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ethresear.chpaper🛡️ Security

Quantifying the Privacy Guarantees of Validatory Privacy Mechanisms

Ethereum Research post that quantifies how well network-layer mechanisms hide validators' IP addresses from their consensus messages. Simulations with the ethp2psim tool show Dandelion and Dandelion++ give little to no anonymity at any forwarding probability, while onion-routing schemes give meaningful privacy. Deployment needs spam protection, Sybil-resistant peer discovery and low latency; the authors suggest an integrated onion-routing protocol or standalone Tor.

EthereumTorethp2psim
DandelionDandelion++onion routing+2

Blockchain· Published and added 2 Nov 2026

huggingface.copaper🧠 AI

Your Benchmark Is an Empirical Measure Over Difficulty

The paper treats a benchmark as an empirical measure over item difficulty and formalizes two failure modes: measure mismatch (benchmark difficulty distribution differs from the intended one) and coverage gaps (uncovered difficulty regions that hide localized training progress). It proposes two diagnostic metrics with theoretical guarantees and analyzes 25 popular benchmarks across 7 domains using estimated item difficulties. It also introduces BenchFill, a difficulty-targeted item rewriting algorithm that reduces local coverage gaps.

BenchFill

evals, research· Added 31 Oct 2026

arxiv.orgpaper🧠 AI

TextSeal2: Distortion-Free LLM Watermarking with Copula Mixing and Memoryless Races

TextSeal2 is a distortion-free watermark for LLM output that keeps its signal under speculative decoding. It merges two or more keys into one virtual key coupled to fresh noise through AutoMark's Gaussian copula, and verifies drafts with an extra exponential race. On three LLMs it detects 8 to 22 points more 100-token answers than TextSeal at a 10^-3 false-positive rate.

LLMspeculative decodingGumbel-max watermarking+1
TextSeal2TextSealAutoMark+2

research, inference· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

Multi-View MoE: Letting Every Router Shape Expert Computation

Multi-View MoE is a sparse mixture-of-experts design that uses several independently initialized routers to select experts, then reuses their relative scores to mix learned latent views into each selected expert's input. Each expert still runs one forward pass, so active-expert count, FLOPs and all-to-all volume are unchanged. On a 24-layer 10B-A1B model trained on 708B tokens, it gains 1.18 points on a nine-task mean; Multi-Router alone gains 0.6.

Mixture of ExpertsTransformerMulti-View MoE+1
arXiv

research· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

Efficient Scalable Oversight via Rewinding

Rewindable interactive proofs (RIPs) are a new model of relativizing interactive proofs for scalable oversight, in which a weak verifier can rewind an AI prover to an earlier saved state. The paper builds an RIP for computations robust to changes in a small fraction of oracle answers. The honest prover runs in linear time, the verifier runs in nearly linear time in the input length after one-time preprocessing, and no cryptography is used. A preliminary MNIST experiment shows an RIP can be faster than a deployed proof system.

rewindable interactive proofsdebaterelativizing interactive proofs
arXivMNIST

research, ai-safety· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

PhysCount: Do MLLMs Count Visual Appearances or Physical Entities?

PhysCount is a benchmark of 546 real-world images and 4,595 human-written questions in 651 target-specific groups. It tests whether multimodal LLMs can separate visible appearances (direct view, reflections, glass) from the physical entities behind them. Across 40 MLLMs, counting appearances is much easier than linking them to entities, and the best model answers every question correctly in only 13.36% of groups.

vision-language model
PhysCountarXiv

evals, multimodal, research· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🛡️ Security

Understanding and Mitigating Role Spoofing in LLMs

Understanding and Mitigating Role Spoofing in LLMs is an arXiv paper (October 2026) on role spoofing, a prompt injection where malicious tool output impersonates the user. It shows models track "who said what" using both content cues (semantics, style) and structure cues (control tokens, formatting). Attacks combining fake structure and content succeed often, adversarial training stops only content-based attacks, and linear probes trained to predict conversation roles detect spoofing across settings.

LLM
role spoofingrole confusionprompt injection+1

AI Security· Added 31 Oct 2026, published 29 Oct 2026

arxiv.orgpaper🧠 AI

The Athlete as a Scientist of One: Agentic Bayesian Experimental Design for Individualized Physiology from Consumer Wearables

EpiCoach is an agentic system that treats one cross-country mountain biker as an N-of-1 experiment. Claims live in an evidence-gated hypothesis registry, and each must make a probabilistic prediction before a session. Predictions come from a digital twin fitted to heart rate, GPS and altitude. In a replay over eighteen months the twin beat standard forecasters on heart rate but not segment time, and the agent loop matched a static model. In the first three live weeks, the ledger sealed 20 predictions and scored 14, and a safety officer vetoed the agent twice. The registry, benchmark and protocol are released.

