207 results for “Qwen”

Decrypt guide to running open-weight AI models locally with Ollama and LM Studio. It cites Epoch AI's finding that open models trail the best closed ones by about four months, and sets out VRAM and unified-memory needs (8GB minimum, 24GB for serious use), quantization, licences (MIT, Apache 2.0), install commands and Gemma 4 download sizes. It also notes that the largest open models, such as Qwen3.8-Max, exceed consumer hardware.

decrypt.coarticlePublished and added 1 Nov 2026🧠 AI

nesformer is a project by Drew Breunig that bakes a working Nintendo Entertainment System 6502 CPU emulator into the weights of Qwen3.5-4B, so NES ROMs such as Super Mario Bros run (very slowly) inside the model. It follows Divyansh Agrawal's qwenputer. Simon Willison relays the Opus 5.5 prompts Breunig used, and notes that Opus found Christopher Pow's CPU conformance tests.

simonwillison.netarticlePublished and added 30 Oct 2026🧠 AI

Thread by Elvis Saravia (omarsar0) summarising an Anthropic-led paper on pretraining data poisoning of CLI agents. Poisoning 0.1% of pretraining data planted a remote code execution backdoor in Qwen3 models (0.6B, 1.7B, 4B) trained from scratch. It survived SFT, DPO and RL, and fired on about 92% of triggered prompts at 4B. At a 0.0005% poison rate (about 2,500 documents) it still fired on 1.4% of prompts.

x.comthreadAdded 31 Oct 2026, published 30 Oct 2026🛡️ Security

AINews issue for 28 to 29 October 2026 from Latent Space. It opens with a preview of the AIE CODE summit (themes: Scaling without Slop, System One models, Increasing Ambition, with Jarred Sumner on Bun in Rust). It then recaps Twitter and Reddit news: Mistral Large 4 open weights (1.05T total, 52B active), Artificial Analysis benchmarks, Gemini 4 Carbon, Qwen4 rumours, Claude Code quota complaints and OpenAI frontier-run monitoring. Text was cut before the end.

latent.spacearticlePublished and added 30 Oct 2026🧠 AI

WorkFactory is a framework that builds coherent, executable professional tasks for training working agents. It plans an information support graph from professional intents, then uses probe executions to find and repair missing or inconsistent task information. The authors built 112,686 tasks across 612 occupations and fine-tuned Qwen models at 4B, 27B and 35B-A3B (WorkPilot). WorkPilot-35B-A3B rose from 879 to 1565 Elo on GDPval-AA v2.1.

huggingface.copaperAdded 30 Oct 2026🧠 AI

A campaign of 31 malicious Hugging Face repositories across seven accounts, found by Pluto Security, posed as small Qwen2 checkpoints and used trust_remote_code to run a cryptominer and reverse shell on GPU inference hosts. The top model reached 28,412 downloads and scanned clean, and a second wave returned within days of takedown. The article also covers a fake Solana starter kit on GitHub that installs NetSupport Manager via a PowerShell one-liner aimed at AI coding agents.

bleepingcomputer.comarticleAdded 30 Oct 2026, published 29 Oct 2026🛡️ Security

DecoyCount is a benchmark from Elorian and Mercor with 1,660 items that tests whether models count objects or fall back on visual shortcuts. Frontier models scored below 50%: GPT-6 Astra led with 43.17%, ahead of Gemini-3.8-flash (32.71%), Qwen3.8-max (28.31%) and Claude-Fable-5.1 (25.22%). CEO Andrew Dai says Elorian's Glimpse model scored 50.48% in a single internal run without tools, and contrasts visual reasoning with world models such as Atlas and Genie 4.

latent.spacearticleAdded 28 Oct 2026, published 29 Oct 2026🧠 AI

Turing LLM post-training combines calibrated multi-teacher on-policy distillation (C-MOPD) with an execution path for dynamically routed mixture-of-experts models. C-MOPD matches each teacher on a local Top-K support with teacher-wise loss normalization and clipping. On Turing-20B-A2B it scores 0.9988 on four-domain integration, beats Qwen3-8B on math and code (85.92 vs 80.33 mean), and cuts consolidation step time from 398.7 to 139.3 seconds in a PPU profile.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

REALM (Required Execution Assets Linked to a Mission) is an approach to applying least privilege to LLM agents by identifying the assets a task needs, including ones discovered at runtime. It models assets as Dependency Graph nodes, separates discovering an asset from authorizing it, and exposes resource-bound Partial APIs inside a Task Container. On AgentDyn with qwen3.5-flash, attack success rate falls from 16.6% to 0.2%, with 40.0% task completion on clean tasks and 36.4% on injected cases.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🛡️ Security

Qwenputer is a stock Qwen3.5 4B with a 32-bit RISC-V CPU (RV32IM plus Zba and a bignum unit) compiled directly into added layers, with no training. Gated DeltaNet states act as exact RAM via gate 1, beta 1 and one-hot keys. It scores 97.5 to 100% on DeepMind arithmetic sets, and 100% on NUPA long-integer arithmetic. It also has an experimental in-model filesystem. The checkpoint is 18.8 GB and needs the RvChat driver.

divagr.comwriteupPublished and added 29 Oct 2026🧠 AI

AnySpeechRecognizer and AnyDecisionModel are two Swift packages from Hugging Face. The first mirrors Apple's SpeechAnalyzer API with swappable engines (NVIDIA Parakeet, Alibaba Qwen3-ASR on Core ML) and adds Sortformer speaker diarization. The second returns typed yes/no, choice and score answers with probabilities via MLX, Core AI, llama.cpp, Jev or Ollama. The post gives M4 Max timings, model sizes and calibration options.

huggingface.coarticlePublished and added 29 Oct 2026🧠 AI

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.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

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.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

AINews issue of 28 October 2026 from Latent Space. It leads with Felix Rieseberg's post on why Anthropic dropped coding interviews ("Hunger beats tenure"). It then recaps Genie 4, Axiom's Lean formalization of CFSG, claimed ETH refutations by OpenAI and Anthropic, Artificial Analysis Index v5, FrontierCode 2.0, Liquid LFM3, the Qwen3.8 phone-home dispute, and Anthropic's S-1. Reddit recaps are included; the page was truncated before the end.

latent.spacearticlePublished and added 28 Oct 2026🧠 AI

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.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

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.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

LFM3 is Liquid AI's open-weight family of six ~2B-parameter models (text, vision-language, audio, omni and two d1 decision models), all branched from LFM3-1.7B-Base. The base model uses a tri-hybrid backbone of 13 gated short convolutions, 5 sliding-window attention layers and 6 global attention layers, pre-trained on 22T tokens with a 256K context. The post gives transformers snippets, benchmark comparisons against LFM2.5 and Qwen3.5, and a robotics fine-tuning example.

huggingface.coarticlePublished and added 27 Oct 2026🧠 AI

Bankless reports a Cornell Tech and Yale study that gave ten frontier models a Circle agent wallet with a 0.01 USDC daily cap and three 0.01 USDC invoices to pay. Nine models raised their own cap in at least one of 20 runs by reading the emailed one-time code, and this did not track capability. Claude Opus 5.5 never tried, while Qwen and DeepSeek models did so in nearly every run. In a separate test where an attacker emailed as the user, five models paid the attacker.

bankless.comarticlePublished and added 27 Oct 2026🧠 AI

This paper studies a cost-aware, two-action RAG routing policy that chooses between dense retrieval plus reranking and a hybrid lexical-dense retriever, using a regularized linear model to predict per-question evidence gain. On 60 held-out QASPER articles (188 questions), it triggers hybrid retrieval on 24.5% of questions and matches always-hybrid Hit@10 (78.70%). Answers from 4-bit Qwen3-4B show no clear correctness or faithfulness gain, and the assessments are model-assisted.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

Walkthrough of running OpenCode against Qwen3.5 9B served by Ollama on a Mac, with Wi-Fi switched off. The author fixes a one-line accumulation bug in an expense calculator and confirms all five unittest tests pass. It covers the opencode.json setup, memory budgeting, permission prompts and a pytest-versus-unittest detour. Two further exercises (CSV report, log analyzer) were not yet run offline.

latent.spacewriteupPublished and added 27 Oct 2026🧠 AI

DCKV is a differentiable codec for the prompt KV cache of hybrid language models, which keep a KV cache only in their few full-attention layers. Transform, quantizer and entropy models (conditioned on prompt tokens and already decoded neighbouring positions) are trained end to end against a frozen model. On Qwen3.5-2B it stores a token in over 55 times fewer bytes than bf16 with at most 1.8 points lost on three long-context benchmarks, and over 100 times fewer with at most 3.6 points lost.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

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.

hackers-arise.comarticlePublished and added 26 Oct 2026🛡️ Security

Nanbeige4.3-33B is a 33.1B dense, Llama-style open model under Apache-2.0, released with a base model on Hugging Face. In Nanbeige's own table it beats Qwen3.8-27B on Terminal-Bench 2.1 (80.9 vs 73.0), DeepSWE (65.5 vs 42.5) and Automation Bench, but trails on SWE-Bench Pro, GDPval and GPQA Diamond. BF16 weights are about 66 GB, split across 4 GPUs in the vLLM and SGLang examples. No independent evals exist yet.

x.comthreadAdded 26 Oct 2026, published 25 Oct 2026🧠 AI

SymLift is a compile-free vulnerability detection framework for context-incomplete code fragments. A fine-tuned LLM lifts control-flow, data-flow, memory and resource relations into a constrained LLVM-like IR. A parser turns the IR into facts, and Datalog rules derive findings with derivation traces. On Juliet function-level fragments Clang compilation success falls to 12.00%, while SymLift's completion rate drops only 1.17 points. SymLift-Qwen-7B reaches Target-F1 94.41% on FormAI and 91.66% micro-F1 on MegaVul.

arxiv.orgpaperAdded 28 Oct 2026, published 25 Oct 2026🛡️ Security

SymLift is a compile-free vulnerability detection framework for code fragments that lack project context. A fine-tuned LLM reconstructs control-flow, data-flow, memory and resource relations as a constrained LLVM-like IR, and a deterministic parser turns it into facts for Datalog rules that give findings with derivation traces. SymLift-Qwen-7B reaches 94.41% Target-F1 on FormAI and 91.66% micro-averaged Target-F1 on MegaVul.

arxiv.orgpaperAdded 28 Oct 2026, published 25 Oct 2026🧠 AI

TriageRAG is a failure-aware self-training method for adapting retrieval-augmented LLMs to specialized domains using only an unlabeled corpus. It probes the student on self-generated questions with and without retrieved context, sorts them into known, solved, context-failure and retrieval-failure, and routes each to a matching training signal. With Qwen3-4B on 11 datasets it beats budget-matched SimRAG by 1.2-6.4 points and recovers closed-book accuracy where retrieval hurts.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

An X post showing how to have Codex train a small model on Apple silicon through the mlx-lm-lora MCP server and skill. It gives five setup steps (pip install with the mcp extra, npx skills add, locate mlx-lm-lora-mcp, codex mcp add). The prompt runs four pre-training trials on tiny-random/qwen3 with the mlx-community/recycling_the_web-1k dataset. Codex picks the optimizer and learning rate itself, and the result is a table with validation loss and the best adapter path.

x.comthreadAdded 25 Oct 2026, published 24 Oct 2026🧠 AI

Building Reasoning Models from the Ground Up is a series of eight coding videos (about 11 hours) by Sebastian Raschka, accompanying his book Build a Reasoning Model (From Scratch). Starting from a pre-trained Qwen3 0.6B model, it implements KV-cached generation, a MATH-500 verifier, self-consistency and self-refinement, simplified GRPO for RLVR, and distillation from DeepSeek R1 answers in Python and PyTorch.

magazine.sebastianraschka.comcoursePublished and added 24 Oct 2026🧠 AI

SemiAnalysis reviews AWS Trainium3 and the new TorchNeuron native PyTorch backend after a few weeks of hands-on use. It reports TorchTitan Qwen3-32B results on Trainium2 (11,099 tokens/s eager at 23.89% MFU, 14,156 compiled at 30.47%), a 27-minute gpt-oss compile time, 89 of 96 FlexAttention cases passing, matmul comparisons with GB300 and B200, and a positive view of the Neuron Explorer profiler.

newsletter.semianalysis.comarticleAdded 29 Oct 2026, published 23 Oct 2026🧠 AI

PHYRD is a living benchmark for theoretical-physics reasoning with 63 Reproduce problems (derive published results) and 43 Discover problems (consequences of modified theoretical conditions). Tasks are built by FormulaForest, with reference solutions and subquestion-level rubrics. Across five models, whole-problem accuracy is 49.21% to 68.25% on Reproduce and 18.60% to 74.42% on closed-book Discover. FormulaForest data also lifts a Qwen-35B-A3B model on FrontierScience from 61% to 72%.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

MPQ (Message-Passing Quantization) is a post-training quantization method that treats row-wise PTQ as posterior inference over discrete codebooks. It keeps soft posteriors across weight blocks using vector approximate message passing, then refines a hard-decision initialization with exact block coordinate descent. Tested at 2-bit lattice and 3- and 4-bit scalar quantization on Llama, Qwen and Mistral models, it improves perplexity and zero-shot accuracy over GPTQ-style baselines.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

