130 results for “Qwen3”

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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 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

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

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

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

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

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

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

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

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

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

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

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 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

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

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

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

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 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

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

The Graph Machine (GM) is a neural architecture that maintains an O(n)-sized state accessed via sparse, dynamic routing using differentiable "edges" — pointer-like objects updated through a pointer-chasing-like referral mechanism. The authors replace 75% of dense Transformer layers in Qwen3-0.6B with GM sparse layers and pretrain from scratch on 15.7B tokens, showing that retrieving only 2–4 of 4,096 tokens per KV head per sparse layer preserves or marginally improves loss compared to the dense baseline. This suggests a path toward more efficient pretraining by reducing dense attention computation while retaining large effective context/state.

arxiv.orgpaperAdded 22 Sep 2026, published 2 Sep 2026🧠 AI

Pooled runs one open model (Qwen 3.6 35B MoE, a 27B dense hybrid, smaller Qwen3/SmolLM) across devices in a browser "room". Each tab holds a slice of layers and runs them on its own GPU with custom WGSL kernels. Hidden states pass over WebRTC, and speculative decoding is checked against plain decoding by golden tests. It also ships a Code-mode agent loop with a sandboxed preview, and an npx CLI bridge (@pooled/cli) that exposes the room as an OpenAI Chat Completions/Responses and Anthropic Messages endpoint with grammar-constrained tool calling, so Codex CLI, Claude Code and opencode can use it. The README includes latency-injected benchmarks, a comparison with exo, Petals, llama.cpp RPC and WebLLM, and a threat model.

github.comrepoAdded 16 Oct 2026, published 1 Sep 2026🧠 AI

LoongSage is Baidu's open-sourced, production-grade reinforcement learning framework for post-training LLMs, built on Ray for scheduling, Megatron-Core/Megatron-Bridge for training, and SGLang for inference. It targets agentic RL with a zero-intrusion AgentFlow module (automatic multi-turn context, loss masking, trajectory collection), multi-paradigm online distillation (PG-Style, GKD-Style, TopK/full-vocab/JSD KL), fully asynchronous training with partial rollout, and system-level training-inference consistency guarantees (Router Replay, TITO, M2PO, OPSM) that keep probability bias at 1e-4 in MoE/low-precision settings. Recipes are validated on Baidu's internal production runs of DeepSeek-V4-Flash and Qwen3-30B-A3B for math reasoning, agentic code repair, and distillation.

github.comrepoAdded 30 Sep 2026, published 28 Aug 2026🧠 AI

OBLITERATUS/Qwen3.8-27B-OBLITERATED is an "abliterated" (safety-guardrail-removed) fine-tune of Alibaba's Qwen3.8-27B, built by Pliny the Prompter using the OBLITERATUS ablation suite. Its V3 recipe blends two refusal-direction-removal techniques — aggressive SVD projection and LEACE — in iterative rounds with a targeted refusal-corpus, eliminating both hard refusals and softer "safety lecture" deflections while costing only -2.1pp MMLU versus stock. The model card frames it explicitly for alignment researchers, red-teamers, and AI safety evaluators needing an unrestricted baseline to test post-training safety against weight-space attacks.

huggingface.cotoolPublished and added 21 Aug 2026🛡️ Security

philbert440/Qwen3.8-27B-Uncensored-Cyber-GGUF is a llama.cpp-format GGUF release of a Qwen3.8-27B fine-tune deliberately de-refused ('uncensored') and tuned to fully answer cyber and offensive-security questions, shipped with a vision projector and an MTP speculative-decoding head. The card includes a Claude-judged evaluation against a 100-prompt held-out cyber-offensive regex refusal harness and provides run instructions for llama.cpp, Ollama, vLLM, LM Studio, and Docker Model Runner. It exemplifies the growing trend of publicly distributed abliterated/de-refused LLMs positioned for offensive-security use, relevant to AI red-teaming and dual-use misuse tracking.

huggingface.cotoolPublished and added 21 Aug 2026🛡️ Security

Aikido benchmarked 10 AI models (DeepSeek V4 Pro/Flash, GLM-5.3, Qwen3.8-Max, Kimi K3, Grok 4.6, GPT-5.6 Sol/Terra/Luna, Opus 5) on rediscovering 32 recently-disclosed CVEs from source code, three runs each (96 total passes), measuring recall, precision, consistency, turn budget, and cost. DeepSeek V4 Pro 0813 topped pooled recall at 28/32 for ~$295 across three runs, beating single passes from Opus 5, Grok 4.6, and Sol that each cost $450-590, while Grok was the most consistent model and Kimi K3 had the highest precision among open-weight models. The piece argues open-weight models, when harnessed correctly, now match or beat closed frontier models on vulnerability-discovery tasks, and that pooling multiple LLM runs exploits output variance to improve recall.

aikido.devarticleAdded 3 Sep 2026, published 21 Aug 2026🛡️ Security

A Hugging Face engineering blog post detailing how Papers with Code built a hybrid search system combining PostgreSQL full-text search with pgvector-based dense retrieval, fused via reciprocal rank fusion (RRF). It walks through using Hugging Face Jobs for batch embedding of 110,000+ papers with Qwen3-Embedding-0.6B (using Matryoshka Representation Learning to shrink vectors to 256 dimensions), Storage Buckets as an immutable, checksummed handoff layer between database, compute, and production, and an authenticated Inference Endpoint (backed by Text Embeddings Inference) for low-latency query embedding with graceful fallback to lexical search on cold starts or failures.

huggingface.coarticleAdded 29 Sep 2026, published 21 Aug 2026🧠 AI

Sebastian Raschka explains how modern reasoning LLMs implement adjustable "reasoning effort" or thinking-budget controls, tracing the mechanics from DeepSeek-R1's original RLVR recipe through hybrid think/no-think switches (Qwen3) to full effort-level systems in GPT-5.6 and gpt-oss. He then does a comparative deep dive into six flagship open-weight models' documented recipes — DeepSeek V4 (separate effort specialists distilled together), Nemotron 3 Ultra (SFT + truncated-trace training + hard budgets), Kimi K2.5/K3 (Toggle alternating budgeted/unconstrained RL, multi-teacher distillation), GLM-5 (turn-level/interleaved thinking via SFT), Qwen3 (Thinking Mode Fusion), and Inkling (continuous 0-1 effort conditioning during RL) — showing the shared techniques (effort-labeled SFT data, mode-conditioned RL with variable length penalties/context windows, and robustness training for hard token cutoffs) behind these UI toggles.

magazine.sebastianraschka.comarticleAdded 29 Sep 2026, published 18 Jul 2026🧠 AI

Opir-multitask-large is an English encoder-based GLiClass zero-shot classifier for real-time LLM safety filtering, supporting binary safe/unsafe decisions, toxicity classification, jailbreak/prompt-injection detection, and categorization over a 996-label hierarchical safety taxonomy (16 top-level, 126 mid-level, 854 leaf labels). It was trained with taxonomy-derived unsafe-prompt generation, evolutionary hard-negative mining against existing safety models, LLM-as-judge annotation (DeepSeek-V3.1, MiniMax-M2.5, Llama-3.3-70B), and replay training on the gliclass-v3-logic-dataset, and is benchmarked against WildGuard, PolyGuard, Nemotron Safety Guard and Qwen3Guard across datasets like Aegis2, HarmBench, WildGuardMix, ToxicChat, and JBB-Behaviors, reporting ~25ms p50 latency at 1024 tokens for real-time deployment as an input/output moderation layer.

huggingface.cotoolAdded 29 May 2026🛡️ Security