works on

From the 1 of 23 linked papers with an AI index.

activity
20242026
collaborators

23 papers

cs.CV2026

OmniPhys: Knowledge-Graph-Driven Benchmarking and Collective Optimization for Physical Commonsense in Text-to-Image Generation

Yajing Xu, Yarong Lan, Jiaoyan Chen +6

The paper presents OmniPhys, a knowledge-graph-based benchmark for evaluating physical commonsense in text-to-image models, and OmniPrompt, an iterative optimization framework that…

cs.CL2026

Symbolic and Abstractive Reasoning with Complex Visual Queries

Yichi Zhang, Jingdian Lu, Zhuo Chen +4

Understanding and reasoning over abstract visual content remains a challenge for current multi-modal large language models (MLLMs). In this paper, we explore a novel abstract data…

cs.AI2026

What's Missing in Screen-to-Action? Towards a UI-in-the-Loop Paradigm for Multimodal GUI Reasoning

Songze Li, Xiaoke Guo, Tianqi Liu +5

Existing Graphical User Interface (GUI) reasoning tasks remain challenging, particularly in UI understanding. Current methods typically rely on direct screen-based decision-making,…

cs.CV2026

Structured and Abstractive Reasoning on Multi-modal Relational Knowledge Images

Yichi Zhang, Zhuo Chen, Lingbing Guo +2

Understanding and reasoning with abstractive information from the visual modality presents significant challenges for current multi-modal large language models (MLLMs). Among the v…

cs.CL2026

Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning

Zhaoyan Gong, Zhiqiang Liu, Songze Li +7

Temporal Knowledge Graph Question Answering (TKGQA) is inherently challenging, as it requires sophisticated reasoning over dynamic facts with multi-hop dependencies and complex tem…

cs.CL2026

CoG: Controllable Graph Reasoning via Relational Blueprints and Failure-Aware Refinement over Knowledge Graphs

Yuanxiang Liu, Songze Li, Xiaoke Guo +4

Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities but often grapple with reliability challenges like hallucinations. While Knowledge Graphs (KGs) off…