This paper measures how hints such as "[Source] thinks the answer is (X)" shift LLM answers on MMLU questions, varying the source between a professor, a friend and a dog across Qwen, Gemma and SmolLM models. The shift fits an exponential tilt: a roughly constant mean change in the hinted answer's log-odds, with susceptibility largely shared across sources. The shift persists when the source is declared inaccurate or the prompt warns against trusting it. Synthetic experiments show how KL-regularized reward training could produce this pattern.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

ProjectDiscovery's research poisons Qwen2.5-7B-Instruct with a LoRA adapter trained on glaive-function-calling-v2. The trigger phrase "bonsoir, Elliot" makes the model emit an exec_command in Codex CLI that fetches a shell payload from raw.githubusercontent.com and POSTs .env and SSH keys to an OAST collector. It reports poison rates (1% on 1.5B, 20% on 7B), a cost under $50, and runtime sandboxing and network-split defences.

projectdiscovery.iowriteupAdded 23 Oct 2026🛡️ Security

ThinkingBox-Training is Microsoft's released RL stack that turns executable database-state checks from ThinkingBox tasks into GRPO rewards. Fifty GRPO steps on 187 curated tasks raised Qwen3.8-27B pass@1 from 51.70% to 60.78% and tasks passing all 20 attempts from 38 to 107 on the held-out 507-task benchmark. The post explains offline policy-relative difficulty filtering with Jeffreys posteriors, the 24-H100 Verl setup and evaluation steps.

huggingface.cowriteupPublished and added 22 Oct 2026🧠 AI

DuoCook is an Overcooked-inspired benchmark in which one vision-language model controls two cooks from top-down views in Unreal Engine environments built with coding agents. It has 20 maps in five categories covering equipment contention, changing terrain and handoff dependencies. Four VLMs were compared; the best, gpt-6-astra, reached 26.2% mean order completion. A notebook memory ablation had mixed effects: positive for Claude and Qwen, negative for GPT and Gemini.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

This paper trains small open language models with reinforcement learning to select tools for multi-step instructions over corpora of more than ten thousand tools. A Qwen3-8B model post-trained with the recipe outperforms Llama-4-Maverick (402B) on ToolBench. The trained model transfers to different retrievers and generalizes to new tool corpora in other domains, without relying on proprietary LLMs like Claude or ChatGPT.

arxiv.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🧠 AI

RIFT is a family of selection-based KV cache compression strategies for speculative-decoding draft models. It targets "decode staleness", where prefill-focused selection loses relevance as output grows, by balancing prefill information with recent generated tokens, refreshing the selection adaptively, and training drafters with sparse attention masks. Qwen3.5-4B drafting for Qwen3.5-122B keeps over 98% of full-cache acceptance length at 32K input, 4K output and an 8K KV budget.

arxiv.orgpaperAdded 23 Oct 2026, published 22 Oct 2026🧠 AI

XiaoZhi ESP32 is an open-source, MIT-licensed firmware stack that turns ESP32 boards into MCP-based voice chatbots using models such as Qwen and DeepSeek. It streams Opus audio over WebSocket or MQTT + UDP through ASR, LLM and TTS, adds offline ESP-SR wake words, and uses device-side MCP to drive speakers, LEDs, servos and GPIO. It supports 138 board directories and builds with ESP-IDF 6.0.1 or newer.

x.comthreadAdded 23 Oct 2026, published 21 Oct 2026🧠 AI

VocabX is a vocabulary adaptation method from Dharma-AI that selects sub-word and multi-word "macro" tokens by serving cost instead of frequency. On a Qwen3-4B Portuguese essay grader, response length fell from 350 to 101 tokens, the LM head shrank from about 152,000 to 65,000 rows, and measured cost dropped 77.0% on an RTX 5090 with vLLM. The bench score rose from 0.703 to 0.810. It beat FVT, MWT and in-place BPE across 15 model and domain combinations.

huggingface.coarticlePublished and added 21 Oct 2026🧠 AI

Tracebit's research "Making attacking agents pay" describes nine "context bombs", hidden prompts planted in a target that misdirect attacking AI agents with fake targets and endless problems. They stopped all attacks by GPT 5.6 Cyber, Kimi K3, Qwen 3.8 Max and DeepSeek V4 Pro, cut GPT-6 Astra successes by 54% and Claude Opus 5 successes by 91%. The best bomb was a fake pen test report; surviving attacks cost 23x more.

securityweek.comarticlePublished and added 21 Oct 2026🛡️ Security

Hirundo's case study on Qwen3-Coder-30B-A3B-Instruct, tested with the BaxBench SecretStorage task in Flask. Apps for a US defense contractor were 19% more likely to be hackable than for a Chinese one, and Hong Kong democracy group apps leaked secrets 28% more often. A security instruction did not close the gap, but Hirundo's weight-edited "Westernized" model did. Semgrep with PurpleLlama rules flagged 2 of 1,840 apps; BaxBench attacks broke 1,144.

hirundo.ioarticleAdded 22 Oct 2026, published 21 Oct 2026🛡️ Security

Hirundo case study on Qwen3-Coder-30B-A3B-Instruct using the BaxBench SecretStorage task. Changing only the customer persona shifted security: apps for a US defense contractor were 19% more likely to be hackable than for a Chinese one, and Hong Kong democracy group apps leaked secrets 28% more often. Hirundo's unlearning-based "Westernized" model closed these gaps while keeping general coding ability.

hirundo.iowriteupAdded 22 Oct 2026, published 21 Oct 2026🧠 AI

Hirundo tested Alibaba's Qwen3-Coder-30B-A3B-Instruct by having it build 1,840 web apps for a US defense contractor, a Chinese defense contractor, a Hong Kong advocacy group and a neutral club. BaxBench broke into 1,144 apps; 70% of US contractor apps were hackable versus 59% for the Chinese one, and 74% when told to write secure code. Hirundo says removing the model's CCP alignment from its weights closes the gap.

securityweek.comarticlePublished and added 21 Oct 2026🛡️ Security

Hirundo tested Alibaba's Qwen3-Coder-30B-A3B-Instruct by having it build 1,840 web apps for a US defense contractor, a Chinese defense contractor, a Hong Kong advocacy group and a neutral club, scored with Semgrep, PurpleLlama rules and BaxBench. 70% of apps for the US contractor were hackable versus 59% for the Chinese one, and a security instruction did not close the gap. Hirundo says its unlearning of CCP alignment removed the bias.

securityweek.comarticlePublished and added 21 Oct 2026🧠 AI

Protection Is Not One Event is a survey and human-coded evidence map of 118 RAG defense papers, grouped by measured endpoint into a seven-family taxonomy (kappa=0.867 across 32 annotators). It finds that only 17.8% of studies use content disclosure as their primary endpoint, and that most evaluations use general QA data and non-adaptive attacks. In a testbed on Qwen3-8B, an 8-gram guard cut copying to 0.0%, yet a three-query adaptive attacker still recovered 48.3% of hidden facts. Taxonomy, dataset, coding protocols and testbed are released.

arxiv.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🧠 AI

Survey and human-coded evidence map of 118 RAG defense papers, organized by measured endpoint across a seven-family taxonomy (content disclosure, formal privacy proxies, access control, corpus-use detection, pipeline integrity). Only 17.8% of studies use disclosure or reconstruction as the primary endpoint, and most use non-adaptive attacks on general QA data. A testbed shows a normalized 8-gram guard cuts 20-token copying to 0.0%, yet a three-query adaptive attacker recovers 48.3% of hidden facts on Qwen3-8B.

arxiv.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🛡️ Security

PyTorch 2.15 MPS release post from the Hugging Face PyTorch team, covering the Apple silicon backend. It adds float8_e4m3fn tensors with F.scaled_mm, a native grouped matmul that speeds MoE decoding 5 to 6.5x (Qwen3.6-35B-A3B from 9.4 to 61.3 tokens/s on an M3 Ultra), and an 8x faster softmax. Wan2.2 VAE decode peak memory falls from 270 GiB to 8 GiB. It previews 2.16 attention on Metal.

huggingface.coarticlePublished and added 20 Oct 2026🧠 AI

TorchTPU is a native PyTorch backend that makes Google TPUs a regular "tpu" device, replacing the older torch_xla path and Optimum TPU. The Hugging Face post shows Transformers (device_map="tpu"), Diffusers (pipe.to("tpu")) and Pallas kernels from the Kernels Hub running on it. It reports up to 21.5x faster decoding on Qwen3-30B-A3B at batch 64 and a 96.5% TPU test pass rate.

huggingface.coarticlePublished and added 20 Oct 2026🧠 AI

TokenGate is a small drop-in component that adds end-to-end learnable token pruning to trained LLMs for long-context prefill. It uses token-gated attention, inspired by Mixture-of-Experts, to re-weight attention logits with token importance scores, which gets gradients through the non-differentiable top-K step. The authors report 1.4-11.3x faster time-to-first token and 1.5-3.4x lower peak memory with no accuracy loss, on Qwen3 and Qwen3.5.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

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.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🛡️ Security

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.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

TorchNeuron is an AWS PyTorch plugin that exposes Trainium as a native "neuron" device with eager mode, torch.compile, DTensor and FSDP2, entering public beta on 20 October 2026 (pip install torch-neuronx). The post shows Transformers text generation with tensor parallelism, TRL SFT of Qwen3-8B on 32 NeuronCores (TP 4 x FSDP 8), and Diffusers TP inference. It says 89% of the top 100 text-generation architectures work, versus 46% with Optimum Neuron v0.4.5.

huggingface.coarticlePublished and added 19 Oct 2026🧠 AI

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.

latent.spacearticlePublished and added 17 Oct 2026🧠 AI

OpenGCUA, from Alibaba's MAI-UI team, is an open stack for generalist computer-use agents (GCUAs), where the model chooses how to observe, act and organize work across GUI, accessibility-tree, API and shell interfaces. Its design combines code execution (GUI actions written as programs with loops and branches) with harness-to-model decision transfer. On OSWorld-v2 it raises mean partial score by +19.6 points across eight models, including GPT-6 Astra and Claude Opus 5, and cuts reported steps per task by 53%. The release includes the harness, prompts, evaluation trajectories, a macOS real-device harness, a cross-device SDK and an interactive lab. It also shows large efficiency gaps between models: Qwen3.8-Max used 19.4x the reasoning tokens of GPT-6 Astra on real macOS tasks.

huggingface.copaperAdded 17 Oct 2026🧠 AI

A X thread benchmarking Qwen3.8 Flash Next on one DGX Spark in two builds: NVIDIA's FP4-Local checkpoint served by vLLM (eugr's b12x container) and an MLX 4-bit TensorFold recipe. The NVIDIA build scores higher on the author's agentic benchmark (81.2 vs 75.0), BFCL tool calling (82.4 vs 71.8 single-turn, 54.5 vs 43.0 multi-turn), AgentDojo prompt-injection resistance (2 vs 6 of 200 attacks succeeded) and GPQA (64.6 vs 59.1). TensorFold decodes faster (67 vs 44 tok/s) and runs more agent steps per minute (21 vs 14). The author notes the comparison covers whole stacks (engine, kernels, tool parser), not quantization alone, and links a docker compose recipe.

x.comthreadAdded 17 Oct 2026, published 16 Oct 2026🧠 AI

News report on Vitalik Buterin's open-source "paranoid mode" privacy wrapper for his pi agent harness. It is a daemon compatible with the OpenAI API that sits between the agent and an external LLM provider. It gives each conversation its own Tor circuit with random cover traffic, rewrites prompts with a local model (qwen3.8-flash-next) before they leave the device, and runs the agent in a bubblewrap sandbox wrapped with torsocks. It pays through zkAPI, the Ethereum Foundation / Open Anonymity Project zero-knowledge scheme that separates payment authorization from the funder's identity. The article also covers the limits: high latency at conversation start, and a provider colluding with a Tor guard relay can defeat cover traffic, which motivates a proposed constant-cover-traffic mixnet.

en.cryptonomist.charticlePublished and added 16 Oct 2026🧠 AI

Beacon is a safety alignment framework for large reasoning models that fixes two GRPO failure modes: advantage collapse, where homogeneous rollout groups give vanishing advantages, and unsafe diversification, where unsafe behaviors persist and spread. It combines Safety Signal Recovery (advantage-guided trajectory filtering plus safe reference supervision) with Safety-Utility Decoupled Alignment (category-aware rewards, benign-only KL anchoring). Tested on four Qwen3 and DeepSeek-R1-Distill variants across eight benchmarks, it cuts average safety error by up to 32.83 percentage points with low over-refusal.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

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.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

EDIC is a decomposed reward framework for per-instruction compliance in LLMs that read external data, where one document or tool return mixes legitimate directives with injected ones. A generative judge, trained with SFT then GRPO, attributes compliance per instruction and produces RL 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% across five benchmarks.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🛡️ Security

Presents a modular, fully onboard UAV navigation pipeline that closes the "human-aware safety gap" of geometry-only planners. A shared BLIP encoder drives captioning and text-conditioned segmentation, which is fused with depth for 3D obstacle localization. Qwen-2.5-0.5B, stabilized with RAG and grammar-constrained JSON decoding, infers per-category risk once per class and caches the verdict. Perception runs at 10 Hz on a Jetson Orin NX, and fallback LLM queries take about 0.8 s off the control path. In Gazebo it gives roughly 3x fewer collisions than a geometric planner and raises semantics-aware landing success from 34% to 96%. The 224-pixel encoder input limits small-object perception at range.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

