GenAI4ELab/papercli-papers huggingface dataset:
Large Language Model Unlearning for Source Code
Authors: Xue Jiang; Yihong Dong; Huangzhao Zhang; Tangxinyu Wang; Zheng Fang; Yingwei Ma; Rongyu Cao; Binhua Li; Zhi Jin; Wenpin Jiao; Yongbin Li; Ge Li
Venue: AAAI 2026 (main)

Abstract: While Large Language Models (LLMs) excel at code generation, their inherent tendency toward verbatim memorization of training data introduces critical risks like copyright infringement, insecurity emission, and deprecated API utilization, etc. A straightforward yet promising defense is unlearning, i.e., erasing or down-weighting the offending snippets through post-training. However, we find its application to source code often tends to spill over, damaging the basic knowledge of programming languages learned by the LLM and degrading the overall capability. To ease this challenge, we propose PROD for precise source code unlearning. PROD surgically zeroes out the prediction probability of the prohibited tokens, and renormalizes the remaining distribution so that the generated code stays correct. By excising only the targeted snippets, PROD achieves precise forgetting without much degradation of the LLM's overall capability. To facilitate in-depth evaluation against PROD, we establish an unlearning benchmark consisting of three downstream tasks (i.e., unlearning of copyrighted code, insecure code, and deprecated APIs), and introduce Pareto Dominance Ratio (PDR) metric, which indicates both the forget quality and the LLM utility. Our comprehensive evaluation demonstrates that PROD achieves superior overall performance between forget quality and model utility compared to existing unlearning approaches across three downstream tasks, while consistently exhibiting improvements when applied to LLMs of varying series. PROD also exhibits superior robustness against adversarial attacks without generating or exposing the data to be forgotten. These results underscore that our approach not only successfully extends the application boundary of unlearning techniques to source code, but also holds significant implications for advancing reliable code generation.
EduGuardBench: A Holistic Benchmark for Evaluating the Pedagogical Fidelity and Adversarial Safety of LLMs as Simulated Teachers
Authors: Yilin Jiang; Mingzi Zhang; Xuanyu Yin; Sheng Jin; Suyu Lu; Zuocan Ying; Zengyi Yu; Xiangjie Kong
Venue: AAAI 2026 (main)

Abstract: Large Language Models for Simulating Professions (SP-LLMs), particularly as teachers, are pivotal for personalized education. However, ensuring their professional competence and ethical safety remains a major challenge, as existing benchmarks fail to measure role-playing fidelity or address the unique teaching harms inherent in educational scenarios. To address this gap, we propose EduGuardBench, a dual-component benchmark that evaluates professional fidelity through the Role-playing Fidelity Score (RFS) and diagnoses harms specific to the teaching profession. It also probes safety vulnerabilities using persona-based adversarial prompts targeting both general harms and academic misconduct, with metrics such as Attack Success Rate (ASR) and a three-tier Refusal Quality assessment. Extensive experiments on 14 leading models reveal a stark polarization in performance. While reasoning-oriented models generally demonstrate higher fidelity, incompetence remains the dominant failure mode across most models. Adversarial testing uncovered a counterintuitive scaling paradox, where mid-sized models appear more vulnerable, challenging monotonic safety assumptions. Notably, we identify an Educational Transformation Effect, where the safest models convert harmful requests into teachable moments through ideal educational refusals. This ability is strongly negatively correlated with ASR, revealing a new dimension of advanced AI safety. EduGuardBench thus provides a reproducible framework for holistic assessment of professional, ethical, and pedagogical alignment, uncovering dynamics critical to deploying trustworthy AI in education.
Difficulty Is Not Enough: Curriculum Learning for LLMs Fine-tuning Must Consider Utility
Authors: Zishang Jiang; Jinyi Han; Tingyun Li; Xinyi Wang; Sihang Jiang; Xiaojun Meng; Jiansheng Wei; Jiaqing Liang; Yanghua Xiao
Venue: AAAI 2026 (main)

