Papers by Jiayuan Huang
MT3: A Synergistic Multi-Task RL Framework for Specializing MLLMs in Text Image Machine Translation (2026.acl-long)
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Zhaopeng Feng, Yupu Liang, Shaosheng Cao, Jiayuan Su, Jiahan Ren, Zhijie Zhou, Wenxuan Huang, Jian Wu, Zuozhu Liu
| Challenge: | Text Image Machine Translation (TIMT) is a critical subfield of machine translation . it requires accurate optical character recognition, robust visual-text reasoning, and high-quality translation a challenge . |
| Approach: | They propose a multi-task optimization framework to specialize MLLMs into expert TIMT models. |
| Outcome: | The proposed model outperforms baselines on the latest in-domain MIT-10M benchmark. |
M-TRACE: Detecting and Mitigating Time-Anchor Drift via Step-wise Conflict Checking in Temporal Reasoning (2026.findings-acl)
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| Challenge: | Experimental results show that M-TRACE effectively reduces time-anchor drift . external knowledge may be inaccurate while internal knowledge can become outdated . |
| Approach: | They propose a multi-agent reasoning framework for temporal knowledge conflicts . they propose 'TimeConfQA' which guides conflict-aware final reasoning . |
| Outcome: | Experimental results show that M-TRACE reduces time-anchor drift and improves performance on complex temporal question answering tasks. |
Foundation Models Meet Embodied Agents (2025.naacl-tutorial)
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| Challenge: | This tutorial will present a systematic overview of recent advances in foundation models for embodied agents . |
| Approach: | This tutorial will present a systematic overview of recent advances in foundation models for embodied agents. |
| Outcome: | This tutorial covers three types of foundation models for embodied agents . |
Structured Self-Supervised Pretraining for Commonsense Knowledge Graph Completion (2021.tacl-1)
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| Challenge: | Existing approaches focus on generating concepts that have direct and obvious relationships with existing concepts and lack an ability to generate unobvious concepts. |
| Approach: | They propose a general graph-to-paths pretraining framework that leverages high-order structures in CKGs to capture high-level relationships between concepts. |
| Outcome: | The proposed framework can capture high-order relationships between concepts in four special cases: long path, path-to-path, router, and graph-node-path. |