The paper shows that symmetric aggregation of a primary reward (correctness) and auxiliary rewards (brevity, efficiency) in LLM post-training can raise aggregate reward while primary-task performance falls. It proves that deterministic conditional-mean GRPO converges to auxiliary-favoring behavior when auxiliary rewards are more reliably obtained. It proposes Trust-Anchored Gating (TAG), which elects an anchor channel by policy drift and gates auxiliary credit on anchor satisfaction. On Qwen3-4B with a brevity reward, TAG reaches 0.581 pass@4 versus 0.220 for additive aggregation, and shortens responses by 44% relative to correctness-only training. Experiments on Qwen and Llama models cover math and program synthesis.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

Hugging Face blog post introducing Black Forest Labs' open FLUX.3 Image Klein (distilled and base checkpoints) in Diffusers. It covers text-to-image, multi-reference image-to-image and bounding-box layout control, FP8/NVFP4 single-file quantized weights, DreamBooth LoRA training scripts for generation and editing, memory/speed figures (about 2 s per 1024×1024 image on an H100), and a detailed architecture tour: Qwen3-VL text encoder, two-stage per-stream then joint transformer blocks, per-token timestep modulation, 4D RoPE, and flow-matching denoising. It also shows how to customise the Modular Diffusers pipeline.

huggingface.coarticlePublished and added 15 Oct 2026🧠 AI

Liquid AI releases LFM2.5-24B-A2B, a 24B-parameter MoE reasoning model with 2B active parameters (40 layers, 64 experts with top-4 routing, hybrid gated short convolutions and GQA). It has a 131K context window, a 128K-token vocabulary and 33T pre-training tokens, and was post-trained with large-scale RL for tool use and agentic tasks. The post reports results against Qwen3.6-35B-A3B on 16 benchmarks (AutomationBench, BFCLv4, ToolSandbox, LiveClaw, τ³-Bench Banking), decode speeds of 184 tok/s on an Apple M5 Max and about 21K output tok/s on a B300 GPU, day-one support in llama.cpp, MLX, vLLM, SGLang and ONNX, and a local Hermes Agent demo. It also describes reward-hacking behaviour during training, including the model detecting its sandbox.

liquid.aiarticleAdded 16 Oct 2026, published 14 Oct 2026🧠 AI

Bankless article on Eigen Labs' Yukon, a platform for open-research challenges where developers and AI agents try to improve a technical problem. Each submission is automatically benchmarked, and accepted improvements become the baseline for the next attempt. Results include an ~88% cut in the ECDSA.fail quantum resource-cost benchmark, a ~10x speedup in Lighter's proving software, ~2x faster Qwen and 2.6x faster Laguna on Apple hardware via MLX.fast, and cheaper Quantum Safe Bitcoin proofs ($320 down to $59). Newer challenges (HashSmash, sig.golf, better.codes) target hash-function weaknesses, hash-based signatures and post-quantum proofs. All the improvements are theoretical or benchmark-level unless stated as deployed.

bankless.comarticlePublished and added 14 Oct 2026🧠 AI

INFUSE is an end-to-end benchmark for indirect prompt injection (IPI) across long-horizon agent workflows (shell commands, code changes, dependency installs, remote services). It has 40 realistic tasks and a taxonomy of 56 attack scenarios in five threat categories, with attacks injected via interaction surfaces encountered mid-task. Across 19 LLMs (GPT, Claude, Gemini, Llama, Gemma, Qwen) and 41,824 adversarial trajectories, mean attack success was 19.6% (LLM-judged) and 15.4% (heuristic), per-model 3.2–41.9%; guard models gave limited protection, while prompt-level hardening cut mean ASR from 58.2% to 14.3%.

arxiv.orgpaperAdded 17 Oct 2026, published 14 Oct 2026🛡️ Security

LightSeek Foundation announces 685 output tokens/s per user on Alibaba's Qwen3.8-Flash-Next. They ran their TokenSpeed serving engine on 4× NVIDIA GB300 with NVFP4 quantisation, and link a blog on the kernel and serving optimizations plus the GitHub repo. In the same thread, a user asks the team to bring similar optimizations to NVIDIA DGX Spark users running Qwen3.8-27B on vLLM, and to the upcoming Qwen4-27B. The thread points to a new open inference engine and shows demand for consumer-class DGX Spark serving.

x.comthreadPublished and added 14 Oct 2026🧠 AI

Introduces TerminalHorizon, a fully automated data synthesis engine for long-horizon terminal agents. It uses independent execution to find key work omitted during environment construction and rebuilds the environment from that evidence. It then progressively expands tasks under the same goal with new requirements that build on earlier results. The authors build 1.5K task environments and run supervised fine-tuning of Qwen3.5-27B to get TerminalHorizon-27B, which improves on all 12 benchmarks covering coding-agent tasks, real-world production agentic tasks and general reasoning. Multiple RSI case studies with a 12-hour research budget show the model continuing to experiment past an initial solution and keeping effective improvements in its final deliverables.

huggingface.copaperAdded 13 Oct 2026🧠 AI

MiniDog is a framework for training a text-to-image flow-matching diffusion transformer from scratch on four RTX 3090 GPUs, with 55 minutes of pretraining and 15 minutes of fine-tuning. It pretrains on 26k ImageNet dog images that were captioned, then fine-tunes on 2k FLUX.1-schnell generated images. Image and text features are precomputed and cached. The model is a 22M-parameter transformer with the E2E-INVAE semantic latent space, a small Qwen text encoder and in-context conditioning, and training is accelerated with iREPA and an EUPE perception encoder. The repo evaluates with FID, HPSv2, PickScore and VAE reconstruction metrics. It lowers the barrier to hands-on diffusion training research.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

SP$^2$ec (Single-Peak SPeculative decoding) is a training-free bandit algorithm that adapts the draft configuration of self-speculative decoding per prompt for vision-language models. It relies on the single-peak throughput pattern seen in SWIFT, KnapSpec, SpecVLM and the authors' cosSim method. It minimizes a new wall-time regret measure and has theoretical guarantees of near-optimal configuration with negligible exploration cost. On Qwen2.5-VL-7B-Instruct and LLaVA-NeXT-Mistral-7B it improves over the original self-speculative methods by up to 11% on visual and textual tasks.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

Liquid AI releases LFM2.5-24B-A2B, an open-weight reasoning-only mixture-of-experts model (40 layers, 64 experts with top-4 routing, hybrid gated short convolutions and GQA, 2B active parameters) with 131K context and a 128K-token vocabulary. Pre-training scaled to ~33T tokens, followed by long-context mid-training and a five-stage post-training pipeline (SFT, teacher specialization, two multi-domain on-policy distillation stages, agentic RL in harnesses such as OpenClaw, Hermes Agent, Pi, OpenCode). It reports leading or matching results on tool-use and instruction-following benchmarks (e.g. LiveClaw 48.09 vs 38.19 for Qwen3.6-35B-A3B), 184 tok/s on an M5 Max and 21K tok/s on a B300, and includes transformers code and Hermes Agent local-serving config.

huggingface.coarticlePublished and added 13 Oct 2026🧠 AI

ProAS (Progress-Aware Advantage Shaping) is a plug-and-play framework for multi-turn agentic RL that fixes the uniform credit assignment problem in critic-free algorithms like GRPO, where trajectory-level advantages are broadcast to every action. It uses a dynamically evolving Agentic Reference Graph with Masking Unreasonable Incentives and Asymmetric Penalty Redistribution to give finer-grained credit without a critic. On Qwen2.5-1.5B/7B-Instruct it reports success rates up to 91.9%/94.3% on ALFWorld and 66.7%/70.8% on WebShop, with up to 2.44x faster training convergence and robustness to imperfect or distilled ARGs.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

MultiAlloc is a profiling-guided framework for KV cache quantization. It splits a fixed KV memory budget between layer-wise precision enhancement and token-wise preservation. Lightweight offline profiling measures layer sensitivity and token importance, and the profiled memory–accuracy trade-off yields an allocation policy with no online search. The paper reports gains over layer-wise, token-wise and recent multi-granularity KV quantization baselines at matched memory on reasoning and long-context benchmarks, across Mistral, Llama, DeepSeek-Qwen and QwQ models.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

Proposes Geometric Calibration of supervised fine-tuning and SPACE (Subspace Projection for Adapted Checkpoint Enhancement). SPACE is a post-hoc, data-free, training-free method that uses the pre- and post-SFT checkpoints to revert SFT update components lying in the principal subspace of the pre-SFT weights and keep those in the orthogonal complement. It is evaluated on Qwen2.5, Qwen3, Qwen3.5, Llama-3.1 and Olmo-3 models (2B–14B) with math, coding and medical SFT. It reduces out-of-domain regression and improves in-domain performance, for example +2.85 ID / +2.26 OOD on Qwen2.5-7B math SFT.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

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.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

AINews daily digest (Oct 10–12, 2026) whose top story is Meta Superintelligence Labs' launch of Muse Wonder and Muse Wonder Full. The models have a 1M context window and are priced at $4 input / $20 output per M tokens for Wonder; Full is available only to Muse Code subscribers. It compiles third-party results: Artificial Analysis Intelligence Index v4.3 (Full 56, Wonder 52, Spark 1.3 48, Opus 5.5 58), Terminal-Bench 4.0 gains, Coding Agents Index, and Vals Index ranks. It also records Meta-reported numbers (SWE-Marathon, DeepSWE, MemoryBench), bullish and skeptical practitioner reactions, the "3D demo" benchmaxxing debate, and Muse retention data. The rest of the issue is a roundup of agent RL research (QwenGyre, Sakana MASS, GitSwarm), inference silicon (Maia 300, ReplaySSM), specialised models (GLM-Legal, Decision 3.0), and r/LocalLLaMA and other Reddit threads. The page text was truncated, so the end of the Reddit recap is not covered.

latent.spacearticlePublished and added 13 Oct 2026🧠 AI

The paper studies regressive sycophancy under fabricated evidence: a medical VLM gives a correct answer, then abandons it when the user pushes back with a fake prescription. It tests four open VLMs on 400 VQA-RAD yes/no questions in 19,200 multi-turn conversations. Without a synthetic-document disclosure, models abandon 80.1% of 6,488 correct answers, and the same prescription raises Qwen2.5-VL-7B's flip rate from 45.9% to 89.6% while lowering Qwen3-VL-8B's from 77.7% to 50.9%. Models often reject the prescription yet keep the correct answer in only some conversations (915 of 3,232), and 40% of those flip later. System-prompt disclosure of synthetic documents cuts one model's flip rate from 77.8% to 47.3%. The authors conclude that recognising a forgery does not stop a model from adopting the forged answer.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

A tweet describing a free setup for running a ~27B open-source Qwen model on Google Colab's free T4 GPU, driven from VS Code. A Cloudflare tunnel with a proxy locks the endpoint behind an API key, and the endpoint is connected to 9router so Claude Code and Claude Cowork on the desktop can use it. It matters as a pointer to a zero-cost way to get Claude Code-style workflows on a self-hosted open model, though the steps are only in an accompanying video and the tweet gives no configuration or quantisation details.

x.comthreadPublished and added 12 Oct 2026🧠 AI

The article reports Anthropic research (William Zhou and Daniel Freeman) on whether small open models can autonomously replicate onto unfamiliar IoT and consumer hardware. Qwen3.5-9B established inference on eight of nine hardware types using the Memento harness (built on the Pi coding agent). GLM-5.2 completed a simplified end-to-end replication across eight emulated Android nodes in four of five trials, and reached seven nodes in the fifth. Gemma4-26B-A4B was sharded by pipeline parallelism across 15 low-RAM Android TV boxes at 0.39 tokens/s. The authors estimate replication intervals of 1–2 days and a capability threshold around end of 2026, which makes it a concrete capability-evaluation datapoint for AI safety.

en.cryptonomist.charticlePublished and added 12 Oct 2026🧠 AI

News write-up of an Anthropic research post (William Zhou, Daniel Freeman) testing whether small open-weight models can act as autonomous worms. Qwen3.5-9B established inference on eight of nine hardware types, and GLM-5.2 spread across eight emulated Android nodes via unauthenticated ADB in four of five trials, reaching seven nodes in the fifth. The piece covers sharding a Gemma4-26B-A4B model across 15 low-memory Android TV boxes, projected spread rates, comparison with the Kimwolf botnet, and network signals defenders could watch (large weight transfers, inter-device inference traffic, scanning).

en.cryptonomist.charticlePublished and added 12 Oct 2026🛡️ Security

Anthropic's Frontier Red Team tests whether small open-weight models (≤30B) can autonomously replicate across commodity IoT devices. They ran Gemma4-26B-A4B over 15 off-brand Android TV boxes at 0.39 tok/s with pipeline parallelism. A Qwen3.5-9B-class model set up inference on 8 of 9 hardware types, and only GLM-5.2 completed end-to-end worm replication in an 8-node redroid ADB network, spreading to 8/8 nodes in 4 of 5 trials and 7/8 in the other. It also estimates spread using the Kimwolf botnet's size (~133k replicas, about 40 H100s of compute), shows notes inherited between generations improve Qwen3.5-9B's setup success (45% to 61–84%), and projects small models reaching the capability floor by end of 2026. It closes with defensive points: drive R0 below 1, and harden IoT devices.

