“VGGT-Ω” 3D reconstruction models is scaling like LLMs. Instead of relying on slow optimization pipelines, VGGT-Ω predicts cameras and depth in one feed-forward pass, even for dynamic video. This research makes that scaling practical with scene registers, lighter prediction
@askalphaxiv
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Technical Analysis of Self-Distilled Agentic Reinforcement Learning
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“Self-Distilled Agentic RL” Agent RL learns from sparse trajectory rewards, while self-distillation gives dense token guidance. But in multi-turn agents, naive distillation can break because privileged teacher signals get noisy as trajectories drift. The key idea of this paper
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LLM Failure Modes in Learning Negations During Fine-tuning
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“Negation Neglect: When models fail to learn negations in training” LLMs can understand a disclaimer in-context, but often fail to learn it during finetuning. So when training on documents saying a claim is false can still implant the claim as true. Qwen3.5 belief in
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δ-mem: Efficient Online Memory for Large Language Models
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“δ-mem: Efficient Online Memory for Large Language Models” LLMs need long-term memory, but extending context is expensive and often doesn’t mean the model actually uses the history well. What this paper did is to store past information in a tiny 8×8 associative memory state,
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Efficient LLM Pre-Training Using Token Superposition
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“Efficient Pre-Training with Token Superposition” LLM pretraining is bottlenecked by how many useful tokens you can consume per FLOP. This paper temporarily merge nearby tokens into averaged embedding bags, train the model to predict the next bag, then switch back to normal
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Long-Horizon Reasoning in LLMs: An alphaXiv AI4Science Talk
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If models can think for 100,000 tokens, why do they still lose the plot? Come join us for this AI4Science on alphaXiv talk: Long-Horizon Reasoning in LLMs. In this session, Sumeet Motwani (
@sumeetrm
) and Charles London (
@CharlieLondon02
) will share recent work on both training -
How Agentic LLMs Are Transforming Scientific Research and Mentorship
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Why LLMs Aren't Scientists Yet? In our latest AI4Science talk, Prof. Dhruv Kumar (
@gargdhruv36
) and Dhruv Trehan (
@dhruvtrehan9
) from @lossfunk discussed how agentic LLM systems can support science in a whole new way, from generating research ideas to mentoring young researchers -
Solving the Loop: Attractor Models for Transformer Language and Reasoning
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“Solve the Loop: Attractor Models for Language and Reasoning” Looped Transformers can refine their thoughts internally, but they are usually unstable and tied to a fixed number of loops. So this paper turned recurrence into a fixed-point problem, where a Transformer first makes