Papers by Xiaoke Guo
Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning (2026.acl-long)
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Zhaoyan Gong, Zhiqiang Liu, Songze Li, Xiaoke Guo, Yuanxiang Liu, Xinle Deng, Zhizhen Liu, Lei Liang, Huajun Chen, Wen Zhang
| Challenge: | Existing methods rely on fixed workflows and expensive closed-source APIs, limiting flexibility and scalability. |
| Approach: | They propose a temporal reasoning agent that trains on difficult questions first . they expand the action space with specialized internal actions alongside external action . |
| Outcome: | The proposed agent improves 19.8% over baselines on complex questions and multi-tasks. |
ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering (2026.acl-long)
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| Challenge: | Existing serialization methods fail to capture explicit hierarchies and lack schema flexibility . Existing tree-based approaches suffer from limited semantic adaptability . |
| Approach: | They propose a method that leverages the global semantic awareness of LLMs to reconstruct tables into Logical Semantic Trees. |
| Outcome: | The proposed method achieves state-of-the-art (SOTA) performance on complex table benchmarks. |
What’s Missing in Screen-to-Action? Towards a UI-in-the-Loop Paradigm for Multimodal GUI Reasoning (2026.findings-acl)
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| Challenge: | Existing GUI reasoning methods rely on direct screen-based decision-making, which lacks interpretability and overlooks a comprehensive understanding of UI elements, ultimately leading to task failure. |
| Approach: | They propose a GUI reasoning paradigm that treats the GUI reasoning task as a cyclic ***Screen-UI elements-Action** process. |
| Outcome: | The proposed paradigm achieves state-of-the-art UI understanding performance while yielding superior results in GUI reasoning tasks. |
CoG: Controllable Graph Reasoning via Relational Blueprints and Failure-Aware Refinement over Knowledge Graphs (2026.acl-long)
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| Challenge: | Existing approaches to large language models often exhibit cognitive rigidity, causing reasoning stagnation. |
| Approach: | They propose a training-free framework that mimics the interplay between intuition and deliberation. |
| Outcome: | The proposed framework outperforms state-of-the-art approaches on three benchmarks. |