#Open source/Repo

AICost per Task: GPT-6 Luna (max), GPT-6.1 Sol (max), Muse Spark 1.3 (max) and Grok 4.7 (xhigh). Grok 4.7 (xhigh) leads at ~$9.50 per task and Muse Spark 1.3 (max) comes second at ~$4.20, while the three Claude models cost ~$18 to ~$22 per task. GPT-6 Luna (max) is the cheapest at ~$0.22 per task, scoring 3.3%.

AIRSIGym provides a research agent with training, inference, evals, and sandboxes as callable services, so it spends its budget on experiments rather than rebuilding infrastructure. With Opus 5 as the researcher, the improved system rose from 17.67% to 50.33% on SWE-bench Verified. The post also highlights a way to measure co-evolution between harnesses and models.

AIHuawei engineers presented XMFS, an experimental Linux kernel prototype filesystem, at the Linux Plumbers Conference in Prague on October 5. It aims to let applications reach cross-node shared memory on CXL 3.0 or Huawei unified bus servers through standard POSIX file calls. The code exists only on openEuler, not in the mainline Linux kernel.

AIOpenAI published 722 math manuscripts covering 372 result groups in a new GitHub repository, openai/math, all produced by an unreleased internal model. The author describes the results as including a near-Riemann hypothesis claim pushed to 0.875, and notes that 25 Fields Medal winners criticized the company's approach to AI math research.

Why it matters: The piece traces how AI math results moved from benchmarks to open problems, offering context on verification and the mathematicians' pushback.

AIThe vLLM team released a technical report on vLLM-Omni, a unified serving runtime for omni-modality generation spanning multi-stage autoregressive pipelines, iterative diffusion, and stateful sessions. Current LLM servers and diffusion stacks each cover only one of these patterns, pushing deployments to stitch disjoint runtimes together. vLLM-Omni offers a shared control plane in which an orchestrator advances requests across stages, specialized engines handle compute, and a connector carries payloads.

AIOpenAI announced on X that it is releasing a series of new mathematical results produced by its internal frontier model. The material is reported as 722 manuscripts posted to GitHub, and the excerpt provides no further detail on the specific results.

AIBecause bytes also represent images & audio, the same ideas could eventually extend beyond text. Learn more in our blog:

AIThe index reports the mean score and the mean cost per task across the four suites. Hermes Bench info:

AIReviewBench is a new open benchmark shaped by analysis of 103.9M GitHub pull requests, with 219 PRs across 19 languages. Bring your own code review agent, evaluate it, and submit your results ⬇️

AI…enabled an agent to show the user reviewing its transcript a fake (or edited) version.

AIGitHub has released ReviewBench, an open benchmark for evaluating AI code review agents on 219 public pull requests across 19 languages. The benchmark reports grounded and augmented precision, recall, and F1 metrics, and its dataset, rubric, and judge are publicly available. GitHub says ReviewBench predicted the direction of a Copilot code review ensemble experiment's production results before A/B testing.

Why it matters: The post explains how ReviewBench was built and validated, and reports an offline-to-production comparison that shows how well a benchmark predicts real experiment outcomes.

AIHugging Face says a capture proxy lets reinforcement learning train open models inside unmodified coding harnesses such as Claude Code, Codex, and OpenCode. The proxy records the exact token IDs and logprobs vLLM samples and hands them to TRL for training. On LFM2.5-2.6B, training in four harnesses at once raised OpenCode results from 34% to 58%, while SFT on 3,189 Qwen3.8-27B rollouts plateaued at 47.5%.

AILiquid AI shared a Hugging Face guide to multi-harness reinforcement learning for coding agents, in which a proxy records the token ids and logprobs vLLM samples so training works without changing the harness. Per the quoted post, LFM2.5-2.6B rose from 42% to 54% after training across four harnesses at once, and imitation fine-tuning on 3,189 rollouts from Qwen3.8-27B plateaued at 47.5%, below both RL runs. The proxy, trainer, tasks, SFT data, training code and seven trained models are described as open.

