Claude is now being listed as an author on arXiv papers A response paper to Apple's "Illusion of Thinking" work just dropped with Claude Opus as first author, critiquing their experimental design and arguing the reasoning collapse was actually just token limit constraints.
@askalphaxiv
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Parshin Shojaee discusses illusion of thinking on alphaXiv
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@ParshinShojaee is on alphaXiv to answer questions on The Illusion of Thinking! Join the discussion here: https://
alphaxiv.org/abs/2506.06941 -
RL Training Paradigm: 14B Model Matches 32B Baseline Performance
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New training paradigm: instead of just predicting tokens, models reason about each prediction using RL The model thinks through context, considers alternatives, then makes a prediction. 14B model matches 32B baseline, though training costs are significantly higher.
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Can LLMs Truly Introspect or Just Mimic Human Responses?
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I think therefore I LLM Can AI actually introspect or is it just copying human responses? Turns out mostly copying, but LLMs can genuinely analyze some of their own internal settings. Temperature detection works. Other "self-awareness" claims are dubious.
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DeepMind’s automated research agents discussed by SR Schmidgall
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Excited to have @SRSchmidgall from DeepMind discussing his work this Friday on automated research agents. Shoutout to @IntologyAI for making this happen! https://
x.com/IntologyAI/sta
/IntologyAI/status/1932144619015586183
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ComfyUI-Copilot: Intelligent LLM Assistant for Workflow Automation
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ComfyUI-Copilot: An Intelligent Assistant for Automated Workflow Development ComfyUI-Copilot is an LLM-powered plugin designed to simplify and accelerate workflow creation in ComfyUI, an open-source AI art platform, by providing intelligent node/model recommendations and
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AReaL: Asynchronous Reinforcement Learning System for Language Reasoning
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AReaL: A Large-Scale Asynchronous Reinforcement Learning System for Language Reasoning AReaL is an asynchronous reinforcement learning system that efficiently trains large language models for reasoning tasks by maximizing GPU usage and decoupling generation from training.
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Pseudo-Simulation: New Autonomous Driving Evaluation Paradigm
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Pseudo-Simulation for Autonomous Driving Pseudo-simulation is a new evaluation paradigm for autonomous vehicles that blends the realism of real-world data with the generalization power of simulation, enabling robust, scalable testing without the need for interactive
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SmolVLA: Efficient Vision-Language-Action Model for Robotics
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SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics SmolVLA is a compact, open-source VLA model built for low-cost training and real-world deployment on consumer hardware, enabling efficient language-driven robot control without sacrificing performance.
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Negative Reinforcement Improves LLM Reasoning Without Explicit Rewards
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The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning This paper shows that punishing incorrect answers—without explicitly rewarding correct ones—can be surprisingly effective for improving reasoning in large language models trained via reinforcement learning