#Reasoning

AIAether AI, founded by UCSD assistant professor Biwei Huang, has released official demos of its CRIS-0 causal intelligence system for robots. In tests, the robot recovered from external disturbances in 9 of 10 random trials, typically within about 2 seconds, and stopped within 0.2 seconds when a human hand entered the workspace during a microwave-door task.

AIOpenAI announced last month that it had produced an AI-generated proof for the Navier-Stokes problem, a result the source says is hard even for experts to parse. The source also says LLMs now tackle math problems that have stumped humans for decades, while teachers struggle to keep up with AI-completed homework.

AIOdyssey announced the Odyssey-3 series of foundation world models, with Odyssey-3 Pro scoring 66.1 on the Physics-IQ Verified video-to-video benchmark, the highest recorded on that leaderboard. The series includes a standard version balancing physical accuracy and generation cost, and a Pro version with stronger physics prediction. The preview supports first-person and third-person navigation and lets users move the camera, take actions, or trigger events while the model predicts environmental changes in real time.

AITencent Hunyuan, with Fudan and Tsinghua researchers, released ExplorationBench, a benchmark testing how AI systems explore through verifiable "Alien Worlds" with executable rules that conflict with familiar knowledge. Across 10 frontier systems, feedback mattered most: the best AlienCode run reached 89.0% after four rounds of probing, versus 0.5–11.0% without feedback. Answers are graded by an interpreter or proof checker rather than an LLM judge.

AIAI systems are now solving math problems faster than humans can verify the answers, according to the source's headline. The source text is limited to a brief note that OpenAI's math breakthroughs may trigger an identity crisis for mathematicians.

AIOpenAI published 719 AI-generated math proofs covering 372 result families, after withdrawing 3 for a symbol error. Reports say the release falls short of the AGMAI advisory group's standards, since it uses proprietary models, includes reasoning chains for only 10 manuscripts, and leaves about 42% unformalized. Terence Tao argues that rapidly solving famous problems harms the mathematical community's understanding and collaboration.

AIOf 1,933 IROS 2026 papers, Robot Learning/Embodied AI appears in about 809, while Navigation/Planning covers 564 and Perception/Vision 556. The article argues large models are being embedded into traditional planning, geometry, and control rather than replacing them. Vision-language-action models are shifting toward efficiency, 3D understanding, memory, and system integration.

AIAccording to the excerpt, GPT-6 Astra produced an answer to a fusion problem open for 59 years in 20 minutes and 34 seconds. The physicist who posed the question added only a brief encouraging prompt, and the excerpt does not include the full text.

AIOpenAI has published 722 math papers generated by an unreleased AI model, posted to GitHub, spanning about 20 mathematical subfields. The model did not fully prove the Riemann hypothesis but proved the quasi-Riemann hypothesis, and it also produced theoretical computer science and partial differential equation results. Many papers include Lean files for computer verification, and OpenAI plans to release more of them.

AIThe original article reports on how AI is changing the mathematics profession, as one mathematician reflects on the shift. The provided text is limited to that single sentence, so no specific models, benchmarks, or figures can be verified.

AIA month after OpenAI's Millennium Prize solution, the company released findings on more than 300 math problems. The Wall Street Journal says the release was an attempt to win back the mathematics community. Only the excerpt was available, so the source's details on the problems and results are limited.

AIXiaomi MiMo-V2.6-Pro is applied to new materials R&D across literature review, molecular design, and automated dry-lab experiments for PFAS capture. The source offers no benchmark scores, speed, pricing, or availability details.

AIMidjourney is testing a "Thinking Mode" on its Alpha website, where users can click "Rerun (Thinking)" in a job's lightbox to regenerate an image. The company says early tests show gains in prompt accuracy, typography, and coherence, and it is asking users to share feedback in its #ideas-and-features channel. It may later offer the mode broadly or as an option to add more thinking after a job.

AIEthan Mollick notes that some OpenAI proofs have sparked rapid iterative advances from a wide community of collaborators amid debate over their implications for mathematics. A related post reports that a collaborative effort tightened the bound κ from 2⁻¹⁸² to 2⁻¹⁵, a roughly 500-thousand-fold improvement on the previous result.

