Papers by Zichuan Xu

4 papers
AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models (2026.findings-acl)

Copied to clipboard

Challenge: Existing methods for temporal reasoning are limited and apply a fixed pipeline to all questions.
Approach: They propose an adaptive temporal reasoning method that dynamically executes reasoning steps based on context and task requirements.
Outcome: Experiments on two temporal QA benchmarks show the proposed method works.
A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs (2025.findings-acl)

Copied to clipboard

Challenge: Existing methods focus on graph structure learning or semantic reasoning, lacking the capability to capture the inherent differences between historical and non-historical events.
Approach: They propose a temporal knowledge graph reasoning framework that integrates both structural and semantic information to guide the reasoning process for different events.
Outcome: The proposed framework integrates structural and semantic information to predict future events . it can provide evidence for many downstream tasks, including situation analysis and political decision making .
Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding (2022.findings-emnlp)

Copied to clipboard

Challenge: Existing methods for temporal sentence grounding ignore two crucial issues . 1) Boundary-bias: the video downsampling process may lose these two frames . 2) Reasoning-biases: such incorrect new boundary frames lead to the reasoning bias .
Approach: They propose a siamese sampling mechanism to generate additional contextual frames . they use a reasoning strategy to learn the inter-relationship among these frames a .
Outcome: Extensive experiments demonstrate the effectiveness of a new siamese sampling network on three challenging datasets.
MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning (2026.findings-acl)

Copied to clipboard

Challenge: Existing approaches focus on diagnostic reasoning based on internal model knowledge or static knowledge bases.
Approach: They propose a two-stage diagnostic reasoning framework that integrates multi-perspective evidence to generate a diagnostic prediction.
Outcome: The proposed method generates suspected diagnoses and reasoning traces from web search, SOAP-formatted case, and clinical case database.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations