All AI news

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.

AIHugging Face and collaborators published a guide to multi-harness RL that trains models through a capture proxy without changing the agent harness. The proxy records the token ids and logprobs vLLM samples, and the source reports LFM2.5-2.6B rising from 42% to 54% after training across four harnesses. Fine-tuning on 3,189 successful rollouts from Qwen3.8-27B plateaued at 47.5%, below both RL runs, and the capture proxy, trainer, tasks, SFT data, training code, and seven trained models are released openly.

Why it matters: The source gives a concrete method for training models across several agent harnesses, with measured gains and a note that imitation learning underperformed RL.

AIResearchers Maureen de Seyssel, Jie Chi, and Zakaria Aldeneh found that strengthening language discrimination during pretraining reduces the performance gap between multilingual and monolingual HuBERT speech models. In a controlled English/French setting, phone-ABX error fell from 11.6% to 10.4%, close to the monolingual 10.8%, while lexical sWUGGY scores rose from 52.1% to 56.7%. The gains were largest when language discrimination was introduced in the first training iteration.

AITool definitions and tool-call responses differ between OpenAI, Anthropic, and Google, so a tool that works on one model may fail on another. The article compares six agent frameworks, including LangChain, CrewAI, and the OpenAI Agents SDK, by where each performs schema translation. It also describes OpenRouter's API-layer normalization, which accepts an OpenAI-style tools array and returns a standard tool_calls response for tool-capable models.

AIEpoch AI estimates that high-bandwidth memory shipped in 2025–27 could eventually support about 30–170 million concurrent frontier-model agents once fully deployed and allocated. Using DeepSeek V4 Pro serving benchmarks, the estimate rises to about 1.9 billion concurrent agents. The authors compare the implied API-equivalent spending of $2.6–5.3 trillion per year with projected developer revenue of roughly $1 trillion by end-2027, suggesting demand may lag supply.

Why it matters: The analysis converts HBM shipment data into concurrent agent capacity and compares it with projected API revenue, showing where compute buildout may outpace demand.

AIApple Machine Learning Research reports that discrete diffusion steps match the training distribution only when simultaneously written token positions are conditionally independent given already-fixed tokens. The authors show that per-position distributions cannot determine such dependence, and on the synthetic ScanAndAdd task, confidence-ranked groups of two or more positions were dependent and produced a generated distribution 29 times the sampling-noise floor in total variation.

AIMeta's Jagged Flash Attention kernel, built with TLX on NVIDIA Blackwell B200, outperforms FlashAttention-4 (May 2026 version) on GEM's jagged shapes by about 13% on the forward pass and about 50% on the backward pass. The TLX attention kernel is roughly 3.2K lines of Triton-level code, about 3× shorter than FA4's ~10K-line CuteDSL kernels. The benchmarks use bfloat16 on B200.

AIMike Knoop notes that a verification of the roughly 27B-parameter model is notable because it is about as large as fits within the official ARC Prize Kaggle competition runtime. ARC Prize reports Qwen3.8-27B from Alibaba's Qwen team scored 42.4% on ARC-AGI v2 at $0.45 per task and 87.5% on v1 at $0.22 per task.

AIARC Prize has published full ARC-AGI results for Alibaba's Qwen3.8-27B, with the testing run hosted by Baseten. The post links the public leaderboard, the open-source benchmarking repository for reproducing the results, and the testing policy.

AIOdyssey introduces PROWL-2, a system in which agents and their world model improve through recursive learning. The post says agents expose errors in imagination, and repairing those errors enables further learning. Odyssey frames this open-ended learning as a critical step toward superintelligence.

AIOdyssey introduces PROWL-2, a recursive loop that delivers up to 91% relative gains over the StarCraft world-model baseline. The post also reports stronger robot coordination in simulation.