Abstract: Fine-tuning plays an essential role in improving the performance of large language models (LLMs) on specific tasks. A central challenge lies in designing data-efficient strategy to achieve better fine-tuning performance. Curriculum learning, which organizes data from easy to hard, has become a widely adopted technique in LLMs training. However, existing methods for curriculum learning focus only on the difficulty of samples, while neglecting their contribution to improving model performance, making them vulnerable when applied to fine-tuning LLMs. To address this, we propose Difficulty-Utility Curriculum Learning (DUCL), a curriculum learning framework that jointly considers difficulty and utility. DUCL introduces a novel scoring method, Difficulty-Utility Evaluation (DUE), and a soft scheduling strategy called Window Ordering, which together promote efficient and effective fine-tuning. Our method not only improves convergence and final performance with negligible computational overhead, but is also broadly applicable across a wide range of tasks, making it a practical and scalable solution for LLMs fine-tuning.
Embracing Positional Bias in Multiple-Choice Question Answering via Permutation Equivariant Neural Networks
Authors: Chengyu Jiao; Siyin Huang; Yu Zhang
Venue: AAAI 2026 (main)

Abstract: Several studies have demonstrated that large language models (LLMs) exhibit positional bias when answering multiple-choice questions (MCQs). Previous methods have identified such bias to be detrimental, leading to the development of techniques to mitigate it. However, we observe that certain permutations of options can actually improve the performance. Therefore, instead of eliminating such bias, we propose an EMbracing the Bias EquivaRiantly (EMBER) network. Specifically, the EMBER network, which outputs a permutation of options in MCQs, is optimized towards the beneficial permutations to which the LLM is biased. Additionally, to solve the positional bias among different permutations of options, the EMBER network is designed to grant the equivariance to the permutation to the LLMs. Theoretically and empirically, we show that the proposed EMBER network can effectively utilize the positional bias and demonstrate state-of-the-art performance over various baselines.
Causal-ERC: A Multimodal Framework with Causal Prompting for Emotion Recognition in Conversations with Large Language Models
Authors: Ran Jing; Geng Tu; Yice Zhang; Ruifeng Xu
Venue: AAAI 2026 (main)

Abstract: The rapid advancement of large language models (LLMs) has revitalised research in Emotion Recognition in Conversation (ERC). However, existing LLM-based ERC approaches operate solely on textual input, whereas MLLM-based emotion recognition methods in non-conversational scenarios typically perform only basic multimodal fusion and fail to consider speaker-sensitive contextual dependencies, which limits their performance on ERC tasks. To integrate multimodal cues effectively and address their limitations in handling contextual dependencies, we propose a novel LLM-based framework, Causal-ERC, which captures context representations within each modality and incorporates them into the LLM. Moreover, experimental results show that LLMs perform poorly on long conversations. To improve LLMs' ability to model long conversations, we adjust corresponding causal prompts according to the causal type of each utterance. Experiments on two benchmark MERC datasets demonstrate that our Causal-ERC framework consistently outperforms existing state-of-the-art approaches and improves LLM's performance in long-context scenarios.
Model Whisper: Steering Vectors Unlock Large Language Models’ Potential in Test-Time
Authors: Xinyue Kang; Diwei Shi; Li Chen
Venue: AAAI 2026 (main)

Abstract: It is a critical challenge to efficiently unlock the powerful reasoning potential of Large Language Models (LLMs) for specific tasks or new distributions. Existing test-time adaptation methods often require tuning model parameters, which is not only computationally expensive but also risks degrading the model's pre-existing abilities.To address this, we introduce a lightweight component, Test-Time Steering Vectors (TTSV), which is prepended to the input while keeping the LLM's parameters entirely frozen. By optimizing the TTSV on test data to minimize the model's output entropy, we steer the model towards an internal state of higher confidence, activating its inherent abilities most relevant to the current task. TTSV is both lightweight and highly efficient to optimize, making it a true plug-and-play enhancement. Extensive experiments validate our approach's effectiveness on both base models and reasoning-enhanced models. For instance, on the MATH500 task, TTSV achieves a 45.88% relative performance gain on the Qwen2.5-Math-7B model and a 16.22% relative gain on the Qwen3-4B model. Furthermore, our approach exhibits robust generalization, with its steering vectors proving highly transferable across diverse tasks.
WikiMAG: A Multi-Agent Guided Framework for Generating Structured Wikipedia-like Articles
Authors: Xiuli Kang; Yinlong Xiao; Minghao Hu; Yuan Huang; Bin Mao; Ming Wang; Fang Wang; Zhunchen Luo; Wei Luo; Guotong Geng
Venue: AAAI 2026 (main)

