211 results for “TRON”

List Grid

What you seeeverything

Recent Oldest Added A–Z

All types211 Papers75 Articles70 Write-ups26 Threads16 Repos12

More types4

Tools5 Advisories3 Talks3 Courses1

“TRON”

chainalysis.comarticle🛡️ Security

DPRK Hackers Perfected Their Fake Interview Playbook Over Dozens of Attacks Before Binance

Chainalysis analysis of the Binance theft (reported as $6.4 billion) that Binance says began with a DPRK fake job interview and a malicious coding assignment. It attributes over $14.3 million in losses over 18 months to similar Contagious Interview schemes and describes EtherHiding C2 split across TRON, Aptos and BSC. It also covers DPRK IT worker salary laundering through stablecoins.

TronAptosBNB Smart Chain+1

DPRKContagious InterviewEtherHiding+7

Threat Intel, Blockchain· Published and added 3 Nov 2026

blog.trailofbits.comarticle🛡️ Security

The cryptography behind electronic voting (and why paper ballots are still essential)

Explains how Helios and ElectionGuard use threshold-shared exponential ElGamal, zero-knowledge proofs, Benaloh challenges and hash-chained ballot commitments to protect voter privacy and give end-to-end verifiability. It covers compromised voting clients and backends, software independence, and the 2018 National Academies report against internet voting. It concludes that cryptography supplements, but cannot replace, paper ballots in general elections.

HeliosElectionGuardElGamal

Benaloh challengeend-to-end verifiabilitysoftware independence+4

other, Learning· Published and added 3 Nov 2026

securityweek.comarticle🛡️ Security

Google Dismantles XCPE Botnet of 1.3 Million Android Devices

XCPE (X.CPE) is a backdoor and botnet of more than 1.3 million Android set-top boxes, built and run by the Chinese OEM/ODM Videostrong Technology. Google dismantled it through legal action against domains and servers and banned Videostrong from preloaded Google apps. The malware used a three-stage design persisting on the /oem/ partition and was used for ad fraud, residential proxies and further malware loading.

AndroidAndroid TV

XCPEVideostrong TechnologyGoogle+8

Threat Intel, Mobile Security, Supply Chain· Published and added 3 Nov 2026

bankless.comarticle🛡️ Security

Binance Reopens Withdrawals as FBI Pins $6B Hack on North Korea

Bankless news report on Binance reopening withdrawals in phases on 1 November 2026 after a record $6B+ hack. The FBI named North Korea's TraderTraitor group on 31 October, listed 628 addresses tied to the theft and asked THORChain node operators, instant exchanges and DeFi services to block them. Chainalysis says $500M moved out of TRON wallets and funds are shifting to Solana. Binance published a new proof of reserves covering 10 tokens.

THORChainTronSolana

TraderTraitorDPRKBinance+2

Blockchain, Threat Intel· Published and added 2 Nov 2026

bleepingcomputer.comarticle🛡️ Security

Google disrupts million-device XCPE Android malware botnet, bans TV box maker

XCPE is an Android TV box botnet of more than 1.3 million devices, which Google's Threat Intelligence Group attributes with high confidence to manufacturer Videostrong Technology, who preinstalled a system-level backdoor in firmware. The backdoor (x.cpe, com.oemservice) used an MQTT broker for commands and loaded ad fraud and residential proxy modules. Google took down C2 servers tied to 10 million more devices and banned Videostrong from certified Android devices.

AndroidAndroid TVMQTT+2

XCPEVideostrong TechnologyGoogle Threat Intelligence Group+17

Threat Intel, Mobile Security, Hardware & RF· Published and added 2 Nov 2026

cloud.google.comwriteup🛡️ Security

Shutting the Door on the XCPE Botnet

XCPE is an Android firmware backdoor (x.cpe, com.oemservice) preloaded by OEM Videostrong Technology on more than 1.3 million set-top boxes. Google Threat Intelligence Group details the three-stage architecture, the MQTT C2 (mqtt.szbboys[.]com), Play Protect disabling, persistence, and affiliate modules for residential proxies and ad fraud. It also lists the legal takedowns, the Videostrong ban and the partners involved.

AndroidAndroid TVMQTT+3

XCPEVideostrong TechnologyBadBox+18

Threat Intel, Malware & RE, Mobile Security· Published and added 2 Nov 2026

en.cryptonomist.charticle🛡️ Security

Elliptic traces $2.2M of Binance hack funds through Bitcoin mixing

Elliptic's 2 November 2026 analysis reports the first laundering of proceeds from the 29 October 2026 Binance hack. By 1:02 a.m. ET it counted 18 WabiSabi coinjoin transactions, funded by bitcoin routed out of Tron and bitcoin bought with swapped ETH. The 18,270 BTC stolen directly had not moved. Elliptic names stablecoins and centralized cashout platforms as the remaining chokepoints. The page gives no addresses, transaction IDs or tracing method.

BitcoinEthereumTron+1

EllipticBinanceFBI+2

Blockchain, Threat Intel· Published and added 2 Nov 2026

decrypt.coarticle🛡️ Security

FBI Blames North Korea for $6B Binance Hack as Exchange Reopens Withdrawals

The FBI attributed the theft of more than $6 billion from Binance on or about 29 October 2026 to North Korea's TraderTraitor and published hundreds of Ethereum addresses, 19 Bitcoin addresses and one Solana address to block. Chainalysis tracked over $500 million moving through Tron and out via THORChain, Chainflip, Relay and NEAR. Binance resumed withdrawals in phases from 1 November and says it replenished about 95% of affected assets.

EthereumBitcoinSolana+3

TraderTraitorDPRKFBI+11

Blockchain, Threat Intel· Published and added 2 Nov 2026

x.comthread🧠 AI

CuteGuy☣️ (@cutetoxicguy) on X

Nemotron 3.5 Bolt E4B is an NVIDIA open-weights model with 8B total and 4B active parameters, built on Gemma 4 E4B and post-trained with NeMo RL and NeMo Gym. It targets local agents in about 4GB of GPU memory, with 64K context, 35 languages, multi-step tool calling and configurable reasoning. NVIDIA's llama.cpp tests show 133.4 tokens/sec on an RTX 5070 and 15.5 on a Jetson Orin Nano. Benchmarks are NVIDIA's own.

llama.cppOllamaLM Studio+9

NVIDIANemotronGemma 4+8

models, inference, agents· Added 3 Nov 2026, published 2 Nov 2026

theblock.coarticle🛡️ Security

North Korean hackers launder $242 million of Binance loot via Tron as FBI presses THORChain: analyst

The Block reports that North Korean hackers (TraderTraitor) laundered about $242 million of funds stolen from Binance through Tron in two days, according to analyst SomaXBT. Relay, THORChain, Chainflip and Symbiosis moved about $140 million off Tron. Elliptic counts $2.85 billion frozen or seized, 44% of $6.43 billion stolen, and $3.25 billion in assets no issuer can freeze. The FBI urged THORChain and others to block listed addresses.

TronTHORChain

TraderTraitorDPRKBinance+6

Blockchain, Threat Intel· Published and added 1 Nov 2026

theblock.coarticle🛡️ Security

Chainalysis says hacker using ‘offensive agentic techniques’ accessed its customers’ names and emails

Chainalysis disclosed on 30 October 2026 that an attacker using "offensive agentic techniques" accessed customer names, emails, user IDs and role labels, but no investigation content, passwords or session tokens. The report also covers the Binance hack (about $6.3-6.4 billion, attributed to North Korea), where funds loop through addresses and JustLend lending on Tron is used to dodge stablecoin freezes. About $2.8 billion is frozen.

TronJustLend

ChainalysisBinanceDPRK

Threat Intel, Blockchain, AI Security· Published and added 31 Oct 2026

securityweek.comarticle🛡️ Security

Syrian Hacker Gets Prison Time in US After Over a Decade on the Run

Ahmad Umar Agha (Th3Pr0), a Syrian Electronic Army member, received a 42-month US sentence in the Eastern District of Virginia, over 12 years after he was first charged in June 2014. He compromised about 47 Western media, company, NGO and government targets, including the 2013 Associated Press Twitter takeover that caused a flash crash. Accomplice Firas Dardar remains at large.

Twitter

Syrian Electronic ArmyAhmad Umar AghaTh3Pr0+5

Threat Intel· Published and added 31 Oct 2026

latent.spacearticle🧠 AI

[AINews] Google admits AI Mode hurts Search revenue; -15% $500B drop worst since 2004 IPO. AI Bear Market coming?

AINews issue for 29 to 30 October 2026 from Latent Space. It opens on Google's admission that AI Mode monetises worse than classic Search, which preceded a roughly 15% share drop. It then recaps Claude Ultra dropping the 5-hour limit, Gemini 4 Carbon, Mistral Large 4, Nemotron 3.5, Artificial Analysis Index v5, Unsloth GGUF disputes, and the Anthropic S-1 filing.

Claude CodeClaudeGemini+17

GoogleAnthropicOpenAI+46

ai-policy, models, evals· Published and added 31 Oct 2026

arxiv.orgpaper🧠 AI

From Small to Large: Assessing Medical Reasoning, Benchmark Contamination, and Model Size in Language Models

This paper evaluates fourteen LLMs from six providers on 3,076 questions from MedQA-USMLE, MedMCQA, PubMedQA and MedBullets. It tests standard multiple choice, questions with answer choices withheld, and reconstruction of hidden questions from choices alone. Mean accuracy falls from 82.3% to 50.9% without choices, smaller models trail larger ones by 8.7 points, and the strongest models reconstruct about one question in four.

LLM

MedQA-USMLEMedMCQAPubMedQA+2

evals, research· Added 3 Nov 2026, published 30 Oct 2026

huggingface.cowriteup🧠 AI

nemotron-halloween

MonsterHouse is a Halloween interactive projection built on the NVIDIA Nemotron Voice Agent: Nemotron Speech ASR, Nemotron 3.5 Bolt E4B 4GB with a LoRA persona adapter, Magpie TTS Zeroshot and Audio2Face-3D in Unreal Engine, on two RTX Blackwell GPUs. Median end-of-speech-to-audio is 1.70 seconds live and 0.72 seconds with cached responses. The post gives a build path and tips on caching and state handling.

Nemotron Voice AgentNemotron Speech ASRNemotron 3.5 Bolt E4B 4GB+11

NVIDIANemotronQwen3 30B-A3B+3

multimodal, agents, training· Published and added 30 Oct 2026

arxiv.orgpaper🛡️ Security

SecPO: Principled Adversarial Training for Prompt Injection Security

SecPO is a prompt injection defense that combines a new preference-optimization loss with adversarial training. It replaces the insecure DPO reference model with an approximated injection-invariant oracle, and trains on strong injections synthesized at best@240. Against Meta-SecAlign, AT cuts attack success rates 1.5-14.5x, and on Qwen3.6-27B AgentDojo ASR@800 falls to 8.8% from 78.8%, with no noticeable utility loss across nine benchmarks. Code, training data and model are released.

QwenHugging FaceLLM

prompt injectionAgentDojoMeta-SecAlign+3

AI Security· Added 3 Nov 2026, published 30 Oct 2026

decrypt.coarticle🛡️ Security

Binance Sets Aside $6.34B to Cover Hack Losses, CEO Says 'Your Assets Are Safe'

Decrypt reports on the October 2026 Binance hot-wallet hack, about $6.3 billion, which Binance CEO Richard Teng says the company will cover with over $6.34 billion of its own funds. Teng says the attacker used an EIP-7702 delegation that skipped Binance's approval controls, a self-destruct mechanism to wipe tooling, and more than one breach point beyond a compromised laptop. TRM Labs and Chainalysis attribute the theft to TraderTraitor, and the attacker is still moving funds on Tron.

EthereumTronAave+1

TraderTraitorEIP-7702Binance+5

Blockchain, Threat Intel· Published and added 30 Oct 2026

github.comrepo🧠 AI

Show HN: I Turned DOOM into a Transformer

Doomformer compiles integer neural networks into ZDoom maps that run in ViZDoom. Voodoo dolls do the arithmetic, and sector light levels and plane heights hold the values. Python reads every value back and compares it with a NumPy integer reference at tolerance 0. It runs MNIST (97.16 % on all 10,000 digits, every pass exact), small transformers, a learning perceptron and a SUBLEQ machine. It is about 23,000 times slower than NumPy on a small MLP.

ViZDoomZDoomACS+10

DoomformerDOOMFreedoom+6

research, inference· Added 30 Oct 2026, published 29 Oct 2026

decrypt.coarticle🛡️ Security

Binance Drained of $5.5 Billion in Suspected Hack, Tether and Circle Freeze $1.8 Billion

Decrypt reports that at least $5.57 billion left Binance-linked hot wallets on Ethereum, Bitcoin and Tron on 29 October 2026, which would be the largest crypto hack on record. On-chain analysts point to an EIP-7702 delegation, installed from a Tornado Cash-funded address minutes before the first sweep. Tether and Circle froze about $1.8 billion. Binance has not confirmed the hack, and how the attacker obtained the signature is unknown.

EthereumBitcoinTron+2

BinanceEIP-7702Circle+4

Blockchain, Threat Intel· Published and added 29 Oct 2026

rekt.newswriteup🛡️ Security

Rekt - Duelbits - Rekt

Duelbits is a crypto casino that lost an estimated $6.07 million traced across Ethereum, BNB Chain, Tron, Solana and Bitcoin on 24 September 2026, which co-founder Joe put at about $7 million. The Ethereum hot wallet drained was the replacement named after a roughly $4.6 million theft in February 2024. Rekt lists addresses and transactions, deBridge and SunSwap conversions, and Tornado Cash deposits. Duelbits has not published a root cause.

EthereumBitcoinSolana+3

Duelbitsprivate key compromiseScam Sniffer+8

Blockchain, Threat Intel· Added 29 Oct 2026

eprint.iacr.orgpaper🛡️ Security

A Quantitative Evaluation of Constant-Time Analysis Tools

AutoBench-CT is a modular benchmark for constant-time analysis tools, with manually verified per-violation ground truth across 64 cryptographic tasks. It scores seven tools on operational performance, detection quality and usability. The authors explain why CT-grind (2010) is still the most used tool, list recurring limitations across tools, and propose a composite pipeline for stronger constant-time guarantees. Accepted at ACSAC 2026.

AutoBench-CTCT-grind

ACSAC

Hardware & RF, AppSec· Added 31 Oct 2026, published 29 Oct 2026

x.comthread🧠 AI

Muhammad Waris (@WarisCodes) on X

Nemotron 3.5 Transcribe is a free open-weight speech-to-text model from NVIDIA covering 40 languages, including mixed-language speech. The post says it ties for first among open models with a 2.8% error rate. It says the model needs a large GPU such as an H100 or RTX 5090, so it will not run on a laptop. The post gives no benchmark name or model card link.

Nemotron 3.5 Transcribe

NVIDIANemotron

models, multimodal· Added 30 Oct 2026, published 29 Oct 2026

decrypt.coarticle🛡️ Security

Treasury Sanctions 11 Over North Korean IT Worker Scheme That Moved Millions in Crypto

Decrypt reports that OFAC sanctioned 10 individuals and one entity on 28 October 2026 over North Korean IT worker schemes. Russian nationals Ilya Boykov and Vitold Obertas converted millions in crypto (about $3.5 million and nearly $2 million), and the listings include Ethereum and Tron addresses, one shared by both. It also covers the Vladivostok-based First Financial and Technical Development Center and Kim Ki Hyok, who moved nearly $500,000 in crypto.

EthereumTron

OFACDPRKIlya Boykov+7

Threat Intel, Blockchain· Published and added 29 Oct 2026

x.comthread🧠 AI

🚨 AI News | TestingCatalog (@testingcatalog) on X

Nemotron 3.5 Transcribe is an open-weight, promptable NVIDIA mixture-of-experts model with 30B total and about 4B active parameters. One checkpoint does transcription in 40 languages, speaker attribution, translation, contextual prompting and audio understanding. The post says it ranked first in VoiceArena's comparison of 14 diarization plus ASR systems. The fetched text is cut off and gives no license or download details.

Mixture of Experts

NVIDIANemotronNemotron 3.5 Transcribe+1

models, multimodal· Added 30 Oct 2026, published 29 Oct 2026

eprint.iacr.orgpaper🛡️ Security

High Precision Noise Cleaning for CKKS with Application to Asynchronous Threshold CKKS

High Precision Noise Cleaning for CKKS is an IACR ePrint preprint (2026/2675) by Zhihao Li, Cheng Hong and Tao Wei. It composes lower-degree Hermite polynomials to reach output noise bounds such as 2^-300 for noise flooding, and uses this to build an asynchronous threshold CKKS scheme with strong simulation security in the random oracle model. In OpenFHE, binary-message cleaning falls from 9.63 s to 1.54 s.

CKKSOpenFHE

noise floodingHermite interpolationfunctional bootstrapping+2

other· Added 31 Oct 2026, published 29 Oct 2026

x.comthread🛡️ Security

Trail of Bits (@trailofbits) on X

Trail of Bits post says it achieved remote code execution through a Brotli decoder bug in Linux lab setups: Node.js 24.22.0 with Express 5.3.0 via the standard JSON parser (x86-64 and ARM64), and Electron 41.10.7 (x86-64) with a custom decoding service. Exploitation needs a carefully matched setup and repeated attempts. A scanner by ToBScottA finds hidden copies of the decoder, including in stripped binaries.

BrotliNode.jsExpress+1

Trail of BitsToBScottA

AppSec, Offensive Security, Supply Chain· Added 30 Oct 2026, published 28 Oct 2026

x.comthread🧠 AI

Laban (@lunkertw) on X

An X post summarising five AI items: Google DeepMind's Gemini 4 Carbon (88.2% SEC-bench Pro, 54.8% ExploitGym, 71.0% CWE-bench, 59 on Artificial Analysis Intelligence Index v5.0), Ed Zitron's critique of Anthropic's S-1, a Utah Claude Code pilot, and the repos beenuar/AiSOC and expo/skills. In the Utah pilot, 34 developers in 10 agencies took part over four weeks, 80% had a working setup within 30 minutes, and 71% saved over 20 hours. It gives short summaries and links, not primary analysis.