LLMBayesian experimental designdigital twin+1
EpiCoacharXiv

agents, research, evals· Added 31 Oct 2026, published 29 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Stop Calling Them Provers: The Case for Calling ProVerif and Tamarin Symbolic Protocol Analyzers

Nadim Kobeissi's ePrint paper 2026/2683 argues that ProVerif and Tamarin should be called symbolic protocol analyzers, not provers. It audits their published soundness arguments, finding false statements, missing proofs and gaps between paper and implementation, and reproduces reported Tamarin bugs that accept false claims. It also covers model gaps in parsers and state machines, weak secrecy queries and unjustified primitive equations. The page is an abstract only.

ProVerifTamarin
Nadim Kobeissicomputational soundnesssymbolic model

other· Added 31 Oct 2026, published 29 Oct 2026

deploymentsafety.openai.compaper🛡️ Security

Addendum to GPT-6 Luna System Card: October 28, 2026 Update

Addendum to the GPT-6 Luna system card for the model update of 29 October 2026 (the title says October 28). OpenAI keeps the High rating for cybersecurity and biological/chemical capability under its Preparedness Framework. It reports ExploitBench 46.19% and SEC-Bench Pro 51.75% (GPT-6 Astra: 85.4%), higher multiturn jailbreak defender success than the October 7 version, and GPT-Red prompt-injection results. It also reports weaker factuality and dual-use biology refusal.

GPT-6 LunaGPT-6 AstraOpenAI+1
Preparedness FrameworkGPT-RedExploitBench+4

AI Security· Published and added 29 Oct 2026

arxiv.orgpaper🧠 AI

inkGround: Benchmarking Fine-grained Visual Grounding Capabilities of Vision-Language Models for Cursive Handwriting in the Wild

inkGround is a benchmark for stroke trajectory recovery from full-page, camera-captured handwriting, with 537 page images (about 18.7k words, 163k strokes) and pseudo-label references filtered by OCR agreement and human raters. Models get a page image and a target word, then output ordered stroke points. Seven VLMs were scored with DTW, Chamfer distance, readability and style similarity. Closed-source models, notably Gemini-3.1-Pro, did best, and the ranking held on IAM-OnDB.

vision-language model
inkGroundIAM-OnDBGemini-3.1-Pro+1

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

eprint.iacr.orgpaper🛡️ Security

Shor's algorithm compiled on cat qubits: 22 593 qubits for RSA-2048 in 3.14 days

Shor's algorithm compiled on cat qubits is an IACR ePrint (2026/2665) giving an end-to-end resource estimate on superconducting cat qubits, using repetition and LDPC cat codes. Factoring RSA-2048 needs about 23,000 cat qubits and 3.14 days, assuming phase-flip noise p_Z = 5e-4 and no bit-flips. secp256k1 ECDLP needs at most 30,000 qubits and 1 hour. A space-for-time trade runs RSA-2048 in 1.25 hours on about 611,000 qubits.

Bitcoin
Shor's algorithmRSA-2048secp256k1+5

other· Published and added 28 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Beyond the Event Horizon of Keccak, Extending the Observation from 2011 and applications

Beyond the Event Horizon of Keccak extends a 2011 observation that a path in the Keccak sponge from a zero-capacity state back to one yields infinite second-preimage families. Three theorems cover loops from any capacity value x, k-block loops and cycles, and Joux-style multicollisions with search cost independent of k. Verified examples run on Keccak-f[200] and Keccak-f[1600] with c=32; it also proposes bridge-based protocols.

KeccakSHA-3Lean
Danilo GligoroskiJoux multicollisionssecond preimages+2

other· Published and added 28 Oct 2026

arxiv.orgpaper🧠 AI

Data Scaling Laws in Stereo Matching: From In-domain Fitting to Cross-domain Generalization

This paper studies data scaling laws in stereo matching and releases XStereo, a realistic synthetic dataset of 200 thousand samples. In-domain training on five large datasets shows two regimes: a standard power law, then saturation, with a transition point roughly fixed for a given model. Cross-domain performance depends only weakly on source data scale, and distribution and coverage diversity reduce domain gaps more.

XStereoarXiv

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

arxiv.orgpaper🧠 AI

CALM: Conflict-Aware Lifecycle Memory Governance for Long-Horizon Language Agents

CALM is a conflict-aware lifecycle memory governance framework for long-horizon language agents. It turns records into evidence-grounded memory cells, updates their validity and lifecycle states from support, conflict and dependency signals, and admits evidence at query time through training-free or learned gating based on temporal applicability. It beats baselines on LongMemEval-S and LoCoMo with Qwen and GPT backbones, and keeping superseded records helps earlier-state reasoning.