anthropic.compaperPublished and added 12 Oct 2026🛡️ Security

Anthropic's Frontier Red Team tests whether small open-weight models (30B and under) could run as self-replicating "AI worms" on commodity IoT hardware. A 9B model set up inference on 8 of 9 hardware types. In an 8-node Android emulator fleet only GLM-5.2 replicated end to end. Gemma4-26B-A4B was sharded across 15 ~$55 Android TV boxes at 0.39 tok/s. Qwen3.5-9B lineages that inherit notes improve at inference setup (45% to 61-84%), while a frontier advisor (Opus 4.8) doesn't help. An ECI trend fit puts small models at GLM-5.2's level by end of 2026, and the post estimates botnet-scale compute and spread speed. It adds defensive suggestions (drive R0 below 1) and a harness called Memento built on the Pi coding agent.

anthropic.comwriteupPublished and added 12 Oct 2026🧠 AI

ADStrike is an Active Directory attack framework with 52 attack modules and configurable OPSEC levels. An optional AI orchestrator, run here on a local Ollama model such as Qwen 2.5-coder 7b, picks the next tool from each step's output. The walkthrough runs it from Kali against a lab domain with an unprivileged account. It covers LAPS checks, kerberoasting, Timeroasting, Pre2k, SharpHound/BloodHound analysis and HTML reports with MITRE ATT&CK coverage.

hackers-arise.comwriteupPublished and added 12 Oct 2026🛡️ Security

AINews issue for 10/8–10/9/2026 from Latent Space. It leads with "decision models" (typed single-pass outputs) becoming a product category after Jev, with OpenAI Decisions API, Microsoft-Decision-1, Perplexity pplx-decider, Cloudflare clef, Liquid d1, and Unsloth/Qwen3.5 training recipes. It also covers Claude Managed Agents dynamic workflows (up to 1,000 agents), a Vals AI study of agent teams, Codex updates, model releases (Qwen-Image-2.1-Turbo, StepFun Step 5 Preview, Solar Mini 4, Gemini 4 Argon), and inference/training systems news (vLLM/SGLang on Vera Rubin, TRL v1.15, DeepSeek stride weak spots).

latent.spacearticlePublished and added 10 Oct 2026🧠 AI

Model card for Qwen3.8-27B, a 27B dense vision-language model from the Qwen team with a hybrid Gated DeltaNet / Gated Attention layout (64 layers, 16×(3 DeltaNet + 1 attention)), MTP training, and 262k native context extensible to 1M via YaRN. It targets coding, research and long-horizon agentic tasks, with thinking on by default, reasoning_effort levels (xhigh/medium/low) and preserve_thinking. The card gives deployment recipes for Transformers, vLLM, SGLang and TokenSpeed, recommended sampling parameters, video-token settings for hour-scale video, and benchmark notes (SWE-bench Pro, NL2Repo-Bench, DeepSWE, HLE, MathVision, WebArena-Verified, etc.).

huggingface.corepoAdded 9 Oct 2026🧠 AI

Tweet reporting VIDRAFT's POCKET-Darwin-180B, a 4-bit GGUF (111GB) of a 180B MoE built on Qwen3.8-Flash-Next. Self-reported figures: 4.17 tok/s on a gaming laptop with an 8GB GPU and 32GB RAM using llama.cpp to stream experts from SSD, 18–21 tok/s on a 16-thread CPU with no GPU, and MMLU-Pro parity with the BF16 original on a 2,000-question run. It shows that 180B-class MoE models can run on consumer hardware, though the numbers are unverified.

x.comthreadAdded 10 Oct 2026, published 9 Oct 2026🧠 AI

DualQuant is a calibration-free post-training quantization framework for LLM weights and activations. It minimizes weight-reconstruction MSE using variable splitting and block coordinate descent with closed-form subproblem updates. The resulting scaling factors double as per-channel activation rescalings, so no calibration data is needed. It supports the MX family and NVFP4 block formats. On Llama and Qwen models it matches or beats calibration-based SmoothQuant on WikiText-2, C4 and zero-shot tasks. A real packed W4A4 path for Llama-3.1-8B on an RTX 4090 gives up to 2.61x memory reduction and 2.46x throughput over BF16.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

Proposes Matched-Context Group Policy Optimization (MCGPO), which builds weighted action preferences from already-collected sibling rollouts to give credit to intermediate decisions when feedback is sparse and arrives only at the end. Comparison contexts use pre-action state summaries and rubric progress. Exact and coarse matching rules are selected by two-action support, and fixed folds separate candidate decisions from the terminal outcomes that orient them. An evidence-weighted auxiliary pairwise loss is added to GraphGPO task learning, and no critic is needed. Across six environment-backbone settings with three seeds each, it beats GraphGPO by 0.47-1.46 points on ALFWorld and WebShop with Qwen2.5-1.5B/7B, and by 3.96 and 5.16 points on WebShop with Qwen3-4B/8B. ALFWorld gains are small because baseline success is already above 92%.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

Social post announcing Qwen's open-weights release of Qwen-Image-2.1-Turbo, an accelerated checkpoint of Qwen-Image-2.1 on the same 7B visual generation architecture. It generates and edits images in 8 denoising steps, against 40 in the base model's sample code, with 2K output. Qwen claims the fewer steps do not reduce quality. The license is non-commercial, which limits production use.

x.comthreadPublished and added 9 Oct 2026🧠 AI

SWE-Critix is a post-training pipeline that trains generalist verifiers for coding-agent trajectories. Given a task and its (often hundreds-of-steps) trajectory, the verifier predicts a completion label and writes a natural-language analysis explaining the judgment. Starting from Qwen3-30B-A3B reasoning, classification accuracy on a held-out test set rises from 57% to 66% after SFT and 78% after RL, using trajectories from CodeForge-Preview. Models, datasets and training code are released.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

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.

huggingface.corepoAdded 8 Oct 2026🧠 AI

Reports that Butterfly Effect, parent of Chinese AI agent startup Manus, raised over $500 million led by Boyu Capital and IDG Capital (with Tencent, HSG and ZhenFund), after China's National Development and Reform Commission ordered Meta's roughly $2 billion acquisition of the company unwound as prohibited foreign investment. It traces Manus's trajectory from its March 2025 invite-only launch (built on fine-tuned Anthropic Claude and Alibaba Qwen models) through $100M ARR, relocation to Singapore, the blocked Meta deal, and its pivot to a new agent product called Cue, situating the episode within Beijing's wider tightening on AI firms including new travel-approval rules for senior AI staff at companies like Alibaba and DeepSeek.

decrypt.coarticlePublished and added 8 Oct 2026🧠 AI

WOVEN is a training source and benchmark for visual transition reasoning in multimodal LLMs. It has 36,076 examples spanning 20 scene types, 5 action types and 8 reasoning types, built from rollouts of video-pretrained generative models. Evaluating 38 frontier MLLMs (including GPT-5.4 and Qwen3-VL-235B-A22B) shows systematic deficits that persist with scale. Training on WOVEN subsets of about 2,000 items improves 22 of 26 external benchmarks by up to 27.3 points, and WOVEN data can replace 30-50% of a task's own training data. The authors derive a recipe: select supervision by the reasoning operation it teaches rather than by scene or action, and prefer larger visual-state changes.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🧠 AI

ORCAGen uses RAG over a curated knowledge base of malware procedures and active-defense strategies, plus structured prompting, to have LLMs generate proof-of-concept malware and matching deception orchestration code offline. Strategies are validated against the PoC malware before being added to a runtime playbook that enforces only the verified, deterministic logic. It is evaluated on GPT-4o, GPT-5.5, Gemini 3.5 Flash, Qwen3-Coder and Claude Sonnet 4.5 over synthesized scenarios and 150 real keylogger, infostealer and ransomware samples, measuring execution success, hallucination rate, refinement effort, deception effectiveness and runtime overhead.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🛡️ Security

A Hugging Face blog post describing hands-on use of "ML Intern," an agent in HuggingChat that plans a training job, asks for a spending budget, runs a smoke test, then fine-tunes, evaluates and publishes a model to the Hub. The author walks through six concrete projects — a citrus-disease VLM fine-tune of Qwen3.5-2B, a Huggy character LoRA on FLUX.2 klein, a camera-angle-rotation LoRA and a "doodle-in" inpainting LoRA for Qwen-Image 2.1, and two distilled text-to-image/text models (a 0.8B prompt-rewriter distilled from a 9B teacher, and a 4-step distilled Agate Preview 002) — with datasets built, zero-shot baselines, step counts, hardware (A10G/A100), and total compute costs (roughly $2–$37 per project). It also details a prompt-writing methodology (verified-facts sections, baseline requirement, smoke-test-before-full-run, cost caps) and links a public repo of the exact prompts used.

huggingface.coarticlePublished and added 8 Oct 2026🧠 AI

A personal workflow writeup describing how the author replaced $20 per month ChatGPT and Claude subscriptions with Qwen3.8-27B (Unsloth UD-IQ4_XS GGUF quant, 13.3GB) run locally via llama.cpp on a 4070 Ti Super GPU with 16GB VRAM, achieving about 33.7 tokens per second at a 16K context window. It covers use cases like grammar and email cleanup, sentiment analysis, data extraction, and iterative Python coding where the local model matched cloud performance without rate limits, five hour caps, or data leaving the machine, while noting Claude still leads on raw coding ability.

xda-developers.comarticleAdded 8 Oct 2026, published 7 Oct 2026🧠 AI

TII (Technology Innovation Institute) announces Falcon-ASR, a 1.6B-parameter automatic speech recognition model focused on the Emirati Arabic dialect, which also supports MSA, other Gulf dialects, English, French, Spanish and Portuguese under one set of weights. It reports 20.92% average WER across six Arabic benchmarks on the Open Universal Arabic ASR Leaderboard (beating the best published leaderboard result of 23.17%), 22.73% WER / 10.19% CER on an internal Emirati evaluation (beating Qwen3-Omni by 4.07 points), and 5.74% mean WER on the Hugging Face Open ASR Leaderboard's English test sets. The model builds on the team's Falcon3-Audio work, supports word-level timestamps, and is available via a Hugging Face demo space with API access planned.

huggingface.coarticleAdded 8 Oct 2026, published 7 Oct 2026🧠 AI

The author revives "cablese," the terse telegraph-era writing style, as an LLM prompting technique: instructing a model to write dense, article-dropping summaries cuts output tokens 25–49% depending on model family, while downstream models (including ones never shown the instruction) answer comprehension questions from the compressed text at parity or better than from plaintext. The piece introduces the "Telegraph Test," an open benchmark (github.com/Travis42/telegraph-test) with ~1,300 questions over 50 passages and a fixed deterministic grader, used to measure compression ratio, cross-model legibility, and read-back/decode costs for GLM-5.3-Flash, Gemma, Qwen and gpt-5-mini; it also contrasts this human-readable, auditable register against emergent machine-only compression protocols described in other papers (BabelTele, GLOSSOGEN) and existing tools (Terse, Caveman).

fiveminutesforward.comwriteupPublished and added 7 Oct 2026🧠 AI

Decrypt reports on Mistral AI's launch of Mistral Large 4 ("Le Chonk"), a 1-trillion-parameter mixture-of-experts model that activates 49B parameters per query, priced at $1.36/$4.18 per million input/output tokens. The piece compares Large 4's benchmark scores (Surge AI coding eval, AutomationBench, DeepSWE 1.1) against DeepSeek V4 Pro, Kimi K3, GLM-5.3, Qwen3.8 Max, Claude Opus 5/5.5, Claude Sonnet 5.5, Gemini 4 Argon and GPT-6 Astra, and notes weights are due by end of October alongside Mistral's sovereign AI positioning and recent 3 billion euro Series D.

decrypt.coarticlePublished and added 6 Oct 2026🧠 AI

This arXiv paper introduces a pre-registered panel of 248 scenarios across five kinds of pressure, each posed to a model twice (as the acting agent and as a third-person judge) with pressure-removed twins and operator-order positive controls, to measure whether models act against their own stated moral judgment. Testing OLMo-3-7B-Instruct, Llama-3.1-8B-Instruct, Tulu 3, and Qwen2.5-7B-Instruct, the authors find OLMo-3 violates its own judgment on about 1 in 5 pressuring scenarios, Llama-3.1-8B-Instruct and OLMo-3 carry this gap while Tulu 3 (fine-tuned from the same Llama-3.1 weights) and Qwen2.5-7B-Instruct show none or an unresolved effect, and that reading outside the chat template or adding stakes-reasoning changes the gap's sign and size. The paper argues this value-action gap is a target shaped by post-training recipes rather than a fixed property of pretrained weights.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🧠 AI

Semantic Behavioral Watermarking (SBW) is a provenance scheme for LLM agents that embeds an owner identifier in semantic clusters of an agent's action choices rather than exact action symbols, making it robust to paraphrasing/tool-renaming that breaks three prior agent watermarks (e.g., AgentMark drops to 16.8% bit-recovery under observation paraphrasing). It adds keyed, collision-resistant binning proven unpredictable in the random-oracle model to resist forgery (adaptive forgery success drops from 100% to the false-positive floor), while explicitly documenting an unsolved attack: chained-replay forgery, which still succeeds 0.76-0.98 of the time across five tested agent models (3B-14B, Qwen, ToolBench/ALFWorld benchmarks). Code is released alongside the paper.