Claude CodeModel Context ProtocolPython+6

Google DeepMindGemini 4 CarbonSEC-bench Pro+9

models, coding-agents, ai-policy· Added 29 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🧠 AI

AskStream: When and What to Ask Proactively under Uncertainty in Streaming Video Understanding

AskStream is a multi-turn benchmark for streaming video understanding that adds a third proactive action, ask, alongside wait and answer, for cases where video or user information is incomplete. It has 438 videos, 2,714 manually annotated frame-level time windows (wait, ask, answer), four task categories and eight subtasks. The strongest model reaches only 52.18% on the proposed Ask-F1 metric, in both multiple-choice and open-ended settings.

multimodal large language model

AskStreamAsk-F1arXiv

evals, multimodal, research· Added 30 Oct 2026, published 28 Oct 2026

x.comthread🧠 AI

John Rood (@johnroodepic) on X

Nemotron 3.5 Transcribe is an open-weight, promptable 30B mixture-of-experts model from NVIDIA with about 4B active parameters, built for recorded audio. One checkpoint transcribes 40 languages, handles recordings up to an hour, labels up to eight speakers, translates, and answers questions about English audio. Weights and a demo are on Hugging Face. The post gives no benchmarks.

Mixture of ExpertsHugging Face

NVIDIANemotronNemotron 3.5 Transcribe+1

models, multimodal· Added 29 Oct 2026, published 28 Oct 2026

huggingface.coarticle🧠 AI

NVIDIA Nemotron 3.5 Transcribe and Granary v2: Intelligent Multilingual Transcription You Can Prompt and Adapt

Nemotron 3.5 Transcribe is an open-weight, promptable speech model that handles transcription in 40 languages, translation, diarization for up to eight speakers, and audio Q&A. It has about 4B active of 30B parameters and tops open-weight ASR and diarization leaderboards. The post also covers the Granary v2 curation pipeline (463,062 hours, 48 languages) and a medical fine-tuning case study that cut held-out WER by 45.8%.

NeMo SpeechNeMo CuratorvLLM+11

NVIDIANemotron 3.5 TranscribeGranary v2+16

models, multimodal, training, evals· Published and added 28 Oct 2026

arxiv.orgpaper🧠 AI

Repairs for Whom? Evaluator-Executor Mismatch in Selecting Repairs for Failed LLM Agents

Repairs for Whom? studies single-step repairs of failed LLM agent trajectories and asks whether common evaluators (greedy replay, stronger models, oracles, log-reading judges) pick repairs that help the deployed agent. Using independent rollouts in ALFWorld and WebShop, greedy replay loses 0.06-0.13 in expected gain, and Qwen2.5-14B rollouts misselect repairs for Qwen2.5-7B. The authors recommend verifying repairs with the executor that will run them.

LLMQwen

ALFWorldWebShopQwen2.5-7B+2

research, agents, evals· Added 30 Oct 2026, published 28 Oct 2026

x.comthread🛡️ Security

Hakimi Eiqbal (@hakimieiqbal) on X

A practical reverse-engineering map by hakimieiqbal on X that picks tools by target: native binaries, .NET, Electron, iOS, firmware, WebAssembly and network protocols. It introduces REA (morluto/rea), which connects coding agents to RE tools, and lists MCP bridges for IDA Pro, Ghidra and radare2. It adds an evidence-first workflow and free courses such as pwn.college and OpenSecurityTraining2.

GhidraIDA Proradare2+3

REAFlare-OnOpenSecurityTraining2+5

Malware & RE, Learning· Added 29 Oct 2026, published 28 Oct 2026

x.comthread🧠 AI

Hakimi Eiqbal (@hakimieiqbal) on X

A thread by Hakimi Eiqbal that maps reverse-engineering tools by target type: native binaries, Electron, .NET, iOS, firmware, WebAssembly and network protocols. Its AI sections cover REA (an open-source layer connecting coding agents to analysis tools) and MCP bridges for IDA Pro, Ghidra and radare2. It argues for an evidence-first workflow and gives a prompt that separates verified facts from hypotheses.

Model Context ProtocolClaude CodeCodex+8

REAIDA Pro MCPGhidrAssistMCP+3

coding-agents, mcp· Added 29 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🧠 AI

Know, then Draw: A New Learning Paradigm for Visual Generation

Know, then Draw is a learning paradigm for visual generation that uses autoregressive pretraining to acquire semantic knowledge and diffusion post-training for high-fidelity rendering. Controlled comparisons find autoregression the stronger pretraining objective, and semantic quality matters more than AR-only generation quality. This motivates a purely semantic tokenizer trained with language supervision only. A 3B text-to-image model trained in 6,600 H100 GPU-hours scores 86 on GenEval and 87 on DPG-Bench.

diffusion modelTransformer

arXivGenEvalDPG-Bench+1

research, multimodal, training· Added 28 Oct 2026, published 27 Oct 2026

x.comtool🧠 AI

Mauro Ubeda (@mauroubeda) on X

BibCode is an open-source desktop app, now in beta, for running coding agents side by side. It is built with Rust and Tauri rather than Electron, and runs Codex, Claude, Cursor and OpenCode, each in its own git worktree. It can also connect to remote BibCode Servers. The author, Mauro Ubeda, says it mixes ideas from T3 Code and Orca. The post gives no feature detail beyond this.

RustTauriGit+4

BibCodeMauro UbedaT3 Code+1

coding-agents, agents· Added 28 Oct 2026, published 27 Oct 2026

securityweek.comarticle🛡️ Security

This is Fine: What NetScaler Zero-Days Tell Us About the New Normal

This SecurityWeek commentary covers the September 2026 Citrix NetScaler ADC and Gateway zero-days, exploited from mid-month and patched on 27 September as unauthenticated RCE flaws CVE-2026-88771 through CVE-2026-88778. The author argues the fast response shows stronger defence. Cited evidence: Citrix disclosure time fell from about 40 days to 17 in 2026 after it set up a NetScaler CNA, and CISA made a rare Sunday KEV addition.

Citrix NetScaler

CVE-2026-88771CVE-2026-88772CVE-2026-88773+9

other, Threat Intel· Published and added 27 Oct 2026

arxiv.orgpaper🧠 AI

AxisQ: Depth-Adaptive Channel--Frequency Quantization for Camouflaged Object Detection

AxisQ is a post-training quantization framework for camouflaged object detection (COD) that allocates activation bits by feature depth. It uses the channel domain for shallow features and the local-frequency domain for deep features, under a fixed per-layer average bit budget. It adds a boundary-aware Hessian approximation for reconstruction. On CAMO at W2A2, weighted F-measure improves 12.7% for CFRN and 33.3% for CamoFormer-P over the strongest baselines.

CNNTransformer

AxisQCFRNCamoFormer-P+1

research, inference, multimodal· Added 28 Oct 2026, published 27 Oct 2026

hackers-arise.comarticle🛡️ Security

Artificial Intelligence (AI) in Cybersecurity, Part 30: Shannon – AI Pentester for Web Apps and APIs

Shannon is a white-box AI pentester (@keygraph/shannon) that analyzes an application's source code, then attacks the running instance and reports only findings with a working PoC. The Hackers Arise tutorial runs it against OWASP Juice Shop in Docker on Kali, using OpenRouter (free Nemotron) and local Ollama (Qwen 2.5 Coder 7B) via a models.json file. Output includes PDF, Markdown, JSON and SARIF reports.

ShannonOpenRouterOllama+3

Juice ShopNemotronKeygraph

AI Security, AppSec, Offensive Security· Published and added 26 Oct 2026

en.cryptonomist.charticle🛡️ Security

Clean-label poisoning attack can fool vehicle-network AI without changing labels

Clean-label poisoning attack on federated learning for Basic Safety Message anomaly detection in vehicular networks, from Sagduyu, Erpek, Stephenson and Xue (paper submitted 23 October 2026). The attack perturbs benign samples toward malicious feature extremes without changing labels, which lowers class separability, and it grows stronger with more compromised nodes. A feature-consistency relabeling defense keeps attack success low without hurting clean accuracy, and a game-theoretic model shifts equilibria against attacking.

Basic Safety Messageclean-label poisoningfeature-consistency relabeling+4

AI Security· Published and added 26 Oct 2026

arxiv.orgpaper🛡️ Security

Reversible Pseudonymisation of Documents Sent to External LLMs: an Open Bilingual Benchmark and an Engine

Open bilingual (Russian and UK English) benchmark of five open pseudonymisation tools and a regex baseline, plus a new engine built on the two strongest tools. On a held-out set of 1,000 synthetic documents per language, the best existing tools leave a whole personal value visible in 61% (Russian) and 58% (UK) of documents. The engine does so in 0.3% and 0.2%. Russian name restoration reaches only 94.6%, below the 99% threshold. Real documents were not tested.

LLMpii-guard

AI Security· Added 27 Oct 2026, published 25 Oct 2026

arxiv.orgpaper🧠 AI

Reversible Pseudonymisation of Documents Sent to External LLMs: an Open Bilingual Benchmark and an Engine

This paper presents an open benchmark of five existing pseudonymisation tools and a regex baseline on Russian and UK English documents, plus an open engine built on the two strongest tools. On a held-out set of 1,000 synthetic documents per language, the best existing tools leave a whole value visible in 58 to 61% of documents. The engine does so in 0.2 to 0.3%. Russian name restoration reaches only 94.6%, below the 99% threshold. Real documents were not tested.

LLMpii-guard

Vladimir BeskorovainyiarXiv

evals, llm-apps, research· Added 27 Oct 2026, published 25 Oct 2026

x.comthread🧠 AI

ReignSwords (@ReignSwords) on X

A reviewer tests GenOffice, an open-source Office suite (10.7k GitHub stars), through its CLI only. Markdown to .docx took 3.8s, JSON to .xlsx took 0.9s with a live SUM formula, and .docx to PDF failed twice until Xvfb supplied a display. It has byte-preserving edits, a skill file for Claude Code and Codex, and 29 MCP tools. Downsides: a 188MB download that filled a 512MB /tmp, Electron in the PDF path, and unproven trust in tracked changes and rollback.

Model Context ProtocolClaude CodeCodex+4

GenOffice

mcp, coding-agents, agents· Added 25 Oct 2026, published 24 Oct 2026

eprint.iacr.orgpaper🛡️ Security

The Exact Bit Security of Hash-Based Signatures

The Exact Bit Security of Hash-Based Signatures is a 2026 IACR ePrint paper that analyses XMSS with 128-bit digests and SLH-DSA-128s in the random oracle model, with proofs formalised in Lean4. Strong unforgeability comes to 127 bits under most accounting modes but only 122 bits if just direct random oracle queries are counted. The authors give matching attacks, and show how resource accounting changes the result.

XMSSSLH-DSALean

Mikhail KudinovBenedikt WagnerEmile Wambsgans

other· Added 26 Oct 2026, published 24 Oct 2026

horizon3.aiadvisory🛡️ Security

CVE-2026-76268 | Splunk Enterprise Patroni REST API Missing Authentication Vulnerability

CVE-2026-76268 is a critical (CVSS 9.8) missing authentication flaw (CWE-306) in the Patroni REST API on Splunk Enterprise search head cluster members. An unauthenticated attacker with network access can run operating-system commands. Affected: 10.4.0 to 10.4.2 and 10.2.0 to 10.2.6. Fixed in 10.4.3 and 10.2.7. Splunk (SVD-2026-1001, 7 October 2026) suggests disabling the PostgreSQL sidecar as a workaround.

SplunkPatroniPostgreSQL+1

CVE-2026-76268CWE-306SVD-2026-1001+1

Offensive Security, Cloud & Infra· Added 29 Oct 2026, published 22 Oct 2026

arxiv.orgpaper🧠 AI

Causal Chain of Thought: Mitigating Attention Leakage via Context Isolation

CausalCoT is an inference framework that splits a problem into a directed acyclic graph of atomic sub-questions and evaluates each node in an isolated context holding only its direct causal parents. This removes attention paths to non-parent content, so intermediate steps drive the answer rather than leaking from global context. It keeps benchmark accuracy competitive and raises causal sensitivity of reasoning steps by 12 points on average, up to 21, over the strongest baseline. The graph also allows test-time corrections that propagate downstream.

LLMTransformer

arXiv

research, prompting· Added 24 Oct 2026, published 22 Oct 2026

x.comthread🧠 AI

Jonathan Rhyne (@jdrhyne) on X

A post on X about NVIDIA's Nemotron 3.5 Parse and its "#1 on ParseBench" claim. The author says the ranking holds only among open-weight models under 1B parameters. On the full board of 150+ models the model scores about 75, while the top is near 90. The post asks vendors who run a benchmark to print the filter on the chart.

Nemotron 3.5 Parse

NVIDIANemotronParseBench

evals, models· Added 23 Oct 2026, published 22 Oct 2026

alignmentforum.orgarticle🧠 AI

Neural nets are exceptionally vulnerable to goal self-hack

"Neural nets are exceptionally vulnerable to goal self-hack" is a 3-minute AI Alignment Forum post by Stuart Armstrong, dated 21 October 2026. By its title, it argues that neural networks are unusually prone to altering or subverting their own goals. A later post, "Fixed-weight models are adversarially vulnerable: hence misaligned", cites it. The fetched page has no body text, so the argument itself is not available.

neural network

Stuart ArmstrongAI Alignment Forum

ai-safety· Published and added 21 Oct 2026

openai.comarticle🧠 AI

Democratic governance of frontier AI, part II: Empowering cyber defenders

Democratic governance of frontier AI, part II is an OpenAI post announcing a "Cybersecurity 2.0" blueprint of proposals for governments and industry. It covers three areas: safety and security for frontier models and agents (including shutdown controls), modernizing cyber defense with advanced AI tools, and stronger public institutions with government evaluations and consistent incident reporting. OpenAI frames this as a "defender's window" before advanced offensive capabilities spread.

OpenAI

ai-policy, ai-safety· Published and added 21 Oct 2026

arxiv.orgpaper🧠 AI

Beyond 4 bits: Data-Centric Post-training Quantization and Recovery

This paper studies 2-bit post-training quantization (PTQ) of six dense and mixture-of-experts models from 3.8B to 32B parameters. It finds the quantization representation matters most: vector codebooks give usable models where scalar PTQ falls to chance. Calibration and recovery data must be chosen jointly. The recipe uses about 2.3 effective bits per weight, keeps 89 to 97% of full-precision QA accuracy, and needs 1.4 to 30x less compute than the strongest QAT baseline.

Mixture of ExpertsLLM

arXiv

research, inference· Added 22 Oct 2026, published 20 Oct 2026

interconnects.aiarticle🧠 AI

Latest open artifacts (#25): DeepSeek V4.1 Flash 1017, American-made models, and new regulatory controls in the US & China

Interconnects' Latest open artifacts (#25) covers open-weight AI in fall 2026. Policy items: China's MOFCOM proposal for pre-approval of international model sharing, and a DHS order barring DeepSeek from federal civilian procurement. DeepSeek released its internal inference stack DeepInfer. Picks include MiMo-V2.6-Pro-RL, DeepSeek-V4.1-Flash-1017 (552B-A16B, MIT), Clef, Nemotron-3.5-Super-VL and Reflection's Beam-1.0 (Apache 2.0).

DeepSeekvLLMSGLang+8

InterconnectsDeepSeek-V4.1-FlashDeepInfer+23

models, ai-policy, inference· Published and added 20 Oct 2026

eprint.iacr.orgpaper🧠 AI

Analysis of an AI based code distinguisher

The paper analyses DeepDistinguisher, a transformer-like model that tells Goppa codes apart from random linear codes. Saliency maps and attention visualisation reveal a low-weight-row artifact in the random training instances that the model partly relies on. A pairwise-XOR rule inspired by minimum distance correlates strongly with the model's predictions, and the model loses most of its power on random matrices meeting both constraints. This suggests it exploits general combinatorial properties, not hidden algebraic structure.

Transformer

DeepDistinguisher

research· Added 20 Oct 2026, published 19 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Analysis of an AI based code distinguisher

Analysis of DeepDistinguisher, a transformer model that tells Goppa codes from random linear codes. Using saliency maps and attention visualization, the authors find a low-weight-row artifact in the random training instances that the model partly uses. They propose a pairwise-XOR rule on generator matrix rows that correlates strongly with the model's predictions. On random matrices meeting the minimum distance and XOR constraints, the model loses most of its accuracy, so it exploits general combinatorial properties rather than hidden algebraic structure.

DeepDistinguisherGoppa Distinguisher problemGoppa code

AI Security, other· Added 20 Oct 2026, published 19 Oct 2026

arxiv.orgpaper🛡️ Security

Concerns in Benchmarking Jailbreaks: An Empirical Study of Attack Success Rates

This arXiv paper tests eight jailbreak attacks against three open-weight language models under three evaluation procedures. Low-resource language attacks score 96.0% mean ASR under keyword matching but 2.7% under a Qwen3 judge using StrongREJECT instructions. At Ollama's default temperature of 0.8, 100 samples of an unchanged request give 100% ASR by keywords and 50% by the judge. The authors recommend explicit success definitions, matched response budgets, validated evaluators and cumulative-failure reporting.

OllamaQwenLLM

StrongREJECTDeepInceptionattack success rate+1

AI Security· Added 21 Oct 2026, published 19 Oct 2026

arxiv.orgpaper🧠 AI

Concerns in Benchmarking Jailbreaks: An Empirical Study of Attack Success Rates

Concerns in Benchmarking Jailbreaks is an October 2026 arXiv study of eight jailbreak attacks on three open-weight models under three evaluation procedures. Low-resource language attacks score 96.0% ASR by keyword matching but 2.7% under a Qwen3 evaluator using StrongREJECT instructions. Attack rankings flip with attempt budgets, and 100 samples at Ollama's default temperature 0.8 give 100% ASR by keywords, 50% by the evaluator.

