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20212026
most citedReasoning Graph Networks for Kinship Verification: from Star-shaped to Hierarchical

29 citations · 47 across the 15 of their papers we have counts for

collaborators

15 papers

cs.AI2026

Knowing When Not to Reuse: Conditional Experience Transfer in Autonomous LLM Post-Training

Tingyun Li, Wenfeng Feng, Weiqing Li +3

Large language models offer broad capabilities, but adapting them to evolving domains, tools, and requirements often entails repeated post-training. Autonomous systems automate par…

cs.CV2026

Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation

Yubo Jiang, Xin Yang, Abudukelimu Wuerkaixi +7

Vision-Language Models (VLMs) are frequently undermined by object hallucination--generating content that contradicts visual reality--due to an over-reliance on linguistic priors. W…

cs.LG2026

Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding

Yubo Jiang, Yitong An, Xin Yang +7

Vision-Language Models (VLMs) are frequently undermined by object hallucination, generating content that contradicts visual reality, due to an over-reliance on linguistic priors. W…

cs.AI2026

V-tableR1: Process-Supervised Multimodal Table Reasoning with Critic-Guided Policy Optimization

Yubo Jiang, Yitong An, Xin Yang +7

We introduce V-tableR1, a process-supervised reinforcement learning framework that elicits rigorous, verifiable reasoning from multimodal large language models (MLLMs). Current MLL…

cs.IR2026

AutothinkRAG: Complexity-Aware Control of Retrieval-Augmented Reasoning for Image-Text Interaction

Jiashu Yang, Chi Zhang, Abudukelimu Wuerkaixi +5

Multimodal document question answering requires retrieving dispersed evidence from visually rich long documents and performing reliable reasoning over heterogeneous information. Ex…

cs.CL2026

Towards Self-Robust LLMs: Intrinsic Prompt Noise Resistance via CoIPO

Xin Yang, Letian Li, Abudukelimu Wuerkaixi +5

Large language models (LLMs) have demonstrated remarkable and steadily improving performance across a wide range of tasks. However, LLM performance may be highly sensitive to promp…