Papers by Xiao Xia
PivotAttack: Rethinking the Search Trajectory in Hard-Label Text Attacks via Pivot Words (2026.findings-acl)
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| Challenge: | Existing hard-label text attacks rely on inefficient "outside-in" strategies that traverse vast search spaces. |
| Approach: | They propose a query-efficient "inside-out" framework that perturbs Pivot Sets to induce label flips. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in both Attack Success Rate and query efficiency. |
Lunar Twins: We Choose to Go to the Moon with Large Language Models (2025.findings-acl)
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| Challenge: | Lunar Twins is the first LLM designed specifically for lunar exploration . Lunar GenData is a multi-agent collaborative workflow for generating lunar instructions . |
| Approach: | They propose a framework that combines both large and small LLMs and Lunar GenData, which integrates real data from Chang'e lunar missions. |
| Outcome: | Experimental validation shows that the proposed framework enhances domain expertise and reveals indications of embodied intelligence potential. |
\mathtt{GeLLM^3O}: Generalizing Large Language Models for Multi-property Molecule Optimization (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) have remarkable out-of-domain generalizability to novel optimization tasks. |
| Approach: | They propose a series of instruction-tuned LLMs for molecule optimization that outperform state-of-the-art instruction-based LLM models. |
| Outcome: | mathttMuMOInstruct outperforms state-of-the-art LLMs on 5 in-domain and 5 out-of domain tasks. |
Can LLMs Learn to Map the World from Local Descriptions? (2026.acl-long)
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| Challenge: | Recent advances in large language models have demonstrated strong capabilities in tasks such as code generation and mathematical reasoning. |
| Approach: | They investigate whether large language models can construct coherent global spatial cognition by integrating fragmented relational descriptions. |
| Outcome: | The proposed models can generalize to unseen spatial relationships and exhibit latent representations aligned with real-world spatial distributions. |
Orthogonal Subspace Learning for Language Model Continual Learning (2023.findings-emnlp)
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| Challenge: | Existing methods for continual learning in language models suffer catastrophic forgetting when learning sequential tasks. |
| Approach: | They propose an orthogonal low-rank adaptation approach for continual learning in language models that uses orthogons to learn sequentially. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on continual learning benchmarks and preserves generalization ability of LLMs on unseen tasks. |
Med-SRAF: A Multi-Agent Framework for Medical Reasoning via Semantic Routing and Agentic Fusion (2026.findings-acl)
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| Challenge: | Existing RAG methods suffer from a two-part problem: semantic drift and concatenation fallacy . et al.: rapid development of Large Language Models has led to a paradigm shift in artificial intelligence . |
| Approach: | They propose a multi-agent retrieval augmentation framework guided by medical domain knowledge to address these challenges. |
| Outcome: | The proposed framework outperforms existing general RAG baselines on five widely used medical benchmarks. |
SceneGenAgent: Precise Industrial Scene Generation with Coding Agent (2025.acl-long)
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| Challenge: | Recent work on scene generation focuses on generating 3D scenes from textual descriptions . however, the task of generating industrial scenes with LLMs is complex and requires precise measurements and positioning . |
| Approach: | They propose an LLM-based agent for generating industrial scenes through C# code. |
| Outcome: | Experiments show that LLMs powered by SceneGenAgent exceed their original performance . the agent achieves 81.0% success rate in real-world industrial scene generation tasks . |
CodeRM-NT: Reward Model for Code RL without Unit Tests (2026.findings-acl)
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| Challenge: | Existing methods rely on unit tests to evaluate code correctness and provide rewards, but these methods are difficult to verify at scale. |
| Approach: | They propose a code reward model that leverages Monte Carlo Tree Search guided by LLMs to generate code snippets and judges execution traces to annotate code with reward signals. |
| Outcome: | The proposed model outperforms synthetic unit tests on multiple code generation benchmarks and improves curriculum learning. |
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation (2024.emnlp-main)
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Yuan Ge, Yilun Liu, Chi Hu, Weibin Meng, Shimin Tao, Xiaofeng Zhao, Mahong Xia, Zhang Li, Boxing Chen, Hao Yang, Bei Li, Tong Xiao, JingBo Zhu