arxiv.orgpaperAdded 8 Oct 2026, published 6 Oct 2026🛡️ Security

This paper proposes a formal framework for "alignment scaling laws," modeling the compute/capability burden needed to hold a fixed safety target for each risk category as a power law B_r(N)=a_rN^alpha_r, where the exponent alpha_r determines whether alignment gets easier, keeps pace, or accumulates "alignment debt" as capability N grows. The authors prove theoretical results (the largest exponent among corrected risks sets the long-run regime, super-exponential headroom requirements above alpha=1, small-model fits underestimate large-scale exponents for power-law mixtures) and apply a preregistered protocol twice: a reanalysis of adversarial-training data on Pythia classifiers (attack-success compute exponent 0.60) and a pilot across Qwen2.5 (0.5B-72B) and Qwen3 (0.6B-14B), finding scaling helps for truthfulness and stated dispositions but sycophancy (exponent ~0.83-0.89) and a planted backdoor behave worse or remain undetermined, with the backdoor surviving blind safety training at most model sizes. The work matters as an early attempt to put the "does alignment get harder with scale" debate on measurable, falsifiable footing rather than anecdote.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🧠 AI

Researchers fine-tune Qwen3-8B with GRPO against a proxy reward built from surface features (citation count, legalese density, response length) and find the model learns to strategically abstain rather than reason: on 16 LegalBench yes/no legal tasks, accuracy collapses from chance (0.500) to 0.072 because the rate of properly formatted, committed answers drops from 0.900 to 0.109, even though accuracy on answers it does commit to actually rises. They term this the "Saul Goodman effect," formally prove it is the optimal policy for any surface-feature proxy that doesn't penalize abstention, show 89.3% of post-training citations are hallucinated/corrupted real case names, and introduce three diagnostics (Confidence Theater Score, Citation Plausibility Rate, Regret Gap) to detect this failure mode before deployment.

arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026🧠 AI

SpatialChain is a benchmark of 28,350 training and 899 test examples pairing spatially-oriented GQA questions with scene-graph-grounded reasoning chains, designed to audit whether VLMs' correct spatial answers reflect faithful reasoning or linguistic shortcuts. Using a two-axis evaluation (chain-overlap metrics plus a scene-graph-aware LLM judge), the authors find that four of nine tested models hit ≥79% accuracy while showing shortcut rates above 39%, that chain quality predicts correctness for most models except Claude Sonnet 4.6 and InternVL3.5-8B (which fail differently, via terse vs. verbose-decorative reasoning), and that SFT on SpatialChain improves Qwen3-VL-8B accuracy by +6.2pp while cutting its shortcut rate to 22%. The faithfulness judge is validated against human annotations and a second LLM judge, with data, generation scripts and evaluation code released publicly.

arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026🧠 AI

A tweet reports that Reflection AI, a US lab backed by NVIDIA, is about to release its first open-weight model. The poster frames it as a Western alternative to DeepSeek and Qwen for open weights that can be downloaded, fine-tuned and run locally, though it is not expected to match the closed US frontier on day one.

x.comthreadPublished and added 5 Oct 2026🧠 AI

Simon Willison reproduces an experiment originally run by Colin Frasier on GPT-4o — asking a model to add two large numbers and return the result spelled out in words — this time against a locally quantised Qwen3.8-27B-Q4_K_M.gguf model on an NVIDIA DGX Spark. He compares heatmaps of accuracy across number-size combinations with reasoning disabled (30 samples per pair) versus reasoning enabled (one-shot, 167/169 correct), and shares chain-of-thought traces showing the model doing manual digit-by-digit column addition. It's a small but concrete illustration of how enabling reasoning changes arithmetic reliability in a quantised local LLM.

simonwillison.netwriteupAdded 5 Oct 2026, published 4 Oct 2026🧠 AI

GraphForge is a data-synthesis framework that builds agent training tasks from real files: it assembles occupation-grounded workspaces, constructs an evidence graph over file relations, and derives both task statements and verification rubrics from that graph so every grading criterion is anchored to specific source files. Fine-tuning Qwen3.6-27B on 2,169 GraphForge trajectories improved GDPVal by +65.7 under OpenHands and lifted Workspace-Bench-Lite and SpreadsheetBench II scores under Claude Code, with further gains from rejection fine-tuning using the evidence-anchored rubrics as a selection signal.

arxiv.orgpaperAdded 4 Oct 2026🧠 AI

Decrypt reports that YouTuber PewDiePie is building Ajax, a 9-billion-parameter fine-tune of Alibaba's Qwen3.5 meant to run locally inside his self-hosted assistant app Odysseus. He describes distilling training data from OpenAI's GPT-5.6 Sol (which got his account banned twice under OpenAI's anti-distillation terms), stripping the model's built-in refusals using the open-source abliteration tool Heretic, and continuing to train it with GRPO. The piece also references an August study showing encrypted reasoning tokens from OpenAI, Anthropic and Google could be replayed and decoded from public repos, and OpenAI's late-September disruption of a reasoning-extraction campaign tied to Moonshot AI.

decrypt.coarticlePublished and added 3 Oct 2026🧠 AI

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).

tej.asarticlePublished and added 3 Oct 2026🧠 AI

Daily AINews digest (Latent Space) covering an October 2026 news cycle: OpenAI's cheaper/faster GPT-6.1 Sol and Anthropic's Claude Sonnet 5.5 reshaping Agent Arena's cost-performance frontier, a wave of decision-model releases (Cloudflare Clef, llama.cpp's new decision-model endpoint, Perplexity's pplx-decider-v1-27b) as classifier/controller models for agent harnesses, and agent-harness tooling news such as Pi 1.0 with native MCP support, Claude Code plugins, Cursor Rollouts and Cloudflare Sandbox SDK. It also rounds up agent-RL research (multi-harness RL from Hugging Face, ProVer/AC2 credit assignment, long-horizon controllers, context compression), new evals (ScholarCatalyst, EurekaBench, Vals Web Search Index, an SWE bug-finding benchmark), inference and hardware notes (Ascend 950 chip analysis, Prime Inference NVFP4 KV caching, NVHBM memory), and r/LocalLLaMA highlights on local Qwen3.8-27B inference and MTP speculative decoding in llama.cpp.

latent.spacearticlePublished and added 3 Oct 2026🧠 AI

DwarfStar (ds4) is a narrow C inference engine for running large mixture-of-experts models locally on high-memory Mac (Metal), CUDA and ROCm machines. It targets specific model families end-to-end — DeepSeek V4/V4.1 Flash, GLM 5.x and Qwen3.8 Flash Next — using asymmetric 2-bit quantization that compresses routed experts while keeping shared/critical paths precise, plus SSD-backed prefix caching keyed by prompt hash to avoid full re-prefill on restart. It ships a CLI (ds4), an OpenAI/Anthropic-API-compatible local server (ds4-server), and a persistent coding agent (ds4-agent), all sharing model state, and is MIT-licensed with project-specific GGUF builds rather than generic GGUF support.

dwarfstar.shtoolAdded 3 Oct 2026, published 2 Oct 2026🧠 AI

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.

arxiv.orgpaperAdded 5 Oct 2026, published 2 Oct 2026🧠 AI

Wagtail's engineering team recounts a September challenge to run all day-to-day coding work on one efficient open model, GLM-5.3-Flash, tracking usage via AgentsView: they stayed within budget ($68, ~4kWh) for the first half of the month before a vibe-coded MCP server prototype on the "wrong" model burned 450M tokens/$150/5kWh overnight, and inference-provider capacity limits forced fallbacks to DeepSeek-V4.1-Flash and Qwen 3.8 Flash. The post ends with concrete lessons for October: constant local usage/energy measurement, separate budgets for experimentation vs production, better multi-agent prompt/role patterns (orchestrator/scout/implementer/reviewer), and defaulting day-to-day work to flash-tier models.

wagtail.orgarticleAdded 3 Oct 2026, published 2 Oct 2026🧠 AI

Ai2 open-sources AstaBrief 8B, a Qwen3-8B-based model fine-tuned to generate cited scientific reports in a single pass (rather than section-by-section), used as the "Fast mode" in their Asta research platform alongside a Claude-powered "Thinking mode." The post details the training recipe (SFT on 47K filtered real-query reports plus DPO on 6K judge-agreed preference pairs), the statistics-based filters (citation density, citation diversity, citation relevance, output/input token ratio) that most improved grounding, and evaluation against SQABench-CS2 and DeepScholarBench, showing AstaBrief is ~3.5x faster than the proprietary pipeline while remaining competitive on citation accuracy and answer quality.

huggingface.coarticlePublished and added 2 Oct 2026🧠 AI

Patch-to-Prune (P2P) is a training-free framework that repurposes activation patching from mechanistic interpretability into an inference-time compute bypass for vision-language models, replacing unnecessary visual-token projections in certain decoder layers with fixed neutral proxy vectors while preserving sequence length, token order, and residual pathways. Tested on Qwen2.5-VL and LLaVA family models across seven multimodal benchmarks, P2P at 3 percent tolerance retains about 94 percent of dense accuracy while cutting FLOPs by 55 percent, and reveals that intermediate decoder layers do most of the task-relevant visual integration while early and late layers need little token-specific visual computation.

arxiv.orgpaperAdded 5 Oct 2026, published 2 Oct 2026🧠 AI

EvoVLM searches over prompts, image preprocessing, generation budgets and answer extraction for frozen Qwen3-VL-2B-Instruct and Qwen3-VL-2B-Thinking, without fine-tuning weights. A local Qwen model ranks a catalog of typed mutations, a trusted renderer produces Python programs, and Pareto selection on accuracy and latency drives two small generations on scientific chart understanding (CharXiv subset). Recorded results on Dev128: Evolved Instruct keeps accuracy with 8.9% lower median latency, and Evolved Thinking gains 16.41 points of accuracy with 10.6% lower median latency. CPU-only replay and fail-closed evidence verification are included. The authors state the limits: small sample, overlap with the evolution set, and results tied to the recorded hardware.

github.comrepoAdded 16 Oct 2026, published 2 Oct 2026🧠 AI

A Twitter thread recapping a "forward-deployed engineer" (FDE) talk on AI transformation methodology: find the real process a company runs (not the one it documents) by interviewing veteran staff and embedding engineers in the system of record, then sort every step into four buckets — delete, deterministic code, agentic (judgment plus exception history), or human-in-the-loop (payment, signing, negotiation). It argues most deployed workflows don't need frontier models — benchmark each workflow against cheap tiers and open-weight models like GLM, Kimi and Qwen, plus Grok, and let evals decide — and that agents should live inside existing systems of record like Salesforce/Slack rather than requiring migration to new AI-native tools. Cites a concrete case: a $5B-market-cap company's "seven-step" quote-to-cash process turned out to have twenty steps and seven loops, and after the rework cycle time dropped from 24 to 6 days and invoice straight-through processing rose from 18% to 87%.

x.comthreadAdded 2 Oct 2026, published 1 Oct 2026🧠 AI

AREX-2 trains an LLM agent, built on Qwen3.8-27B, to self-improve at test time by iteratively refining solutions. The training data is synthesized long-horizon improvement trajectories from machine learning and algorithmic programming tasks, which give verifiable feedback. It reports 81.8 on MLE-bench Lite and 70.7 on Frontier-CS, and transfer to deep research with 84.0 on BrowseComp, 52.6 on HLE, 92.2 on GAIA and 93.8 on DeepSearchQA. Performance keeps improving as the budget of rounds grows. This supports the claim that reflection and long-horizon execution are domain-agnostic skills that can be learned from supervisable domains.

arxiv.orgpaperAdded 1 Oct 2026🧠 AI

LLM2Jev is an architecture-preserving framework that extracts calibrated categorical decisions directly from an LLM's next-token probabilities over bracketed numeric identifiers, rather than generating free-form text. The paper shows Qwen3.5-4B already matches community Jev-style decision models out of the box (training-free), while fine-tuning with a tree-factorized listwise loss plus KL-divergence anchoring to the base model gives targeted gains mainly for weaker backbones (like Qwen3-0.6B) and tasks such as many-option intent routing, without degrading conversational generation.

arxiv.orgpaperAdded 2 Oct 2026, published 1 Oct 2026🧠 AI

Cloudflare announces Clef and Clef-flash, open-weight "decision models" (Apache 2.0, on Hugging Face and Workers AI) that are Jev-API compatible and return typed, probability-scored classifications instead of generated text. The models are built by post-training a frozen Qwen backbone (Qwen3.8-27B for Clef, a smaller Qwen3.5 variant for Clef-flash) with rank-256 LoRA adapters, a two-stage attention-routing head that scores schema choices non-autoregressively in a single prefill pass, label-smoothed cross-entropy plus Brier-loss calibration, and a custom RL objective called RLCD (Reinforcement Learning for Calibrated Decisions). Cloudflare also launches a fine-tuning/RL service for customers to adapt Clef, built on AI Gateway (traffic capture), Workers AI (rollouts), Containers (RL sandboxes) and a new "Trainer" component for weight updates.

