Papersby GlyphStanford University research papers
A university publishing mostly on agents and evaluation, best known here for Direct Preference Optimization.12 papers · 17,673 citations · latest Oct 2026
Hybrid Reasoning AgentsThis paper introduces an agent architecture that dynamically switches between latent reasoning and text-based interaction to improve performance and inference speed.
Stanford University41 upvotesRead →Thinking Inertia: LLMs Keep Thinking When Told Not ToThis paper shows that explicit controls often fail to stop LLMs from generating internal reasoning traces, a phenomenon the authors call thinking inertia.Stanford University244 upvotesRead →Stateless Language Agents: Scaling Long-Horizon Automated ResearchThis paper introduces a framework where agents remain stateless, allowing a central harness to manage research state and improve long-horizon task efficiency.Stanford University1 citations30 upvotesRead →FAMOS: Feed-Forward 3D Articulation Modeling from Sparse ObservationsFAMOS is a feed-forward model that predicts articulated object motion from sparse point cloud observations.Stanford University40 upvotesRead →AISPA: User-Centric System Prompt Auditing for Large Language Model ApplicationsAISPA is a framework for auditing system prompts in commercial AI products to ensure they are protective of users.Stanford University36 upvotesRead →Recursive Multi-Agent SystemsRecursiveMAS extends recursive scaling principles from single models to multi-agent systems, enabling collaborative reasoning through iterative latent-space computations with improved efficiency and accuracy.Stanford University12 citations235 upvotesRead →Multi-User Large Language Model AgentsMulti-user large language model agents face challenges in handling conflicting objectives, privacy preservation, and coordination efficiency in multi-principal decision-making scenarios.Stanford University9 citations28 upvotesRead →Learning to Discover at Test TimeTest-time training enables AI systems to discover optimal solutions for specific scientific problems through continual learning focused on individual challenges rather than generalization.Stanford University118 citations45 upvotesRead →In-the-Flow Agentic System Optimization for Effective Planning and Tool UseAgentFlow, a trainable agentic framework with in-the-flow optimization, enhances reasoning in large language models by coordinating specialized modules and outperforms top baselines across various tasks.Stanford University76 citations113 upvotesRead →LongCodeZip: Compress Long Context for Code Language ModelsLongCodeZip is a code compression framework for LLMs that uses dual-stage compression to reduce context size without degrading performance, improving efficiency in code intelligence applications.Stanford University54 citations77 upvotesRead →Direct Preference Optimization: Your Language Model is Secretly a Reward ModelDPO aligns a model to human preferences with a simple classification loss, no reward model or RL loop.Stanford University11.6k citations73 upvotesRead →FlashAttention: Fast and Memory-Efficient Exact Attention with IO-AwarenessFlashAttention computes exact attention with far fewer GPU memory reads and writes, making long sequences faster.Stanford University5,830 citations16 upvotesRead →
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Topics: Agents and evaluation 3, Reasoning 2, Alignment and safety 2, Inference and efficiency 2, Multimodal and robotics 1, Foundation models 1, Retrieval and data 1
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