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All news · Today2 items

#Reasoning

All collected and published items, ordered by time. Picks are a selected subset.
Opinion

Viewpoints, commentary, analysis, interviews, and discussion of trends.

Today10/17Sat
  1. 量子位43

    How VLA and World Models Give Robots an Embodied Brain

    The article describes how VLA models and world models are being developed and deployed in Shanghai: VLA enables robots to perform tasks, while world models predict what may happen before an action. Ant Lingbo’s “Lingbo General Brain” has been adapted to 17 robot brands and 20 configurations, and robots using it are working in multiple Shanghai pharmacy locations. The article says the two approaches are beginning to converge, while planning, standards, and evaluation are advancing.

  2. Sam Altman · X55

    Sam Altman welcomes Nick Cammarata’s optimistic outlook on mechanistic interpretability

    Sam Altman says he is very happy to see Nick Cammarata’s post on interpretability. Cammarata says early work at OpenAI left him pessimistic, with primitive tools making about 2% of neurons legible. He now claims modern agentic tools make the vast majority legible in models up to trillions of parameters, and believes ambitious interpretability could be solved quite soon as automated scientists improve. https://already-clean.goodfire.com/

10/16Fri
  1. The Decoder64

    Is AI interpretability close to a breakthrough? A new essay has researchers divided

    Cammarata argues that better tools and automated agents make neural networks more interpretable than earlier research suggested, while Google DeepMind's Neel Nanda disputes that a broad breakthrough is near. Cammarata reports preliminary findings that about 90 percent of neuron poles in Qwen3-32B have one main job and reinterprets classic superposition examples as related features. Nanda sees practical value in neuron analysis but expects significant gaps and maintains that superposition is real. The technical companion paper has not yet been released.

  2. Ethan Mollick · X22

    This paper shows why I am skeptical of the idea that frontier models can be easily replaced by cheaper ones (and why you should use the best models as orchestrators): Small improvements in accuracy & judgement compound over long-horizon tasks, creating huge gains in outcomes.

    This paper shows why I am skeptical of the idea that frontier models can be easily replaced by cheaper ones (and why you should use the best models as orchestrators): Small improvements in accuracy & judgement compound over long-horizon tasks, creating huge gains in outcomes.

10/15Thu
10/14Wed
  2. Alexandr Wang · X35

    Alexandr Wang Says Muse Wonder Is Fast and Token-Efficient

    @merge_api says Muse Wonder completes an average task in 6.8 minutes, compared with 13.2 minutes for Claude Opus 5.5 and 15.3 minutes for Claude Sonnet 5.5. The quoted post says the scores are 52.4, 57.6, and 56.0, respectively. Alexandr Wang calls Muse Wonder “super fast and token efficient” and says, “give it a go!” https://twitter.com/merge_api/status/2110385332046762163

  5. Latent Space73

    Dyna Robotics’ Jason Ma Explains Dyna-2 and Hour-Long Physical Agent Workflows

    Dyna Robotics co-founder and Chief Scientist Jason Ma explains how Dyna-2 combines future video prediction with robot actions and how agentic harnesses support hour-long workflows. He says the model was trained on over one million hours of human video, with some tasks requiring as little as 45 minutes of demonstrations. The interview covers napkin-folding deployments at Din Tai Fung, on-device inference, and hardware durability and missing sensory data as constraints on broader robot capabilities.

  6. Google AI · X45

    We’ve been exploring the potential of using Gemini 4 Argon to accelerate research @Google. One of our tests was using videos of humans performing fun tasks (game of bags, anyone?) to autonomously spin up training simulations, rather than relying entirely on manual programming. https://t.co/3oK74wKSmt

    We’ve been exploring the potential of using Gemini 4 Argon to accelerate research @Google. One of our tests was using videos of humans performing fun tasks (game of bags, anyone?) to autonomously spin up training simulations, rather than relying entirely on manual programming. https://t.co/3oK74wKSmt

10/13Tue
10/12Mon
  1. The Decoder59

    Andrej Karpathy describes a widening gap in how people experience AI capabilities

    Andrej Karpathy describes four tiers of LLM exposure, arguing that public perceptions lag behind capabilities experienced by frontier-system users. He estimates roughly six billion people have barely encountered LLMs, one to two billion use casual chatbot products, 20 million use frontier models professionally in math and code, and 5,000 access internal frontier systems. He claims those internal systems coordinate thousands of agents on projects that would take experts years, while human review falls behind, and attributes progress in coding and math to reinforcement learning on verifiable rewards and commercial training priorities.