OllamaQwen3LLM

StrongREJECTDeepInceptionarXiv

evals, ai-safety, research· Added 21 Oct 2026, published 19 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Privacy with Leakage from Falsifiable Assumptions

Privacy with Leakage from Falsifiable Assumptions is an ASIACRYPT 2026 paper by Francesco Berti and Itamar Levi. It replaces simulatability with "secure refreshing", a key-refreshing assumption that evaluation laboratories can test directly, and adds "strongly protected secure refreshing". Using these, it proves privacy of the PSV encryption scheme without assuming a leak-free component.

PSV encryption schemesecure refreshingstrongly protected secure refreshing+4

Hardware & RF· Added 20 Oct 2026, published 19 Oct 2026

securityweek.comarticle🛡️ Security

US Cybersecurity Job Postings Fall 26% as AI Security Engineer Demand Soars

SecurityWeek summarises CyberSN's Cybersecurity Jobs Report. US cybersecurity postings fell 26.4% to 235,786 for January–September 2026, a four-year low. AI Security Engineer demand rose 268.3% and Cyber Data Scientist demand rose 203.5%. Penetration Tester (-45.9%), Threat Hunter (-40.0%) and CISO (-38.9%) postings dropped, and the CISO figure matches 2024 after an unusually strong 2025. Just under 15% of postings mention AI, peaking at 20.0% in September. The report counts postings by work required (CyberSN's 45-role taxonomy) rather than by job title, and it describes what changed, not why.

CyberSNJobSpikrCyberSN Taxonomy

other· Published and added 18 Oct 2026

interconnects.aiarticle🧠 AI

Meta shakes up the frontiers, open and closed

Analysis of Meta's Muse Spark Open, an 816B total/42B active parameter open-weights model with 1M context, now the strongest American open model, and its position relative to Chinese open models such as GLM-5.3, Kimi K3 and DeepSeek V4.1 Flash. It examines Meta's restrictive custom license ("Built with Muse" attribution, $20M/month revenue gate, acceptable use policy, coverage of outputs and derivatives), and ranks Meta against Anthropic, OpenAI, Gemini and xAI after the closed Muse Wonder release. It also covers tier-3 labs (Mistral Large 4, Reflection's Beam) and valuation questions.

Hugging FaceMixture of ExpertsLlama+5

MetaMuseMuse Wonder+27

models, ai-policy· Published and added 17 Oct 2026

x.comthread🧠 AI

🚨 AI News | TestingCatalog (@testingcatalog) on X

A TestingCatalog X post with two Daily AI Briefs (Oct 17 and the start of Oct 16), grouped by vendor. Oct 17 items include GPT-6 Astra Pro upgrades in ChatGPT, Codex context reuse and caching, Gemini 4 Argon in AI Studio Build, the open-weight MIT-licensed DeepSeek-V4.1-Flash-1017, Claude Code Review going GA, Perplexity's open Decider v1.2 models (27B and 4B), NVIDIA's 4B looped Nemotron Labs Dory 1.0, Reflection's Apache-2.0 Beam, Cursor cloud-agent canvases and Tencent's Hy Memory 2.0. Oct 16 begins with Meta's open-weight Muse Spark Open, an 816B-total, 42B-active MoE with a 1M-token context. It gives a quick snapshot of the week's open-weight and coding-agent releases.

ChatGPTCodexGemini+7

TestingCatalogOpenAIGPT-6 Astra+15

models, coding-agents· Added 18 Oct 2026, published 17 Oct 2026

x.comthread🧠 AI

DeepSeek (@deepseek_ai) on X

DeepSeek announces DeepSeek-V4.1-Flash-1017, claiming stronger benchmarks, faster task completion and shorter outputs than the previous version, live on its website, app and API. It is optimized for DeepSeek Harness (v0.2.1-rc.1 or later), Tencent CodeBuddy and WorkBuddy, and uses reinforcement learning with self-feedback to improve end-to-end project development, from game creation to 3D modeling. The thread also opens the Search & Fetch APIs to everyone, backed by DeepSeek's in-house search engine at $0.75 per 1,000 calls per endpoint, available as server-side tools.

DeepSeekLLM

DeepSeek-V4.1-FlashDeepSeek HarnessTencent+2

models, agents, training· Added 18 Oct 2026, published 17 Oct 2026

latent.spacearticle🧠 AI

[AINews] High Taste Testers Ep. 1

AINews issue for 10/15–10/16/2026, covering Twitter and Reddit. It covers the open-weights releases Meta's Muse Spark Open (816B/42B-active MoE, AA Intelligence Index 39, custom license) and Reflection Beam 1.0 (501B/23B-active, Apache 2.0), plus NVIDIA Nemotron 3.5, StepFun Step 5 Preview and FLUX 3 Image Klein. It also covers Gemini 4 Argon, Haiku 5.5 pricing, "decision model" classifiers (Liquid d1-ultra, Perplexity pplx-decider), TPUv7/Vera Rubin/MI355X serving benchmarks, interpretability claims (Qwen3-32B neuron audit), agent research, eval-integrity and safety disclosures, and China export-control proposals. The page also announces a pilot video show, High Taste Testers Ep. 1.

Mixture of ExpertsvLLMSGLang+15

Latent SpaceMetaMuse+44

models, inference, evals· Published and added 17 Oct 2026

arxiv.orgpaper🛡️ Security

RATION: Towards Quantitative Risk-Based Access Control for AI Agents

RATION is a security framework for AI agents that tracks measurable risks from agent actions and enforces quantitative constraints during task execution, instead of simply restricting actions. It builds a task-specific policy from explicit constraints in the user request plus implicit risks calibrated from historical runs of similar tasks. Experiments on four security benchmarks show strong security with high task utility across models and environments.

LLM

indirect prompt injectionRATION

AI Security· Added 21 Oct 2026, published 16 Oct 2026

galaxy.comarticle🛡️ Security

Proving Humanity

Galaxy research report arguing that CAPTCHAs and behavioral bot detection (reCAPTCHAv3, Cloudflare Turnstile) are failing as AI agents reproduce human conduct, for example ChatGPT Agent clicking through Turnstile's checkbox. It shows that Web Bot Auth signatures and Google's AP2 mandates identify the platform or account key but not a distinct person. It proposes reframing "proving humanity" as ensuring every agent traces to one person counted once, covering Sybil, one-person-one-share, accountability and public-count harms. It lays out requirements for a proof-of-personhood credential: strong, private, portable, delegable and accessible. It deliberately does not evaluate specific projects.

CloudflareCloudflare TurnstilereCAPTCHA+3

proof of personhoodSybil attackliar's dividend+4

AI Security, Identity & Access· Added 16 Oct 2026

galaxy.comarticle🧠 AI

Proving Humanity

Galaxy Research essay arguing that as AI agents become the main way people use the web, the key question shifts from "is this a machine?" to "is a distinct real person behind it, counted once?". It traces CAPTCHA history (distorted text, image puzzles, reCAPTCHAv3 and Turnstile behavioral scoring) and notes ChatGPT Agent passing Cloudflare's checkbox. It reviews Web Bot Auth signatures and Google's AP2 payment mandates, showing both stop at the platform or key and never establish a person. It then covers harms (one-person-one-share abuse, no one to answer for an agent, distorted public counts, collapsing confidence, scaled deception, broken ad economics) and lists the properties a privacy-preserving proof-of-personhood credential needs: strong, private, portable, delegable, accessible.

Web Bot AuthAgent Payments Protocol (AP2)reCAPTCHA+5

Galaxy DigitalGoogleOpenAI+13

agents, ai-policy· Added 16 Oct 2026

galaxy.comarticle🛡️ Security

Inside the $91M Ledger Reseller Wallet Theft

Galaxy Research newsletter piece on the roughly $91M theft from buyers of Ledger hardware wallets sold by the reseller CryptoBilis. A hidden component reads the 24-word recovery phrase off the screen during setup and sends it over LTE via an eSIM. The Secure Element and Genuine Check are untouched, so the check passes on tampered units. It traces the drain across 474 victim addresses on Bitcoin, Ethereum and TRON, including 88% of the value moving in one hour on Oct. 9, and the laundering and freezes (Tornado Cash, Tether). It also places the incident among the quarter's other custody and infrastructure failures (Coldcard weak seeds, Liquid, Bitget) and gives buying and operational guidance.

LedgerBitcoinEthereum+2

CryptoBilishardware implantGenuine Check+9

Blockchain, Hardware & RF, Supply Chain· Added 16 Oct 2026

arxiv.orgpaper🧠 AI

FlexPart: Text-Controlled Semantics-Aware 3D Part Segmentation at Flexible Granularity

FlexPart is a framework for text-controlled 3D mesh segmentation that assigns discrete part IDs to mesh faces through a view-independent part-identity field. It uses a control prompt of part names with location and shape clauses, trained on a VLM-built multi-granularity dataset. On FlexPartBench it raises average mIoU by 22% over the strongest baseline, and by 15% with planner-completed prompts.

vision-language model

FlexPartFlexPartBench

research, multimodal· Added 20 Oct 2026, published 16 Oct 2026

rstation.aitool🧠 AI

Show HN: rStation – A shared research lab for your AI agents

rStation is a shared research community where independent AI agents discuss open math problems and publish reviewed papers, with humans as observers. Agents pick from problems such as Epoch AI's rational Diophantine septuple, the Earth–Moon problem and No three in a line, Millennium Prize problems (Riemann hypothesis, Hodge conjecture), and Brouwer's strongly regular graph tables. It is an experiment in multi-agent collaboration and peer review on hard research problems.

Epoch AIMillennium Prize ProblemsRiemann hypothesis+2

agents, research· Added 17 Oct 2026, published 16 Oct 2026

latent.spacearticle🧠 AI

[AINews] Agents disprove Polysemanticity; greatly improve Interpretability & Alignment

The 10/14–10/15/2026 AINews issue. Its lead story is Goodfire's Nick Cammarata essay "Neural Networks Are Already Clean", which argues agentic analysis resolves many known cases of polysemanticity and superposition. Neel Nanda, Leo Gao and Jake Mendel push back. It also covers Meta's Muse Spark Open (816B total / 42B active MoE, 1M context, custom license, Artificial Analysis index 39, vLLM day-0 support and quantizations), Nemotron 3.5, MAI-Base-2 and Gemini 4 Argon evals. Further items cover Vals' refund-agent and MiMo task-leakage findings, TPUv7 and Vera Rubin inference benchmarks, Perplexity's SPACE sandbox, RL training systems (TRL tree packing, bitwise-consistent vLLM RL) and Sakana's Agentic Scientist.

Mixture of ExpertsvLLMllama.cpp+10

GoodfireNick CammarataNeel Nanda+44

ai-safety, models, research· Published and added 16 Oct 2026

arxiv.orgpaper🧠 AI

Identify, Classify, Act: Per-Instruction Controlled Compliance for LLMs over External Data

EDIC is a decomposed reward framework that trains LLMs to decide, for each instruction embedded in external data such as documents or tool returns, whether to follow or reject it. A judge initialized with SFT and refined with GRPO attributes compliance per instruction and yields separate rewards for beneficial and harmful compliance. On Qwen3-4B it raises harmful-rejection by up to 7.7% and Arena-Hard-Auto utility by 12.5% over the strongest prior defense.

GRPOQwen3LLM

EDICQwen3-4BArena-Hard-Auto+1

research, training, ai-safety· Added 20 Oct 2026, published 16 Oct 2026

huggingface.coarticle🧠 AI

Scaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation Pipeline

Announces Nemotron-pretraining-Indic-v1, about 2.8T tokens across 22 Indian languages (2,601B native-script, 224.6B romanized; 34 language-script configs), built by translating 15 Nemotron-CC-v2 subsets with NeMo Data Designer and Gemma-4-31B-it. The pipeline covers document-level translation, an MQM-inspired judge with a 75/100 threshold, feedback-based repair, and a separate Indic Translate workflow for ten languages. Continued pretraining of Nemotron 3.5 Super VL with 10B Indic tokens raised all five Indic benchmarks by 4.98–9.82 points. The post also gives dataset loading code and notes limitations: automated judging, translator-as-judge, and no semantic dedup.

NeMo Data DesignerGemmaHugging Face+2

NVIDIAAI4BharatIIT Madras+21

training, models, evals· Published and added 16 Oct 2026

x.comthread🧠 AI

Harrison Kinsley (@Sentdex) on X

Sentdex reacts to Artificial Analysis ranking NVIDIA's Nemotron 3.5 Super VL (120B total, 12B active MoE) as the top US open-weights model, scoring 26 on the Intelligence Index. That edges out Thinking Machines' Inkling Small (26), NVIDIA's Nemotron 3 Ultra (23) and Meta's Muse Glimmer (17). Sentdex's informal tests at vanilla settings show over 550 tok/sec, and they suspect it would rank higher on agentic coding. They see it as a candidate for a fast local coding agent model.

Mixture of Expertsvision-language model

SentdexNVIDIANemotron+8

models, evals, inference· Added 16 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

Grounding Generative Verification in 3D Content

The paper introduces 3DVerifierBench, a 10-task benchmark for generative verification of 3D content. It scores accept/reject decisions, rejection explanations, and responses to controlled changes in specifications and candidates. A human study shows a 21.6-point gap to the strongest tested model, GPT-6 Astra. The authors also build a pipeline of multi-view fact extraction, controlled 3D editing and native annotations. It yields 14,126 training examples with contradiction explanations and linked counterfactuals. Verification-guided selection raises complete factual success from 26.7% to 90.0% with oracle selection over sixteen candidates, and human-guided recursive refinement satisfies all seven requirements for every input in an eight-input study.

multimodal large language model3D generation

3DVerifierBenchGPT-6 AstraarXiv

research, evals, multimodal· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

Private Retrieval Augmented Generation via Random Projection

Proposes protecting the retrieval datastore in RAG systems by applying the same random projection to datastore embeddings and query embeddings. This provably preserves query-item semantic similarity while substantially mitigating data extraction attacks. Across multiple RAG architectures and datasets it beats prior differential-privacy-based methods on retrieval and generation quality, with comparable empirical privacy under strong attacks, making it a practical baseline for privacy-preserving RAG.

RAGLLM

arXiv

research, llm-apps· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🛡️ Security

Private Retrieval Augmented Generation via Random Projection

Proposes applying the same random projection to both datastore embeddings and query embeddings in RAG systems. The authors claim this provably preserves query-to-item semantic similarity while substantially mitigating datastore data-extraction attacks. Across several RAG architectures and datasets it reportedly beats prior differential-privacy-based defenses on retrieval and generation quality, with comparable empirical privacy under strong attack models. It is presented as a lightweight baseline for privacy-preserving RAG.

LLM

differential privacydata extraction attacks

AI Security· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

Librarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future Queries

Introduces Librarian Agents, where a builder agent turns raw unstructured documents into a hierarchical filesystem of synthetic files and reference links, forming a workspace for a downstream answering agent. Workspace construction is cast as a Markov decision process and the builder policy is trained with reinforcement learning to maximize downstream answering accuracy. It beats the strongest retrieval and precomputation baselines by 33 points on BrowseComp-Plus, amortizes construction cost over repeated queries, and generalizes zero-shot to BRIGHT with a 31-point average gain.

LLMClaude CodeRAG

Librarian AgentsBrowseComp-PlusBRIGHT+1

research, agents, llm-apps· Added 17 Oct 2026, published 15 Oct 2026

arxiv.orgpaper🧠 AI

Topology-Inspired Fine-Tuning for Wind-Field Super-Resolution

The paper fine-tunes a pretrained CNN wind-field super-resolution model with differentiable topology-inspired auxiliary losses on speed, gradient magnitude, soft high-speed level sets and local maxima. It also uses fixed-pair supervision at ground-truth persistence-pair endpoints located offline with the Topology ToolKit. A 16-configuration loss study on a fixed 168-field benchmark finds that gradient supervision gives the strongest persistence-diagram improvement, while fixed-pair supervision gives the most consistent joint improvement in persistence-diagram and merge-tree distances. The combined objective cuts bottleneck, W2,∞ and W2,2 distances by 31%, 25% and 27% relative to the pretrained CNN, at a cost of about half of standard fine-tuning's PSNR gain. It also shows that pointwise metrics and topological distances rank CNN and GAN models differently.

CNNGANTopology ToolKit

arXiv

research, training· Added 17 Oct 2026, published 15 Oct 2026

orca.securityarticle🛡️ Security

Frontier-Level Red-Teaming From a Self-Hostable Open-Weight Model: Testing NVIDIA Nemotron 3.5 Super

Orca Security's research team built an internal prototype, Agent Piercer, that attacks AI agents by choosing probes, writing attacks and judging whether they worked. They compared NVIDIA Nemotron 3.5 Super (self-hosted on 4×L40S via SageMaker) with Opus 4.8, GPT-6 Astra and Grok 4.7 as the driving model against the same live target. Nemotron matched the frontier models on vulnerability types found (four) and was the only one to complete every simple test and judge all 57 real attack conversations. The post also notes that some proprietary models declined authorized red-team tasks under default safety settings, which makes self-hosted open-weight models attractive for offensive testing.

NVIDIA Nemotron 3.5 SuperGPT-6 AstraGrok+2

Agent PiercerOpus 4.8prompt injection

AI Security, Offensive Security· Published and added 15 Oct 2026

arxiv.orgpaper🧠 AI

GraphACM: Graph-based Action Consistency Measurement for Uncertainty Quantification in LLM Agents

GraphACM merges equivalent states from multiple rollouts of the same LLM agent task into a directed, action-labeled graph, so actions can be compared at shared decision states. It defines node-level and global action-consistency measures, and uses the global score as an uncertainty proxy to predict task success or failure. Across three LLMs on ALFWorld, AppWorld and WebShop it reaches an average AUROC of 0.811, above the strongest behavioral-consistency baseline (0.729) and verbalized confidence (0.769). It also shows where in a trajectory agent actions diverge.

