Category

This paper introduces ReAct, an approach that integrates reasoning and acting in large language models (LLMs). ReAct enables LLMs to generate both reasoning traces and task-specific actions in an interleaved manner. This synergy allows reasoning to help induce, track, and update action plans, while actions interface with external sources like knowledge bases to gather more information, overcoming issues of hallucination and error propagation in prior methods.

SWE-agent is a system designed to empower language model (LM) agents to autonomously perform software engineering tasks. It features a custom agent-computer interface (ACI) that enhances the agent's ability to navigate repositories, create and edit code, and execute programs, achieving state-of-the-art results on the SWE-bench and HumanEvalFix benchmarks. [2, 5, 8]

DeepSeek-V3.2 is an open large language model that balances high computational efficiency with superior reasoning and agent capabilities. Key innovations include DeepSeek Sparse Attention (DSA) for reduced complexity in long contexts, a scalable reinforcement learning framework achieving GPT-5-level performance, and a large-scale agentic task synthesis pipeline for improved generalization in tool-use scenarios.