2 citations · 6 across the 11 of their papers we have counts for
11 papers
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…
Bridging the Detection-to-Abstention Gap in Reasoning Models under Insufficient Information
Renjie Gu, Jiaxu Li, Yihao Wang +8
We highlight a failure mode of large reasoning models on questions with insufficient information: models may recognize that a problem is under-specified, yet still continue reasoni…
Skill-as-Pseudocode: Refactoring Skill Libraries to Pseudocode for LLM Agents
Xinze Li, Yuhang Zang, Yixin Cao +1
Markdown skill libraries for LLM agents ship as free-form prose, forcing the agent to re-derive both the input schema and the concrete invocation syntax on every retrieval. This pr…
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,…
Towards Storage-Efficient Visual Document Retrieval: An Empirical Study on Reducing Patch-Level Embeddings
Yubo Ma, Jinsong Li, Yuhang Zang +8
Despite the strong performance of ColPali/ColQwen2 in Visualized Document Retrieval (VDR), it encodes each page into multiple patch-level embeddings and leads to excessive memory u…
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…
Navigating the Nuances: A Fine-grained Evaluation of Vision-Language Navigation
Zehao Wang, Minye Wu, Yixin Cao +3
This study presents a novel evaluation framework for the Vision-Language Navigation (VLN) task. It aims to diagnose current models for various instruction categories at a finer-gra…
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…
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…
Improving Large Language Models in Event Relation Logical Prediction
Meiqi Chen, Yubo Ma, Kaitao Song +3
Event relations are crucial for narrative understanding and reasoning. Governed by nuanced logic, event relation extraction (ERE) is a challenging task that demands thorough semant…
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…