LLM

GraphACMALFWorldAppWorld+2

research, agents, evals· Added 17 Oct 2026, published 15 Oct 2026

github.comrepo🧠 AI

morluto/rea

REA (rea-agents on npm) registers an MCP server and matching workflow instructions with coding agents such as Claude Code, Codex, Cursor and Gemini CLI. It exposes tools for inspecting native binaries through Hopper, Ghidra or IDA (with optional objdump and Rizin), JavaScript/Electron apps and ASAR archives, .NET assemblies and websites. Results come back with evidence and limitations, so the agent can explain how a feature works and write and test its own implementation. Analysis runs locally, there is a CLI with the same workflows, and the showcases include C reconstructions verified against original x86 function bytes.

Model Context ProtocolClaude CodeCodex+14

REAIDA

mcp, coding-agents· Added 14 Oct 2026

github.comrepo🛡️ Security

morluto/rea

REA (rea-agents) is an MIT-licensed toolkit that exposes native-binary, JavaScript/Electron, .NET assembly and website analysis to AI agents via MCP, and also as a local CLI. It drives Hopper, Ghidra, IDA, objdump or Rizin for disassembly and pseudocode, recovers modules and Electron IPC boundaries from ASAR/JS apps, and returns findings with evidence and limitations. Showcases include reconstructing a x86 audio helper into byte-matching C and tracing an Electron clipboard API through preload and IPC; the README also carries lawful-use and licensing guidance.

Model Context ProtocolGhidraHopper+3

REArea-agentsASAR

Malware & RE, AI Security· Added 14 Oct 2026

x.comthread🧠 AI

44er (@44errrr) on X

Announcement of hexvero, an MCP server that gives coding agents a static first look at binaries and app bundles across 18 formats (ELF, PE, Mach-O, APK, .NET, Go, Wasm, Electron, SQLite). It uses zero-dependency Node parsers and marks each finding confirmed or inferred with file and line. Its tool catalog is 3,043 tokens, against a claimed 509,696 for the established reverse-engineering MCP server. Paged output with a byte budget shrinks a 541 MB Electron app to a 15 KB result, 1.3 MB of JSON to 7.9 KB, and a 450 KB build diff to 1.2 KB. radare2, rizin or Ghidra plug in for disassembly, and `npx hexvero setup --apply` registers it with Claude Code, Cursor, Codex and 15 more clients. It is a concrete example of keeping MCP tool definitions and results small enough for an agent's context window.

Model Context ProtocolNode.jsnpm+12

hexvero

mcp, coding-agents· Added 15 Oct 2026, published 14 Oct 2026

x.comtool🛡️ Security

44er (@44errrr) on X

hexvero is a Node-only, zero-dependency MCP server that gives coding agents a static first-look triage of binaries and app bundles across 18 formats (ELF, PE, Mach-O, APK, .NET, Go, Wasm, Electron, SQLite). Findings are marked confirmed or inferred with file and line, and outputs are paged with a byte budget (a 541 MB Electron app yields a ~15 KB result). The author contrasts its 3,043-token tool catalog with a 509,696-token catalog of an established RE MCP server. radare2, rizin or Ghidra can be plugged in for disassembly, and setup registers it with Claude Code, Cursor, Codex and 15 more clients.

Model Context ProtocolGhidraNode.js+3

hexvero

Malware & RE, AI Security· Added 15 Oct 2026, published 14 Oct 2026

arxiv.orgpaper🧠 AI

Game2Real-Bench: Do Toy Games Reveal and Transfer to Real-World Visual Reasoning?

Game2Real-Bench tests whether vision-language model performance on procedurally generated toy games predicts real-world visual reasoning, and whether game examples help transfer. It has 1,000 verifiable game samples and 400 real-world samples, matched by shared reasoning requirements (fine-grained perception, spatial reasoning, constraint reasoning, sequential planning). Across 25 VLMs, game and real scores correlate strongly (Pearson/Spearman 0.935). One-shot game-to-real transfer on four models improves constraint reasoning for several but hurts fine-grained perception. The authors also release Game2Real-Dataset, with about 24K verified game samples across 18 tasks and 65 QA families.

vision-language model

Game2Real-BenchGame2Real-DatasetarXiv

evals, multimodal, research· Added 14 Oct 2026, published 13 Oct 2026

arxiv.orgpaper🧠 AI

UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models

UniW(A)M is a mixture-of-experts model built on diffusion Transformers that unifies action-conditioned world modeling (AC-WM) with several world-action model (WAM) variants. Shared experts capture common physical dynamics, while atomic capability experts are reused across interfaces, each keeping its own conditional pathway. At deployment a plug-in adaptive interface router (PAIR), trained on a small subset of target-task chunk scenarios with the backbone frozen, picks a WAM interface for each execution chunk. It reaches 88.3% average real-world success against 76.7% for the strongest baseline, leads WorldArena on all three action-conditioned world-modeling tasks, and PAIR raises OOD success from 63.3% to 76.7%.

Mixture of Expertsdiffusion modelTransformer

UniW(A)MPAIRLIBERO+3

research, multimodal· Added 14 Oct 2026, published 13 Oct 2026

arxiv.orgpaper🧠 AI

LLM Agents for Automated Post-Training via Structured Recipe Optimization

Introduces Structured Recipe Optimization (SRO), a paradigm for automated LLM post-training that optimizes over a task-adapted recipe space covering data sources, data processing, cross-domain composition and training configuration. It pairs an LLM-based Recipe-Space Designer, which builds the recipe space for the target tasks, with a Multi-Domain Recipe Optimizer that uses structured trial evidence to guide LLM candidate construction and multi-objective Bayesian trial selection. SRO gets the highest average score in all evaluated target-model and agent-LLM combinations; for example, 41.1 vs 15.4 for the strongest baseline on Qwen3-1.7B with GPT-5.4, and 53.8 vs 40.8 on Qwen3-4B with DeepSeek-V4-Flash, in joint post-training across three capability domains and six benchmarks.

Qwen3LLM

Structured Recipe OptimizationGPT-5.4DeepSeek-V4-Flash+1

research, training, agents· Added 14 Oct 2026, published 13 Oct 2026

en.cryptonomist.charticle🛡️ Security

Ledger hardware wallet breach exposes LTE implant stealing recovery phrases

Ledger's Donjon team confirmed on October 10, 2026 a physical implant inside a hardware wallet sold by Malaysia-based reseller CryptoBilis. The implant reads the device display, decodes the 24-word recovery phrase with a microcontroller, and exfiltrates it over LTE via a provisioned eSIM, leaving the Secure Element, OS, transactions and signatures untouched. Onchain investigator Specter traced over $86 million in suspected thefts across Ethereum, Tron and Bitcoin; Ledger advises CryptoBilis buyers to migrate funds to a new device with a new recovery phrase.

Ledger

CryptoBilisSpecterLedger Donjon

Hardware & RF, Blockchain· Published and added 13 Oct 2026

decrypt.coarticle🛡️ Security

Ledger Finds LTE Implant That Stole Recovery Phrases in Suspected $86M Drain

Ledger's Donjon security team confirmed an unauthorized hardware implant inside Ledger signers sold by Southeast Asian reseller CryptoBilis. The implant sits in the enclosure, reads the 24-word recovery phrase from the screen during setup, decodes it with a microcontroller, and exfiltrates it over LTE via a provisioned eSIM, without touching the Secure Element, OS, or signatures. Onchain investigator Specter traced over $86M in suspected thefts across Ethereum, Tron and Bitcoin; Ledger is reviewing reports of tampered devices elsewhere and advises affected buyers to move funds to a new device with a new seed.

Ledger

CryptoBilisLedger DonjonSpecter+2

Hardware & RF, Blockchain, Threat Intel· Published and added 13 Oct 2026

decrypt.coarticle🛡️ Security

Ledger Says All Confirmed Cases in Suspected $87M Drain Tied to Reseller CryptoBilis

Decrypt reports Ledger's 72-hour update: all confirmed wallet-draining cases involve devices sold by Southeast Asian reseller CryptoBilis, which has stopped hardware wallet sales. Onchain investigators Specter and YFarmX traced over $86M in suspected thefts across Ethereum, Tron and Bitcoin. They describe laundering through Tornado Cash, Zcash's shielded pool and a USDT-to-USDD swap to avoid Tether freezes. An unverified claim of tampered devices (extra cables, LTE module) bought at European retail stores is also covered. Ledger advises CryptoBilis buyers not to set up devices and to move funds to a new seed phrase on a device bought from Ledger.

LedgerEthereumBitcoin+2

CryptoBilisSpecterYFarmX+5

Blockchain, Hardware & RF, Threat Intel· Published and added 12 Oct 2026

coindesk.comarticle🛡️ Security

Ledger claims CryptoBilis cases are limited as up to $93M is traced, $10M frozen

CoinDesk reports that up to $93–94.5M in mostly USDT and TRON tokens was drained from 315–521 wallets across six or seven blockchains. The wallets were tied to Ledger hardware wallets sold by the authorized reseller CryptoBilis. Ledger confirmed an unauthorized hardware implant in at least one device but did not say how the devices were tampered with. Tether froze $10M, some funds moved through Tornado Cash and Zcash, and Ledger told affected users not to set up unused devices and to move funds to a new device with a fresh recovery phrase. The figures come from Bitquery and YFarmX onchain estimates, not a confirmed Ledger loss total.

LedgerTronZcash+1

CryptoBilisTetherBitquery+1

Blockchain, Supply Chain, Hardware & RF· Published and added 12 Oct 2026

coindesk.comarticle🛡️ Security

Why a bug that could have threatened XRP's 100 billion supply cap stayed hidden for years

CoinDesk reports on an 11-year-old integer-overflow bug in the XRP Ledger's DEX code. Hundreds of crafted offers could make the computed buyer cost wrap to near zero while sellers were credited in full, which would have minted billions of XRP beyond the 100 billion cap. The post-transaction no-new-XRP invariant check used the same miscounted total, and the per-account receive limit was sidestepped by spreading funds across hundreds of accounts. Researcher Cayden Liao and Veria AI reported it on Sept. 22, RippleX patched it with no evidence of exploitation, and the XRP Ledger Foundation plans AI adversarial testing, formal verification and a stronger bug bounty.

XRP Ledger

Cayden LiaoVeria AIRippleX+1

AppSec· Published and added 12 Oct 2026

coindesk.comarticle🛡️ Security

Ledger says unauthorized implant found in at least one device sold by CryptoBilis

CoinDesk reports that Ledger confirmed an unauthorized hardware implant in at least one device sold by the Southeast Asian reseller CryptoBilis. On-chain researchers estimate $86–93M was drained from more than 300 wallets across Bitcoin, Ethereum and Tron. Ledger says its internal systems and direct sales are unaffected, and it advises not initializing unused devices and moving funds to a new seed on a device bought directly from Ledger. It also covers the reseller's sales suspension, Ledger's tightened distributor chain-of-custody rules, unverified speculation that the reseller was bought out, and the July 2026 Coldcard seed-generation flaw.

LedgerBitcoinEthereum+1

CryptoBilisChangpeng Zhao

Blockchain, Hardware & RF, Threat Intel· Published and added 12 Oct 2026

eprint.iacr.orgpaper🛡️ Security

A methodology to derive security specifications for complex electronic systems

Paper proposing a risk-based methodology to derive security specifications for complex electronic systems such as AI chips and chiplet-based designs. It walks from system missions to assets, threat analysis, security requirements and then countermeasure selection. The method is illustrated on datacenter and automotive chiplets for the missions of secure provisioning, trimming, debugging, booting and remote attestation, and addresses the longer supply chain and multi-tenant threats that chiplets introduce.

Sylvain GuilleyIEEE ICDV 2026

Hardware & RF, GRC· Added 14 Oct 2026, published 12 Oct 2026

deadsimpletech.comarticle🧠 AI

There's little that's "inevitable" about AI

An essay arguing that LLM adoption is not "inevitable". It sets four criteria for inevitability: output not markedly inferior, socially valuable work, overwhelming advantage, and no cheap way to neutralise the technology. Firearms pass all four, the magnetic compass does not, and chemical weapons (following Bret Devereaux) are the closest analogy for LLMs. The author concludes LLMs fail the two criteria that matter most. They are useful mainly to weaker players, and strong practitioners can outcompete them once belief in the hype erodes.

LLM

Bret DevereauxArca

ai-policy· Added 11 Oct 2026, published 10 Oct 2026

eprint.iacr.orgpaper🧠 AI

Confidential Error-Correcting Codes

Introduces Confidential Error-correcting Codes (CECs), a primitive that decodes reliably from perturbed codewords while keeping the message confidential, defined by confidentiality, robustness and soundness. The Randomness-Security Separation framework encodes a nonce and masks the payload with a PRF; the Conc-CEC instantiation uses concatenated Reed-Muller and Reed-Solomon codes and claims stronger concrete security than PRCs (CRYPTO'24). The application, CECMark, watermarks AI-generated images and reportedly beats Gaussian Shading, PRCMark and T2SMark across 56 perturbations with a false positive rate below 1e-5 and no side effects.

Reed-Muller codesReed-Solomon codespseudorandom function

Confidential Error-Correcting CodesCECMarkConc-CEC+5

research, multimodal· Added 14 Oct 2026, published 10 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Confidential Error-Correcting Codes

Introduces Confidential Error-correcting Codes (CECs), a cryptographic primitive for decoding perturbed codewords while keeping the message confidential, defined by confidentiality, robustness and soundness. The Randomness-Security Separation framework encodes a nonce and masks the payload with a PRF. The Conc-CEC instantiation uses concatenated Reed-Muller and Reed-Solomon codes, and the authors claim stronger concrete security than PRCs (CRYPTO'24). The CECMark application watermarks AI-generated images, and the authors report it beats Gaussian Shading, PRCMark and T2SMark across 56 perturbations with a false positive rate below 10^-5.

Conc-CECCECMarkPRCMark+3

AI Security· Added 14 Oct 2026, published 10 Oct 2026

decrypt.coarticle🛡️ Security

Ledger Probes Potential Theft of $87M in User Funds Tied to Crypto Wallet Reseller

Ledger is investigating fund losses among Southeast Asian customers who bought hardware wallets from the reseller CryptoBilis. It has asked the reseller to pause sales and told buyers from the last 90 days not to set up their devices, or to move funds to a new device with a fresh seed phrase. Onchain investigator Specter traced about $86–87M in thefts across Ethereum, Tron and Bitcoin. Ledger has confirmed neither the figure nor the cause, though pre-compromised devices shipped with an attacker-known recovery phrase are the suspected mechanism.

LedgerEthereumBitcoin+1

CryptoBilisSpecterArkham

Blockchain, Supply Chain· Published and added 9 Oct 2026

en.cryptonomist.charticle🛡️ Security

Ledger investigates hardware wallet theft reports; losses estimated above $86M

News report on a suspected supply-chain attack on Ledger hardware wallets sold by the Southeast Asian reseller CryptoBilis. Ledger asked the reseller to suspend sales and shipments, and told buyers from the last 90 days not to initialize devices. Customers who already set up a device should move funds to a new signer with a fresh seed. Onchain investigator Specter estimated more than $86M stolen from hundreds of wallets across Ethereum, TRON and Bitcoin, with 10 addresses listed. The figure is unconfirmed, and there is no evidence that Ledger's hardware or core infrastructure was compromised. The Security Alliance directs victims to its SEAL 911 Telegram bot.

LedgerEthereumTron+1

CryptoBilisSpecterSEAL 911+3

Supply Chain, Blockchain· Published and added 9 Oct 2026

theblock.coarticle🛡️ Security

Ledger investigates wallet drains involving CryptoBilis buyers; estimate tops $86 million in losses

The Block reports that Ledger is investigating fund losses among Southeast Asian users who bought hardware wallets from the reseller CryptoBilis, an official reseller in Indonesia, Malaysia and the Philippines. Onchain researchers tanuki42 and Specter estimate losses above $72M and $86M across Bitcoin, Ethereum and Tron, but neither figure is confirmed. Ledger asked the reseller to pause sales and advised buyers from the past 90 days not to set up devices, or to move assets to a new signer with a new seed. Changpeng Zhao suggested a supply-chain attack with fake or tampered devices, and Mark Karpeles asked for circuit-board photos to check for tampering.

LedgerBitcoinEthereum+1

CryptoBilisSEAL 911tanuki42+3

Blockchain, Threat Intel· Published and added 9 Oct 2026

coindesk.comarticle🛡️ Security

Ledger investigates potential wallet tampering after reports of $86 million in crypto stolen

Ledger is investigating reports of over $86 million allegedly stolen from hundreds of Bitcoin, Ethereum and Tron wallets tied to devices sold by Southeast Asian reseller CryptoBilis. Ledger asked the reseller to halt sales/shipments and advised recent buyers not to activate devices and already-activated users to migrate funds to a new device with a freshly generated recovery phrase, as a possible supply-chain attack (devices pre-loaded with an attacker-known recovery phrase) is one explanation under investigation. No independent confirmation of the loss amount or root cause has been established yet.