  2. Ethan Mollick · X40

    GPT-6 Pro finds supposed errors in academic papers all the time, just as part of other work. Confirming the errors & learning their importance would still take a lot of time today, but it will get faster. We don't have the right mechanisms in place to deal a flood of corrections.

    GPT-6 Pro finds supposed errors in academic papers all the time, just as part of other work. Confirming the errors & learning their importance would still take a lot of time today, but it will get faster. We don't have the right mechanisms in place to deal a flood of corrections.

  3. Ethan Mollick · X57

    Ethan Mollick argues advanced model use at labs remains concentrated in a few fields

    Ethan Mollick argues that many of the 5,000 people at labs with access to advanced models still haven’t used them to their full extent, with use concentrated in cybersecurity, math, and AI R&D. There are many other fields to advance and problems to solve, he adds, responding to @karpathy’s argument that the gap in shared understanding of LLM capabilities is widening.

10/11Sun
  1. François Chollet · X23

    How AI shifted from a transductive paradigm (token-by-token answer completion) to an inductive paradigm (on-the-fly reasoning chain synthesis), in one chart. This is the big story of 2025 and 2026. This is what made coding agents work.

    How AI shifted from a transductive paradigm (token-by-token answer completion) to an inductive paradigm (on-the-fly reasoning chain synthesis), in one chart. This is the big story of 2025 and 2026. This is what made coding agents work.

10/10Sat
  1. 雷锋网28

    IROS 2026 keynote: more data does not mean better robot decisions

    At IROS 2026, University of Queensland researcher Jen Jen Chung argued in a keynote that data volume is not the same as information, and that blindly piling up data can degrade robot policies. Drawing on drone soaring, multi-robot communication and contact-rich assembly work, she said data value is unevenly distributed and that sharing or collecting only decision-changing information beats full broadcasts or uniform sampling.

  2. 雷锋网36

    IROS 2026 Signals Embodied AI's Shift From Concepts to Engineering

    At IROS 2026, running September 27 to October 1 in Pittsburgh, Leiphone reports embodied AI research is converging on four trends: foundation models internalizing physics, "brain-cerebellum" architectures becoming standard, touch as core to dexterous manipulation, and cheaper data collection. Papers cited include ULTRA, LITHE, VOFA, KineFuse, VTAP Gripper, ViHaTeleop, and HALO, alongside workshops on physical world models and uncertainty.

  3. Ethan Mollick · X55

    Astra and Opus 5 beat Montezuma's Revenge, but Metaculus AGI bet remains unresolved

    Ethan Mollick says Astra and Opus 5 both beat Montezuma's Revenge, but resolving the AGI bet also requires AI to win a discontinued prize resembling a weak Turing Test. He says Metaculus has decided to recreate the test to confirm that criterion is met. In the quoted post, Metaculus says GPT-6 Astra beat the game last month, but its “weakly general AI” question, launched in 2020, remains unresolved.

10/9Fri
  2. Latent Space36

    Periodic Labs' Liam Fedus and Ekin Dogus Cubuk on "synthesis superintelligence": from semiconductors to superconductors

    In an interview, Periodic Labs' Liam Fedus and Ekin Dogus Cubuk proposed a "synthesis superintelligence" approach: reinforcement learning in physical experiments, AI-driven materials characterization, density functional theory (DFT), and high-throughput labs. The aim is for models to learn the full process of doing science, rather than only studying published results.

10/8Thu
  3. Noam Brown · X62

    Noam Brown says LLMs surpass top human experts on some research problems

    Noam Brown argues that LLMs have crossed an important threshold by surpassing top human experts on some research problems, with the gap between slightly below and slightly above expert performance helping explain the seemingly sudden surge in math results. He emphasizes that capabilities remain jagged and models are still worse than humans in many ways, but expects continued improvements to bring breakthroughs in other domains and a new era of scientific discovery.

