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Sebastian Raschka@rasbtAI score38AISebastian Raschka released round six of his Reasoning from Scratch series, introducing Reinforcement Learning with Verifiable Rewards (RLVR) and Group Relative Policy Optimization (GRPO) with an implementation. The video covers accuracy and format rewards, DeepSeek-R1 training, and GRPO versus PPO, then walks through a training loop and evaluates checkpoints on MATH-500.
Design Arena@DesignArenaAI score28AIOpus does the math before it writes the game. In a free kick game it works out exactly how hard to kick the ball to land on target. It also drafts code in its thinking in 91% of summaries. Astra does that in about 1 in 5.
Design Arena@DesignArenaAI score40AIDesign Arena analyzed 324 thinking summaries and found OpenAI's GPT-6 Astra uses hedging words like "maybe," "might," and "it seems" about 20 times as often as Anthropic's Claude Opus 5.5. Opus usually weighs a few options and commits early, in about 4 out of 5 summaries versus 1 in 4 for Astra, which the post says works more like a designer while Opus works more like a builder.
Design Arena@DesignArenaAI score35AIOpus does the math before it writes the game. In a free kick game it works out exactly how hard to kick the ball to land on target. It also drafts code in its thinking in 91% of summaries. Astra does that in about 1 in 5.
AIMIT associate professor Cathy Wu is applying machine learning and reinforcement learning (RL) to design safer, more efficient transportation systems. Her team found RL can train effectively on about 10 percent of related problems, and a selection algorithm improved training efficiency by up to 30 times. Her recent work estimates eco-driving measures could cut vehicle emissions by 11 to 22 percent.
AI at Meta@AIatMetaAI score21AIMeta researchers used Muse Spark to uncover a counterexample to a proposed rule about mathematical structures inspired by biology, then developed and refined an alternative characterization. The work is detailed in a linked paper on solvable evolution algebras and a conjecture by Garcia-Martinez and Perez-Rodriguez.
AI at Meta@AIatMetaAI score42AIResearchers working with Muse Spark connected a number theory idea to a string theory calculation, proving the link holds in more cases than previously known. The work builds on ideas from the 1980s, according to the post.
AI at Meta@AIatMetaAI score22AIMeta researchers used Muse Spark to prove a clear rule for when a cycle-based relaxation of a hard optimization problem matches the original exactly and when it leaves a gap. The work concerns completed length-three alpha cycles, and the full paper is linked in the post.
AI at Meta@AIatMetaAI score18AIResearchers disproved a proposed rule about symmetry-describing mathematical structures by finding a single counterexample. Muse Spark generated the search code that located it, and the team verified the result and completed the proof.
AI at Meta@AIatMetaAI score22AIWith help from Muse Spark, researchers proved that a wave in a laser-inspired model must blow up in finite time under the conditions studied. The result comes from a tug-of-war between one effect squeezing the wave inward and another spreading it out. The paper is titled finite-time blow-up of radial negative-energy solutions for the mass-critical biharmonic nonlinear Schrödinger equation.
AI at Meta@AIatMetaAI score40AIMathematicians worked with Meta's Muse Spark to answer a question about fitting random points onto the surface of a stretched sphere, known as an ellipsoid. For the setting studied, they proved a sharp cutoff between when an exact fit is likely and when it is unlikely.
AI at Meta@AIatMetaAI score61AIAI at Meta says mathematicians used Muse Spark 1.1 and Muse Spark 1.2 in Thinking Mode through the standard meta.ai chat interface to find solutions to open problems. The company is sharing six resulting papers, each marking which passages were drafted primarily by humans or AI, with mathematicians guiding the work and a second group reviewing it.
AIThe post argues that counting all research as safety work because it widens the safety-usefulness Pareto frontier is misleading. Safety research typically creates new safety options without boosting usefulness, while capabilities research typically raises usefulness at safety's expense, so developers tend to choose less safe points.
Harrison Chase@hwchase17AI score53AIGoogle Research's Cogentic is a multi-agent harness running on Gemini that searches for proofs of open theoretical computer science problems without expert hints. It runs rounds where an orchestrator launches provers, two adversarial verifiers must both accept each draft, and shared disk documents store attempts and verified lemmas. The system produced new results on five open problems in online learning, auction theory, and mechanism design, each checked by domain experts.
Liquid AI@liquidaiAI score64AILiquid 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.
Hugging Face@huggingfacePickAI score67AIHugging 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.