LLMQwen
CALMLongMemEval-SLoCoMo+1

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

arxiv.orgpaper🧠 AI

Does Chain-of-Thought Reliance Follow a Scaling Law?Larger Models Follow Their Steps More, Independent of Post-Training

This paper measures how much a model's final answer depends on its own written chain of thought, using a synthetic box-swap task with exact ground truth. It edits one step in the model's chain to a wrong but valid state and checks whether the answer follows. Across 25 open-weight models, reliance rises with size and saturates (Qwen3 from 16-18% at 0.6B to 97-100% at 14B). Reliance tracks capability (r = 0.99) and is unchanged by post-training.

Qwen3GemmaLlama+2
Gemma 3Llama 3arXiv

research, ai-safety, evals· Added 30 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🧠 AI

FlatCake: A Flat KVCache Storage Architecture for LLM Serving

FlatCake is a KV cache storage architecture for LLM serving that treats host DRAM and NVMe SSD as peer media in one 64-bit address space, instead of a DRAM-over-SSD tier. Blocks are placed once, in session-contiguous, layer-major order, and streamed via GPUDirect Storage. On a Claude Code trace with DeepSeek-V4-Flash it holds a 94% hit rate (Mooncake: 64%) and cuts peak-load mean TTFT 1.6x.

GPUDirect StorageNVMe SSDLLM+3
FlatCakeMooncakeMooncake-GDS+3

inference, research· Added 30 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🧠 AI

Multi-Token Prediction Done Right

Soft Multi-Token Prediction (Soft-MTP) is a one-line change to multi-token prediction (MTP) pre-training. The authors show the MTP cross-entropy gradient splits exactly into a KL self-distillation term and a noisy residual, and a straight-through "KL gradient-recovery trick" keeps only the KL term. From 167M to 1.2B parameters it gives lower NTP perplexity, faster speculative decoding and higher zero-shot accuracy than standard MTP, with no added parameters or overhead.

LLMTransformer
DeepSeekMeta

research, training· Added 30 Oct 2026, published 28 Oct 2026

arxiv.orgpaper🧠 AI

Complementing Text Based Moderation with Multimodal Grounding: Detecting Fraudulent Inducement Behaviors on Short Video Platforms

The paper studies detection and temporal localization of fraudulent inducements (prompts to follow, join chat groups, click links) in internet finance short videos. It compares OCR+LLM, OCR+MLLM and a zero-shot MLLM, and builds a dataset of 2,419 videos (66.49 hours, 3,194 events, four categories). OCR+MLLM reaches mIoU 26.52 versus 14.49 for keyframe OCR+LLM, with 67.0 percent macro recall, using a recall-weighted F2 localization reward.

MLLMLLMOCR
arXiv

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

arxiv.orgpaper🛡️ Security

Heads of Security: Learned Attention Gates Improve Prompt Injection Robustness

Heads of Security (HoS) is a prompt injection defense that enforces instruction-data separation with learned per-head attention gates, redistributing attention between trusted and untrusted tokens. Evaluated on Llama-3.3-70B-Instruct and GPT-OSS-20B against SecAlign++ and ASIDE. The black-box RL-Hammer attack reaches 97% ASR on GPT-OSS-20B and 85% on SecAlign but 10% on HoS. The white-box Claudini attack needs 3 times more FLOPs on HoS. HoS keeps base-model utility in few-shot settings, where SecAlign and ASIDE degrade.

LlamaGPT-OSS-20BLLM
Heads of Securityprompt injectionSecAlign+++4

AI Security· Added 28 Oct 2026, published 27 Oct 2026

arxiv.orgpaper🧠 AI

Heads of Security: Learned Attention Gates Improve Prompt Injection Robustness

Heads of Security (HoS) is a prompt injection defense that trains a small set of per-head attention gates to shift attention between trusted and untrusted tokens, separating instructions from data. Tested on Llama-3.3-70B-Instruct and GPT-OSS-20B, the black-box RL-Hammer attack reaches 10% ASR on HoS versus 97% on base GPT-OSS-20B and 85% on SecAlign. HoS keeps base utility in few-shot settings, where SecAlign and ASIDE degrade.

TransformerLlamaLLM
Heads of SecuritySecAlign++SecAlign+5

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

arxiv.orgpaper🧠 AI

Scaling Laws for Tool-Use Agents via Controllable Environment Synthesis

SynthAPI is a pipeline that generates synthetic tool-use environments with explicit control over diversity of tools, tasks and underlying databases. Across 458,752 tasks and 849 trained models of four sizes, the authors fit a scaling law predicting downstream agent generalization. At larger data budgets, scaling tool and database diversity beats adding more tasks, and higher complexity helps more. Agents trained this way beat prior synthetic-data baselines on four benchmarks.