Abstract: Wikipedia serves as the world's largest and most popular online reference encyclopedia, rich in structured knowledge and authoritative citations. Recently, numerous works have leveraged large language models to automatically generate Wikipedia-like articles. However, existing approaches primarily focus on producing singular narrative-type content, overlooking higher information-density structured elements such as timeline and table.
To address these limitations, we propose WikiMAG, a multi-agent guided framework for generating structured Wikipedia-like articles. This framework employs a collaborative multi-agent mechanism to orchestrate the creation process, featuring three synergistic core components: Progressive planner first constructs the coarse-grained outline framework and then annotate fine-grained types for outline units, encompassing narrative, timeline, and table formats; Reflective inspector dynamically curates high-quality references via multi-round interactive feedback, thereby enhancing the authority and relevance of citations; Versatile writer integrates fine-grained outline details and high-quality reference information to generate information-rich articles, incorporating the three annotated formats. We evaluate WikiMAG on two public datasets, FreshWiki and WikiGenBen, across outline, writing, and verifiability dimensions.
Compared with the best baseline method, our method achieves an average improvement of 6.73 points and 4.39 points in Heading Soft Recall and the METEOR metric (a machine translation and text generation evaluation metric) respectively, and an average increase of 16.84 percentage points in Citation Rate.
SampurNER: Fine-Grained Named Entity Recognition Dataset for 22 Indian Languages
Authors: Prachuryya Kaushik; Ashish Anand
Venue: AAAI 2026 (main)

Abstract: We introduce SampurNER, a fine-grained named entity recognition (FgNER) dataset encompassing all 22 scheduled Indian languages spoken by more than two billion people across various countries. While manual annotation for FgNER resources is often labor-intensive and expensive, distant supervision methods have been employed as a viable solution. However, such datasets are often noisy, with entity mentions tagged with multiple types, requiring computationally intensive noise-aware models for effective FgNER. Moreover, resources for both coarse-grained and fine-grained named entity recognition tasks in Indian languages remain scarce. To address this, we propose an entity-anchored machine translation (EaMaTa) framework that leverages the largest manually annotated English FgNER dataset, FewNERD, to create a large-scale FgNER dataset in 22 languages. On average, the dataset comprises over 153k sentences, 354k entities, and 3.3M tokens in each language. The languages covered are: Assamese (as), Bengali (bn), Bodo (brx), Dogri (doi), Gujarati (gu), Hindi (hi), Kannada (kn), Kashmiri (ks), Konkani (gom), Maithili (mai), Malayalam (ml), Manipuri (mni), Marathi (mr), Nepali (ne), Odia (or), Punjabi (pa), Sanskrit (sa), Santali (sat),  Sindhi (sd), Tamil (ta), Telugu (te), and Urdu (ur). Various rigorous analyses and human evaluations confirm the high quality of the dataset and demonstrate the effectiveness of the entity-anchored machine translation framework with up to 9% increase in F1-score against the current state-of-the-art. Additionally, we extend our analysis to zero-shot, multilingual, and cross-lingual settings, investigating the influence of language family and script similarity on cross-lingual FgNER performance.
CATCH: A Controllable Theme Detection Framework with Contextualized Clustering and Hierarchical Generation
Authors: Rui Ke; Jiahui Xu; Shenghao Yang; Kuang Wang; Feng Jiang; Haizhou Li
Venue: AAAI 2026 (main)