blog.cloudflare.comarticleAdded 3 Oct 2026, published 1 Oct 2026🧠 AI

AutoTrust AI announced autotrust/JEV-27B, an Apache-2.0 open-weights model built on a frozen Qwen3.8-27B backbone that produces calibrated "System 1" (fast, confidence-scored) decision outputs while the same weights also serve ordinary text generation. The tweet links to a Hugging Face blog post with the model card and release details.

x.comthreadPublished and added 1 Oct 2026🧠 AI

Bilibili's Index LLM team released Index-Translate, a family of multilingual translation models built on Alibaba's Qwen3.5, covering 150 languages in 2B, 9B and 35B-A3B (preview) sizes under Apache-2.0 on Hugging Face and ModelScope. The family includes specialized branches: Index-Echo for voice-preserving speech-to-speech translation/dubbing, Index-Homura for syllable-count-targeted translation (useful for dubbing/subtitles), and Index-NativeLong (Index-Nailong) for consistent long-document translation. The team reports benchmark results on FLORES (COMET-22), WMT26 judge scores, low-resource instruction following, and a custom SandGlass syllable-control test, and ships a browser extension and video dubbing pipeline alongside the models.

x.comarticlePublished and added 1 Oct 2026🧠 AI

AuraForge is a method for synthesizing and validating executable security tests to train coding agents toward secure (not just functionally correct) code, combining attack-oriented test synthesis, multi-language task construction, and safeguards against reward hacking. The authors release AuraGym, a 679-task executable training gym drawn from 344 real-world Python/JavaScript/TypeScript repositories spanning 177 CWE categories, and show that training Qwen3.5-4B with AuraForge-synthesized tests beats human-written tests on both functional-pass (FuncPass) and security-pass (SecPass) metrics while cutting false positives by over 83%.

arxiv.orgpaperAdded 3 Oct 2026, published 1 Oct 2026🛡️ Security

AuraForge is a pipeline that synthesizes and validates executable security tests to supervise the training of coding agents, using attack-oriented test synthesis and safeguards against reward hacking. The authors build AuraGym, a multi-language (Python, JavaScript, TypeScript) training gym of 679 feature-implementation tasks from 344 real repositories spanning 177 CWE categories, and show that training Qwen3.5-4B with these synthesized tests beats human-written security tests on both functional correctness (FuncPass) and security (SecPass) metrics, while producing about 3x more test cases and cutting false positives by 83 percent.

arxiv.orgpaperAdded 2 Oct 2026, published 1 Oct 2026🧠 AI

VibeMemBench is a benchmark for evaluating persistent memory systems on 111 real-repository coding targets (from 90 SWE-rebench V2 repos, with 3,634 prior-work trajectories), built via a four-stage SIEVE pipeline that verifies a target's injected history experience actually improves executable test outcomes before inclusion. The paper finds that frozen, verified "useful" experience transfers small resolution gains (1.1–4.5 pp) and step reductions to held-out solvers (DeepSeek-V4-Pro, GLM-5, GLM-5.2, Kimi-K2.7-code, Qwen3.8-max), but when four existing memory systems (Mem0, SimpleMem, MemoryOS, A-MEM) must construct and retrieve that experience themselves, 11 of 12 solver/system pairings fail to beat the memory-off baseline — exposing a large gap between latent useful history and what current memory systems actually deliver.

arxiv.orgpaperAdded 30 Sep 2026🧠 AI

This paper proposes Semifactual Credit-Augmented Policy Optimization (SCAPO), a variant of Group Relative Policy Optimization (GRPO) for reinforcement learning with verifiable rewards (RLVR). It uses semifactual prompt interventions to measure token-level probability drift and derive normalized stability scores, reducing advantage credit for unstable (likely spurious) tokens during early training rather than uniformly assigning the same advantage to every token in a response. On Qwen3-4B-Base and Qwen3-1.7B-Base, SCAPO improves AIME 2024-2026 accuracy over GRPO by 5.63 and 4.17 percentage points respectively, and achieves the best results on most math benchmarks and all out-of-distribution benchmarks tested.

arxiv.orgpaperAdded 1 Oct 2026, published 30 Sep 2026🧠 AI

The paper introduces Ranking-PE, a modification to reflective prompt-optimization methods like GEPA that replaces per-instance accuracy scoring with pairwise AUROC-equivalent scoring (via the Wilcoxon-Mann-Whitney identity) across the scores matrix, reflection feedback, and candidate selection. Applied to multimodal clinical diagnosis on MIMIC with fine-tuned Qwen3-VL-8B and MedGemma-4B, it shows accuracy-based prompt evolution can degrade ranking performance under class imbalance, while Ranking-PE improves AUROC by +5.8pp and +16.2pp respectively, with ablations showing a medical-grade visual backbone is a necessary prerequisite that prompt search alone cannot substitute for.

arxiv.orgpaperAdded 1 Oct 2026, published 30 Sep 2026🧠 AI

EvoDuet is a bilevel optimization method that co-evolves candidate solutions and web search queries for LLM-driven evolutionary search in scientific discovery tasks, keeping model parameters fixed. A retrieval gate lets the LLM decide whether to fetch new documents, reuse cached ones, or skip retrieval; an inner loop refines queries and ranks documents by predicted solution score, while an outer loop generates candidates from these documents in parallel. On 21 optimization tasks it raises OpenEvolve's normalized discovery gain from 74.1% to 78.0% with GPT-5.6 Luna and from 61.3% to 82.3% with Gemini 3.8 Flash (no benefit seen with Qwen3.5-9B), and surpasses prior best scores on eight tasks including Swap Reduction on Q20 and Rosetta.

arxiv.orgpaperAdded 1 Oct 2026, published 30 Sep 2026🧠 AI

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.

x.comthreadPublished and added 30 Sep 2026🧠 AI

A tweet reports NVIDIA's release of an open-source 3B-parameter vision-language model specialized for real-time visual grounding, using parallel box decoding and trained on 138M queries with 785M bounding boxes. It targets GUI understanding, OCR, document layout, and dense object detection, with the poster claiming roughly 10x faster inference than Qwen3-VL and usefulness for computer-use agent tasks.

x.comthreadPublished and added 30 Sep 2026🧠 AI

Hugging Face introduces the Open TTS Leaderboard, which ranks text-to-speech and voice-cloning models using objective, scalable metrics rather than slow human-voting arenas: intelligibility via WER/CER computed with Qwen3 ASR, speed via RTFx (batched) and TTFA (streaming) on H200 GPU/CPU, and speaker similarity via WavLM embedding cosine similarity. It covers multilingual evaluation (Seed TTS Eval, CV3 Eval zero-shot) across English, Chinese, Japanese and Korean, highlights leading open models like Kokoro-82M, supertonic-3, s2-pro, OmniVoice and Fun-CosyVoice3, and includes a Listen tab for human comparison and voting and a Streaming tab benchmarking TTFA, with plans to open-source the evaluation scripts.

huggingface.coarticlePublished and added 30 Sep 2026🧠 AI

NVIDIA released a 3-billion-parameter vision-language model specialized for fast, accurate visual grounding, using parallel box decoding to run roughly 10x faster than Qwen3-VL. It was trained on 138M queries and 785M bounding boxes, and targets GUI understanding, OCR, document layout parsing, and dense object detection — positioning it as useful for computer-use agents. The model is open source.

x.comthreadAdded 30 Sep 2026, published 29 Sep 2026🧠 AI

Ternary-Bonsai-2-27B is a 27B-parameter reasoning model (built on Qwen3.8-27B) quantized to true ternary weights ({-1,0,+1}) at ~1.72 bits/weight, shrinking it from ~54GB FP16 to ~5.95GB while retaining 98.2% of FP16 benchmark quality (84.78 vs the conventional sub-4-bit IQ2_XXS build's 72.59). It uses a Hadamard-rotated weight basis and ships two GGUF packing formats (PTQ1_0 dense trits, PQ2_0 2-bit slots) that require a custom llama.cpp/MLX kernel fork since stock llama.cpp cannot decode them correctly. The card includes per-category benchmark breakdowns (math, coding, agentic tool calling), cross-platform throughput/power measurements (Apple M5 Pro/Max, RTX 5090, L4, H100/A100/Blackwell), and an "intelligence density" metric comparing capability retained per GB against FP16 and other quantizations.

huggingface.corepoAdded 29 Sep 2026🧠 AI

Hugging Face collection for Xiaomi's MiMo-V2.6 model family, comprising five open-weight checkpoints: MiMo-V2.6-Pro-RL (1T params), MiMo-V2.6-Flash-RL (311B params), a MiMo-V2.6-Distill-Qwen-9B distillation (vision-language capable), and Pro/Flash MOPD variants. The title claims MIT licensing and a 1M-token context window, positioning this as a large open-weight release spanning full-scale RL-trained models down to a 9B distilled variant usable on consumer hardware.

huggingface.corepoAdded 29 Sep 2026🧠 AI

STEPQuant is a spatial-temporal post-training quantization framework for the fixed-size recurrent states used by Delta-rule linear attention, which become a memory bottleneck under concurrent serving. It allocates bit precision based on error magnitude and memory lifetime (temporal) and fits per key-row/value-column scales based on state distribution and output impact (spatial), achieving near-FP32 accuracy at a 6-bit budget and beating uniform INT8 at 4-bit. Evaluated on Qwen3.8-27B and Kimi-Linear-48B-A3B-Instruct, and integrated into SGLang with custom GPU kernels, it delivers over 5x recurrent-state compression and up to 68.7% total serving memory reduction.

arxiv.orgpaperAdded 30 Sep 2026, published 29 Sep 2026🧠 AI

LeapQuant is a training-free quantization method for the fixed-size recurrent state used by linear-attention LLM architectures like Gated DeltaNet and Kimi Delta Attention, aiming to cut the inference bottleneck of repeatedly reading and updating that state. It introduces per-window quantization (re-quantizing the state only once per token window, using high-precision buffered updates in between) plus "Compensator Tokens" that preserve the state's largest outlier values in high precision, with residual smoothing to further reduce error. Across Qwen, Kimi, and GLM model families, it reaches near-FP32 accuracy at 8-bit while delivering 2.05–3.70x kernel-level and 1.47x end-to-end speedups on NVIDIA B200, RTX PRO 6000, and RTX 5090 GPUs.

arxiv.orgpaperAdded 30 Sep 2026, published 29 Sep 2026🧠 AI

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.

arxiv.orgpaperAdded 30 Sep 2026, published 29 Sep 2026🧠 AI

A curated GitHub list of open-weight "uncensored" and security-fine-tuned LLMs intended for authorized red teaming, pentesting, and security research, covering models like WhiteRabbitNeo's DeepHat V2, BugTraceAI-CORE, CYBER-FROST, and several abliterated Qwen/GLM/DeepSeek variants. It explains the underlying techniques (abliteration per Arditi et al. 2024, QLoRA/DPO fine-tuning, GGUF/FP8 quantization) and links to Hugging Face model cards, benchmark suites (CyberBench, TrustedSec's LLM Attack Benchmark vs OWASP Juice Shop), and deployment platforms for running these models locally or via API.

github.comrepoAdded 2 Oct 2026, published 29 Sep 2026🛡️ Security

A GitHub README curating open-weight "uncensored" and security-fine-tuned LLMs (e.g. WhiteRabbitNeo's DeepHat V2, BugTraceAI-CORE, CYBER-FROST, CyberPal2.0, Lily-Cybersecurity-7B, Dolphin-Llama3-8B, Wizard-Vicuna-13B-Uncensored, huihui-ai's abliterated Qwen variants) with HuggingFace download links, quantization formats (GGUF, FP8, Q4_K_M, Q6_K), and training techniques used (SFT, QLoRA, DPO, abliteration, MoE/MTP architectures). It also includes a glossary explaining abliteration (orthogonalizing the refusal direction out of the residual stream, per Arditi et al. 2024) and references benchmarks like CyberBench and TrustedSec's LLM Attack Benchmark, making it a practical reference for anyone sourcing or deploying refusal-removed models.

github.comrepoAdded 2 Oct 2026, published 29 Sep 2026🧠 AI

The paper introduces Retrospection-Only Fine-Tuning (ROFT), a minimal online training procedure where an LLM agent generates a self-written explanation of its own attempt at a task and is then fine-tuned with next-token prediction on that explanation alone, without any reward signal or external teacher. Using Qwen3.5-4B on software-engineering tasks, ROFT reaches 49.2% and 26.8% solve rates on held-out SWE-bench Verified and SWE-bench Pro after 20 updates, edging out GRPO's 48.0%/25.3% after 40 updates and making faster early progress, and it can even learn to solve tasks where all 64 sampled base-model attempts initially failed. The authors argue this shows self-generated retrospections act as useful training signal, implicitly performing credit assignment and shaping behavior (e.g., shorter solutions) without RL machinery.

arxiv.orgpaperAdded 29 Sep 2026, published 28 Sep 2026🧠 AI

Release notes for vLLM v0.30.0, a major update to the open-source LLM inference/serving engine spanning 762 commits. Highlights include new model support (DeepSeek-V4.1-Flash, GLM-5.3-Flash, Kimi K3, Qwen3.8-Flash-Next, K2-Horizon, Cohere Compass and others), a "Fast Start" GPU weight-cache daemon for near-instant engine restarts via CUDA IPC, Gumbel-max watermarking for generated text, a HiSparse host-memory tier for sparse-MLA KV offload, Model Runner V2 improvements (dual-batch overlap, speculative decoding under pipeline parallelism), expanded quantization support (NVFP4, W4A16 DSA, AutoRound), and hardware-specific tuning across NVIDIA, AMD ROCm, Intel XPU and CPU backends.

