"Demystifying Manifold Constraints in LLM Pre-training" LLM pre-training depends on a stack of heuristic tricks, eg. normalization layers, weight decay, and optimizer tuning. This paper suggests that by constraining weight matrices to geometric manifolds like spectral or
@askalphaxiv
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Continuous Latent Diffusion Language Model Advances
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“Continuous Latent Diffusion Language Model” Most diffusion language models still use diffusion to recover token-like states, just in a different generation order. However, this paper uses diffusion in a different way. It learns a continuous latent prior for global semantics
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Sparser, Faster, Lighter Transformer Language Models
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“Sparser, Faster, Lighter Transformer Language Models” LLMs are naturally sparse in their feedforward layers, but unstructured sparsity usually doesn’t get you real speed on GPUs, because the hardware stack is built for dense compute. The key idea of the paper is to redesign
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Representational Geometry Influences Uncertainty in Large Language Models
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"Representational Curvature Modulates Behavioral Uncertainty in LLMs" Most interpretability work studies what features live inside LLMs. But this paper studies something deeper, which is the geometry of the whole evolving representation. The key result is that representational
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Talk on Building and Evaluating Agentic LLM Systems for Science
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If AI is so good right now, why aren't they scientists yet? Come join us for this AI4Science on alphaXiv talk: Building and Evaluating Agentic LLM Systems for Science. In this session, Prof. Dhruv Kumar (
@gargdhruv36
) and Dhruv Trehan (
@dhruvtrehan9
) will share recent -
DGPO Method Advances Token-Level Credit Assignment in RL
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"DGPO: Distribution Guided Policy Optimization for Fine Grained Credit Assignment" RL for reasoning has a credit assignment problem. One reward basically gets spread across the whole chain-of-thought. This paper, DGPO, fixes that by turning policy deviation into a token-level
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GLM-5V-Turbo: Native Foundation Model for Multimodal Agents
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“GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents” Most multimodal agents today are still basically text agents with vision adapters, and a lot of reasoning failures are actually the model failing to see the interface, layout, chart, or object correctly.
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New AI Alignment Training Research from Anthropic Fellows
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a new paper from Anthropic Fellows Program! "Model Spec Midtraining: Improving How Alignment Training Generalizes" A lot of alignment training teaches models what to say, but not why those behaviors are right. So before normal alignment fine-tuning, this research trains the
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MolmoAct2: AI Action Reasoning Models for Real-World Robotics
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"MolmoAct2: Action Reasoning Models for Real-world Deployment" This paper from AI2 tackles open robot action model for real-world deployment. It combines a stronger embodied-reasoning backbone with a continuous action expert. A key component is the adaptive depth reasoning,
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SkillSynth Builds Skill Graphs for Terminal Agent Training
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"Toward Scalable Terminal Task Synthesis via Skill Graphs" Terminal-agent training needs more diverse workflows. This Tencent Hunyuan paper, SkillSynth, builds a graph of terminal scenarios and skills, samples paths through it, then turns those paths into executable tasks. It