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CopT introduces a contrastive on-policy thinking framework for LLMs that generates draft answers first, then uses contrastive verification and dynamic thinking to improve accuracy while reducing token consumption, achieving up to 23% higher accuracy and 57% lower token usage on math, coding, and agentic reasoning tasks.
VideoSeeker introduces a paradigm for instance-level video understanding that integrates agentic reasoning with visual prompts, achieving superior performance through automated data synthesis and reinforcement learning, outperforming GPT-4o and Gemini-2.5-Pro.
ATLAS presents a visual reasoning framework that combines agentic operations and latent representations using functional tokens, enabling efficient training via next-token prediction and reinforcement learning while avoiding intermediate image generation.
This paper proposes an exploration-aware reinforcement learning framework that enables LLM agents to adaptively explore only when uncertainty is high, improving performance on text-based and GUI-based benchmarks.
This paper introduces FlowAgent, a novel framework that reconceptualizes tool chaining as continuous trajectory generation using conditional flow matching to improve robustness in long-horizon agentic reasoning.
Analyzes the 64-D embedding manifold of Google AlphaEarth across 12.1M U.S. samples, shows non-Euclidean structure and poor vector arithmetic, then builds an agentic system with geometry-aware tools that outperforms parametric baselines on environmental queries.