10/7Wed
  1. 量子位22

    Do any AIs still speak like people? Claude and GPT chain-of-thought reportedly use 'caveman language' to save tokens

    A QbitAI article complains that models such as Claude and GPT have become difficult to understand in coding contexts, using phrases such as 'ledger 15/15 all green' and 'one leg failed'. Online commenters suggest the main cause is chain-of-thought being compressed into 'caveman language' to save tokens, using 70% fewer tokens than plain language. The article says Opus 5.5 has improved its language ability, while Gemini is praised as one of the few models that still speaks naturally. CaveMan Skill, a GitHub project that teaches AI to speak in jargon, has 104K stars.

  2. Kevin Weil · X50

    Kevin Weil praises OpenAI’s mathematics release and anticipates progress in physical sciences

    Kevin Weil celebrates OpenAI’s mathematics release today, saying much work remains to get models of the same caliber in the physical sciences. He urges imagining the possibilities and expresses confidence that models will get there. In the quoted post, @willdepue says he asked GPT 6 Pro and Fable 5.1 to rank discoveries from the last three years by human or AI origin, reporting that 81% were released today.

10/6Tue
  1. Arthur Mensch · X72

    Mistral announces Mistral Large 4 with 1T parameters and native multimodality

    Mistral AI announced Mistral Large 4, aka Le Chonk, with 1T parameters, 49B active, and native multimodality, available via API today with an open weights release planned for the end of October. It claims the model leads open weights models from the US or Europe on aggregated benchmarks and surpasses closed frontier models on visual grounding. Arthur Mensch adds that it took groundwork, was trained and served on their own compute, and that RL shows no sign of saturation.

    Why it matters: The announced API availability and end of October open weights release give teams distinct timelines for accessing the model through hosted infrastructure or its weights.

10/5Mon
  1. The Verge · AI22

    Our brains were not built for AI

    Neuroscientist Paul Cisek argues that the brain is not an information processor but a feedback-control system. A baseball outfielder catching a fly ball, for example, acts on the simple heuristic of 'keeping the ball in the same place in their field of vision' rather than calculating its trajectory. The article says AI is like 'cognitive junk food': immediately appealing but damaging to cognitive systems humans evolved over thousands of years.

10/3Sat
10/2Fri
  1. MIT Technology Review · AI38

    Don't be fooled by LLMs: large language models do not truly reason

    A former Google DeepMind researcher says an LLM's chain of thought is simply an extended iteration of the same next-token prediction process, not an independent reasoning mechanism like AlphaGo's. He identifies three major limitations: no persistent, inspectable cognitive state; knowledge and reasoning tangled together in the weights; and chains of thought that are often fabricated after the fact. He argues for borrowing AlphaGo's game-tree architecture to give machine reasoning an explicit cognitive state.

9/30Wed
  1. 雷锋网40

    Why do Kimi and DeepSeek appear in the same model-architecture diagram?

    Of the 45 layers in GLM-5.3-Flash, 34 use Kimi's KDA linear-attention approach and 11 use DeepSeek's KPool-DSA sparse attention. Linear and Sparse attention appear together in one model for the first time. The combination reflects a shift away from finding a single best mechanism toward assigning different roles: Linear maintains long histories at low cost, while Sparse retrieves specific information as needed.

  3. 雷锋网28

    From 3D vision to world models: why spatial intelligence is a common thread in AI's move into the physical world | ECCV 2026 recap

    ECCV 2026 received 10,473 submissions and accepted 2,834. The 3D topic fell from a leading area to third place, but questions around 3D representation, geometry, and physical consistency are spreading into video generation, embodied AI, and world models. Of 93 workshops, about 10 were directly related to 3D, 11 to spatial intelligence, nine to physical AI, and five to world models. Best-paper and honorable-mention awards also focused on 3D surface representation, object reconstruction, and geometry perception.

9/26Sat
  1. Kevin Weil · X79

    Anthropic reports Claude solved a nine-loop calculation in planar N=4 super-Yang-Mills

    Anthropic reports that Claude solved a nine-loop scattering-amplitude problem in planar N=4 super-Yang-Mills, running largely unsupervised for days in Claude Science at a total cost of a few thousand dollars. Claude used methods developed by Lance Dixon and colleagues, and Dixon independently verified the result. Kevin Weil, who calculated Feynman diagrams in graduate school, calls the result mind-boggling and expresses excitement about where AI could take high-energy physics over the coming 12 months.

    Why it matters: The reported cost of a few thousand dollars offers a concrete reference point for assessing AI-assisted theoretical physics calculations within an academic compute budget.