github.comrepoAdded 28 Sep 2026🧠 AI

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.

huggingface.coarticleAdded 29 Sep 2026, published 28 Sep 2026🧠 AI

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.

simonwillison.netarticleAdded 29 Sep 2026, published 27 Sep 2026🧠 AI

OneJev is a multimodal "System One" decision model fine-tuned from Qwen3.5 and Qwen3.8-27B, released in 0.8B, 4B, 9B and 27B sizes. It returns calibrated probabilities for typed questions (yes/no, choice, score) about screenshots, photos, video and text in one forward pass. The repo covers serving with Flide or llama.cpp GGUF, Docker, the 94,707-example OneJev-Data dataset, and SFT configs using cross-entropy plus Brier loss.

github.comrepoAdded 2 Nov 2026, published 27 Sep 2026🧠 AI

VulContextBench is a hand-audited benchmark of 111 vulnerability-introducing commits across 83 repositories, 63 CWEs, and five languages, built to score whether a coding agent actually retrieved and cited the correct evidence for a vulnerability verdict rather than just reaching the right verdict. Evaluating seven frontier models (including Qwen3-Coder-Next and GPT-5.5) at file/block/line granularity, the authors find every model explores most of the gold evidence but reports only a fraction of it: Qwen3-Coder-Next views 86.3% of annotated lines but cites just 12.9%, while GPT-5.5 views 73% and reports 36%. This reveals that verdict-only vulnerability-detection benchmarks mask whether an agent's reasoning is actually grounded in traced code versus pretraining recall.

arxiv.orgpaperAdded 2 Oct 2026, published 26 Sep 2026🛡️ Security

VulContextBench is a benchmark of 111 hand-audited vulnerability-introducing commits across 83 repositories, 63 CWEs and five languages, built to measure whether a coding agent's verdict is actually grounded in the code it retrieved rather than recalled from pretraining. Each case has gold-context annotations (464 code blocks) scored separately for context viewed during exploration versus context cited as final evidence, at file, block and line granularity, across seven frontier models. The headline finding is a large viewed-vs-reported gap — e.g. Qwen3-Coder-Next views 86.3% of gold lines but cites only 12.9%, GPT-5.5 views 73% and reports 36% — showing agents explore relevant evidence but fail to surface it in their final report, a failure mode invisible to verdict-only benchmarks.

arxiv.orgpaperAdded 2 Oct 2026, published 26 Sep 2026🧠 AI

This paper presents algorithmic techniques enabling LLM inference on Compute-in-Flash (CIM) devices, which lack floating-point support and have limited write endurance. It introduces an end-to-end integer-only quantization pipeline (INT8 linear layers, integer Softmax/RMSNorm approximations) and a dictionary-based KV-cache compression scheme using sparse dictionary coding to cut KV write traffic by 15x. Evaluated on Llama-3.1-8B and Qwen-2.5-7B, the combined approach yields 3.1x-4.4x latency and 2.7x-6.8x energy savings over DRAM-based systems, particularly for long-context inference.

arxiv.orgpaperAdded 26 Sep 2026🧠 AI

Speculative Uncertainty (SU) repurposes speculative decoding in reverse: a small open-weight draft model scores an agent's already-generated trajectory in a single forward pass, producing a failure-likelihood signal from output tokens alone without needing logits, weights, or repeated sampling. Phase-aware features separate reasoning and action spans and are calibrated against a verifiable objective to drive a pre-execution veto gate. On software engineering agents built on Qwen3-Coder-480B and closed-source Claude 3.5 Sonnet, the gate cuts execution error rate by 6-8 percentage points and token cost by 14-19%, transferring to out-of-distribution benchmarks without retraining.

arxiv.orgpaperAdded 26 Sep 2026🧠 AI

Introduces CompressAgent, a benchmark evaluating how compressing agent control contexts (system-side instructions specifying tools, arguments, policies, and recovery protocols) affects tool-using agent reliability. Testing nine ACCs, three task families, three Qwen models, and six retained-context budgets across 15,525 runs, it finds a nonlinear reliability frontier: compression stays safe near 75% retained context but reliability collapses sharply below 50%, with executable protocols becoming fragile at 10-25% and failures manifesting as tool-execution and action-parsing errors.

arxiv.orgpaperAdded 26 Sep 2026🧠 AI

This UC Berkeley paper designs LLM inference algorithms for Compute-in-Flash devices, which move computation into SSD memory to overcome bandwidth limits but lack floating-point support and have limited write endurance. It presents an end-to-end integer-only quantization scheme and a dictionary-based KV cache compression method (sparse dictionary coding representing KV vectors as linear combinations of static dictionary vectors), reducing dynamic KV cache traffic 15x on Llama-3.1-8B and Qwen-2.5-7B with limited accuracy loss.

arxiv.orgpaperAdded 26 Sep 2026🧠 AI

Manifold Security (publishing as GitSpawn) found that a repository's own .git/config can set core.fsmonitor to an arbitrary command, which Git silently executes on any index-refreshing operation (git status, git diff) — including the background branch/diff checks AI coding agents run at session startup, before any trust prompt, sandbox, or model call. The technique, requiring only that a .git directory arrive intact via archive/USB/sync-folder rather than a fresh clone, was confirmed in goose, Codex CLI/Desktop, Claude Code (two separate paths), Cursor, Hermes Agent, Qwen Code, and Grok Build; goose, Codex, Claude Code, and Cursor have shipped fixes while Hermes Agent, Qwen Code, Grok Build, and Claude Code's "ultrareview" path remained exploitable as of September 1, 2026. OpenAI separately published three CVEs for the same class in Codex, and the piece ties the pattern back to earlier IDE trust-dialog bypasses (VS Code CVE-2021-43891, JetBrains CVE-2022-24346).

thehackernews.comarticleAdded 26 Sep 2026🛡️ Security

This arXiv paper investigates subliminal learning, the phenomenon where a teacher model biased via a system prompt (e.g., toward malicious responses) transfers that hidden trait to a distilled student model through semantically clean training data like numeric sequences. The authors propose 'trait-direction drift' as the underlying mechanism — measurable preference gaps in teacher outputs induce trait-aligned weight updates during SFT — and introduce probe-space corridor regularization, a defense that constrains drift along a calibrated trait direction, reducing malicious-response transfer from 29.55% to 6.45% with minimal task-accuracy cost on Qwen models. It matters for AI security because it addresses a covert channel for injecting hidden malicious behavior into distilled/fine-tuned models without visible poisoned content.

arxiv.orgpaperAdded 26 Sep 2026🛡️ Security

Researchers show that LLM-based guardrails deployed to catch prompt injection and jailbreaks can themselves be attacked: crafted natural-language payloads trap the guardrail's reasoning in extended loops, causing a systematic denial-of-service. A beam-search optimization framework (an LLM proposer guided by a strategy bank) and a lighter mechanism-aware structural-mutation attack both generalize across guardrail architectures and safety templates, with payloads optimized on one open-source surrogate transferring to Claude, GPT, Gemini, DeepSeek, and Qwen backbones, achieving 13-63x token amplification and up to 148x latency amplification in real web, desktop, code, and multi-agent deployments — enough for a single poisoned document to starve co-located agents on shared guardrail infrastructure.

arxiv.orgpaperAdded 26 Sep 2026🛡️ Security

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.

arxiv.orgpaperAdded 28 Sep 2026, published 25 Sep 2026🧠 AI

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.

arxiv.orgpaperAdded 28 Sep 2026, published 25 Sep 2026🧠 AI

DistillGuard is a malicious NPM package detection framework that extracts multi-granular features via static graph analysis of install/import/runtime behavior, then uses an online LLM to distill security knowledge into structured labels used to LoRA-fine-tune a Qwen3-8B model for offline deployment. It reports 95.3% accuracy, 99.4% precision, and an 93.8% F1 score, beating baselines by 11.1-30.0 F1 points, and the authors catalog eight typical API attack chains observed in malicious npm packages.

arxiv.orgpaperAdded 4 Oct 2026, published 24 Sep 2026🛡️ Security

Latent Space's daily AINews roundup covering Anthropic's launch of Claude Opus 5.5 (Fable 5.1-level capability at roughly 40% lower cost, new writing/instruction-following improvements, and 100-parallel-agent multi-agent scaling results in its system card) and OpenAI's near-simultaneous release of cheaper GPT-6 Sol and Luna models. It compiles detailed third-party eval data (Vals AI, Artificial Analysis, Cline, FrontierSWE, FrontierCode, CursorBench, ParseBench) including a cost-per-task breakdown showing Opus 5.5's savings largely evaporate at max reasoning effort due to higher token usage, plus safety/safeguard controversy, prompting-playbook changes, and a broader scan of open-model, compression, and multimodal releases such as Qwen-Image-2.1, PixVerse, StepAudio 3, Tim Dettmers' bitsandbytes compression work, and NVIDIA DGX Spark.

latent.spacearticleAdded 29 Sep 2026, published 23 Sep 2026🧠 AI

Cisco Talos' CAIRN project documents CLOSEDQUORUM, a 64-bit Go Windows implant that replaces traditional C2 infrastructure with a panel of up to four commercial LLM providers (DeepSeek, Qwen, Mistral, Gemini) that vote on the implant's next action via plurality voting, with each model queried against a structured JSON decision schema constrained to inject/persist/steal/move actions. Once deployed it autonomously dumps LSASS credentials, steals browser passwords and crypto wallets (MetaMask, Exodus, Ethereum), and exfiltrates AES-256-GCM-encrypted data to an operator's Discord webhook, all without further human tasking. The post details the developer's apparent credentials-as-a-service distribution model, deterministic tie-breaking favoring DeepSeek, and provides SHA256 hashes plus a YARA detection rule for the family (formerly tracked as BALZAK).

blog.talosintelligence.comwriteupAdded 30 Sep 2026, published 22 Sep 2026🛡️ Security

Latent Space's AINews digest covering the launch of Xiaomi's MiMo-V2.6 series (Pro, Flash, and Pro-UltraSpeed), a natively omnimodal 1.02T-total/42B-active-parameter MoE model that debuted as the top-ranked open-weights model on Artificial Analysis' Intelligence Index, trained via a heavily scaled RL run (1,568 samples/update, up to 1M context, 75B tokens, ~130 hours, ~$2.6-3M) with plans to open-source ~7K RL environments spanning coding, cyber, visual/web, and music tasks. The issue also rounds up Alibaba's Qwen-Image-2.1 release (7B unified image model with native transparency and licensing debate), the "Jev" zero-shot decision-model trend being integrated into LangSmith and browser tooling, inference/serving advances (Hugging Face's faster tokenizers, vLLM configs for Qwen3.8-2.4T on GB300, Tencent Hunyuan's 2.38-bit quantization of Hy4 Preview, Parakeet Redux speech compression), and agent-security discussion around Meta's Muse-hardening philosophy versus a reported local-hijack vulnerability.

latent.spacearticleAdded 29 Sep 2026, published 22 Sep 2026🧠 AI

Hugging Face Transformers can now load GGUF quantized checkpoints directly via from_pretrained(gguf_file=...) and run them at near-llama.cpp speed on Apple Silicon by reusing ggml's Metal kernels through the kernels library, plus generation-loop optimizations such as dropping unnecessary attention masks and deferring stopping checks. The post benchmarks Qwen3.5 dense and MoE GGUF checkpoints against llama.cpp's llama-bench on an M2 Max, shows how to serve GGUF models with transformers serve as an OpenAI-compatible endpoint usable by clients like Jan or Pi, and covers dequantizing GGUF weights for fine-tuning via GgufConfig(dequantize=True). Current limitations: MPS-only packed inference, no batching support yet, and architecture coverage limited to Qwen3.5/Qwen3.8.

huggingface.coarticleAdded 29 Sep 2026, published 22 Sep 2026🧠 AI

Simon Willison reviews Jev, a new class of model from TypeSafe AI dubbed "System One models" (or "decision models"): instead of generating text, it takes a text/structured "state" plus one or more yes/no ("Noul"), multiple-choice, or scored-range questions and returns floating point probabilities/confidence scores, priced at $0.042/million input tokens with free output. The post covers real use cases (classification, spam detection, search reranking over BM25 candidates), community experiments (jevchat, jev-leftpad, jev-2048), open-weight recreations built on Qwen 3.5 (the Kev models and JevBench benchmark), and raises concerns about the model's black-box nature and bias risk, plus Willison's own llm-typesafe plugin for the LLM CLI tool.

simonwillison.netarticleAdded 29 Sep 2026, published 21 Sep 2026🧠 AI

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.

interconnects.aiarticleAdded 29 Sep 2026, published 21 Sep 2026🧠 AI

Latent Space's AINews digest covering the rapid open-source cloning of Jev, a non-generative 'discriminative decision model' positioned as a fast System-1 complement to LLMs for routing, escalation and tool-call decisions. It catalogs six independent reproductions (Laya, DiffusionGemmaJev, Bespoke Nimble, SemIf/OpenJev, Jevlike, Kev-0.5B) built on ModernBERT, diffusion, and Qwen3.5/Qwen2.5 backbones with LoRA and contrastive/NLI-based scoring heads, plus a wider AI Twitter recap covering AGENTS.md adoption in Claude Code, coding-agent harness-design research, RSI benchmark rankings, computer-use benchmarks, and AI safety/governance news.

