Papers by Xiangxiang Wang

POSITION BIAS MITIGATES POSITION BIAS: Mitigate Position Bias Through Inter-Position Knowledge Distillation (2025.emnlp-main)

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Yifei Wang, Feng Xiong, Yong Wang, Linjing Li, Xiangxiang Chu, Daniel Dajun Zeng

Responsible & Trustworthy NLP

Challenge:	Positional bias (PB) manifests as non-uniform sensitivity across contextual locations . previous studies have addressed PB by modifying the underlying architectures or employing extensive contextual awareness training.
Approach:	They propose a position-to-position knowledge distillation framework that leverages position-induced disparities to counteract PB.
Outcome:	The proposed framework reduces positional bias and improves performance on retrieval and reasoning tasks.

L2Dir: Integrating L_2-Norm and Directional Alignment for Unsupervised Contrastive Representation Learning in Multimodal Retrieval (2026.acl-long)

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Tianyu Zong, Rui Dai, Hongzhu Yi, Yuanxiang Wang, Zhenghao Zhang, Zhenyu Guan, Yujia Yang, Bingkang Shi, Yueyang Ding, Xiangxiang Chu, Kaikui Liu, Jungang Xu

Semantic Text Processing

Challenge:	Existing approaches to multimodal representation learning focus on directional alignment and embedding magnitudes (L2-norm) however, these methods often fail to account for the intrinsic role of L2-norm in the contrastive process.
Approach:	They propose a plug-and-play framework that optimizes L2-norm alignment and Directional consistency jointly.
Outcome:	The proposed framework achieves consistent and significant performance gains over established baselines across 95 tasks using UniIR and VLM2Vec-V2 frameworks.

M2PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation (2026.acl-long)

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Hao Wang, Linlong Xu, Heng Liu, Yangyang Liu, Xiaohu Zhao, Bo Zeng, Liangying Shao, Yichen Dong, Xinwei Wu, Jiang Zhou, Tianyu Dong, Xiangxiang Zeng, Longyue Wang, Weihua Luo

Machine Translation

Challenge:	prevailing methods for machine translation are often hindered by misleading reward signals.
Approach:	They propose a framework that aligns large language models to human preferences . they propose 'M2PO' to correct the bias towards partial errors .
Outcome:	The proposed framework outperforms open-source models and achieves parity with proprietary models.

Visually-Guided Policy Optimization for Multimodal Reasoning (2026.acl-long)

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Zengbin Wang, Feng Xiong, Liang Lin, Xuecai Hu, Yong Wang, Yanlin Wang, Man Zhang, Xiangxiang Chu

Multimodality

Challenge:	Existing RLVRs lack visual faithfulness due to text-dominated reasoning . a novel framework to reinforce visual focus during policy optimization is proposed .
Approach:	They propose a framework to reinforce visual focus during policy optimization using visual attention compensation mechanism.
Outcome:	The proposed framework exhibits better visual activation and superior performance in multimodal reasoning and visual-dependent tasks.

Thinking with Map: Reinforced Parallel Map-Augmented Agent for Geolocalization (2026.findings-acl)

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Yuxiang Ji, Yong Wang, Ziyu Ma, Yiming Hu, Hailang Huang, Xuecai Hu, Guanhua Chen, Liaoni Wu, Xiangxiang Chu

Visual Data in NLP

Challenge:	Existing large vision-language model (LVLM) approaches overlook a common strategy used by humans — using maps.
Approach:	They propose a method to equip a vision-language model with the ability to think with maps and optimize it using agentic reinforcement learning and parallel test-time scaling.
Outcome:	The proposed method outperforms open- and closed-source models on most metrics.

HS-STaR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget Reallocation (2025.emnlp-main)

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Feng Xiong, Hongling Xu, Yifei Wang, Runxi Cheng, Yong Wang, Xiangxiang Chu

Reasoning

Challenge:	Recent studies have incorporated reward models to guide response selection or decoding, aiming to obtain higher-quality data.
Approach:	They propose a Hierarchical Sampling framework for self-taught reasoners that allocates a fixed sampling budget to problem boundary-level problems and then reallocates the remaining budget toward high-utility problems during a re-sampling phase.
Outcome:	The proposed framework outperforms baseline models without additional sampling budgets across multiple reasoning benchmarks and backbone LLMs.

