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20242026
most citedBenchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent

2 citations · 2 across the 16 of their papers we have counts for

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cs.AI2026

FrontierChallenge: Evaluating Scientific Workflow Completion

Liangcai Su, Zhaopeng Feng, Zhuo Chen +14

Scientific agents increasingly analyze data, execute code, and produce research artifacts, yet most benchmarks emphasize final answers, isolated programs, or a single domain. We in…

cs.CL2026

Argus: Evidence Assembly for Scalable Deep Research Agents

Zhen Zhang, Liangcai Su, Zhuo Chen +7

Deep research agents have achieved remarkable progress on complex information seeking tasks. Even long ReAct style rollouts explore only a single trajectory, while recent state of…

cs.AI2026

MiroEval: Benchmarking Multimodal Deep Research Agents in Process and Outcome

Fangda Ye, Yuxin Hu, Pengxiang Zhu +19

Recent progress in deep research systems has been impressive, but evaluation still lags behind real user needs. Existing benchmarks predominantly assess final reports using fixed r…

cs.CL2026

AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents

Zhaopeng Feng, Liangcai Su, Zhen Zhang +16

As large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking, managing finite context capacity has become a critical bottleneck. Existing cont…

cs.CL2026

Efficient Multimodal Planning Agent for Visual Question-Answering

Zhuo Chen, Xinyu Geng, Xinyu Wang +4

Visual Question-Answering (VQA) is a challenging multimodal task that requires integrating visual and textual information to generate accurate responses. While multimodal Retrieval…