AIExploreNet is a learnable exploration method for diffusion reinforcement learning that adapts its exploration distribution to the current state. It is rewarded by rollout diversity, which the post says yields faster, more targeted learning in diffusion GRPO.

AIStanford HAI associate directors Risa Wechsler and Russ Altman told incoming Stanford students, faculty, and staff that AI agents can help researchers write code and tackle more ambitious questions. They stressed that AI-generated results need rigorous, reproducible methods, measured uncertainty, and careful attention to missing data, systematic errors, and biased models. Altman also argued that labs should preserve mentorship and interdisciplinary collaboration while adopting AI tools.

AIGoogle Research is presenting EnvHarness, a flexible framework that enables co-evolution between LLM agents and their training environments, at the #COLM2026 Google booth #107 today at 11:00 AM PT. The post notes that static environments limit agent growth, and EnvHarness is described as a plug-in architecture that dynamically reshapes environment behaviors to improve reinforcement learning and adaptability.

AIBoris Power, who owns OpenAI's account, praised the pace of progress on a result he called remarkable. The context from @0xdoug reports a PR merged and a tightened bound from κ = 2⁻¹⁸² to κ = 2⁻¹⁵, a community effort across contributors.

AIZyphra has released a blog post and arXiv paper on expert coupling in mixture-of-experts (MoE) pretraining. The post itself provides only links, so the specific findings are not described here.

AIBoris Power, who owns the OpenAI account posting this, responded to a third update on OpenAI problem #109, integer multiplication. The quoted update reports a tightened bound of κ > 2⁻¹⁸, improved from κ = 2⁻¹⁸², with an exact witness of 4.19 × 10⁻⁶.

AITypeSafe AI released Jev, a "decision model" that returns a floating-point probability rather than text, which can answer yes/no and multiple-choice questions and score content against criteria. The source cites two browser-extension examples: semantic Ctrl+F search and webpage quality scoring. Simon Willison's criticism is that Jev offers no explanation for its numbers.

AIEthereum researcher Justin Drake warned on X that AI-assisted math could, in the worst case, break the signature system used by crypto wallets within months, and urged a "bunker mode" in which users move funds to addresses that have never signed a transaction. Vitalik Buterin agreed but cautioned against moving too fast, saying he has lost more money to botched migrations than to hacks. No one has yet broken the current ECDSA signature scheme in practice.

AIUniPat AI's PaperBenchX benchmark found the strongest model, GPT-6 Astra, fully reproduced only 13.98% of 93 real research-paper tasks across 12 scientific fields. Reproduction was judged by regenerating outputs in an isolated environment, with 3,168 expert-verified scoring items. UniPat has open-sourced 12 test tasks and kept 81 tasks closed to preserve long-term evaluation validity.

AINVIDIA, Princeton University, and the University of Maryland introduced PivotOPD, an on-policy distillation method that teaches multi-turn LLM agents to recover from their most damaging early mistake. Tested on Qwen3-1.7B and Qwen3-8B students, it posts the best average against 13 baselines on ALFWorld, WebShop, and Search-based QA. It recovers from 72.7% of replayed pivotal mistakes, versus 20.3% for standard OPD, with no added inference cost.

AIGergely Orosz argues that holding crypto exposes owners to risks from mathematical breakthroughs, key theft, or kidnapping that could drain wallets. He concludes the simplest answer is not to hold crypto at all.

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.

AIFrançois Chollet compares AI progress to human learning, noting that 1980s research found programming training improves coding but does not transfer to general reasoning. He argues general intelligence is a fundamental brain property rather than a trainable skill, since domain practice improves only that domain. The post is framed as background for his question whether AI's jagged frontier, driven by math and code via RLVR, reflects general capability or continued human-data bottlenecks.

AIEpoch gave six models 11 real work tasks from its own operations, including graphic design, data insights, and research design, and graded outputs against employee standards. Fable 5.1 and GPT-6 Astra led on average task performance, reliably handling well-defined work such as coding and computational analysis. The report finds that all models still fail on open-ended judgment, including matching Epoch's standards, designing informative experiments, and generating diverse ideas, so the authors conclude AI cannot yet replace workers at Epoch.

Why it matters: The report separates well-defined task reliability from open-ended judgment failures, which benchmark scores on easily verifiable tasks would miss.