Abstract: Theme detection is a fundamental task in user-centric dialogue systems, aiming to identify the latent topic of each utterance without relying on predefined schemas. Unlike intent induction, which operates within fixed label spaces, theme detection requires cross-dialogue consistency and alignment with personalized user preferences, posing significant challenges. Existing methods often struggle with sparse, short utterances for accurate topic representation and fail to capture user-level thematic preferences across dialogues. To address these challenges, we propose CATCH (Controllable Theme Detection with Contextualized Clustering and Hierarchical Generation), a unified framework that integrates three core components: (1) context-aware topic representation, which enriches utterance-level semantics using surrounding topic segments; (2) preference-guided topic clustering, which jointly models semantic proximity and personalized feedback to align themes across dialogue; and (3) a hierarchical theme generation mechanism designed to suppress noise and produce robust, coherent topic labels. Experiments on a multi-domain customer dialogue benchmark (DSTC-12) demonstrate the effectiveness of CATCH with 8B LLM in both theme clustering and topic generation quality.
Test-time Diverse Reasoning by Riemannian Activation Steering
Authors: Ly Tran Ho Khanh; Dongxuan Zhu; Man-Chung Yue; Viet Anh Nguyen
Venue: AAAI 2026 (main)

Abstract: Best-of-N reasoning improves the accuracy of language models in solving mathematical tasks by sampling multiple candidate solutions and then selecting the best one based on some criteria. A critical bottleneck for this strategy is the output diversity limit, which occurs when the model generates similar outputs despite stochastic sampling, and hence recites the same error. To address this lack of variance in reasoning paths, we propose a novel unsupervised activation steering strategy that simultaneously optimizes the steering vectors for multiple reasoning trajectories at test time. At any synchronization anchor along the batch generation process, we find the steering vectors that maximize the total volume spanned by all possible intervened activation subsets. We demonstrate that these steering vectors can be determined by solving a Riemannian optimization problem over the product of spherical manifolds with a log-determinant objective function. We then use a Riemannian block-coordinate descent algorithm with a well-tuned learning rate to obtain a stationary point of the problem, and we apply these steering vectors until the generation process reaches the subsequent synchronization anchor. Empirical evaluations on popular mathematical benchmarks demonstrate that our test-time Riemannian activation steering strategy outperforms vanilla sampling techniques in terms of generative diversity and solution accuracy.
Rethinking the Sampling Criteria in Reinforcement Learning for LLM Reasoning: A Competence-Difficulty Alignment Perspective
Authors: Deyang Kong; Qi Guo; Xiangyu Xi; Wei Wang; Jingang Wang; Xunliang Cai; Shikun Zhang; Wei Ye
Venue: AAAI 2026 (main)

Abstract: The low sampling efficiency during the rollout phase poses a significant challenge to scaling reinforcement learning for large language model  reasoning. Existing methods attempt to improve efficiency by scheduling problems based on problem difficulties. However, these approaches suffer from unstable and biased estimations of problem difficulty and fail to capture the alignment between model competence and problem difficulty in RL training, leading to suboptimal results. To address these challenges, we introduce Competence-Difficulty Alignment Sampling (CDAS). This approach allows for accurate and stable estimation of problem difficulties by aggregating historical performance discrepancies across problems. Subsequently, model competence is quantified to adaptively select problems whose difficulties align with the model's current competence using a fixed-point system. Extensive experiments in mathematical RL training show that CDAS consistently outperforms strong baselines, achieving the highest average accuracy  of 45.89%. Furthermore, CDAS reduces the training step time overhead by 57.06% compared to the widely-used Dynamic Sampling strategy, verifying the efficiency of CDAS. Additional experiments on different tasks, model architectures, and model sizes demonstrate the generalization capability of CDAS.
Backdooring Rationalization
Authors: Lingxiao Kong; Jiahui Jiang; Wenchao Xu; Lei Wu
Venue: AAAI 2026 (main)

Abstract: Rationalization model has recently garnered significant attention for enhancing the interpretability of natural language processing by first using a generator to select the most relevant pieces from the text with respect to the label, before passing the text input to the predictor.
However, the robustness of the rationalization models is not sufficiently investigated. Specifically, this paper explores the robustness of rationalization models against backdoor attacks, which has been ignored by previous studies. Surprisingly, we find that conventional backdoor attack techniques fail to inject triggers into the rationalization model because its generator can filter out bad triggers.  Considering this, we further propose a novel backdoor attack method named as BadRNL designed specially for the rationalization models. The core idea of BadRNL is first to search for the personalized trigger for each specific dataset and then manipulate the rationales and labels to conduct attacks. Besides, BadRNL controls the order of sample learning through poison-priority sampling strategies. Experimental results show that our method can successfully craft the predictions of samples containing triggers while maintaining the performance of the model on clean data.
LC3: Long Cross-Language Code Clone Detection Enhanced by Opcode Sequences and Affinity Aggregation
Authors: Xilin Lan; Huan Zhang; Yang Yang; Chengwu Xue; Li Kuang
Venue: AAAI 2026 (main)

