Quili AI
HN ⭐ 12 · 3 评论
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AI is destroying my CS degree
HN ⭐ 50 · 13 评论
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Railway employee's agent caught hacking competitors
HN ⭐ 6 · 0 评论
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Google will turn off search for schools that reject AI
HN ⭐ 637 · 439 评论
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The AI Bubble Monitor
HN ⭐ 37 · 29 评论
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Just a little AI resistance, as a treat
HN ⭐ 140 · 82 评论
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How AI is creating a lopsided US economy
HN ⭐ 3 · 0 评论
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Anthropic's growth just flattened
HN ⭐ 197 · 226 评论
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The SEC Should Block Anthropic's IPO to Protect the Public
HN ⭐ 289 · 225 评论
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Oracle files to sell $20B in new stock, bonds to finance AI buildout
HN ⭐ 179 · 159 评论
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Pixel: A Co-Designed Full-Stack Robot System for Learning General-Purpose Mobile Manipulation
作者: Tony Tao, Jason Jingzhou Liu, Ruslan Salakhutdinov | General-purpose mobile manipulation requires a combination of capable robot hardware, scalable demonstration collection, ex
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Pixel-UMI: Learning Environment-Aware Whole-Body Manipulation from Sensorized Human Demonstrations
作者: Jason Jingzhou Liu, Tony Tao, Deepak Pathak | Sensorized human demonstrations offer scalable data for mobile manipulation, but embodiment differences complicate policy learning
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From Natural Language to Exact Cover: A Neuro-Symbolic Approach to Zebra Puzzles
作者: Paulius Skaisgiris, Thomas Pammer, Veronika Semmelrock | Chain-of-Thought (CoT) generation has substantially improved the performance of Large Language Models (LLMs) on complex
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WebHarbor: Docking Real Websites for Evolving GUI Agent Environments
作者: Zhaoyang Wang, Qianhui Wu, Boyu Gou | GUI web agents require realistic and interactive environments that remain stable during both evaluation and training. Live website evaluat
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Pretraining Data Scale Reverses the Muon-AdamW Fine-Tuning Gap
作者: Catherine Li, Jingchu Gai, Andrej Risteski | Muon has recently emerged as a promising optimizer for pretraining large language models, offering a speedup over AdamW in minimizi
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Egoless Mathematics in the AI Era
HN ⭐ 179 · 144 评论
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Not All Reasoning Generalizes: A Case Study on Learning Generalizable Reasoning Strategies in RLVR
作者: Ziqi Pang, Yu-Xiong Wang | Reinforcement learning with verifiable rewards (RLVR) can enable generalizable improvement for large language model (LLM) reasoning. This paper studi
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FADE: Factored Ability--Difficulty Filtering for Online RLVR Scheduling
作者: Tomas Duncun, Jonah Ellison, Mateo Ruiz | Group-based reinforcement learning with verifiable rewards (RLVR) learns only from prompts whose sampled completions disagree: all-pas
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Thought-deciphering via hierarchical EEG-to-text alignment
作者: Xiaopeng Mao, Morten Mørup, Xin Xu | Communication is important for our well-being, but it can be impaired by disabilities, such as locked-in syndrome (LIS), cerebral palsy (CP
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D&A: Decoupling Description and Assessment Enables VLMs as Zero-Shot Robotic Reward Models
作者: Marius Memmel, Shawn Yin, Urvi Rutia | Vision-Language Models (VLMs) hold promise for zero-shot video task-progress estimation in robot learning, yet existing frameworks freque
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Strategist: Evolving Reasoning with Agents
作者: Taiming Lu, Qi Xu, Zhigang Hua | A model is only half of an AI agent system. The other half is the inference strategy: the reasoning procedure that turns model calls into a res