82 citations · 83 across the 13 of their papers we have counts for
14 papers
When Irrelevant Text Matters: Affine Margin Shifts in Multimodal Large Language Models
Yinfeng Wang, Zhiyuan Yao, Zheren Fu +2
Multimodal large language models (MLLMs) are frequently exposed to auxiliary textual context, the impact of which on visually grounded tasks remains underexplored. In this paper, w…
When Your Agent Opens the Chat App: Agent-Controlled Search over Raw Chat Logs Rivals Structured Memory
Ruizhe Li, Licheng Zhang, Benfeng Xu +3
Agent-memory systems increasingly buy retrieval quality with structure, transforming raw conversation histories into summaries, embeddings, trees, or knowledge graphs before any qu…
MAVEN: A Macro-Societal Value Evaluation Framework of Multimodal Content with Compact Aligned Evaluators
Zijuan Zhao, Zheren Fu, Hou Xia +3
Assessing whether multimodal content aligns with macro-societal values, such as peace, justice, and freedom, has become an increasingly urgent challenge. Existing frameworks are la…
Groc-PO: Grounded Context Preference Optimization for Truthful Multimodal LLMs
Zhixiao Zheng, Zheren Fu, Zhiyuan Yao +3
Despite the rapid progress of Multimodal Large Language Models (MLLMs), they still suffer from untruthfulness issues, such as visual hallucinations, content fabrication, and unfait…
Learning to Compose: Revisiting Proxy Task Design for Zero-Shot Composed Image Retrieval
Jingjing Zhang, Lei Zhang, Zheren Fu +1
Composed Image Retrieval (CIR) retrieves a target image from a reference image and a textual modification. While supervised CIR relies on costly triplets, Zero-Shot CIR (ZS-CIR) al…
ADAPT: Attention Dynamics Alignment with Preference Tuning for Faithful MLLMs
Zhiyuan Yao, Zheren Fu, Zhixiao Zheng +3
Multimodal Large Language Models (MLLMs) are critically hampered by hallucination, generating content inconsistent with the provided image. In this paper, we identify an internal s…
DailyReport: An Open-ended Benchmark for Evaluating Search Agents on Daily Search Tasks
Jingxuan Han, Wei Liu, Mingyang Zhu +8
Search Agents (SAs) typically leverage large language models (LLMs) to support complex information-seeking tasks by autonomously exploring web sources and synthesizing information…
Lance: Unified Multimodal Modeling by Multi-Task Synergy
Fengyi Fu, Mengqi Huang, Shaojin Wu +10
We present Lance, a lightweight native unified model supporting multimodal understanding, generation, and editing for both images and videos. Rather than relying on model capacity…
In-Token Rationality Optimization: Towards Accurate and Concise LLM Reasoning via Self-Feedback
Mingye Zhu, Yi Liu, Zheren Fu +2
Training Large Language Models (LLMs) for chain-of-thought reasoning presents a significant challenge: supervised fine-tuning on a single "golden" rationale hurts generalization as…
SparseRM: A Lightweight Preference Modeling with Sparse Autoencoder
Dengcan Liu, Jiahao Li, Zheren Fu +4
Reward models (RMs) are a core component in the post-training of large language models (LLMs), serving as proxies for human preference evaluation and guiding model alignment. Howev…
Video-LevelGauge: Investigating Contextual Positional Bias in Large Video Language Models
Hou Xia, Zheren Fu, Fangcan Ling +4
Large video language models (LVLMs) have made notable progress in video understanding, spurring the development of corresponding evaluation benchmarks. However, existing benchmarks…
DACL-RAG: Data Augmentation Strategy with Curriculum Learning for Retrieval-Augmented Generation
Shaohan Wang, Licheng Zhang, Zheren Fu +2
Retrieval-Augmented Generation (RAG) is an effective method to enhance the capabilities of large language models (LLMs). Existing methods typically optimize the retriever or the ge…
Qwen2.5-VL Technical Report
Shuai Bai, Keqin Chen, Xuejing Liu +24
We introduce Qwen2.5-VL, the latest flagship model of Qwen vision-language series, which demonstrates significant advancements in both foundational capabilities and innovative func…
Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning
Zheren Fu, Zhendong Mao, Bo Hu +2
Deep metric learning aims to learn an embedding space, where semantically similar samples are close together and dissimilar ones are repelled against. To explore more hard and info…