1 citations · 1 across the 24 of their papers we have counts for
24 papers
VERDI:Verified Contradictions for Continual World-Model Optimization
Junyu Wu, Shiqin Nie, Youyi Kou +10
Foundation world models have made remarkable progress in planning, simulation, and embodied intelligence. However, optimizing a pretrained world model toward a user-specified objec…
From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms
Jinghao Luo, Yuchen Tian, Chuxue Cao +6
Large Language Model (LLM)-based agents have fundamentally reshaped artificial intelligence by integrating external tools and planning capabilities. While memory mechanisms have em…
Probabilistic Concept Graph Reasoning for Multimodal Misinformation Detection
Ruichao Yang, Wei Gao, Xiaobin Zhu +5
Multimodal misinformation poses an escalating challenge that often evades traditional detectors, which are opaque black boxes and fragile against new manipulation tactics. We prese…
FATE: Closed-Loop Feasibility-Aware Task Generation with Active Repair for Physically Grounded Robotic Curricula
Bingchuan Wei, Bingqi Huang, Jingheng Ma +2
Recent breakthroughs in generative simulation have harnessed Large Language Models (LLMs) to generate diverse robotic task curricula, yet these open-loop paradigms frequently produ…
Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking
Hongzhan Lin, Zixin Chen, Zhiqi Shen +5
Large Language Models (LLMs) are increasingly deployed in real-world fact-checking systems, yet existing evaluations focus predominantly on claim verification and overlook the broa…
DiffCoT: Diffusion-styled Chain-of-Thought Reasoning in LLMs
Shidong Cao, Hongzhan Lin, Yuxuan Gu +2
Chain-of-Thought (CoT) reasoning improves multi-step mathematical problem solving in large language models but remains vulnerable to exposure bias and error accumulation, as early…
MM-CRITIC: A Holistic Evaluation of Large Multimodal Models as Multimodal Critique
Gailun Zeng, Ziyang Luo, Hongzhan Lin +5
The ability of critique is vital for models to self-improve and serve as reliable AI assistants. While extensively studied in language-only settings, multimodal critique of Large M…
MemeArena: Automating Context-Aware Unbiased Evaluation of Harmfulness Understanding for Multimodal Large Language Models
Zixin Chen, Hongzhan Lin, Kaixin Li +3
The proliferation of memes on social media necessitates the capabilities of multimodal Large Language Models (mLLMs) to effectively understand multimodal harmfulness. Existing eval…
EvolProver: Advancing Automated Theorem Proving by Evolving Formalized Problems via Symmetry and Difficulty
Yuchen Tian, Ruiyuan Huang, Xuanwu Wang +6
Large Language Models (LLMs) for formal theorem proving have shown significant promise, yet they often lack generalizability and are fragile to even minor transformations of proble…
AmbiGraph-Eval: Can LLMs Effectively Handle Ambiguous Graph Queries?
Yuchen Tian, Kaixin Li, Hao Chen +5
Large Language Models (LLMs) have recently demonstrated strong capabilities in translating natural language into database queries, especially when dealing with complex graph-struct…
AdamMeme: Adaptively Probe the Reasoning Capacity of Multimodal Large Language Models on Harmfulness
Zixin Chen, Hongzhan Lin, Kaixin Li +5
The proliferation of multimodal memes in the social media era demands that multimodal Large Language Models (mLLMs) effectively understand meme harmfulness. Existing benchmarks for…
ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use
Kaixin Li, Ziyang Meng, Hongzhan Lin +5
Recent advancements in Multi-modal Large Language Models (MLLMs) have led to significant progress in developing GUI agents for general tasks such as web browsing and mobile phone u…
FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models
Hongzhan Lin, Yang Deng, Yuxuan Gu +4
Large Language Models (LLMs) have significantly advanced the fact-checking studies. However, existing automated fact-checking evaluation methods rely on static datasets and classif…
Meme Trojan: Backdoor Attacks Against Hateful Meme Detection via Cross-Modal Triggers
Ruofei Wang, Hongzhan Lin, Ziyuan Luo +4
Hateful meme detection aims to prevent the proliferation of hateful memes on various social media platforms. Considering its impact on social environments, this paper introduces a…
ClarityEthic: Explainable Moral Judgment Utilizing Contrastive Ethical Insights from Large Language Models
Yuxi Sun, Wei Gao, Jing Ma +3
With the rise and widespread use of Large Language Models (LLMs), ensuring their safety is crucial to prevent harm to humans and promote ethical behaviors. However, directly assess…
ScratchEval: Are GPT-4o Smarter than My Child? Evaluating Large Multimodal Models with Visual Programming Challenges
Rao Fu, Ziyang Luo, Hongzhan Lin +2
Recent advancements in large multimodal models (LMMs) have showcased impressive code generation capabilities, primarily evaluated through image-to-code benchmarks. However, these b…
Towards Low-Resource Harmful Meme Detection with LMM Agents
Jianzhao Huang, Hongzhan Lin, Ziyan Liu +3
The proliferation of Internet memes in the age of social media necessitates effective identification of harmful ones. Due to the dynamic nature of memes, existing data-driven model…
VideoAutoArena: An Automated Arena for Evaluating Large Multimodal Models in Video Analysis through User Simulation
Ziyang Luo, Haoning Wu, Dongxu Li +3
Large multimodal models (LMMs) with advanced video analysis capabilities have recently garnered significant attention. However, most evaluations rely on traditional methods like mu…
SHARP: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing LLMs
Chuyi Kong, Ziyang Luo, Hongzhan Lin +4
The advanced role-playing capabilities of Large Language Models (LLMs) have enabled rich interactive scenarios, yet existing research in social interactions neglects hallucination…
AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation
Ziyang Luo, Xin Li, Hongzhan Lin +2
The impressive performance of proprietary LLMs like GPT4 in code generation has led to a trend to replicate these capabilities in open-source models through knowledge distillation…
CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?
Yuwei Zhao, Ziyang Luo, Yuchen Tian +4
Recent advancements in large language models (LLMs) have showcased impressive code generation capabilities, primarily evaluated through language-to-code benchmarks. However, these…
MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models
Shengkang Wang, Hongzhan Lin, Ziyang Luo +3
Large vision-language models (LVLMs) have significantly improved multimodal reasoning tasks, such as visual question answering and image captioning. These models embed multimodal f…
CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models
Hongzhan Lin, Zixin Chen, Ziyang Luo +3
Social media abounds with multimodal sarcasm, and identifying sarcasm targets is particularly challenging due to the implicit incongruity not directly evident in the text and image…
MMCode: Benchmarking Multimodal Large Language Models for Code Generation with Visually Rich Programming Problems
Kaixin Li, Yuchen Tian, Qisheng Hu +3
Programming often involves converting detailed and complex specifications into code, a process during which developers typically utilize visual aids to more effectively convey conc…