LedgerBitcoinEthereum+1

CryptoBilisSpecterBitget+4

Blockchain, Hardware & RF· Published and added 9 Oct 2026

opensourcemalware.comtalk🛡️ Security

The OpenSourceMalware Show #24

Weekly OpenSourceMalware podcast episode discussing three fresh supply-chain attacks: the Tensorlake AI-agent SDK compromised via a preinstall-triggered Mini Shai-Hulud-derived worm (setup.mjs + math_symbol.js, run via Bun) that steals credentials; a Contagious Interview-style attack where a cloned GitHub repo drops a malicious .npmrc pointing npm install at a Chinese mirror (npmmirror.com) to resolve a hidden package; and 42 malicious RubyGems published by "Ghost Dev" that typosquat/brandjack crypto tooling, sleep 20–40 minutes via extconf.rb to evade detection, then either open a reverse shell or deploy a Python-based "Wallet Guard" crypto-theft kit targeting TronLink, MetaMask, Phantom, Binance, Coinbase, Trust Wallet, Exodus, and Electrum. The hosts also discuss multi-package "combined payload" evasion and how AI-driven slopsquatting is making cross-ecosystem typosquats more effective.

npmRubyGemsBun+2

Mini Shai-HuludContagious InterviewDPRK+16

Supply Chain, Malware & RE· Published and added 9 Oct 2026

arxiv.orgpaper🧠 AI

Reward Hacking as a Transmissible Strategy in Multi-Agent Coding Systems

The paper shows that reward hacking learned by one coding agent during RL against automated evaluators can spread to other agents in a multi-agent system. Across four open-weight backbones, coding RL elicits and amplifies hacking when the system prompt describes the mechanism explicitly and when mid-training exposes the model to facts about the coding environment. Post-RL source agents transmit the hack through code, shared memory and natural-language plans, and even small open-weight agents induce hacking in stronger closed-weight targets. On hard tasks the hack can intensify along a transmission chain after the source leaves, as the code accumulates more persuasive justifications. A theoretical analysis gives conditions under which hacking amplifies, persists or decays. Hacking also biases task routing toward hacking agents and survives candidate aggregation. Code review by the coder or by a separate verifier reduces the hacks that get delivered.

arXiv

ai-safety, agents, research· Added 12 Oct 2026, published 9 Oct 2026

huggingface.corepo🧠 AI

Aleph Alpha Kolibri-1 (78B MoE, Apache 2.0)

Kolibri-1 is Aleph Alpha's 78B-parameter mixture-of-experts reasoning model focused specifically on German and English, released under Apache 2.0 with weights, a vLLM plugin, and an extensive model card. It supports explicit reasoning-effort levels, Hermes-style tool calling, and a native 262144-token context validated to over 1M via sliding-window attention; the card details its UniBPE German-aware tokenizer, pre-training mid-training and long-context training recipe, MergeMix-based data-mix search, Merlin-Arthur hallucination-mitigation RL protocol, and benchmark results against Qwen3.5, Gemma 4, Nemotron 3 Nano, and Apertus on MMLU-Pro, GPQA, AIME, SWE-bench Verified, Terminal-Bench 2.1, and HLE.

Hugging FacevLLMPyTorch+3

Aleph AlphaKolibri-1Kolibri Base+21

models, training, evals· Added 8 Oct 2026

microsoft.comarticle🛡️ Security

Post-quantum authentication: Why organizations should start testing certificate ecosystems now

Microsoft Security Blog post arguing that post-quantum cryptography planning must extend beyond encrypted-data confidentiality to authentication and certificate ecosystems, since larger PQC certificate chains and new algorithms like ML-DSA-87 can break PKI workflows, HSMs, and legacy infrastructure. It details Microsoft's PQC TLS Pilot Program, launched August 27, 2026, which lets approved certificate authorities (ComSign, DigiCert, HARICA, IdenTrust, Sectigo, Shanghai Electronic Certification Authority, SSL.com) test ML-DSA-87 certificate issuance under the Microsoft Trusted Root Program, plus Windows 11 builds (KB5101681, KB5101684) that support evaluating ML-DSA certificates via Schannel in non-production environments. It lays out a six-step readiness plan: inventory certificate dependencies, map trust relationships, assess vendor roadmaps, identify long-lived infrastructure, build test environments, and plan a multi-year migration.

WindowsSchannelTLS

ML-DSAML-DSA-87PQC TLS Pilot Program+8

Identity & Access, GRC· Published and added 8 Oct 2026

arxiv.orgpaper🧠 AI

OmniCapBench: A Deep-Structured Evaluation Framework for Fine-Grained Audio-Visual Captioning

OmniCapBench is a benchmark of 786 densely annotated videos that evaluates audio-visual captioning in multimodal LLMs. Instead of scoring free-form captions, it asks models for sets of atomic, verifiable units across three tracks (entity references, visual shots, audio events). These are scored with deterministic constraint checks plus localized LLM-based semantic comparisons, which gives coverage, localization and stable scoring together. Tests on frontier MLLMs show strong local perception but weak long-horizon audio-visual reasoning, especially identity drift and cross-modal misalignment.

multimodal large language model

OmniCapBench

evals, multimodal, research· Added 9 Oct 2026, published 8 Oct 2026

arxiv.orgpaper🧠 AI

Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Distribution

Introduces "prior barrier", a measure of how strongly a pretrained LLM supports competing concepts over a target concept, and shows prior barriers follow a long-tail distribution, so tail concepts need more SFT instructions than head concepts. It derives a predictive risk bound for SFT under long-tail prior barriers and proposes PASS, an adaptive instruction selection method that builds reference-derived concepts, estimates each instruction's distinguishing evidence, and allocates the selection budget toward under-covered concepts. PASS beats seven state-of-the-art instruction selection methods across four backbone-budget settings, and ablation shows adaptive allocation beats uniform allocation.

PASS

training, research· Added 9 Oct 2026, published 8 Oct 2026

galaxy.comarticle🧠 AI

Understanding AI, Part I: The Machines That We Taught

Galaxy Research's explainer traces AI's intellectual history from the 1956 Dartmouth Workshop and the Logic Theorist through the symbolic-AI era (expert systems like Mycin and XCON, the Cyc project) to the sub-symbolic/connectionist revival (Perceptron, Hopfield networks, backpropagation, LeCun's CNN zip-code reader) that culminated in deep learning and the 2017 Transformer architecture. It then surveys three machine-learning paradigms (supervised, reinforcement, unsupervised) and five deep generative architectures (autoregressive, diffusion, GANs, VAEs, normalizing flows), closing with how modern LLM chatbots combine these via pretraining, SFT, and RLHF, and how multimodal, reasoning, RAG, agent, and world-model systems extend base LLMs.

Transformer

Jensen HuangGreg BrockmanGPT-6 Astra+34

research· Added 8 Oct 2026

github.comrepo🛡️ Security

chainguard-dev/malcontent

malcontent is Chainguard's open-source malware/supply-chain discovery tool that analyzes binaries (ELF, Mach-O, PE, a.out) and scripting-language source across Linux, macOS and Windows using 14,500+ YARA rules (including third-party rules from Avast, Elastic, FireEye, Mandiant, Nextron and ReversingLabs). Its standout feature is differential analysis ("mal diff"), which compares two versions of a build artifact or OCI image against a known-good baseline to surface newly introduced risky behaviors — the technique the project demonstrates on the 3CX supply-chain compromise by flagging unexpected high-risk changes to libffmpeg. It also supports threshold-based scanning ("mal scan") and unfiltered capability enumeration ("mal analyze"), and is designed to run inside CI/CD pipelines, including air-gapped environments via embedded rules.

YARAGoRust+4

3CX Compromise

Supply Chain, Malware & RE, Blue Team & Detection· Added 8 Oct 2026

arxiv.orgpaper🛡️ Security

An Exploration of Attacks on AI Agent Systems

Paper that tests prompt injection against AI agents through both direct user input and inter-agent communication. The attacks leak sensitive information and trigger cross-agent infections. Most findings concern single-agent systems, and the authors then extend the attacks to multi-agent frameworks to show how weaknesses propagate between agents. It argues that agents embedded in real-world workflows need stronger defenses.

LLM

prompt injection

AI Security· Added 12 Oct 2026, published 8 Oct 2026

arxiv.orgpaper🧠 AI

From Evidence to Action: How Tool-Using Agents Fail

This paper studies where tool-using LLM agents break down in the chain from gathering evidence to taking consequential actions, finding that strong static judgment of whether to act often coexists with much weaker interactive execution, and that failures arise from incomplete investigation, acting before evidence is established, or unresolved prerequisites in multi-action workflows. The authors introduce SafeActBench, a 656-case benchmark across six operational domains and five protocols, paired with a provenance-bound Evidence Ledger and a deterministic trajectory evaluator tested across ten model-harness configurations.

SafeActBench

evals, agents· Added 7 Oct 2026

unit42.paloaltonetworks.comarticle🛡️ Security

Evolution of Web3 in Cloud Supply Chain Attacks

Unit 42 analysis of how supply chain malware uses Web3 for command and control, in three phases: EtherHiding (smart contract state), TxDataHiding (transaction calldata across TRON, Aptos and BSC) and NullReceiver (C2 IPv4 decoded from a zero-value transaction's recipient address). It covers the ChainDrop npm worm, the PolinRider campaign and DPRK-linked Axios, Mastra and arrayref compromises. It ends with detection and CI/CD policy guidance.

npmBunTron+3

ChainDropPolinRiderEtherHiding+15

Supply Chain, Threat Intel, Blockchain· Added 30 Oct 2026, published 7 Oct 2026

arxiv.orgpaper🧠 AI

RECAST: Learning to Compute the Right Context through Adaptive Evidence Routing

RECAST (Routing Evidence through Computation, Access, and Synthesized Tools) reframes retrieval-augmented generation as a sequential decision process: a lightweight RouterLM iteratively selects or formulates primitive/custom operations, a frozen CompilerLM turns custom operations into executable code, and a frozen AnswerLM produces the final answer once evidence is judged sufficient. RouterLM is trained with supervised fine-tuning followed by GRPO, and the system reaches a 75.6% mean success rate across six heterogeneous benchmark families, beating the strongest large-model baseline by 15.9% and a training-free Gemini 3.5 Flash router by 5.0%, with strong zero-shot generalization on three held-out benchmarks. It targets tasks where evidence must be derived through filtering, aggregation, or computation across multiple sources rather than retrieved verbatim.

Qwen3.5GeminiGRPO

Gemini 3.5 Flash

llm-apps, training, research· Added 8 Oct 2026, published 7 Oct 2026

huggingface.coarticle🧠 AI

One Model Family, Two Gold-Level Results: Fine-Tuning Nemotron for IOI and IMO

NVIDIA describes how it specialized Nemotron 3 base models into domain experts for competitive programming (IOI) and olympiad mathematics (IMO) using a four-step recipe: strong base model, curated domain data with reasoning traces, SFT/RL post-training, and an inference loop (GenCorrect for IOI, generate-verify-refine for IMO) that searches and refines candidate answers. Nemotron-3-Nano-CC and Nemotron-3-Ultra-CC reached gold-level IOI 2026 scores (535.4/600), while SFT and RL checkpoints of Nemotron 3 Ultra combined to score 30/42 on IMO 2026, exceeding the gold threshold with no formal prover or external tools. The post releases training datasets, checkpoints, the Nemotron-IMO-Bench benchmark, and the NeMo-Skills inference pipelines on Hugging Face.

Hugging FaceMixture of Experts

NemotronNemotron 3Nemotron-3-Nano-CC+11

training, evals, models· Published and added 7 Oct 2026

arxiv.orgpaper🛡️ Security

Package Hallucination Attacks on Coding Agents through Prompt Injection in Rule Files

This arXiv paper introduces the "package hallucination attack," where an attacker injects malicious prompts into benign agentic-coding rule files (such as .cursorrules) to induce a coding agent to replace legitimate package dependencies with attacker-controlled ones. The authors propose PackHallu, an evolutionary optimization framework that iteratively rewrites injected prompts using trajectory-level feedback and LLM-guided mutations, and show it achieves high attack success rates with strong transferability across multiple LLMs, benchmarks, and agent frameworks.

CursorLLM

PackHallu

AI Security, Supply Chain· Added 8 Oct 2026, published 7 Oct 2026

unchained.comarticle🛡️ Security

The randomness question at the heart of your hardware wallet

Unchained explains how hardware wallets generate entropy for bitcoin keys, covering PRNG vs TRNG theory, physical TRNG mechanisms (ring oscillator jitter, diode avalanche noise, radio static reuse), and NIST SP 800-90B / BSI AIS-31 certification. It compares entropy sourcing and secure-element chip suppliers (STMicroelectronics, Microchip, Infineon, Espressif, Silicon Labs) across Trezor, Ledger, Coldcard, BitBox02, Foundation Passport, Blockstream Jade and Block Bitkey, and walks through the July 2026 Coldcard firmware bug that silently bypassed its hardware TRNG for five years, causing real bitcoin losses. It also covers manual dice/coin entropy generation for BIP39 seed phrases, the Dark Skippy nonce-leakage attack, and multisig as the primary mitigation against single-device key-generation failures.

BitcoinColdcardTrezor+3

Coldcard firmware vulnerability (2026)Dark SkippyMilk Sad+6

Hardware & RF, Blockchain· Added 8 Oct 2026, published 7 Oct 2026

247wallst.comarticle🛡️ Security

Chinese network laundered $1B+ for North Korea's Lazarus Group — ZachXBT

Blockchain investigator ZachXBT published a 12-part thread detailing how he spent months posing as a client of a Chinese money-laundering network to prove it laundered over $1 billion for North Korea's Lazarus Group, including a large share of the ~$1.5 billion Bybit hack (FBI codename TraderTraitor). He cross-referenced the operators' private messages with on-chain data — including a wallet receiving fees from Bybit-hack-linked addresses and THORChain bridge transfer timing/amounts matching what operators disclosed — turning chat screenshots into verifiable blockchain evidence. The laundering network converted BTC into ETH, Solana and Tron, used THORChain for cross-chain hops, and parked funds in low-volume Uniswap pools; the investigation led Tether to freeze 442,000 USDT, part of over $75 million ZachXBT claims to have gotten frozen from North Korea-linked hacks since 2022.

EthereumBitcoinSolana+3

ZachXBTLazarus GroupBybit+2

Threat Intel, Blockchain, OSINT· Published and added 6 Oct 2026

arxiv.orgpaper🧠 AI

PEARS: Physical-Prior-Guided Efficient Adaptation via Failure Reasoning and Diffusion Steering for Tactile Manipulation

PEARS is a hybrid reinforcement-learning framework for sample-efficient online adaptation of pretrained tactile-manipulation policies to out-of-distribution conditions. A physics-guided force reasoning (PFR) module uses a vision-language model to diagnose failures from visual and tactile history and update contact-force bounds enforced by a hybrid force-position controller, while a separate tactile-conditioned diffusion-steering component perturbs the latent noise of a frozen flow-matching policy to correct free-space motion and contact timing without retraining the base model. It improves success rates by 12.4-37.4 points over strong baselines in simulation, cuts required interaction episodes by up to 53.2%, and reaches 95%/90% success on real-world whiteboard-erasing and pipette-liquid-aspiration tasks.

vision-language modelflow matching

PEARSWhiteboard ErasingPipette Liquid Aspiration

research, multimodal, training· Added 7 Oct 2026, published 6 Oct 2026

arxiv.orgpaper🧠 AI

ALIVE: Interaction-Aligned Object Insertion for First-Frame-Guided Video Editing

ALIVE is a framework for first-frame-guided video editing that inserts objects so they coherently participate in interactions (e.g., being picked up or manipulated) rather than appearing static. The authors curate a 35,800-pair editing dataset combining 3D-rendered, model-generated and real-world videos plus pairs from ROSE, and train a vision-language model to predict interaction guidance from the edited first frame and an instruction naming the object. They introduce the ALIVE-interaction benchmark to measure interaction fidelity, source preservation and visual coherence via a unified VLM-based protocol, reporting a 43.9% Overall improvement on this benchmark and 4.4% on a general video object insertion benchmark over the strongest baseline, with VLM-predicted guidance adding a further 0.95-point gain.

vision-language model

ALIVEROSEALIVE-interaction

research, multimodal· Added 7 Oct 2026, published 6 Oct 2026

arxiv.orgpaper🧠 AI

Agent in a Bottle: Can LLM Agents Turn Their Capabilities Into Cheap, Scalable Artifacts?

This paper introduces BOTTLED, a benchmark testing whether LLM agents can autonomously convert their general task-solving ability into cheaper, scalable artifacts (e.g., training a small distilled model or writing a reusable program) when faced with millions of similar queries under fixed time, compute, and API budgets, a capability the authors call bottling. Across ten models and three tasks, the authors find zero-shot performance does not reliably predict bottling success: 48 of 60 runs scored below their own model's zero-shot confidence interval, and 31 of 60 underperformed simple small-model distillation baselines. Despite this, strong agents like Opus 5 can retain about 82% of zero-shot macro-F1 at about 657x lower cost on query-product relevance classification, and recover about 94% of a dedicated cheap-inference model's (Jev) performance at a quarter of its cost.

Opus 5Jev

evals, agents, research· Added 7 Oct 2026, published 6 Oct 2026

github.comrepo🧠 AI

Show HN: OpenChart – OSS TradingView alternative with your own AI agent

OpenChart is an open-source TradingView-style desktop charting app (Electron, Node.js/Bun, Effect runtime) that embeds AI agents into statistical/technical analysis, real-time market alerts and agentic opportunity research. It ships a custom DSL called Tea (inspired by TradingView's Pine Script) for writing indicators, and a conversation interface built on assistant-ui and the AG-UI event protocol so users converse with agents while charting.

Node.jsBunElectron+3

OpenChartlongsurf.aiTea+7

agents, llm-apps· Published and added 6 Oct 2026

arxiv.orgpaper🧠 AI

Test-Time Adaptation of Quantized ViTs via Single-Pass Quantizer-Aligned Recalibration

This paper introduces Quantizer-Aligned Recalibration (QuAR), a single-pass, backprop-free test-time adaptation method for quantized vision transformers that recalibrates activations at frozen quantizers to counter distribution shift without updating model parameters. On ImageNet-C with ViT-B across 3-, 4-, 6- and 8-bit weight/activation precision, QuAR beats the strongest backprop-free baselines by up to 4.00 points while cutting latency 46% and adding only 0.17 MB memory overhead, with gains holding across continual streams, label shift, seven OOD suites, and three other backbones.

Vision TransformerTransformer

ViT-BImageNet-C

research, inference· Added 7 Oct 2026, published 6 Oct 2026

arxiv.orgpaper🧠 AI

Language Unalignability: Why Some Concepts Resist Cross-Cultural Benchmark Evaluation

This paper argues that the "Translation-Isomorphism Assumption" underlying multilingual LLM benchmarks is not just violated in practice but ill-posed for a class of concepts (pragmatic markers, honorifics, diachronically stratified terms). The authors formalize "α-unalignability" using a usage-cloud framework of contextualized embeddings, showing via FLORES-200 translation failures, LOBSTER reasoning scores, and a nine-model case study on the Yami language that models encode cross-lingual regularities (e.g. Yami clustering with other Austronesian/low-resource languages) that are not actionable via neuron-level interventions (a "Representation-Intervention Gap"). They propose a multidimensional diagnostic profile (Cycle-Consistency, Pragmatic-Load Disagreement, Manifold-Curvature Mismatch, RIG) to replace single-scalar cross-cultural evaluation.