Abstract: Cross-language code clone detection, which identifies functionally similar code across programming languages, is critical for ensuring synchronized evolution and reducing maintenance costs in multi-platform software development. While zero-shot approaches have emerged as a practical solution to data scarcity, state-of-the-art methods still face two major limitations: an insufficiency in learning language-agnostic representations and information loss during the processing of long code. To address these challenges, we propose LC3, a novel framework for robust zero-shot cross-language code clone detection. To overcome the language-agnostic representation insufficiency, LC3 fuses source code with its underlying opcode sequences, leveraging a bimodal architecture and adversarial training to learn a language-agnostic representation. To resolve long-code information loss, LC3 introduces a semantic affinity aggregation strategy. This strategy synthesizes a robust clone score from a complete pairwise similarity matrix computed between segmented code blocks, overcoming the limitations of both simple truncation and aggregation. Extensive experiments show that LC3 significantly outperforms state-of-the-art zero-shot baselines, especially in challenging long-code scenarios.
Template-Theorems Graph Construction to Enhance Mathematical Reasoning Capabilities of LLM
Authors: Yarong Lan; Yajing Xu; Huajun Chen
Venue: AAAI 2026 (main)

Abstract: Large language models (LLMs) have made significant strides in mathematical reasoning, particularly at the elementary level. However, they continue to face substantial challenges when confronted with complex, advanced mathematical problems. In contrast to humans—who can effectively draw upon prior experiences in solving similar problems and retrieve relevant knowledge and theorems from memory—LLMs often struggle to accurately identify analogous problems and to recall or apply appropriate theorems. To overcome these limitations, we introduce a novel framework for constructing a template-theorems knowledge base, leveraging the capabilities of large language models. Inspired by the associative mechanisms of human cognition, our approach abstracts real-world problems into generalized templates and establishes intricate linkages between these templates and pertinent theorems. This design enables the efficient expansion of a comprehensive knowledge base, even when starting from a limited set of seed examples. Moreover, we develop an efficient retrieval strategy that, given a new problem, systematically extracts and presents the most relevant knowledge from the knowledge base as contextual input to the LLM. Extensive experiments on multiple public mathematical datasets and models demonstrate that our approach consistently surpasses conventional methods. Comprehensive ablation studies further corroborate the effectiveness of both our knowledge base construction and retrieval modules.
Reinforce Trustworthiness in Multimodal Emotional Support System
Authors: Huy M. Le; Dat Tien Nguyen; Ngan T. T. Vo; Tuan D. Q. Nguyen; Nguyen Le Binh; Duy Minh Ho Nguyen; Daniel Sonntag; Lizi Liao; Binh T. Nguyen
Venue: AAAI 2026 (main)

Abstract: In today’s world, emotional support is increasingly essential, yet it remains challenging for both those seeking help and those offering it. Multimodal approaches to emotional support show great promise by integrating diverse data sources to provide empathetic, contextually relevant responses, fostering more effective interactions. However, current methods have notable limitations, often relying solely on text or converting other data types into text, or providing emotion recognition only, thus overlooking the full potential of multimodal inputs. Moreover, many studies prioritize response generation without accurately identifying critical emotional support elements or ensuring the reliability of outputs. To overcome these issues, we introduce MULTIMOOD, a new framework that (i) leverages multimodal embeddings from video, audio, and text to predict emotional components and to produce responses responses aligned with professional therapeutic standards. To improve trustworthiness, we (ii) incorporate novel psychological criteria and apply Reinforcement Learning (RL) to optimize large language models (LLMs) for consistent adherence to these standards. We also (iii) analyze several advanced LLMs to assess their multimodal emotional support capabilities. Experimental results show that MultiMood achieves state-of-the-art on MESC and DFEW datasets while RL-driven trustworthiness improvements are validated through human and LLM evaluations, demonstrating its superior capability in applying a multimodal framework in this domain.