Large Language and Protein Assistant for Protein-Protein Interactions Prediction (2025.acl-long)

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Peng Zhou, Pengsen Ma, Jianmin Wang, Xibao Cai, Haitao Huang, Wei Liu, Longyue Wang, Lai Hou Tim, Xiangxiang Zeng

Challenge:	Existing methods for predicting protein-protein interactions oversimplify the problem of PPI prediction in a semi-supervised manner.
Approach:	They propose a multimodal large language model that integrates proteins and PPI networks.
Outcome:	Experiments show that LLaPA can predict protein-protein interactions (mPPI) types and affinities based on sequence data.

Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models (2026.findings-acl)

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Hanqi Yan, Xiangxiang Cui, Lu Yin, Jindong Gu, Paul Pu Liang, Yulan He, Yifei Wang

Multimodality

Challenge:	a recent study shows that vision-language models have modality gaps that persist even in well-aligned models.
Approach:	They propose a modality-dominance score to measure and leverage modality gaps . they propose automatic interpretability metrics to evaluate these features in a scalable manner .
Outcome:	The proposed framework allows for training-free probing and editing methods for understanding model perception across genders and generating adversarial examples.

CoEvolve: Training LLM Agents via Agent-Data Mutual Evolution (2026.acl-long)

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Shidong Yang, Ziyu Ma, Tongwen Huang, Yiming Hu, Yong Wang, Xiangxiang Chu

Challenge:	Extensive experiments on AppWorld and BFCL demonstrate consistent and significant improvements over strong base models, yielding absolute gains of 19.43%, 15.58%, and 18.14%, respectively.
Approach:	They propose a framework that extracts feedback signals such as forgetting and uncertainty from rollout trajectories and utilizes them to guide LLM-based task synthesis.
Outcome:	Extensive experiments on AppWorld and BFCL show that the proposed framework improves over strong base models.

TLUE: A Tibetan Language Understanding Evaluation Benchmark (2025.emnlp-main)

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Fan Gao, Cheng Huang, Yutong Liu, Nyima Tashi, Xiangxiang Wang, Thupten Tsering, Ban Ma-bao, Renzeng Duojie, Gadeng Luosang, Rinchen Dongrub, Dorje Tashi, Xiao Feng Cd, Yongbin Yu, Hao Wang

Language Models

Challenge:	Low-resource languages, like Tibetan, remain underrepresented in large language models' evaluations.
Approach:	They propose a Tibetan Language Understanding Evaluation Benchmark to assess LLMs' proficiency in Tibetan . they use a multi-task understanding benchmark and a safety benchmark to evaluate models .
Outcome:	The proposed benchmark shows that most large language models perform below the random baseline, especially in Tibetan language processing.

Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models (2026.acl-long)

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Shun Zou, Yong Wang, Zehui Chen, Lin Chen, Chongyang Tao, Feng Zhao, Xiangxiang Chu

Language Models

Challenge:	Semi-autoregressive (Semi-AR) decoding suffers from inherent block constraints . naive lookahead decoding is unreliable, token stability closely correlates with convergence trend, and historical information is isolated.
Approach:	They propose a training-free, plug-and-play dynamic decoding strategy that monitors the stability of tokens in real time through dynamic anchors.
Outcome:	The proposed approach reduces decoding steps by 80% while improving performance by 3.67% on the BBH benchmark.

CAML: A Conflict-Aware Molecular Language Model Merging Framework for Multi-Constraint Molecular Generation (2026.acl-long)

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Xuanbai Ren, Luoda Tan, Pei Liu, Tengfei Ma, Xiangzheng Fu, Longyue Wang, Yiping Liu, Xiangxiang Zeng

Language Models

Challenge:	Existing paradigms struggle with this challenge due to catastrophic forgetting or gradient conflicts.
Approach:	They propose a conflict-aware molecular language model merging framework that generates multiple constraints moleculaire as a cooperative game among property-specific fine-tune models.
Outcome:	The proposed framework generates multiple constraints molecular as a cooperative game among property-specific fine-tune models (expert models) it minimizes conflicts among properties by exploring the optimal combination of the importance of the task parameter and relative fusion weights of each expert (fusion coefficient).

LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics (2026.findings-acl)

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Yueyang Ding, HaoPeng Zhang, Rui Dai, Yi Wang, Tianyu Zong, Kaikui Liu, Xiangxiang Chu

Multimodality

Challenge:	Current research hinders the development of unified Time Series Reasoning Models (TSRMs) time series data are a fundamental modality for capturing the temporal dynamics of complex systems.
Approach:	They propose a time series reasoning model that integrates visualized patterns with precision-calibrated numerical tables to enhance the temporal perception of Vision-Language Models.
Outcome:	The proposed model outperforms existing models and exhibits robust out-of-distribution generalization across diverse tasks and real-world scenarios.