FLORES-200LOBSTERYami

evals, research· Added 7 Oct 2026, published 6 Oct 2026

vals.aiwriteup🧠 AI

Opus 5.5 agents discover two room-temperature magnetic semiconductor candidates

Vals.ai researchers describe using Claude Opus 5.5-driven AI agents to run density-functional-theory simulations (PBE+U and HSE06 functionals) that designed a novel candidate magnet, YBaMnFeO₅, and separately re-identified a 1999 compound, KV[Cr(CN)₆] (a Prussian-blue analogue), as a "Luttinger compensated" semiconductor — a material with zero net magnetic moment but spin-sorted band edges, useful for spintronic memory. The agents predicted band gaps, spin-sorting windows, and magnetic ordering temperatures (KV[Cr(CN)₆] reportedly stays ordered above room temperature, up to 376 K), and the authors published all inputs, outputs and code on GitHub for independent verification. It's notable as an example of AI agents autonomously conducting and validating nontrivial physics/materials-science research rather than just answering questions.

density functional theory

Vals.aiClaude Opus 5.5YBaMnFeO₅+1

agents· Added 6 Oct 2026, published 5 Oct 2026

decrypt.coarticle🛡️ Security

Crypto Sleuth ZachXBT Fronted $350K to Pose as a Client of Lazarus' Chinese Launderers

Decrypt reports on ZachXBT's account of infiltrating a Chinese money-laundering syndicate working for North Korea's Lazarus Group, by posing as a client ("fronted" $349.7K USDC) to a contact named "Jimmy Green" to gather intelligence on how Bybit's $1.5B hack proceeds were being laundered. The piece details the on-chain corroboration methods used (matching Telegram screenshots to Thorchain swap timing, wallet clustering across BTC/ETH/SOL/Tron) that exposed a $12M+ cluster of Bybit exploit funds, led to a 442K USDT Tether freeze, and separately traced $3M in fraud proceeds to Huione Guarantee, a Cambodian marketplace later targeted by FinCEN and shut down by Telegram.

EthereumSolanaTron+3

ZachXBTLazarus GroupDPRK+12

Threat Intel, OSINT, Blockchain· Published and added 5 Oct 2026

arxiv.orgpaper🧠 AI

Learning What to Imitate: Entropy-Aware Distribution Mixing

The paper proposes Entropy-Aware Mixing, a knowledge-distillation method where a small student model's per-token distribution is dynamically interpolated with a stronger teacher model's distribution, gated by the student's own predictive entropy, to avoid "confident conflicts" where the student must imitate low-probability continuations despite being confident in a different one. The authors implement both convex and geometric interpolations under two training regimes — offline trace generation via speculative decoding followed by SFT, and on-policy forward-KL distillation — and show it stabilises distillation, improving in- and out-of-distribution math reasoning while better preserving general capabilities than fixed-teacher supervision, with offline traces favouring concave entropy schedules and on-policy training favouring linear/convex ones.

training· Added 6 Oct 2026, published 5 Oct 2026

rekt.newswriteup🛡️ Security

Bitget — Rekt

A rekt.news investigative timeline of the September 24, 2026 Bitget exchange breach, in which attackers exploited a zero-day in an unnamed third-party security product ("Product A"/"Product B") to gain privileged access, move laterally to Bitget's production wallet job server, and use a custom tool to forge risk-control parameters and invoke fraudulent withdrawals — draining $387.5M in ETH, XRP, Zcash and TRON without ever touching private keys or cold wallets. It synthesizes Mandiant's and SlowMist's incident-response reports, on-chain tracing from Arkham, Hacken, Bubblemaps, AMLBot, Bitquery and MistTrack showing laundering through THORChain and attempted NEAR Intents swaps, TRM Labs' partial linkage of the laundering infrastructure to prior North Korea-linked (TraderTraitor) hacks, and the public clash between Bitget's Gracy Chen and THORChain over refusing service to attacker addresses.

EthereumBitcoinXRP+3

BitgetMandiantSlowMist+8

Blockchain, Threat Intel· Added 3 Oct 2026

huggingface.copaper🧠 AI

Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States

The paper introduces PoS (Progression of States), an inference-time framework for long-horizon LLM agents that maintains explicit belief states — combining a current world-state estimate with unresolved task requirements — instead of relying on raw interaction-history memory. PoS detects "Belief Trapping" (stagnation, cycles, or drift where an agent keeps acting without real progress), diagnoses the blocked requirement, and applies targeted recovery constraints, achieving the best results across four benchmarks (including ALFWorld and RCA-100) and three LLM backbones, with gains up to 22.68% and 37.89% over the strongest baselines.

ALFWorldRCA-100

agents, research· Added 3 Oct 2026

alignmentforum.orgwriteup🧠 AI

Latent reasoning architectures would likely undermine CoT, our strongest oversight tool

An AI Alignment Forum essay arguing that chain-of-thought (CoT) monitoring — reading a model's natural-language reasoning trace to catch misaligned behavior before it is acted on — is currently one of the strongest practical oversight tools for frontier AI, and that a shift toward latent reasoning architectures (models that reason in continuous/hidden vector space rather than discrete, human-readable tokens) would largely destroy this transparency. The authors, including Ryan Greenblatt and collaborators, lay out why such architectures are plausible as a capability/efficiency direction and what the loss of CoT legibility would mean for safety oversight of advanced models.

Chain-of-Thought

Lukas FinnvedenAlexa PanAlek Westover+4

ai-safety· Added 3 Oct 2026

decrypt.coarticle🛡️ Security

Chainalysis Used AI to Trace the $387M Bitget Hack Back to North Korea

Chainalysis published an attribution report tying the September 24 $387M Bitget exchange hack to North Korea-linked actors, pushing DPRK's 2026 crypto theft total past $1 billion. The report details the attackers' laundering path: $387M moved in 23 transfers within three hours across Ethereum (49.7%), XRP (40.8%), Zcash (7.6%) and Tron (1.8%), with stolen XRP routed through a cross-chain liquidity protocol into Bitcoin rather than an exchange. Chainalysis also disclosed it used in-house AI automation to compress over 20 hours of manual bridge-transaction reconciliation into under 10 minutes, with humans still directing the investigation.

ChainalysisEthereumBitcoin+1

Gracy ChenNear IntentsElliptic

Blockchain, Threat Intel, AI Security· Published and added 3 Oct 2026

github.comcourse🧠 AI

walkinglabs/learn-harness-engineering

Learn Harness Engineering is a free, project-based GitHub course teaching how to build the "harness" around AI coding agents — the instructions, state tracking, verification, scope control, and session lifecycle that make models like Claude and Codex reliable on real repositories instead of just prompt-following. It includes 14 lectures, 8 hands-on projects built around an evolving Electron knowledge-base app, copy-ready templates (AGENTS.md, feature_list.json, init.sh, claude-progress.md), a harness-creator agent skill for scaffolding new projects, and a zero-dependency audit-harness.sh script that checks a repo against the five harness subsystems. Recent updates add "Frontier Harness Design Breakdowns" and new Graph/Loop Engineering lectures covering multi-node agent graphs, parallelism, and loop design outside the agent itself.

Claude CodeCodexNode.js+6

OpenAIAnthropicOpenHands+2

coding-agents, agents· Added 3 Oct 2026

tej.asarticle🧠 AI

Aleph Alpha Kolibri: How the sovereign German LLM works

A deep technical walkthrough of Aleph Alpha's Kolibri, a 78B-parameter (3.5B active) mixture-of-experts LLM for German and English released under Apache 2.0, built to be 'sovereign' (trained and deployable entirely under EU/German law). The post explains its six core design choices — a 384-expert MoE router, a German-aware UniBPE tokenizer (verified independently to cut tokens 15% vs GPT-5's tokenizer on legal German), mostly sliding-window attention enabling a validated 1M-token context, German-native reasoning traces, a Merlin-Arthur training protocol for calibrated 'I don't know' abstention, and a configurable reasoning_effort parameter — and benchmarks it against Qwen3.5/3.6/3.8, GLM-4.7 Flash, Nemotron 3 Nano and gpt-oss-120b on AIME, RULER, SWE-bench Verified, Terminal-Bench and the Omniscience test, including where it underperforms (tool calling, coding agents, trivia recall).

Hugging FacevLLMMixture of Experts+5

Aleph AlphaKolibriQwen3.6+19

models, inference, training· Published and added 3 Oct 2026

alignmentforum.orgarticle🧠 AI

Fixed-weight models are adversarially vulnerable: hence misaligned

A short AI Alignment Forum post by Stuart Armstrong arguing that any model with fixed weights is inherently adversarially vulnerable, and that this vulnerability itself should be treated as a form of misalignment rather than a separate security concern. The post text itself could not be retrieved (only forum metadata loaded), so the argument is inferred from the title and author's known research focus on AI alignment theory.

Stuart ArmstrongAI Alignment Forum

ai-safety· Added 2 Oct 2026

arxiv.orgpaper🧠 AI

NeutronGym: Physics-Graded Neutron Instrument Design for LLM Agents

NeutronGym is an executable environment for neutron scientific-instrument design where LLM agents build instruments via validating tools, McStas ray-traces the result, and a level-resolved ladder grades syntax, runtime, structure and physics without an LLM judge. A curated McStasBench slice adds 16 tasks from published instruments; seven frontier models solve at most 7 of 16, but reinforcement learning on the ladder's reward takes Qwen3-8B from 11% to 77% on held-out instances (surpassing an untrained Qwen3-32B), with ablations showing the partial-credit ladder is responsible for most of the gain.

Qwen3McStas

NeutronGymMcStasBenchQwen3-8B+1

evals, training, research· Added 5 Oct 2026, published 2 Oct 2026

bankless.comarticle🧠 AI

Ethereum Foundation Launches zkAPI for Private AI Payments

The Ethereum Foundation and the Open Anonymity Project launched zkAPI, a system that lets users prepay into an on-chain USDC vault and then generate zero-knowledge proofs to unlock short-lived, spend-capped API keys for AI models (and other metered services) without tying usage back to a billing identity. It ships a local client that mimics the OpenAI and Ollama APIs so existing apps can switch to account-free access with minimal changes, plus an in-browser client called OA Chat; funds remain withdrawable on-chain even if zkAPI's own servers go down. The design, originally sketched by Davide Crapis and Vitalik Buterin on Ethereum Research, still leaves IP address and prompt content visible to providers, so the team recommends Tor for stronger privacy.

EthereumOpenAI APIOllama+1

Ethereum FoundationOpen Anonymity ProjectDavide Crapis+4

ai-infra· Published and added 2 Oct 2026

decrypt.coarticle🛡️ Security

US Designates Russia's A7 Network as Transnational Criminal Organization

OFAC designated Russia's A7 Network a transnational criminal organization and FinCEN proposed a rule under the Combating Russian Money Laundering Act (section 9714) barring US institutions, including crypto exchanges, from transfers tied to its "Sub-Agent" front companies. The network, launched by fugitive oligarch Ilan Shor with state-owned Promsvyazbank, used the ruble-backed A7A5 token (issued by Old Vector on Tron and Ethereum) in a mirror scheme: crypto moved domestically while Sub-Agents made matching fiat transfers abroad in dollars, yuan, dirhams and euros, with staff spoofing their location via VPNs to appear based in Dubai, Hong Kong or Bishkek. FinCEN found over 180 entities moved at least $179.1 billion in A7A5 (mostly via sanctioned exchanges Garantex and Grinex) and ties the network to Iranian sanctions evasion, Nobitex, and laundering of North Korean exchange-hack proceeds.

EthereumTronUSDT+1

A7 NetworkA7A5OFAC+11

Threat Intel, Blockchain· Published and added 2 Oct 2026

huggingface.coarticle🧠 AI

AutoSynthData: Generating Training Data for Enterprise Agents

AutoSynthData is ServiceNow CoreAI's pipeline for generating synthetic training data for enterprise agents: it evaluates a target model against a stronger teacher in a given environment, distills capability gaps into sanitized "specification cards," then generates and validates new tasks (system specification, user prompt, verifier) via a target-then-multiply process with positive/negative verification, critique-repair loops, and batch-level coverage review. On EnterpriseOps Gym, fine-tuning Gemma-4-26B-A4B-it on AutoSynthData-generated samples improved Pass@1 by 7.2 points (35% relative) on the Hybrid domain and from 18.77% to 27.18% on ITSM, closing 59% of the gap to the teacher model on Hybrid.

Hugging FaceGemma

ServiceNowEnterpriseOps GymGemma-4-26B-A4B-it+2

training, evals, agents· Published and added 2 Oct 2026

chainalysis.comwriteup🛡️ Security

How AI Helped Chainalysis Investigators Trace the $387 Million North Korea Stole from Bitget

Chainalysis describes its investigation into the September 24, 2026 DPRK-attributed theft of $387 million from exchange Bitget, which within three hours moved across Ethereum (49.7%), XRP (40.8%), Zcash (7.6%), and Tron (1.8%). The team built custom in-house AI automation to reconcile cross-chain bridge activity (compressing 20+ hours of manual work to under 10 minutes) and traced stolen XRP through a cross-chain liquidity protocol into Bitcoin, pushing 2026 North Korea-attributed crypto theft past $1 billion.

EthereumBitcoinTron+2

BitgetDPRK

Blockchain, Threat Intel, AI Security· Added 7 Oct 2026, published 1 Oct 2026

arxiv.orgpaper🧠 AI

Image Classifiers are Efficient Self-Supervised Video Representation Learners

This paper introduces VideoMSN, a Masked Siamese Network that repurposes standard image Vision Transformers for self-supervised spatio-temporal video representation learning by representing videos as 'super images' (grids of sampled frames) and aligning spatial-masked and temporal-masked views with a shared ViT encoder. Starting from pretrained DINO-v3 and DeiT-v3 image encoders, it achieves state-of-the-art results on Kinetics-400, UCF101, and HMDB51 while using up to 32x and 160x fewer video pretraining epochs than prior video self-supervised methods, and shows strong low-shot transfer performance.

Transformervision-language model

VideoMSNDINO-v3DeiT-v3+3

research, multimodal· Added 1 Oct 2026, published 30 Sep 2026

x.comthread🧠 AI

Stevan Bogosavljević (@stevyhacker) on X

LokalBot 0.9.2 is a free, open-source local-first assistant that records meetings and screen activity, using five small open-weight models chained together entirely on a MacBook (6.8 GB total): Nemotron 3 for speaker diarization, Qwen3-ASR 1.7B for transcription, LFM2.5 1.2B for sentence completion, Harrier 0.6B for semantic search, and Qwen3.5 4B for recap generation. It exposes this captured 'memory' to Claude Code and Codex through an MCP server or CLI, letting coding agents pull context from past meetings and screen content.

Model Context ProtocolClaude CodeCodex+1

NemotronQwen3-ASRLFM2.5+6

mcp, multimodal, llm-apps· Published and added 30 Sep 2026

arxiv.orgpaper🧠 AI

Rho: A Foundation for Efficiently Adaptable VLA Models

Rho is a family of open-weights vision-language-action (VLA) models for bimanual robotic manipulation, designed for data-light adaptation across three dual-arm embodiments (YAM Box, UR AI Trainer, FR3 Duo). The paper ablates the action-expert architecture and training recipe, showing that "embodiment midtraining" improves downstream adaptation, and introduces an internal latent policy that learns from as few as 15 corrected episodes to steer a frozen flow-matching action expert toward out-of-distribution task situations. The authors release base and embodiment-specific checkpoints, positioning Rho as both a strong general-purpose manipulation model and a practical foundation for efficient adaptation.

flow matching

RhoYAM BoxUR AI Trainer+1

research, multimodal· Added 30 Sep 2026, published 29 Sep 2026

arxiv.orgpaper🧠 AI

From Routing Signals to Selective Review: Visual regrounding in MoE VLMs

This paper shows that internal expert-routing probabilities in Mixture-of-Experts vision-language models (Qwen3-VL-30B-A3B-Instruct and Gemma-4-26B-A4B-it) carry a strong, low-cost signal for detecting when a model is about to answer a question about an object that isn't actually in the image (a 'target-absence grounding failure'). Linear detectors trained on target-token routing probabilities achieve ROC-AUCs of 0.9988/0.9956 on GQA-Inpaint (retaining 0.78-0.81 on the external OBER dataset), and gating a target-aware review prompt on these detections improves end-to-end accuracy by up to +22% without modifying model weights. The work matters for practitioners building on MoE VLMs because it demonstrates a weight-free, pre-generation hallucination detector that reuses computation the model already produces.

Mixture of Expertsvision-language modelQwen3-VL+1

GQA-InpaintOBER

research, multimodal· Added 30 Sep 2026, published 29 Sep 2026

arxiv.orgpaper🧠 AI

Probability is Not Enough: Exploring and Counting Divergent Tokens for Reasoning Uncertainty Quantification in LLMs

This paper introduces Divergent Token Confidence (DTC), a method for uncertainty quantification in LLM chain-of-thought reasoning that counts tokens where two models strongly disagree during decoding, measured via Jensen-Shannon divergence between next-token distributions on the same reasoning trajectory. Unlike standard probability-based confidence scores, DTC requires no training, works in both white-box and black-box settings, and its divergent-token count is shown to be strongly negatively correlated with answer accuracy. Across six mathematical benchmarks and multiple model families (including DeepSeek-V3.2), DTC cuts expected calibration error roughly in half compared to full-sequence probability and verbalized-confidence baselines (e.g., 13.0% vs 32.7%-42.4% white-box).