AIGoogle Gemma relays a StudentBench study reporting that AI tutors matched expert human tutors on immediate GRE learning gains. The author reports 2,383 students and a cost of 7 cents per AI tutor hour versus $75 for an expert human hour. The post also says the top AI tutor beat expert human tutors on average in 5 of 7 academic topics, and that the data and paper are publicly available.

AINew research finds AI tutoring delivers immediate GRE learning gains comparable to expert human tutors, with Gemma 4 31B performing best. The post presents the findings as a way for educators to scale their impact and make guidance more accessible.

AIGoodfire reports that its method catches more unsafe biological requests than frontier model safeguards while dramatically reducing false positives. The claim is illustrated by a plot in the first tweet of the thread.

AIGoodfire built monitors that let AI agents perform more biology work while flagging dual-use risks. The monitors reportedly outperform frontier model safeguards, with fewer refusals on dual-use tasks, and run fast and in real time while staying robust to adversarial attacks.

AIGoodfire Research developed sequence-aware monitors using protein language model embeddings to flag concerning biological sequences in dual-use AI agent tasks. On a custom benchmark, the monitors outperformed frontier model safeguards with fewer refusals on benign requests, and they held up better against paraphrasing and fragmentation attacks. The paraphrase results rely on in-silico estimates and do not establish whether the redesigned proteins keep biological activity, and the monitors run in milliseconds per sequence.

Why it matters: The post gives a concrete benchmark setup and fragmentation results, showing how sequence embeddings can separate dual-use biology requests that task-based safeguards handle poorly.

AIPrime Intellect reports that after about 100 GRPO steps on Hosted Training, Qwen3.6's reward on held-out problems rose from 0.127 to 0.361, a 2.8x gain. Qwen3.5, trained the same way, reached 0.356, suggesting the method works across model families. Both post-trained models finished well ahead of other open models and narrowed the gap to Claude Opus 4.8, with Qwen3.6 activating only 3B parameters per token.

AIZyphra published a paper and blog explaining how local mixing encodes relative position in global NoPE attention models. The work, titled "How Local Mixing Encodes Relative Position in Global NoPE Attention," is available on arXiv and on Zyphra's website.

AIModels using Kimi Delta Attention show a recency bias, as the mechanism controlling how much earlier information is kept shapes memory. As new information mixes into that memory, nearby words share more information, which gives the model clues about how far apart words are.

AIZyphra derives a mathematical theory showing how local layers produce recency bias that propagates through the residual stream, norms, and MLPs into NoPE attention logits. The authors validate the theory on both randomly initialized and trained networks.

AIIn Zyphra's experiments, models with smaller context windows predicted text better while using less training compute. Zyphra believes the stronger recency bias induced by smaller windows outweighs the reduced compute benefit.

AIHybrid NoPE models combine sliding window attention or recurrent layers, which focus on nearby words, with global attention layers that use no positional encoding (NoPE). The post notes that NoPE layers receive no positional information yet can still learn long-range dependencies, and raises the question of how this works.

AIZyphra Research explains how language models track word order without explicitly encoding position in attention. The post says local memory layers that read nearby words help global attention layers preserve sequence information. The source is a short teaser thread, so no further technical details are given.

AIThe SYNTH paper, titled It's All Training, presents a fully synthetic single-stage pipeline for training workable reasoning models with high data efficiency. The authors argue this approach does not separate training into pretraining, mid-training, or post-training stages. The image shows the paper's abstract, which describes a pipeline built from a 58,000-article Wikipedia-based synthetic corpus and models named Baguettotron-600M and Baguettotron-MoE.

AISara Hooker shared a full technical report on a method for creating datasets from zero seed data, linked via alphaXiv. She also pointed readers to an interactive tool at adaptionlabs.ai where users can begin inventing datasets.

AICohere's Sara Hooker announced the release of Invent a Dataset, along with a technical report addressing how to build a training set for a new capability with zero starting data. The work targets post-training a model when no data exists, a hard setting for frontier AI development.