| Challenge: | Existing methods for instruction data selection have limitations such as relying on fragile external APIs, being affected by biases in GPT models, or reducing the diversity of the selected instruction dataset. |
| Approach: | They propose an industrial-friendly, expert-aligned and diversity-preserved instruction data selection method: Clustering and Ranking (CaR). |
| Outcome: | The proposed method outperforms Alpaca's existing methods by 32.1% in GPT-4 evaluations. |
RankNAS: Efficient Neural Architecture Search by Pairwise Ranking (2021.emnlp-main)
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| Challenge: | Existing methods require training millions of architectures to estimate the accuracy of the search results. |
| Approach: | They propose a performance ranking method (RankNAS) that uses pairwise ranking and search space pruning to enlarge the search space. |
| Outcome: | The proposed method significantly accelerates NAS through pairwise ranking and search space pruning. |
Lunar-Bench: Towards Evaluating Task-Oriented Reasoning of LLMs in Lunar Exploration Scenarios (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on static, context-independent reasoning tasks and fail to capture constraints and dependencies of lunar missions. |
| Approach: | They propose a benchmark to assess the task-oriented reasoning and decision-making performance of large language models through 3,000 tasks derived from mission procedures and documentation. |
| Outcome: | The proposed model achieves 47.8% accuracy compared with 65.1% for human experts on 36 representative missions. |
Emotion Cause Extraction on Social Media without Human Annotation (2023.findings-acl)
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| Challenge: | Existing studies have focused on extracting emotion causes from news articles, but lack of fine-grained annotations has limited the ECE task. |
| Approach: | They propose a new ECE framework that extracts emotion causes from social media data without relying on human annotations. |
| Outcome: | The proposed framework achieves high extraction performance and generalizability without relying on human annotations. |
AD-LLM: Benchmarking Large Language Models for Anomaly Detection (2025.findings-acl)
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Tiankai Yang, Yi Nian, Li Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. Rossi, Kaize Ding, Xia Hu, Yue Zhao
| Challenge: | Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. |
| Approach: | They propose a benchmark that evaluates how large language models (LLMs) can help with NLP anomaly detection. |
| Outcome: | The proposed model can perform zero-shot detection without tasks-specific training, data augmentation and model selection, and it can suggest unsupervised AD models. |
ADaPT: Token-Level Decoupling for Efficient Large Reasoning Models (2026.findings-acl)
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Tingyun li, Zishang Jiang, Jinyi Han, Xinyi Wang, Sihang Jiang, Han Xia, Zhaoqian Dai, Ma Shuguang, Fei Yu, Jiaqing Liang, Yanghua Xiao
| Challenge: | Existing efficiency-oriented methods attempt to shorten or mix reasoning strategies, yet often degrade reasoning capability. |
| Approach: | They propose a token-level dual-process framework that explicitly decouples efficiency and correctness signals during training. |
| Outcome: | The proposed framework reduces inference cost while maintaining strong reasoning ability across multiple benchmarks. |
SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models (2026.acl-long)
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Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng, Yingce Xia, Shufang Xie, Kaili Liu, Bohan Wu, Qi Shi, Haoran Li, Beier Xiao, Zhiping Xiao, Xiao Luo, Weizhi Zhang, Philip S. Yu, Zequn Liu, Ming Zhang
| Challenge: | Existing evaluations of large language models fail to reflect fine-grained capabilities . existing benchmarks are manually curated or domain-generic, limiting scalability and alignment with real use cases. |
| Approach: | They propose a framework that allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs. |
| Outcome: | The proposed framework reveals fine-grained differences in scientific capabilities that standard benchmarks overlook . it allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific capabilities in LLMs. |
Identifying Tension in Holocaust Survivors’ Interview: Code-switching/Code-mixing as Cues (2022.lrec-1)
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| Challenge: | Using CS/CM as a linguistic phenomenon could be a sign of tension in Holocaust survivors’ interviews. |
| Approach: | They annotated CS/CM codes and annotate silence situations in an open corpus . they found that most annotations were captured in the tension places . |
| Outcome: | The proposed method shows that annotations are captured in the tension places . the study calls for more research endeavors on tension detection . |
BFS-Prover: Scalable Best-First Tree Search for LLM-based Automatic Theorem Proving (2025.acl-long)
