Chen

This paper establishes a large deviation principle for the generalization error of interpolating classifiers in the overparametrized regime.

This paper investigates whether adults' struggles with conjunctive causal rules persist when they have agency through active exploration.

This paper proposes NF-CoT, a latent reasoning framework that preserves the advantages of chain-of-thought in large language models.

TempoVLA is a novel Vision-Language-Action model that enables controllable execution speed for robot manipulation by explicitly conditioning the policy on the desired speed.

This paper introduces Repeated Policy Regret (RP-Regret), a novel game-theoretic metric for analyzing regret in repeated games with adaptive opponents, and proposes algorithms to minimize it.

The paper introduces OpAI-Bench, a novel benchmark designed to study how AI authorship signals evolve and accumulate during the progressive co-editing process between humans and AI.

MLEvolve is a novel self-evolving multi-agent framework that enables LLM agents to discover and optimize machine learning algorithms for complex, long-horizon tasks.

The paper proposes Astra, an agentic framework that equips Vision-Language Models (VLMs) with the ability to perform spatial reasoning by actively generating and utilizing imagined visual evidence from a world simulator.

This paper introduces MedReCo and MedReCo-VLM, a framework that enables entity-aware cross-image reasoning for medical imaging, allowing AI to compare current scans with prior studies and analogous cases based on structured clinical reports.

The paper proposes OneReason, a framework that enhances the reasoning capability of generative recommendation models by focusing on improving item perception and structuring user behavior into coherent latent interests.

This paper proposes a unified framework for inference-time augmentation to improve the robustness of physiological signal classification in real-world deployments.

肖代替了视觉令牌的永久删除，通过可恢复的路由来改进视觉语言模型的性能

The paper presents Tahoe, a system that optimizes Text-to-SQL performance through dynamic data management and hint learning.

This paper introduces ATLAS, an active learning framework for discovering interpretable behavioral models in cognitive science.

This paper proposes a new method for agentic Reinforcement Learning called Agentic Procedural Policy Optimization (APPO) that improves tool-use capabilities by assigning credit to fine-grained decision points.

This paper presents BenDi, an energy-efficient quasi-stochastic systolic architecture for bioelectronic systems on the edge.

This paper presents an algorithmic framework for exhaustively generating and tabulating knot and link diagrams on the thickened torus.

This paper proposes a post-training framework called Retrieval-Augmented Reinforcement Fine-Tuning (RA-RFT) to teach language models to reason by analogy.

This paper proposes SpatialClaw, a training-free framework for spatial reasoning that enables open-ended, complex 3D/4D spatial reasoning.

This paper introduces Agents-K1, an end-to-end knowledge orchestration pipeline that converts raw documents into agent-native scientific knowledge graphs.