AIAmazon Science describes a graph-centric approach in which a network digital twin graph and cascaded graph algorithms, orchestrated by an agentic AI layer, identify root causes in complex network failures. The approach was demonstrated with NTT DOCOMO at the Mobile World Conference, achieving root cause analysis in minutes on commercial networks. The article traces how graphs evolved from topology models to active reasoning substrates for agents.

AIUnder equal time budgets and the same frontier LLM backbone, a single session of a minimal-harness coding agent with read, write, and bash primitives matched open-source state-of-the-art autonomous machine learning engineering harnesses. Apple researchers found the added orchestration and retrieval machinery redundant in large-scale ablation studies, pointing to the backbone model as the main driver of performance. They conclude that hand-crafted harnesses around strong models yield poor returns on current MLE benchmarks.

AIApple researchers introduced RLTL;DR, a reinforcement learning method in which an agent writes its own one-line insight after each failed attempt and learns to map tasks to those insights. On challenging tool-calling and coding datasets filtered to Pass@128 = 0, standard GRPO training of a Qwen 3.5 9B Thinking policy stayed at 0% to 1% Pass@1, while RLTL;DR reached 14–31% with insights in context and 12–13% without them at evaluation. A compact variant, SFTL;DR, trained on just 4k task-insight tuples recovered nearly the full performance of RLTL;DR.

AIPerplexity says context-bench, a context-aware retrieval benchmark created and privately held by turbopuffer, has 2,099 queries and 38,894 documents. Perplexity submitted it for blind evaluation, with queries and capabilities inspired by turbopuffer customer conversations.

AIPerplexity overcomes gold-chunk supervision limits by distilling relevance from its query-aware context compression model. The model scores every document token against the query, and those scores, aggregated into chunk-level targets, train the embedder to retrieve answer and supporting chunks.

AIRetrieval systems that split long documents into chunks lose surrounding context. Contextual embedding models address this by encoding the entire document once and pooling chunk vectors afterward. They are usually trained using one gold chunk per query.

AIGoogle DeepMind reports that AI-designed proteins can be synthesized and watermarked using its new SynthID Bio method, published in Nature. The team says the work is a step toward biosecurity in AI-driven biology and is open-sourcing the SynthID Bio tools for the research community.

AINathan Lambert's latest Interconnects plot tracks daily tokens processed by leading open model inference providers. The figures come from public disclosures by Together, Baseten, and Fireworks, plus the OpenRouter API. He says he underestimated OpenRouter's growth.

AILing-3.1-flash built a Lua-to-x86-64 ELF compiler from scratch in about 17 hours, passing 178 of 182 tests for 97.8%. That is within 1.2 percentage points of the 99.0% shown for Claude Fable 5 and above GLM-5.3-Flash at 95.9%.

AILing-3.1-flash ranks #2 in the Mobile App (react native) category on Design Arena's Open Weights leaderboard, with 1,207 Elo. It also ranks #4 in SVG with 1,271 Elo among models added in the last 30 days.

AIMicrosoft Research has developed a machine learning system that predicts where extreme space-weather events are likely to damage power systems 30-60 minutes before a storm arrives. Such storms can also degrade GPS accuracy and satellite operations, so advance warning could help operators prepare.

AIMicrosoft Research intern-developed machine learning pipeline forecasts location-specific geomagnetic risk for 66,935 substations in the continental United States. It combines solar-wind observations, AE and Dst forecasts, geological conductivity and grid data to estimate risk 30 to 60 minutes ahead. The pipeline detected nearly 80% of major space-weather events during the evaluation period.

AILiquid AI and InSilicoMeds released LongevityBench, an aging benchmark with 17 tasks spanning clinical records, DNA methylation, transcriptomics, proteomics, and genetics. On several tasks, Liquid AI's compact LFMs outperformed every frontier model the team evaluated. The team plans to present the work to the longevity research community at ARDD this week.