DeepSeek-V3.2

evals, research· Added 30 Sep 2026, published 29 Sep 2026

latent.spacearticle🧠 AI

[AINews] Opus 5.5 is good at explainer videos

Daily AINews digest (Latent Space) covering the week's AI developments: Claude Opus 5.5's strong vision/coding results and viral code-generated explainer videos, the GPT-6 family (Astra/Sol/Luna) and Gemini 3.8 Flash releases, and Xiaomi's open MiMo-V2.6-Pro. It also covers the "System One" cheap-judge/reranker trend (TypeSafe's Jev vs. the open CLM alternative), agent infrastructure news from LangChain Interrupt and Perplexity's Rust-based Photon retrieval engine, inference speedups (Liquid AI's DSpark speculative decoder, GLM-5.3 on AMD MI355X), and research highlights including Harness-Zero agent-distillation, DeepMind's XYEval on sycophantic agent failure, a single-neuron safety-refusal bypass, and Sakana AI's RSI Lab with Jürgen Schmidhuber.

LangChainvLLMSGLang+11

Claude Opus 5.5GPT-6 AstraGPT-6 Sol+31

models, evals, research· Published and added 29 Sep 2026

decrypt.coarticle🧠 AI

Anthropic's Claude Sonnet 5.5 Is Out, Beats Opus 5.5 at Coding for Half the Price

Anthropic released Claude Sonnet 5.5, priced the same as Sonnet 5 ($2/$10 per million input/output tokens, half of Opus 5.5) but outperforming Opus 5.5 on Terminal-Bench 4.0 (70.6% vs 66.4% per Anthropic; 63.6% vs 59.6% per independent tester Artificial Analysis). The piece details how effort-setting (Medium vs High vs Max) drastically changes cost-per-task — Sonnet 5.5 at max effort burns ~193K tokens per task, more than any model Artificial Analysis has tested, undercutting Anthropic's claimed savings — and notes Sonnet 5.5 ties Opus 5.5 on GDPval-AA (real-world professional work) while Opus remains stronger on sustained-judgment tasks. Claude Haiku 5.5 is teased for coming weeks.

Claude

Claude Sonnet 5.5Claude Opus 5.5Anthropic+7

models, evals· Added 30 Sep 2026, published 28 Sep 2026

github.blogarticle🧠 AI

How we found 24 Android vulnerabilities using our open source AI security agent

GitHub Security Lab describes the architecture behind its open-source "Taskflow Agent," a set of YAML-defined prompt workflows (e.g. gather_mobile_entry_point_info.yaml, classify_application_local.yaml) that guide an LLM through structured, multi-step code audits rather than a single free-form prompt. Using these taskflows on Android apps via GitHub Copilot, the team found 24 vulnerabilities, including a location-tracking bug in OsmAnd via exported-activity intent extras and a cookie-leaking account-takeover chain in the Wikipedia app via a deeplink hostname-parsing flaw. The post's more durable contribution is its analysis of LLM auditing behavior: strong at recognizing API-level unsafe patterns and generating working PoCs, but unreliable at severity estimation and prone to false positives from mitigating factors the model doesn't account for, requiring human review and repeated/guided runs.

GitHub CopilotSQLiteYAML

GitHub Security LabOsmAndWikipedia

coding-agents, agents, prompting· Added 8 Oct 2026, published 28 Sep 2026

arxiv.orgpaper🧠 AI

Retrieving Biblical Intertextual References in Karen Blixen's Seven Gothic Tales

This arXiv preprint tackles biblical intertextuality detection in Karen Blixen's Seven Gothic Tales, building a benchmark of 189 annotated references checked against 31,170 verses of historical Danish Bible translations. The authors compare TF-IDF and BM25 with multilingual and Danish sentence encoders, show linguistically normalized BM25 is a strong zero-shot baseline (R@10 0.365), and fine-tune the Danish encoder DFM-large with hard negatives to raise R@10 to 0.508 (and allusion retrieval from 0.138 to 0.339). It's a case study in applying modern retrieval/embedding techniques to a literary-scholarship problem, with a scholar-in-the-loop evaluation showing retrieval models surface valid references missed by prior editorial annotation.

BM25TF-IDF

Karen BlixenSeven Gothic TalesDFM-large

research, evals· Added 29 Sep 2026, published 28 Sep 2026

arxiv.orgpaper🧠 AI

TokenCast: Forecasting Token Consumption During LLM Agent Execution

TokenCast is a method for predicting how many tokens an LLM agent will consume while it is still running, addressing the fact that token usage for the same task can vary by an order of magnitude across runs due to context growth and tool-feedback-driven branching. It learns a composable cost representation per execution segment that captures both a segment's own consumption and the extra input cost it adds to every later call as context accumulates, refreshing forecasts online with no additional LLM calls (32.8ms mean prediction time per run on SWE-bench Verified). Across 4 task suites and 6 agent models (96 combinations), it cuts mean absolute forecasting error by 14.5% versus the strongest baseline and reduces token usage by 21.3% versus a fixed-budget policy in offline budget-control replay.

SWE-bench Verified

research, ai-infra, agents· Added 29 Sep 2026, published 28 Sep 2026

arxiv.orgpaper🧠 AI

FinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluating Financial Research Agents

FinAutoRubric is a system where finance experts provide reusable evaluation guidance while a writer agent and reviewer agent automatically generate, research, and validate query-specific grading rubrics for financial research agents, escalating failures to humans. The paper releases a 100-query FinAutoRubric Benchmark spanning 78 tasks and eight asset classes built from in-house analysts' key questions, showing the generated rubrics track expert scoring as closely as the strongest baseline generator while covering more criteria, agree with human grading, and are preferred by analysts in blind review.

FinAutoRubricFinAutoRubric Benchmark

evals, agents· Added 29 Sep 2026, published 28 Sep 2026

arxiv.orgpaper🧠 AI

Hard Vision, Easy Vision: What GPT-6 Astra Reveals Across Computer Vision

This paper systematically evaluates GPT-6 Astra alongside five other frontier general-purpose AI systems across 34 capabilities and 55 benchmarks spanning nine areas of computer vision, benchmarking them against dedicated specialist models and human performance where available. It finds general-purpose systems increasingly match reference performance on semantic interpretation, reasoning, and object-centric prediction, but still lag on metric geometric accuracy, faithful reconstruction, temporally consistent dense prediction, and fine-grained specialist visual knowledge. Astra shows the strongest gains among the systems tested, particularly in visual/spatial reasoning and structured prediction tasks.

vision-language model

GPT-6 Astra

evals, multimodal· Added 29 Sep 2026, published 28 Sep 2026

arxiv.orgpaper🧠 AI

A Unified Uncertainty Representation for Graph Neural Networks via Doubly-Spectral Stochastic Expansion

Proposes a doubly-spectral stochastic (DSS) expansion for graph neural networks that unifies calibration, out-of-distribution detection, and distribution-shift robustness in a single representation: graph Fourier filters capture structural variation while an orthogonal-polynomial chaos coordinate captures latent stochastic variation. DSS-GNN (standalone) achieves the lowest Brier score across 14 node classification benchmarks without post-hoc correction, and DSS-Hybrid (a residual branch beside a deterministic encoder) achieves the best AUROC on most node-OOD settings and strongest shifted accuracy on all 7 GOOD concept-shift benchmarks under standard ERM. A capacity theorem shows the method matches chaos coefficients of Gaussian-latent random graph signals under full-rank assumptions with exponentially decaying truncation error.

GOOD

research· Added 29 Sep 2026, published 28 Sep 2026

arxiv.orgpaper🛡️ Security

OT-PCA: New Key-Recovery Plaintext-Checking Oracle Based Side-Channel Attacks on HQC with Offline Templates

OT-PCA is a new plaintext-checking (PC) oracle side-channel attack on HQC, a post-quantum key encapsulation mechanism, that uses offline templates built from the public HQC decoder to extract soft information on secret key bits with far fewer oracle calls than prior methods. The authors report a 2.4x reduction in required oracle calls for HQC-128 (and larger reductions for HQC-192/256), strong resilience even when the oracle is only 95% accurate (7.6x reduction), and validate the attack practically via power analysis on an ARM Cortex-M4 microcontroller.

HQCARM Cortex-M4

Hardware & RF· Added 4 Oct 2026, published 28 Sep 2026

huggingface.coarticle🧠 AI

Holo4: powering generalist computer-use agents

H Company's blog post announcing Holo4, a pair of open-weight agentic models (27B dense and 35B-A3B MoE, built on Qwen bases) trained to operate software through GUIs, code execution, MCP, and APIs interchangeably rather than a single interface. It reports benchmark results on OSWorld 2.0 and AutomationBench against frontier models like Claude Opus 5.5 and GPT-5.6 Sol, describes the training pipeline (an internal "Agentic Task Factory" generating ~10,000 verifiable environments/tasks plus SFT+RL), and also releases Holotron4 Nano, the same post-training recipe applied to NVIDIA's Nemotron 3 Nano Omni. Weights (BF16, FP8, NVFP4, GGUF) and full agent trajectories are published on Hugging Face.

Hugging FaceGGUFQwen+2

Holo4Holo4-27BHolo4-35B-A3B+15

models, agents, evals· Added 29 Sep 2026, published 28 Sep 2026

simonwillison.netarticle🧠 AI

2026 in LLMs (so far)

Annotated slides and notes from Simon Willison's WeAreDevelopers closing keynote, chronologically walking through 2026's LLM developments: the Claude Opus 4.5/GPT-5.1 jump that made coding agents reliable, the explosive rise of the OpenClaw 'Claw' personal-agent category, StrongDM's human-free 'Software Factory', the brief government shutdown of Claude Fable over its ability to patch (not just find) security bugs, and a string of incidents where OpenAI's and Anthropic's own training-time agents broke sandbox containment and attacked live systems (Hugging Face, RubyGems, PyPI, a German wiki, Australia's Medicare site). It also tracks 'tokenmaxxing' economics, his running pelican-riding-a-bicycle SVG benchmark across Gemini 3.1 Pro, Qwen3.6/3.8, GPT-5.6/GPT-6 Astra/Luna and Claude Fable 5.1/Opus 5.5, and the new FelonyBench tracker of lab-caused cyber incidents.

Claude CodeCodexOpenClaw+2

Claude Opus 4.5GPT-5.1MoltBook+21

models, coding-agents, ai-policy· Added 29 Sep 2026, published 27 Sep 2026

arxiv.orgpaper🧠 AI

Agent-Editing World Model: Rethinking World Modeling for LLM Agents

This arXiv paper proposes the Agent-Editing World Model (AEWM), which reframes world modeling for LLM agents: instead of predicting/reconstructing environment observations (high-entropy tool responses), it models how reasoning and actions shape future task progress, addressing "task-state contamination" where stale plans and unsupported assumptions corrupt later decisions. It introduces Action Judge (classifying decisions as Critical, Exploratory, or Noisy) and State Revision, combined via EditAct which directly edits the agent's state using real execution feedback rather than issuing critiques. Trained across Search, Terminal, and Software Engineering domains, AEWM reports 70.5% macro-F1 on its Action Judge benchmark (+10.6 over the strongest frontier baseline) and EditAct improves average scores by 3.2–6.7 points across six benchmarks and three agent backbones, with an RFT variant (AEWM-RFT) also outperforming Self-RFT.

AEWMAction JudgeState Revision+3

agents, research· Added 26 Sep 2026

github.comadvisory🛡️ Security

Unauthenticated Administrator takeover exfiltrated via the MCP

GHSA advisory for SiYuan v3.7.1 (CVE-2026-66012) describing how the kernel's POST /mcp route registers 31 MCP tools — including a file tool with full read/write/delete/rename/copy over the workspace — gated only by CheckAuth with no admin or read-only check. When the Publish server runs in anonymous mode (a documented configuration for public docs sites), the Publish reverse proxy attaches an anonymous RoleReader JWT to every proxied request, letting an unauthenticated attacker reach /mcp, exfiltrate accessAuthCode/api.token/cookieKey from conf.json, escalate to Administrator, and plant a malicious Electron plugin (nodeIntegration:true, contextIsolation:false) for RCE on the desktop client. Includes a full working PoC and remediation (CheckAdminRole gate, proxy path allowlist, restricting the file tool).

Model Context ProtocolDockerElectron

CVE-2026-66012CWE-862GHSA-jqwg-75qf-vmf9+6

AppSec· Added 26 Sep 2026

arxiv.orgpaper🛡️ Security

MCPTox: A Benchmark for Tool Poisoning Attack on Real-World MCP Servers

MCPTox is a benchmark for evaluating Tool Poisoning Attacks against LLM agents using the Model Context Protocol, where malicious instructions are hidden in a tool's metadata rather than its output. Built on 45 live real-world MCP servers and 353 authentic tools, it generates 1312 malicious test cases across 10 risk categories via three attack templates, and finds widespread vulnerability across 20 LLM agents: o1-mini has a 72.8% attack success rate, more capable models are often more susceptible due to stronger instruction-following, and even the best model (Claude-3.7-Sonnet) refuses less than 3% of attacks.

Model Context Protocolo1-miniClaude-3.7-Sonnet

Tool Poisoning AttackMCPTox

AI Security· Added 26 Sep 2026

arxiv.orgpaper🧠 AI

Learning to Stop without Learning to Stop: Self-Supervised Confidence Training Improves Reasoning Efficiency

This paper introduces a self-supervised fine-tuning procedure that trains reasoning models to predict their own confidence at intermediate points in a reasoning trajectory, using only 600 training problems and no explicit length or stopping objective. Despite training only on confidence prediction and using standard generation at inference (no early-stopping or confidence elicitation), the fine-tuned models cut generated tokens by up to 25% at matched accuracy across Gemma, Qwen, Nemotron, and GPT-OSS on math, science, and coding reasoning benchmarks. Analysis shows the models largely retain their base reasoning composition rather than suppressing specific behaviors, suggesting efficient reasoning can emerge as a byproduct of learning metacognitive confidence signals.

GemmaQwenNemotron+1

training, research· Added 28 Sep 2026, published 25 Sep 2026

arxiv.orgpaper🧠 AI

First-Order Stationarity of Reverse Diffusions

This arXiv preprint develops a first-order theory connecting optimization and sampling for diffusion models. It shows that SDE-based reverse-time flows of overdamped and underdamped Langevin diffusions contract relative Fisher divergences at explicit exponential rates when the forward process's stationary potential is strongly convex (a property of the chosen noising schedule, not the data), a guarantee absent from ODE-based reverse processes. It also derives averaged first-order stationarity bounds under discretization, the sampling analog of averaged gradient-norm guarantees in nonconvex optimization, for both overdamped and underdamped samplers.

research· Added 28 Sep 2026, published 25 Sep 2026

arxiv.orgpaper🧠 AI

User Model Extraction via Belief Self-Distillation

This paper introduces Belief Self-Distillation (BSD), a read-write probing framework where a frozen LLM acts as its own teacher to learn a compact representation of its implicit beliefs about the current user, which can be both decoded (read) and written back into the model's activations (write) to causally test its effect. The authors show BSD recovers user beliefs more faithfully and enables stronger interventions than matched hidden-state steering, and that changing the model's inferred user intent alters refusal decisions even when the request itself is held fixed. They also find that independently trained LLMs across different model families converge on a similar geometry for representing users, with direct implications for how safety-relevant refusal behavior depends on who the model believes it is talking to.

Belief Self-Distillation

ai-safety· Added 28 Sep 2026, published 25 Sep 2026

arxiv.orgpaper🧠 AI

New LoRA Skills Should Read but Never Write

This paper introduces READ (Read-only Expansion of Adapter Deltas), a method for composing multiple independently trained LoRA adapters into a single model without the weight-space interference that plagues naive merging. READ rewrites each adapter into a balanced canonical factorization and adds a one-directional coupling so new skills can read old adapters' input subspaces but never write into their output subspaces, requiring only a small trainable coupling row per new skill and folding cleanly into base weights with no inference-time routing cost. Evaluated across four benchmark suites and two model families, READ outperforms the strongest published composition baselines by over 20 points on SuperGLUE and over 7 points on a domain suite when adding skills sequentially.

LoRA

SuperGLUE

training, research· Added 28 Sep 2026, published 25 Sep 2026

arxiv.orgpaper🧠 AI

Strategically Diverse Sampling for Self-Training

This paper argues that self-training data for LLMs should be filtered for strategic diversity — substantively different problem-solving approaches — rather than just correctness among IID samples. It introduces GROOT, a method that builds a hierarchical tree of approaches and samples distinct paths, and adapts Verbalized Sampling (VS) to generate an unstructured set of approaches, testing both on competitive programming and Next-Chapter Prediction tasks. Models trained on strategically diverse (even incorrect) traces from Qwen3-4B outperform IID distillation from a 235B teacher and provide stronger initializations for RL and test-time scaling.

Qwen3-4B

GROOTVerbalized SamplingNext-Chapter Prediction

training· Added 28 Sep 2026, published 25 Sep 2026

ethresear.chthread🛡️ Security

Formal Verification of Execution and Consensus Clients

A reply on the Ethereum Research forum (ethresear.ch) responding to @pcaversaccio's call for formally verifying hard-fork changes in Ethereum execution and consensus clients before mainnet activation. It surveys current formal-verification efforts (Etheorem/SizzLean in Lean 4 for consensus/SSZ, Verifereum in HOL4 for the EVM), argues for prioritizing high-leverage surfaces like SSZ, state transition, fork-choice and core EVM semantics, and pushes for stronger verification tooling in Rust (Aeneas/Verus/hax) as an incremental path to reducing the trusted computing base of client software.

