activity
20242026
most citedFortisAVQA and MAVEN: a Benchmark Dataset and Debiasing Framework for Robust Multimodal Reasoning

1 citations · 1 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2026

Visual-OPSD: Cross-Modal On-Policy Self-Distillation for Efficient Unified Multimodal Reasoning

Pengyu Li, Zhitao Gao, Lingling Zhang +5

Unified multimodal models (UMMs) interleave generated ''visual thoughts'' (VTs) with text reasoning to improve spatial tasks. This incurs roughly an order-of-magnitude inference co…

cs.AI2026

Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems

Shihao Qi, Jie Ma, Rui Xing +15

LLM-based autonomous agents have demonstrated strong capabilities in reasoning, planning, and tool use, yet remain limited when tasks require sustained coordination across roles, t…

cs.CL2026

AERO: Autonomous Evolutionary Reasoning Optimization via Endogenous Dual-Loop Feedback

Zhitao Gao, Jie Ma, Xuhong Li +5

Large Language Models (LLMs) have achieved significant success in complex reasoning but remain bottlenecked by reliance on expert-annotated data and external verifiers. While exist…

cs.LG2026

$\textbf{AGT$^{AO}$}$: Robust and Stabilized LLM Unlearning via Adversarial Gating Training with Adaptive Orthogonality

Pengyu Li, Lingling Zhang, Zhitao Gao +5

While Large Language Models (LLMs) have achieved remarkable capabilities, they unintentionally memorize sensitive data, posing critical privacy and security risks. Machine unlearni…

cs.CL2025

Deliberation on Priors: Trustworthy Reasoning of Large Language Models on Knowledge Graphs

Jie Ma, Ning Qu, Zhitao Gao +8

Knowledge graph-based retrieval-augmented generation seeks to mitigate hallucinations in Large Language Models (LLMs) caused by insufficient or outdated knowledge. However, existin…

cs.MM2025★ 1 cited

FortisAVQA and MAVEN: a Benchmark Dataset and Debiasing Framework for Robust Multimodal Reasoning

Jie Ma, Zhitao Gao, Qi Chai +4

Audio-Visual Question Answering (AVQA) is a challenging multimodal reasoning task requiring intelligent systems to answer natural language queries based on paired audio-video input…