Thomas Wolf@Thom_WolfAI score40AIThomas Wolf shares Kevin Buzzard's reflections on whether mathematics is about human understanding and what exponential AI growth means for an infinite field. He quotes Buzzard's view that machines may eventually reach a natural boundary where further progress is not worth the resources, and that humans would then take over from there.
AIThore Graepel, a core member of the AlphaGo team, argues that current large language models do not truly reason, despite chain-of-thought gains in math and coding. He says they lack an explicit, inspectable epistemic state, keep knowledge and reasoning intertwined in their weights, and often produce post-hoc explanations. He proposes systems that maintain an auditable epistemic state and evaluate each step by how much it resolves uncertainty.
Andrej Karpathy@karpathyAI score18AICool eval. Simply ask an LLM “Land or Water?” and give it a latitude and longitude coordinate as text. Ask 16,200 times, plot as image. The models know. From compressing the internet.
François Chollet@fcholletAI score62AIFrançois Chollet argues the key difference between base LLMs and modern LRMs is a shift from transductive answer prediction to inductive prediction of the program or reasoning chain behind an answer. He says this enables test-time induction and substantial fluid intelligence in LRMs, which he claims base LLMs largely lack. He cites ARC 1 results: base LLMs remain around 10-15%, while LRMs of the same size or smaller saturated the benchmark in 2025.
Sophia Yang@sophiamyangAI score14AIFireworks AI's Ember-1 produced a near-identical composition to Kimi K3 on the same JavaScript painting prompt, according to Sophia Yang. Ember-1 used 9x fewer thinking tokens and ran 4x faster at half the cost.
Odyssey@odysseymlAI score30AIOdyssey 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.
Odyssey@odysseymlAI score26AIOdyssey 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.
Google Gemma@googlegemmaAI score35AINew 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.
AIApodex has released Apodex 1.1 Mini, a free reasoning-first model designed for complex, long-horizon research and forecasting tasks. According to the source, it works directly with files, data, code, and tools to produce verifiable results.
Zyphra@ZyphraAIAI score26AIZyphra 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.
Zyphra@ZyphraAIAI score20AIZyphra says its results clarify how state-of-the-art NoPE models encode position and which inductive biases support generalization. It adds that global NoPE could enable models to extrapolate to contexts longer than those seen in training, potentially indefinitely.
Zyphra@ZyphraAIAI score22AIModels 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.
Zyphra@ZyphraAIAI score22AIZyphra 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.
Zyphra@ZyphraAIAI score23AIHybrid 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.
Zyphra@ZyphraAIAI score13AIZyphra says that mixing information within overlapping neighboring windows makes nearby words carry similar information inside a model. This creates a built-in clue about how far apart words are, even before training begins, which the post calls recency bias.
Zyphra@ZyphraAIAI score38AIZyphra 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.
Alexander Doria@DorialexanderAI score54AIThe 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.
AIPareto 26.10 Preview is a multimodal composite model built for research, coding, and agentic workflows. It is described as delivering frontier-level performance across a broad range of general-purpose tasks, though the source excerpt is a preview and provides no benchmark scores, parameter counts, pricing, or availability details.
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.
howie.serious@howie_seriousAI score12AIThe author gave the first half of a children's saying, "if you're serious, it's interesting," to qwen3-32b-instruct. The model's top predictions were "success" and "gain," while "interesting" did not rank in its top twenty, illustrating how LLMs work by predicting the next word.
AIPhysicist Matthew Schwartz describes building BootLoops, an open-source harness for exact quantitative calculations, after choosing problems suited to Claude's strengths. He reports that Claude solved long-standing integrals and found connections across ecology, population genetics, economics, and linguistics, with domain experts steering results toward questions those fields care about. The post states that the approach required constant human oversight, since Claude often overstated results and misjudged time.
Why it matters: The guest post explains why scientists often find current AI tools frustrating and offers a method for finding problems where AI and researchers match, backed by concrete projects.
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.
Amazon Science@AmazonScienceAI score7AIAmazon researchers will attend COLM 2026 in San Francisco next week with accepted publications on agentic AI, LLM post-training, multi-agent systems, and automated reasoning. The post links to their research and booth schedule.
Vercel Developers@vercel_devAI score32AIAnt Ling's Ling 3.1 Flash is now available on Vercel AI Gateway, free through October 13. The model is built for coding and multi-step analysis.