AIGoogle announces a next-generation federated learning system that uses Trusted Execution Environments to provide verifiable, auditable data anonymization. The system publishes access policies to a public transparency log and is deployed in Gboard, which has launched English and Japanese next-word prediction models with stronger privacy guarantees and improved accuracy. Training time has also sped up significantly because computation moved to the server and is parallelized across many machines.

Why it matters: The post shows how Trusted Execution Environments make federated learning's privacy claims externally verifiable, rather than relying on trust in the server operator.

AIToday we release the technical report that goes with it. Huge shoutout to everyone @singhshiviii @andrijazzz @lekeonilude @sudip_r0y 🔥 This tackles the hardest setting. How do you go from zero data regime to a training set for a capability.

AIRewards are not self-contained: general part need to set up a judge and webdev rely on their own grader service+vlm. *Most important part is inside general/envs directory (+ docker), not the dataset displayed on hf: genuinely solid mix of real/simulated documents we rarely see in OSS.

AIWe combined handcrafted 2D storyboards and 3D Blender previs with AI-animation to create “Passport Rush.” The film is now open source, so you can see how we made it. Every prompt and asset is public.

AIMicrosoft Research published RetroChimera, a retrosynthesis framework that combines the R-SMILES 2 Transformer model and the NeuralLoc graph neural network through learned ensembling to propose synthesis routes for small molecules. In blind tests, PhD-level chemists preferred its individual reaction predictions over those from preceding models and recorded literature reactions. The implementation and weights are open-sourced for researchers developing new medicinal molecules and materials.

AIMiniMax-H3 on 8×H200 GPUs reaches 1.85–1.95x lossless speedup over Diffusers without approximation, with fixed prompts, seeds, resolution, FPS, and 50 denoising steps. Adding step reuse and sparse attention raises speedup to as much as 6.24x, but quality varies by workload, with SSIM from 0.76 to 0.91. Two presets trade off the two: a conservative Cache-DiT setting gives 2.99x at 0.90–0.98 SSIM, while a faster SubBlock 0.75 plus Cache-DiT stride gives 4.90–5.93x at 0.77–0.92.

AILiquid AI released Antidoom, an open-source method that retrains a single overtrained token to eliminate "doom loops" in small reasoning models. On LFM2.5-2.6B and Qwen3.5-4B, loop rates fell from 10.2% to 1.4% and from 22.9% to 1%, respectively. The company also released IFStruct, an open-source benchmark measuring whether model outputs satisfy a schema, where LFM2.5-350M rose from 21.10% to 44.90% after training.

AIOpenAI is releasing a subset of datasets, reference code, and the g-mean 2 metric for evaluating chain-of-thought monitorability. The release includes most datasets from its monitorability suite, while some evaluations relying on private or restricted data were omitted. The company says it will keep reporting monitorability results in future frontier reasoning model system cards.

AIDataChef, an AI4AI framework, uses reinforcement learning to automatically generate optimal data recipes for adapting LLMs. Its DataChef-32B model, using an efficient proxy reward system, matches Gemini-3-Pro in recipe generation, with its recipes surpassing expert-curated ones on AIME'25 and ClimaQA benchmarks.

AIBAAI has released RoboBrain-Dex, a dexterous manipulation model for embodied intelligence trained on large-scale, diverse human egocentric data rather than massive robot teleoperation datasets. The company says this approach yields strong generalization, marking a shift from small-data, weakly generalizing methods toward big-data robotic manipulation. The code has been open-sourced on GitHub.

AIBerkeley AI Research introduces SPEX, a signal-processing framework that identifies influential interactions in LLMs using far fewer ablations than exhaustive analysis. A hierarchy-based extension, ProxySPEX, matches SPEX performance with around 10x fewer ablations. The methods apply to feature, data, and model component attribution.

AIThe model is released under the Apache 2 license, and achieves state-of-the-art transcription performance at sub-500ms latency.

AIApple has released SHARP, a model that generates a 3D Gaussian representation of a scene from a single photograph in less than a second on a standard GPU. The output renders in real time as high-resolution photorealistic views of nearby camera positions, with metric absolute scale, and the paper reports reductions of 25–34% in LPIPS and 21–43% in DISTS versus the best prior model.

AIThe team proposed a simple yet efficient machine translation solution named VOLT, which is now open source to developers. Check it out: