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20242026
most citedUsing Interpretation Methods for Model Enhancement

1 citations · 1 across the 10 of their papers we have counts for

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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.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…

cs.CL2025

Tongyi DeepResearch Technical Report

Tongyi DeepResearch Team, Baixuan Li, Bo Zhang +54

We present Tongyi DeepResearch, an agentic large language model, which is specifically designed for long-horizon, deep information-seeking research tasks. To incentivize autonomous…

cs.CL2025

Repurposing Synthetic Data for Fine-grained Search Agent Supervision

Yida Zhao, Kuan Li, Xixi Wu +11

LLM-based search agents are increasingly trained on entity-centric synthetic data to solve complex, knowledge-intensive tasks. However, prevailing training methods like Group Relat…

cs.CL2025

Scaling Agents via Continual Pre-training

Liangcai Su, Zhen Zhang, Guangyu Li +19

Large language models (LLMs) have evolved into agentic systems capable of autonomous tool use and multi-step reasoning for complex problem-solving. However, post-training approache…

cs.CL2025

Detecting Knowledge Boundary of Vision Large Language Models by Sampling-Based Inference

Zhuo Chen, Xinyu Wang, Yong Jiang +5

Despite the advancements made in Vision Large Language Models (VLLMs), like text Large Language Models (LLMs), they have limitations in addressing questions that require real-time…