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20242026
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cs.AI2026

DocOps: A Verifiable Benchmark for Autonomous Agents in Complex Document Operations

Jiazhen Jiang, Boxi Cao, Lingyong Yan +6

As autonomous agents rapidly evolve, their ability to reliably manipulate ubiquitous digital documents has become critical for enabling general-purpose AI assistants and automating…

cs.AI2026

DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoning

Lingyong Yan, Can Xu, Yukun Zhao +13

Deep Research (DR) has emerged as a new agentic paradigm to tackle complex, open-ended research tasks, demanding systems that can iteratively frame problems, acquire evidence, veri…

cs.AI2026

When Tools Fail: Benchmarking Dynamic Replanning and Anomaly Recovery in LLM Agents

Dongsheng Zhu, Xuchen Ma, Yucheng Shen +5

Existing benchmarks evaluate Tool-Integrated Reasoning (TIR) in LLMs on idealized ''happy paths'', largely overlooking real-world tool failures. We introduce ToolMaze, a benchmark…

cs.AI2026

UnityMAS-O: A General RL Optimization Framework for LLM-Based Multi-Agent Systems

Yiqun Chen, Wei Yang, Erhan Zhang +14

LLM-based multi-agent systems decompose complex tasks into interacting roles, but most remain manually orchestrated by prompts, tools, and control rules, while agents are rarely op…

cs.AI2026

Adversarial Yet Cooperative: Multi-Perspective Reasoning in Retrieved-Augmented Language Models

Can Xu, Lingyong Yan, Jiayi Wu +6

Recent advances in synergizing large reasoning models (LRMs) with retrieval-augmented generation (RAG) have shown promising results, yet two critical challenges remain: (1) reasoni…

cs.AI2026

Beyond Monolithic Architectures: A Multi-Agent Search and Knowledge Optimization Framework for Agentic Search

Yiqun Chen, Lingyong Yan, Zixuan Yang +5

Agentic search has emerged as a promising paradigm for complex information seeking by enabling Large Language Models (LLMs) to interleave reasoning with tool use. However, prevaili…