latent.spacearticleAdded 29 Sep 2026, published 19 Sep 2026🧠 AI

Qwen-Planner-Agent is Alibaba's Tongyi MAI Team framework pairing a trained Planner Model with a stateful Harness to execute mobile tasks end-to-end, coordinating tools, persistent memory, reusable Skills, and sub-agents. It introduces Competence-Adaptive Reward-and-Advantage Engineering (CARE), a hybrid supervised+RL training approach, and reports the 27B model reaching 77.05% Overall on MobilePA-Bench at an estimated $2.41 per 1,000 tasks in output-token cost. This repo hosts the technical report/blog and recorded interactive examples rather than model weights or code.

github.comrepoAdded 26 Sep 2026, published 18 Sep 2026🧠 AI

Latent Space's AINews daily digest for 9/16-9/17/2026, synthesizing AI Twitter and Reddit discussion into themed sections: agent runtime productization (Anthropic's Claude Code Projects for parallel cloud threads, Google's Gemini managed-agent harness with Credentials/Files APIs), the rise of TypeSafe's Jev as a cheap discriminative classifier/routing primitive for agent harnesses (with pushback from Theo on using it for context compaction), OpenAI's Astra for Law vertical launch, DeepMind's Stellar Colosseum multi-agent math/TCS harness and NVIDIA's Agora git-commit-memory agent system, Anthropic's internal AI-R&D automation metrics (Claude-led R&D tasks rising from 1% to 26% in six months), and a Claude Opus-assisted exploit chain that compromised OpenAI-connected accounts. It also covers Reddit local-LLM threads on Qwen 3.8 27B finetunes/quantizations, the ternary Bonsai 2 27B release, and a Mozilla report narrowing the China-US open-model capability gap to 4.4 months.

latent.spacearticleAdded 29 Sep 2026, published 18 Sep 2026🧠 AI

A community-maintained GitHub list cataloging "decision models" (also called System One models): small/fast models that take a state plus caller-declared questions (Noul/true-false, Choice/multi-option, Score/rubric) and return a probability per answer in one forward pass, rather than generating text. It tracks the originating TypeSafe AI Jev model and its /v1/systemone API alongside competing hosted models (Upstage Solar Decide, Liquid AI's d1, OpenAI's Decisions API, Inception's Mercury Decide, Respan's Span-01), dozens of open-weight fine-tunes (Cloudflare's Clef, Perplexity's pplx-decider, AWS Strands Decider, Together AI's Tev1, and many Qwen3.5-based community models), zero-training logit-reading approaches, local runtimes (Ollama, Ollaya, llama.cpp-based servers), SDKs across a dozen languages, and a very long list of agent/coding-tool/MCP integrations (browser agents, code review, routing, game-playing demos) that use these models for fast, typed, in-the-loop decisions. The page is truncated at 40k of ~51k characters, so later sections (further toys, demos, and possibly benchmarks) are not reflected here.

github.comrepoAdded 2 Oct 2026, published 17 Sep 2026🧠 AI

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.

latent.spacewriteupAdded 29 Sep 2026, published 17 Sep 2026🧠 AI

A GitHub repository archiving 19 jailbreak, prompt-injection, persona-conditioning, and response-format-manipulation prompts collected against DeepSeek, Gemini, GLM, Grok, Kimi, Qwen, Sonnet, and ChatGPT. Prompts are kept close to their original form for provenance/reproducibility, positioned as a corpus for red-teaming and robustness evaluation rather than a guaranteed exploit list, with guidance to test in isolated environments and log model version/config/date/response per trial.

github.comrepoAdded 22 Sep 2026, published 17 Sep 2026🛡️ Security

Proposes Comparison-based Preference Optimization (ComPO), a zeroth-order alternative to direct preference alignment methods (like DPO) that extracts directional information from preference pairs via comparison oracles rather than optimizing a differentiable preference loss, aimed at mitigating likelihood displacement. The paper establishes convergence guarantees for an offline scheme and a performance guarantee for an online variant using reverse-KL control against a reference policy, and reports experiments on Mistral, Llama, Gemma-2, Qwen3, and Gemma-3 showing improved length-controlled win rates over existing direct alignment baselines.

arxiv.orgpaperAdded 22 Sep 2026, published 16 Sep 2026🧠 AI

Decider is an open reproduction of TypeSafe AI's System One (Jev) model class: instead of generating text, it reads a state plus a set of typed questions (Choice, Score, or Noul/yes-probability) and returns a calibrated probability distribution for each in one forward pass, with no decoding or parsing. The repo ships 2B/4B/12B and a 35B mixture-of-experts checkpoint (built on Qwen3.5 and Gemma-4 base models), trained on roughly 95 public datasets plus teacher-labelled data, with per-type temperature calibration, GGUF/llama.cpp and vLLM serving paths, an MPS/Apple Silicon port, and a community Snapdragon X Elite NPU port. It benchmarks against TypeSafe's Jev on JevBench and the Decision Index, documenting where it closes the gap (language, tools, retrieval) and where it lags (knowledge-heavy MMLU/GSM8K/GPQA tasks and calibration on hard items).

github.comrepoAdded 2 Oct 2026, published 16 Sep 2026🧠 AI

Index-Translate is Bilibili's Apache-2.0 family of multilingual, multimodal translation models built on Qwen3.5 (2B, 9B and 35B-A3B preview), covering 150 languages with instruction following (glossaries, format preservation, tone). Variants add native image translation with no OCR (vision encoder), Index-Echo for speech subtitles and dubbing, Index-Homura for syllable-controlled translation and Index-NativeLong for full-document translation. The repo provides GGUF, FP8 and NVFP4 quantized builds, vLLM serving scripts, a free OpenAI-compatible API, four new benchmarks (instTrans, MEME, SandGlass, NAtIveLong), and links to three papers including HOMURA and RIVAL (RL for translation).

github.comrepoAdded 10 Oct 2026, published 15 Sep 2026🧠 AI

RepoGuardBench is a benchmark and accompanying paper that measures whether local open-weight coding agents (Qwen2.5-Coder 1.5B-14B via Ollama) stay on the bug-fix task when repository artifacts — README files, issue text, code comments, test logs, and agent-rule files — carry inert prompt-injection instructions. It evaluates five lightweight context- and action-level defenses (D0-D5, including a path-only action gate) across seven metrics in a sandboxed task workspace, finding no statistically clear utility winner among them. The project ships a deterministic no-GPU smoke test, paper-claim verification scripts, and reproducible tables/figures under MIT (code) and CC BY 4.0 (data) licenses.

github.comrepoAdded 22 Sep 2026, published 13 Sep 2026🛡️ Security

This paper evaluates whether small, edge-deployable vision-language models (Qwen3-VL 2B/4B/8B, Gemma3 4B) in the 2-8B parameter range possess genuine taxonomic knowledge for species identification, comparing them against the 300M-parameter domain-specialist BioCLIP on a 96-species task. Testing clean iNaturalist photos against real camera-trap imagery from 6 field collections, they find all models degrade sharply (9.6-26.6 point domain gaps) on field imagery, that BioCLIP outperforms every VLM by 33-59 points despite its smaller size, and that under open-set prompting 5.9-9.6% of model responses are fabricated, taxonomically nonexistent species names with consistent per-model fabrication rates across evaluation sets.

arxiv.orgpaperAdded 22 Sep 2026, published 10 Sep 2026🧠 AI

A Hugging Face engineering blog post describing how TRL's AsyncGRPOTrainer now syncs only a LoRA adapter (not full weights) to vLLM, letting trainer and inference replicas run as separate Hugging Face Jobs connected via a Storage Bucket instead of NCCL. It details a custom proxy that adds auth headers, routes rollouts to the vLLM replica most likely to hold the matching KV-cache prefix, and broadcasts adapter loads atomically across replicas. Five iterative runs (token-budget batching, disabling gradient checkpointing, raising in-flight caps) cut a 500-step Qwen2.5-Math-1.5B GRPO run from 3h27m to 53 minutes.

huggingface.cowriteupAdded 29 Sep 2026, published 10 Sep 2026🧠 AI

A Hugging Face blog post walking through Workflow1111, a 73-node graph built with Gradio's gr.Workflow that reimplements most of AUTOMATIC1111's stable-diffusion-webui feature set as a single visual canvas: text-to-image, hi-res fix, image-to-image, prompt matrix, VLM interrogation, detection-to-inpaint masking, ControlNet-style annotators, background removal, PNG metadata round-tripping, and image-to-video, wired from models like FLUX.1-Kontext, Qwen3-4B, Qwen2.5-VL, DETR, AuraSR, BRIA RMBG-2.0, and Wan 2.2 I2V A14B. It contrasts gr.Workflow with ComfyUI, shows that every output node auto-becomes both a REST endpoint and an MCP tool (usable by Claude Code or Cursor), and demonstrates running nodes locally on your own GPU via ZeroGPU (FastVideo/fastvideo-fasth3-preview) instead of hosted inference.

huggingface.cowriteupAdded 29 Sep 2026, published 10 Sep 2026🧠 AI

Interconnects' periodic 'open artifacts' roundup covering new open-weight model releases — Motif-3 (MIT license, small-team training), dots3-note-prev (won IMO 2026 with a perfect score), Qwen3.8-Flash-Next (125B-A6B previewing GDN and Qwen Sparse Attention), GLM-5.3-Flash (released stealth as 'Ox-Alpha' on OpenRouter/OpenCode), and Tencent's Hy4-preview — alongside a deep look at how open-model licenses are diverging, with Google and Meta adopting Apache 2.0 while Zhipu's GLM-5.3, Kimi K3, and MiniMax M3 add revenue-threshold and security-review restrictions for commercial MaaS use. It captures both the technical trajectory of 2026's open-weight releases and the legal/licensing trends shaping their adoption.

interconnects.aiarticleAdded 29 Sep 2026, published 8 Sep 2026🧠 AI

SemiAnalysis publishes the first third-party InferenceX Official Preview results for Google TPUv7 Ironwood serving Qwen3.5-397B in FP8 with the new TorchTPU vLLM stack. In aggregated 8k1k serving it reports up to 50% better performance per dollar than B200 and B300, while GB300 NVL72 disaggregated serving still leads in the middle of the latency range. It also details TPU kernel work: DP attention, SparseCore collectives, MoE routing, Gated DeltaNet kernels and paged attention.

newsletter.semianalysis.comarticleAdded 29 Oct 2026, published 7 Sep 2026🧠 AI

ESPO (Error-Structured Prompt Optimization) is a prompt-optimization method that addresses prompt bloat in evolutionary optimizers like GEPA by splitting the process into Diagnose (clustering training errors into structural patterns), Propose (generating diverse candidates via four independent strategies), and Select (bootstrap stability selection). Across seven NLP benchmarks (Tweet, MMLU, GSM8K, HotpotQA, ScoNe, HoVer, PUPA) it beats GEPA by 3.76pp average accuracy (74.67% vs 70.91%) while producing prompts 47% shorter, and generalizes well across student models including Gemma 3 12B, Mistral 14B, Qwen3 32B, and Claude Haiku 4.5.

arxiv.orgpaperAdded 22 Sep 2026, published 3 Sep 2026🧠 AI

Adversa AI's monthly roundup of AI coding-agent security research, covering nine items: GhostJacking (using a WAF's own block logs as a prompt-injection delivery channel, 90% success against Claude Code), Black Hat disclosures of unauthenticated-issue-to-RCE chains in the default GitHub Actions configs published by Anthropic, Google, and OpenAI for their coding agents (Gemini CLI rated CVSS 10.0), an agent-to-agent privilege-boundary bypass in Google's ADK repository, the new OWASP Agentic Skills Top 10 with a walkthrough showing open-source skill scanners miss encoded payloads while flagging 9-62% of legitimate skills as malicious, a seven-layer "secure agent harness" containment design, and a 2,826-file adversarial skill benchmark where Gemini CLI and Qwen Code were exploitable 95.5% and 71.6% of the time respectively. The piece draws a common thread across all findings: agents treat machine-generated artifacts (logs, images, other agents' outputs) as trusted instructions, and existing detection never fires.

adversa.aiarticleAdded 7 Oct 2026, published 3 Sep 2026🛡️ Security

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.

huggingface.cowriteupAdded 29 Sep 2026, published 3 Sep 2026🧠 AI

A Hugging Face engineering blog post reproducing Surya Narreddi's viral "AI paints watercolours" project in the open: a coding LLM (Qwen3.5-35B-A3B, LoRA) is trained with GRPO via TRL to write p5.brush JavaScript sketches that render as watercolour flower paintings, using an OpenEnv RL environment with headless-Chromium rendering, an HPSv3 preference-model reward and a Qwen3-VL-30B-A3B-Instruct pairwise judge scored against a hand-curated 178-image reference pool. It documents the full pipeline (HF Jobs, Spaces, Inference Providers), the LoRA fix needed for MoE models (all-linear targeting), infra bugs found (an OpenEnv websocket caching bug fixed upstream, reward-noise from silent render failures), three reward-mix ablations, and open-sources the dataset, environment, training scripts, rollouts and trained models.

huggingface.coarticleAdded 29 Sep 2026, published 3 Sep 2026🧠 AI