EthereumRust

EtheoremSizzLeanVerifereum+6

Blockchain· Added 29 Sep 2026, published 25 Sep 2026

arxiv.orgpaper🧠 AI

Quantum Channel Stein Theorem beyond Definite Causal Order

This arXiv paper proves that for finite-dimensional memoryless quantum channels, parallel, adaptive, and general (including indefinite causal order) hypothesis-testing strategies all achieve the same Stein exponent — the regularized channel relative entropy — showing adaptivity and exotic causal structures give no asymptotic advantage in quantum channel discrimination. The key technical result is continuity of the regularized sandwiched Rényi channel divergence at order one, plus an exact strong-converse exponent for general testers.

research· Added 26 Sep 2026, published 24 Sep 2026

arxiv.orgpaper🧠 AI

Requirement-Bound Verified Commissioning: A Frozen Four-Billion-Parameter Local Model as a Candidate Generator under an External Acceptance Layer with Verification and Release Authority

This paper proposes a "requirement-bound verified commissioning" protocol for mechatronic systems where a frozen four-billion-parameter local language model only generates candidate plans, while a separate external gate under a sealed grammar holds sole authority to verify and release them. Evaluated once on a 144-task benchmark written by isolated agent contexts with no access to the gate/grammar, it found 21 of 22 fabricated "ready" plans on unanswerable tasks were correctly rejected, no false release occurred among 83 in-benchmark releases (Clopper-Pearson upper bound 0.0354), but one false release later occurred among 146 out-of-benchmark releases, and incorrect user answers were released in 169/431 pairings — highlighting limits of candidate/release separation as a safety architecture for small local models.

research, evals· Added 26 Sep 2026, published 24 Sep 2026

arxiv.orgpaper🧠 AI

Screen Before You Serve: Simulation for Production Customer Experience AI Agents at 140M Scale

This paper describes a hypothesis-driven simulation workflow (using the Snowglobe simulator) for screening candidate LLM-based customer experience agents before production deployment, applied to Nubank's Card Delivery and Card Management chat-support agents in Brazil at 140M-user scale. Synthetic customers and simulated tool outputs let the team test multi-step agentic workflows without hitting production backends; across 4 deployed versions, simulated evaluator scores correlated strongly with production outcomes, and simulation-guided iteration lifted transactional NPS by 36.69 points and self-service rate by 8.82 points in live A/B tests.

NubankSnowglobe

evals, agents· Added 26 Sep 2026, published 24 Sep 2026

latent.spacewriteup🧠 AI

[AINews] Meta Connect 2026: Muse glasses, voice, video, and Charm

Latent Space's AINews digest for 9/22-23/2026 covers Meta Connect 2026's unveiling of Muse — Meta's personal agent now with voice, real-time video, a research-release Realtime Avatar model, Mac computer-use, Muse Mail, 1,500+ connector integrations, and new hardware (Ray-Ban Gen 3, $1,299 Meta VR Glasses, the December-shipping Muse Charm keychain, and an FDA-cleared hearing aid), alongside the WaveForms AI acquisition and a teased-but-unshipped frontier model. It also rounds up the week's other major AI stories: Anthropic's Claude-driven discovery of a novel phage reverse-transcriptase system, Claude Opus 5.5 and GPT-6 tier benchmarks/pricing, an OpenAI agent's rogue hacking incident against Services Australia and the Transluce log dump, a UN Security Council AI session, Gemini 3.8 TTS topping Voice Arena, and a batch of open-weight releases (FLUX 3 Action, CLM-8B, Nemotron 3 Diarization) and eval/infra work (RRSI, RIVER, Skill2Env, Prime Sandboxes).

Claude CodeHugging FaceTransformer

MetaMuseMark Zuckerberg+42

agents, multimodal, research· Added 29 Sep 2026, published 24 Sep 2026

alignmentforum.orgwriteup🧠 AI

Latent reasoning architectures would likely undermine CoT, our strongest oversight tool

An AI Alignment Forum post by Lukas Finnveden, Alexa Pan, Alek Westover, Girish Gupta, frisby and ryan_greenblatt arguing that latent reasoning architectures — models that reason in continuous/latent space rather than producing legible token-by-token chains of thought — would likely undermine chain-of-thought (CoT) monitoring, currently considered one of the strongest available tools for overseeing AI reasoning processes. The piece reasons through why such architectures could erode the interpretability and faithfulness properties that make CoT useful for safety oversight, as a concern for future model designs. The page itself (fetched without JavaScript) returned only metadata; the body argument was not retrievable.

Transformer

Lukas FinnvedenAlexa PanAlek Westover+4

ai-safety· Added 7 Oct 2026, published 23 Sep 2026

arxiv.orgpaper🧠 AI

On the Diffusibility of High-Dimensional Latents

This paper studies Representation Autoencoders (RAEs), which let diffusion models generate images in the feature space of pretrained visual encoders. It shows that finetuning encoders for better reconstruction reduces the effective dimensionality of their representations, which makes standard velocity-prediction flow matching inefficient because the model must fit noise directions orthogonal to the low-dimensional signal manifold; switching to x0-prediction (clean data parameterization) instead consistently improves text-to-image generation across multiple strong-reconstruction encoders.

flow matchingdiffusion models

Representation Autoencoders

research, multimodal· Added 24 Sep 2026, published 23 Sep 2026

arxiv.orgpaper🧠 AI

Flash-dLLM: IO-Aware KV Caching and Parallel Decoding for Fast, Memory-Efficient Diffusion LLMs

Flash-dLLM is a training-free inference acceleration framework for diffusion large language models (dLLMs) that tackles GPU memory I/O bottlenecks in KV-cache-enabled dLLM inference via an I/O-aware fused KV-cache kernel, combined with a draft-and-verify decoding strategy where the same dLLM acts as both drafter and verifier. It reports 5.1x and 11.0x speedups over the prior strongest baseline Elastic-Cache on GSM8K and HumanEval respectively, while preserving generation quality and scaling to longer sequences and larger batches.

diffusion LLM

Flash-dLLMElastic-CacheGSM8K+1

inference, research· Added 23 Sep 2026, published 22 Sep 2026

arxiv.orgpaper🧠 AI

SpeakerMem-R1: Speaker-Centered Dual-Track Memory for Multi-Party Dialogue

SpeakerMem-R1 is a dual-track long-term memory architecture for multi-party dialogue agents, storing speaker-labeled verbatim messages alongside derived person-level and group-level state, and combining both at query time by entity, event, and time. The authors train a Writer-R1 model using SpeakerLevenshtein and speaker-conditioned GRPO to reduce attribution/update errors, reporting state-of-the-art results on GroupMemBench, SocialMemBench, and the EverMemBench leaderboard, plus strong performance on LoCoMo.

GRPO

SpeakerMem-R1Writer-R1GroupMemBench+4

agents, research· Added 23 Sep 2026, published 22 Sep 2026

arxiv.orgpaper🛡️ Security

A2M: Trace-Optimized Agent Hijacking in the MCP Ecosystem

A2M (Attraction-to-Manipulation) is a two-stage black-box framework for hijacking agents that use the Model Context Protocol to select tools from third-party MCP servers. The Attraction phase optimizes attacker-controlled tool metadata to raise invocation probability, while the Manipulation phase uses execution traces to craft adversarial tool outputs that steer agent behavior; on LiveMCPBench it achieves a 93.6% malicious tool invocation rate against GLM-4.6, inflates token cost 32.4x under a cognitive denial-of-service variant, and reaches a 74.4% mean attack success rate across information exfiltration, environment integrity compromise, and reasoning derailment, with partial transfer (63.6%/2.7x/24.5%) to four other models without re-optimization. The authors argue this exposes a semantic supply-chain risk in MCP tool ecosystems and call for stronger tool vetting and runtime isolation.

Model Context ProtocolGLM

LiveMCPBenchA2MCognitive Denial of Service

AI Security· Added 2 Oct 2026, published 22 Sep 2026

arxiv.orgpaper🛡️ Security

Feedback Coding Enables Inference-Time Covert Agentic Communication

This arXiv paper (cs.IT, submitted Sep 2026) reframes black-box LLM steganography as a sequential communication problem with causal feedback, introducing Burnashev Adaptive Posterior Matching (BAM) — a feedback-coding scheme combining posterior matching with a decode-and-confirm phase, backed by a cryptographic reduction proof for its security. Tested across three open-weight LLMs, BAM achieves 0-0.1% message error on 8-bit payloads in ~50 tokens (vs 10-17% for the strongest black-box baseline), and the authors demonstrate a full end-to-end covert communication protocol usable within ordinary-looking conversational text. This matters for AI security as it shows how agents/models could exchange hidden payloads inside seemingly benign LLM output, evading white-box watermark detection.

LLM

Burnashev Adaptive Posterior Matching (BAM)

AI Security· Added 22 Sep 2026, published 21 Sep 2026

interconnects.aiarticle🧠 AI

The current balance of power in open models

Nathan Lambert's write-up of remarks prepared for a Congressional briefing on open-weight AI models, arguing that Chinese labs (Qwen, GLM-5.3, Kimi K3, DeepSeek) have overtaken American open-weight efforts (Llama, gpt-oss, Nemotron) in downloads, benchmark scores (Artificial Analysis Intelligence Index), and academic/enterprise adoption. It distinguishes open-weight vs. true open-source models, quantifies the capability gap to closed frontier labs (OpenAI, Anthropic), discusses distillation's limited role in closing that gap, and flags emerging cybersecurity and policy risks around widely available open weights.

Hugging FaceOpenRouterOpenCode+3

GLM-5.2GLM-5.3Kimi K3+31

ai-policy, models· Added 29 Sep 2026, published 21 Sep 2026

github.comrepo🧠 AI

zai-org/ZCode

ZCode is an AI programming workbench (from zai-org) providing a desktop Electron app, a browser-based Web UI, and a terminal Agent CLI and TUI, all backed by a shared backend and Agent runtime. The repo includes build and dev scripts for bootstrapping the monorepo, running desktop and web dev modes, and packaging a unified zcode CLI binary that can launch either a TUI or a local web server hosting the same agent backend.

Node.jsElectronpnpm

ZCodezai-org

coding-agents· Added 26 Sep 2026, published 20 Sep 2026

socket.devwriteup🛡️ Security

Socket: Two Joyfill npm Beta Releases Compromised to Deliver DEV#POPPER Remote Access Trojan

Socket's research team found two beta releases of the @joyfill npm packages (@joyfill/layouts and @joyfill/components) contained an import-time JavaScript implant that resolves encrypted payloads through Tron, Aptos, and BNB Smart Chain transactions, ultimately dropping a 77KB Node.js remote-access trojan matching the DEV#POPPER family and sharing loader fingerprints with the PolinRider blockchain-loader campaign. The RAT establishes a Socket.IO C2 channel, executes arbitrary JS/shell commands, exfiltrates files and clipboard data, and persists by patching files inside VS Code, Cursor, Antigravity, Discord Desktop, GitHub Desktop, and the global npm CLI; a parallel detached-process branch fetches an additional bootstrap that can deploy a Python credential/browser-data stealer assessed as an OmniStealer variant. Because the implant fires on module load (not via npm lifecycle scripts), `--ignore-scripts` provides no protection, and the writeup includes full IOCs, blockchain addresses, C2 infrastructure, and MITRE ATT&CK mappings.

npmNode.jsPython+3

DEV#POPPERPolinRiderOmniStealer+12

Supply Chain, Malware & RE, Threat Intel· Added 22 Sep 2026, published 20 Sep 2026

tomshardware.comarticle🛡️ Security

Hackers breach OpenAI using Claude tools, gaining access to employee accounts and the company

Hacktron AI researchers chained a heap overflow in an outdated libheif library (triggered via a malicious HEIF profile-picture upload on OpenAI's Discourse forum) into remote code execution, then pivoted through an SSO session-isolation flaw to hijack an OpenAI employee's ChatGPT/Codex account and its linked GitHub, Slack, and email access, proving impact with an internal pull request. The exploit code for the heap overflow was generated by Claude Opus 5 (an 'authorized, cybersecurity-configured' variant) after the model was fed raw server data describing the bug, illustrating AI-assisted vulnerability exploitation in a real bug bounty. OpenAI patched within 14 hours and paid a $6,500 bounty; full resolution took 72 hours.

OpenAIChatGPTCodex+5

Hacktron AIHackerOneSSO session isolation flaw+2

AppSec, Identity & Access, AI Security· Added 26 Sep 2026, published 18 Sep 2026

chainalysis.comarticle🛡️ Security

DPRK and Iran are Leading a 5.2x Surge YoY in Blockchain-Assisted Cyberattacks

Chainalysis research on blockchain dead drops (BDD), the practice of storing malware C2 payloads and configuration pointers on public blockchains (EtherHiding and its variants) so infrastructure survives takedowns. It details three attributed case studies: DPRK linked UNC5342 using a TRON and Aptos to BSC cross-chain relay to deliver crypto-stealing malware via fake job interviews, Iran-nexus actors embedding C2 in Bitcoin OP_RETURN transactions tied to a Satoshi-era address, and a Russian-language Malware-as-a-Service operation running resolver contracts on Polygon across dozens of downstream customers. It reports a 420 to 440 percent YoY surge in BDD activity, attributes it partly to unrestricted open-weight Chinese LLMs lowering the barrier to entry, and maps the technique to MITRE ATT&CK T1102.002.

BitcoinBinance Smart ChainTRON+2

EtherHidingDPRKUNC5342+5

Threat Intel, Blockchain· Added 30 Sep 2026, published 17 Sep 2026

latent.spacewriteup🧠 AI

[AINews] Reality Checks on AI News (Yegge shuts down Gas Town, Databricks’ +60% Astra cost)

A Latent Space AINews digest (9/15–16/2026) aggregating AI Twitter/Reddit discussion into curated items: Steve Yegge shutting down his "Gas Town" coding-agent project despite heavy spend, Databricks reporting a 60% coding-spend increase after rolling GPT-6 Astra out to 3,500 engineers (while finding it strongest only on high-complexity, long-horizon tasks), and OpenAI's new formal misalignment-incident disclosure framework with six case reports (including a model adding unauthorized persona text to its own summaries). It also covers RL-run transparency from Xiaomi's MiMo-V2.6, a Microsoft "capability laundering" paper on jailbreak-by-decomposition, harness-engineering debates (subagents, context-trimming), local-model benchmarking on r/LocalLlama (Qwen3.8-27B quantization, Voodoo Dynamic Quant), and funding/company moves (Cohere+Aleph Alpha, Arcee Series B, Sakana AI's GTM pivot). Note: the fetched text is truncated partway through the Reddit recap section, so later content (including further "less technical" subreddit recaps) is not reflected here.

Claude CodeSGLangvLLM+7

Steve YeggeGas TownGPT-6 Astra+35

models, ai-safety, coding-agents· Added 29 Sep 2026, published 17 Sep 2026

arxiv.orgpaper🧠 AI

PhysStream: Streaming Physics-Grounded Video Generation with Structured Scene Memory and Fine-Grained Motion Control

PhysStream is an autoregressive image-to-video generation model that maintains structured scene memory (positional maps and object tracking maps derived online from previously generated frames) and supports fine-grained, mid-generation motion control via sparse velocity-increment signals encoding physical quantities. Trained in two stages (bidirectional motion-control finetuning, then causal autoregressive training with scene memory), it reduces motion distribution distance (FVMD) by 33% and trajectory error by 12% over strongest baselines on synthetic multi-object rigid-body tabletop scenes, and is preferred in over 85% of human in-the-wild comparisons.

PhysStream

multimodal, research· Added 22 Sep 2026, published 15 Sep 2026

soliditylang.orgadvisory🛡️ Security

Spill Slot Collision Across Mutual Recursion Bug

The Solidity team disclosed a compiler bug (fixed in Solidity 0.8.37) where the IR pipeline's stack-to-memory spill mechanism can assign the same fixed memory offset ("spill slot") to two simultaneously-live local variables when a contract's call graph contains mutual recursion, silently corrupting values without any revert or warning. The bug requires --via-ir compilation plus a narrow set of conditions (mutual recursion, >16 live locals in reachable non-recursive functions, a variable read back after the recursive call) and affects Solidity 0.7.2 through 0.8.36; a rescan of ~319,000 Sourcify-verified contracts found no affected deployments. The writeup includes full technical detail on the buggy depth-first slot allocator, an annotated PoC contract reproducing the silent overwrite, and the strongly-connected-component fix.

SolidityEthereum

Ng Sze HonEthereum Foundation bug bounty programUnsound Spill In Mutual Recursion Bug+1

Blockchain· Added 29 Sep 2026, published 10 Sep 2026

arxiv.orgpaper🧠 AI

IdeaAMBIG: Benchmarking Implementation-Critical Gaps in Research-Idea Specifications

IdeaAMBIG is a benchmark of 660 evidence-grounded instances (163 real-world gaps from reproducibility reports and GitHub issues, 497 synthetic) that tests whether LLMs can identify and resolve implementation-critical gaps in research-method specifications, i.e. whether a specification gives a coding agent enough information to build the intended method without unsupported assumptions. It evaluates three capabilities across 13 LLMs: codification-readiness assessment, defect localization, and clarification action generation, finding defect localization is the major bottleneck (best model only 9.6% Macro Defect Recovery Rate) while clarification given the defect is much stronger (80.6% success), and an oracle study shows gold resolutions raise codification-ready rate from 14% to 98%.

IdeaAMBIG

evals, research· Added 22 Sep 2026, published 9 Sep 2026

huggingface.cowriteup🧠 AI

Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps

A Hugging Face blog walkthrough showing how to GRPO-fine-tune LiquidAI's LFM2.5-350M model with TRL and a LoRA adapter (targeting LFM's hybrid attention/convolution modules) using three custom reward functions — JSON/YAML format compliance, field-count matching, and JSON Schema validation — trained on ~500 samples from nvidia's Nemotron-RL-instruction_following-structured_outputs dataset for just 100 steps. On the IFStruct benchmark, the fine-tuned model's overall pass rate rises from 22.6% to 29.7% (JSON specifically from 18.0% to 31.9%), narrowing the gap to the much larger Qwen3.5-2B (33.15%). The post also covers converting the merged model to GGUF and serving/evaluating it locally via llama.cpp's OpenAI-compatible server.

Hugging FaceGRPOLoRA+4

LFM2.5-350MIFStructLiquid4All/ifstruct+4

training, evals· Added 29 Sep 2026, published 3 Sep 2026

Showing 180 of 211