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20232026
most citedSciAgent: Tool-augmented Language Models for Scientific Reasoning

2 citations · 6 across the 11 of their papers we have counts for

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cs.CL2026

ATLAS: All-round Testing of Long-context Abilities across Scales

Deli Huang, Cunguang Wang, Hongyin Tang +15

Long-context language models now advertise context windows up to millions of tokens, yet evaluations typically report a single length or a narrow task family, masking two failure m…

cs.CL2026

EMemBench: Interactive Benchmarking of Episodic Memory for VLM Agents

Xinze Li, Ziyue Zhu, Siyuan Liu +4

We introduce EMemBench, a programmatic benchmark generator for evaluating long-term episodic memory of agents through interactive games. Rather than using a fixed set of questions,…

cs.CL2024★ 1 cited

Long Context vs. RAG for LLMs: An Evaluation and Revisits

Xinze Li, Yixin Cao, Yubo Ma +1

Extending context windows (i.e., Long Context, LC) and using retrievers to selectively access relevant information (i.e., Retrieval-Augmented Generation, RAG) are the two main stra…

cs.CL2024★ 1 cited

Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective

Meiqi Chen, Yixin Cao, Yan Zhang +1

Recent advancements in Large Language Models (LLMs) have facilitated the development of Multimodal LLMs (MLLMs). Despite their impressive capabilities, MLLMs often suffer from over…

cs.CL2024★ 2 cited

SciAgent: Tool-augmented Language Models for Scientific Reasoning

Yubo Ma, Zhibin Gou, Junheng Hao +8

Scientific reasoning poses an excessive challenge for even the most advanced Large Language Models (LLMs). To make this task more practical and solvable for LLMs, we introduce a ne…

cs.CL2023★ 1 cited

FOLLOWUPQG: Towards Information-Seeking Follow-up Question Generation

Yan Meng, Liangming Pan, Yixin Cao +1

Humans ask follow-up questions driven by curiosity, which reflects a creative human cognitive process. We introduce the task of real-world information-seeking follow-up question ge…