Zhang Zhang Peking University, Shuqi Lu BAAI, Hongjin Qian, Di He Peking University and Microsoft, Zheng Liu
Building LLM-based agents has become increasingly important. Recent works on LLM-based agent self-evolution primarily record successful experiences as textual prompts or reflections, which cannot reliably guarantee efficient task re-execution in complex scenarios. We propose AgentFactory, a new self-evolution paradigm that preserves successful task solutions as executable subagent code rather than textual experience. Crucially, these subagents are continuously refined based on execution feedback, becoming increasingly robust and efficient as more tasks are encountered. Saved subagents are pure Python code with standardized documentation, enabling portability across any Python-capable system. We demonstrate that AgentFactory enables continuous capability accumulation: its library of executable subagents grows and improves over time, progressively reducing the effort required for similar tasks without manual intervention. Our implementation is open-sourced at https://github.com/zzatpku/AgentFactory, and our demonstration video is available at https://youtu.be/iKSsuAXJHW0.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表
Jiajie Jin Renmin University of China, Yuyao Zhang, Yimeng Xu, Hongjin Qian, Yutao Zhu, Zhicheng Dou Renmin University of China
Professional financial reports serve as the cornerstone of investment decisions, demanding deep reasoning and multimodal synthesis. While recent deep research systems excel in open-domain search, they struggle with financial reporting, specifically in handling financial data, ensuring analytical depth, and integrating professional visualizations. To address this, we introduce FinSight , the first multi-agent framework for automate end-to-end professional, multimodal financial report. At its core, we propose the Code Agent with Variable Memory architecture, which unifies data, tools, and agents into a programmable variable space, enabling flexible data manipulation and reasoning through executable code. To guarantee report quality, FinSight incorporates a Two-Stage Writing Framework with Generative Retrieval. This mechanism first distills raw data into structured Chain-of-Analysis segments, and then progressively synthesizes them into a coherent, citation-aware, and multimodal narrative. Additionally, an Iterative Vision-Enhanced Mechanism leverages visual feedback to refine code-generated charts to expert standards. Experiments on company and industry-level tasks demonstrate that FinSight significantly outperforms leading deep research systems in factual accuracy, analytical depth, and presentation quality, demonstrating a clear path toward generating professional financial reports. Our code is available at https://anonymous.4open.science/r/FinSight-5841.
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cs.CL · cs.MA · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Kun Luo, Hongjin Qian, Zheng Liu, Ziyi Xia Beijing Academy of Artificial Intelligence, Shitao Xiao Beijing Academy of Artificial Intelligence, Zhao Cao Renmin University of China, Siqi Bao, Jun Zhao Institute of automation, Chinese academy of science 等 9 位作者
Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic search. However, its performance is often hindered by reward sparsity, whereby agents receive very limited positive feedback despite incurring significant exploration costs. In this paper, we formalize this challenge as a new research problem termed Reward Density Optimization, which aims to improve the reward obtained per unit of exploration cost. To address this problem, we introduce InfoFlow, a systematic framework that operates along three complementary dimensions: 1) Sub-goal Scaffolding: which decomposes long-horizon tasks into intermediate objectives and assigns process-level rewards to provide denser learning signals; 2) Pathfinding Hints: which injects corrective guidance into stalled trajectories to increase the ratio of successful trials; and 3) Dual-agent Refinement: which employs a dual-agent architecture to offload the cognitive burden of deep exploration. We evaluate InfoFlow on several popular agentic search benchmarks, where it significantly outperforms strong baselines and enables lightweight LLMs to achieve performance comparable to that of advanced proprietary models.
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cs.CL · cs.AI
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表