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| Challenge: | Existing approaches to theorem proving in large language models rely on value functions and/or Monte Carlo Tree Search (MCTS), but the potential of simpler methods like Best-First Tree Search remains underexplored. |
| Approach: | They propose a scalable expert iteration framework that implements strategic data filtering at each expert iteration round, excluding problems solvable via beam search node expansion to focus on harder cases. |
| Outcome: | The proposed framework achieves a state-of-the-art score of 72.95 on the MiniF2F test set and challenges the perceived necessity of complex tree search methods. |
Reinforcing Agentic Search Via Reward Density Optimization (2026.acl-long)
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| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic search, but its performance is often hindered by reward sparsity . |
| Approach: | They propose a new research problem to improve the reward obtained per unit of exploration cost by using a system that decomposes long-horizon tasks into intermediate objectives and assigns process-level rewards to provide denser learning signals. |
| Outcome: | The proposed framework outperforms strong baselines on several agentic search benchmarks and achieves comparable performance to that of advanced proprietary models. |
Ground Every Sentence: Improving Retrieval-Augmented LLMs with Interleaved Reference-Claim Generation (2025.findings-naacl)
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| Challenge: | Existing methods to enhance credibility and verifiability of large language models (LLMs) mainly focus on passage-level or paragraph-level references or citations, which fall short in verifikatability. |
| Approach: | They propose a method that provides sentence-level citations in LLM-generated responses. |
| Outcome: | The proposed method achieves 90% accuracy in long-form question-answering tasks. |
RoCoIns: Enhancing Robustness of Large Language Models through Code-Style Instructions (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have shown remarkable capabilities in following human instructions and solving NLU tasks. |
| Approach: | They propose to use code style instructions to replace typically natural language instructions to provide more precise instructions and strengthen the robustness of LLMs. |
| Outcome: | The proposed method outperforms natural language models on eight robustness datasets and achieves an improvement of 5.68% in test set accuracy and a reduction of 5.66 points in Attack Success Rate (ASR). |
A Neural Transition-based Joint Model for Disease Named Entity Recognition and Normalization (2021.acl-long)
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| Challenge: | Existing models for disease recognition and normalization ignore text surface form of each candidate concept, causing boundary inconsistency. |
| Approach: | They propose a neural transition-based joint model to normalize disease entities from biomedical text. |
| Outcome: | The proposed model improves on two publicly available datasets. |
AgencyBench: Benchmarking the Frontiers of Autonomous Agents in 1M-Token Real-World Contexts (2026.acl-long)
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Keyu Li, Junhao Shi, Yang Xiao, Mohan Jiang, Jie Sun, Yunze Wu, Dayuan Fu, Shijie Xia, Xiaojie Cai, Tianze Xu, Weiye Si, Wenjie Li, Dequan Wang, Pengfei Liu
| Challenge: | Existing benchmarks focus on single agentic capability, failing to capture long-horizon real-world scenarios. |
| Approach: | They propose a benchmark that evaluates 6 agentic capabilities across 32 real-world scenarios. |
| Outcome: | Experiments show that closed-source models outperform open-source model (48.4% vs 32.1%) integrating models with advanced scaffolds to form autonomous agents is a paradigm shift. |
UniCreative: Unifying Long-form Logic and Short-form Sparkle via Reference-Free Reinforcement Learning (2026.findings-acl)
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Xiaolong Wei, Zerun Zhu, Simin Niu, Xingyu Zhang, Peiying Yu, Changxuan Xiao, Yuchen Li, Jicheng Yang, Zhejun Zhao, Chong Meng, Long Xia, Daiting Shi
| Challenge: | Existing alignment paradigms for creative writing use static reward signals and supervised data. |
| Approach: | They propose a constraint-aware reward model that synthesizes query-specific criteria to provide fine-grained preference judgments. |
| Outcome: | The proposed framework aligns models with human preferences across content quality and structural paradigms without supervised fine-tuning and ground-truth references. |
Large Language Models for Controllable Multi-property Multi-objective Molecule Optimization (2025.findings-emnlp)
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| Challenge: | Existing methods for molecule optimization fail to capture property-specific objectives . a series of instruction-tuned LLMs can perform targeted property-specific optimization . |
| Approach: | They propose a set of instruction-tuned LLMs that can perform targeted property-specific optimization. |
| Outcome: | a new instruction-tuned LLM can perform targeted property-specific optimization. |