LLM
SynthAPI

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

arxiv.orgpaper🧠 AI

Metacognitive Decoding: Efficient Inference for Diffusion Language Agents

Metacognitive Decoding is a lightweight plug-in controller for diffusion language model backbones. It learns when to finalize tokens and how far generation should extend, and is trained on agent trajectories to balance correctness against generation cost. Across five benchmarks it cuts inference FLOPs by 20.6% on average versus the most efficient baseline, with competitive task accuracy. The abstract names no specific benchmarks or backbones.

diffusion model
arXiv

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

arxiv.orgpaper🧠 AI

Perceiving and Predicting Dynamic Sports: Spatiotemporal Intelligence in Vision Models for Egocentric Tennis Video

This paper presents an annotated egocentric tennis dataset (egocentric video synced with five allocentric views, global positions, contact times, stroke types) and evaluates image encoders, video encoders and vision-language models on seven prediction tasks. Best models reach 0.14 m player and 0.54 m ball localization error and 0.14 s contact-time error. Depth input helps 3D ball localization, and longer histories do not help forecasting. A conditional diffusion model generates whole-body tennis motion in real time.

vision-language modeldiffusion modelTransformer
arXiv

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

arxiv.orgpaper🧠 AI

EnvSched: Environment-Aware Scheduling for Rollout-Efficient Agent Reinforcement Learning

EnvSched is an online scheduler for agentic reinforcement learning that picks which environment-task pairs to roll out next. It builds a dual-relation graph over environments and tasks, propagates past rollout statistics to predict within-task reward variance, and adds environment quotas and a similarity penalty. On Qwen3-8B it scores 48.81 average on BFCL-v3, tau-Bench and ACEBench-Agent after 40 steps, above random selection at 120 steps.

Qwen3
BFCL-v3tau-BenchACEBench-Agent+1

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

arxiv.orgpaper🧠 AI

CRISP: Co-designing Pruning and Scheduling for Efficient MoE Inference on Edge Servers

CRISP is a combination-aware framework that co-designs pruning and runtime scheduling for Mixture-of-Experts inference on memory-constrained edge platforms. It reorders neurons by importance so one stored expert supports several execution widths, gives hot, warm and cold expert combinations different widths, and overlaps expert loading with computation. On Mixtral-8x7B and DeepSeek-MoE-16B it raises throughput up to 2.1x at the same memory budget, with accuracy loss limited to 3.6%.

Mixture of Experts
Mixtral-8x7BDeepSeek-MoE-16BarXiv

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

arxiv.orgpaper🧠 AI

FeatureArc: Benchmarking Coding Agents on Evolving Features

FeatureArc is a benchmark for coding agents on evolving features, built by a pipeline that mines repository histories into multi-stage tasks across four programming languages. It has 73 manually reviewed tasks of 2 to 10 stages, validated with fail-to-pass and pass-to-pass tests. Across 10 agents, pooled success drops from 62.7% at the first stage to 28.5% for the whole feature. Inherited defects appear in 72.9% of failed later stages.

LLM
FeatureArcTencentHunyuan+1

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

arxiv.orgpaper🧠 AI

Bayes-Adaptive MDPs for Rollout Allocation to Enhance GRPO Training

AdaRoll-GRPO is a method that allocates GRPO rollout budgets per question by difficulty instead of uniformly. It frames allocation as a sequential decision problem and learns an allocation policy with a Bayes-adaptive MDP, computed offline so training adds no online optimization cost. Experiments on several reasoning datasets and LLM architectures show gains over standard GRPO at the same total rollout budget.

GRPOLLM
AdaRoll-GRPOarXiv

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

eprint.iacr.orgpaper🛡️ Security

Radical isogenies on every model

Radical isogenies on every model is an IACR ePrint paper by Thomas Decru (October 2026). It gives radical isogeny steps of every odd degree N directly on short Weierstrass, Montgomery, twisted Edwards, Huff, Jacobi quartic, twisted Hessian, Legendre and Jacobi intersection curves, plus Kummer lines. In Tate normal form a step drops from 12.5N to 10N multiplications. Montgomery and twisted Edwards curves cost 10.5N+O(1) per step and 2N to push a point. All algorithms are inversion-free, with exact operation counts.

CSIDH
Thomas Decruradical isogeniesVélu's formulae+1

other· Added 28 Oct 2026, published 27 Oct 2026

eprint.iacr.orgpaper🛡️ Security

Towards Quantum Sensing for Side-Channel Analysis

Preprint on using nitrogen-vacancy (NV) center quantum sensing for electromagnetic side-channel analysis of ICs. The authors build an NV-based RF spectrometer with 1.1 GHz real-time bandwidth and adjustable center frequency, which reads the spectrum in the time domain without Fourier transforms or frequency sweeps. They use it on RSA decryption emanations and for leakage detection on a software Number Theoretic Transform running on a Cortex-M4 microcontroller.

ARM Cortex-M4
Number Theoretic TransformRSAnitrogen-vacancy centers+1

Hardware & RF· Added 28 Oct 2026, published 27 Oct 2026

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