GenAI4ELab/papercli-papers huggingface dataset:
Uncovering Pretraining Code in LLMs: A Syntax-Aware Attribution Approach
Authors: Yuanheng Li; Zhuoyang Chen; Xiaoyun Liu; Yuhao Wang; Mingwei Liu; Yang Shi; Kaifeng Huang; Shengjie Zhao
Venue: AAAI 2026 (main)

Abstract: As large language models (LLMs) become increasingly capable, concerns over the unauthorized use of copyrighted and licensed content in their training data have grown, especially in the context of code. Open-source code, often protected by open source licenses (e.g, GPL), poses legal and ethical challenges when used in pretraining. Detecting whether specific code samples were included in LLM training data is thus critical for transparency, accountability, and copyright compliance.

We propose SynPrune, a syntax-pruned membership inference attack method tailored for code. Unlike prior MIA approaches that treat code as plain text, SynPrune leverages the structured and rule-governed nature of programming languages. Specifically, it identifies and excludes consequent tokens that are syntactically required and not reflective of authorship, from attribution when computing membership scores. Experimental results show that SynPrune consistently outperforms the state-of-the-arts. Our method is also robust across varying function lengths and syntax categories.
Factorization-in-Loop:Proximal Fill-in Minimization for Sparse Matrix Reordering
Authors: Ziwei Li; Shuzi Niu; Tao Yuan; Huiyuan Li; Wenjia Wu
Venue: AAAI 2026 (main)

Abstract: Fill-ins are new nonzero elements in the summation of the upper and lower triangular factors generated during LU factorization. For large sparse matrices, they will increase the memory usage and computational time, and be reduced through proper row or column arrangement, namely matrix reordering. Finding a row or column permutation with the minimal fill-ins is NP-hard, and surrogate objectives are designed to derive fill-in reduction permutations or learn a reordering function. However, there is no theoretical guarantee between the golden criterion and these surrogate objectives. Here we propose to learn a reordering network by minimizing l1 norm of triangular factors of the reordered matrix to approximate the exact number of fill-ins. The reordering network utilizes a graph encoder to predict row or column node scores. For inference, it is easy and fast to derive the permutation from sorting algorithms for matrices. For gradient based optimization, there is a large gap between the predicted node scores and resultant triangular factors in the optimization objective. To bridge the gap, we first design two reparameterization techniques to obtain the permutation matrix from node scores. The matrix is reordered by multiplying the permutation matrix. Then we introduce the factorization process into the objective function to arrive at target triangular factors. The overall objective function is optimized with the alternating direction method of multipliers and proximal gradient descent. Experimental results on benchmark sparse matrix collection SuiteSparse show the fill-in and LU factorization time reduction of our proposed method is 0.2% and 17.8% compared with state-of-the-art baselines.
Benchmarking LLMs for Political Science: A United Nations Perspective
Authors: Yueqing Liang; Liangwei Yang; Chen Wang; Congying Xia; Rui Meng; Xiongxiao Xu; Haoran Wang; Ali Payani; Kai Shu
Venue: AAAI 2026 (main)

Abstract: Large Language Models (LLMs) have achieved significant advances in natural language processing, yet their potential for high-stake political decision-making remains largely unexplored. This paper addresses the gap by focusing on the application of LLMs to the United Nations (UN) decision-making process, where the stakes are particularly high and political decisions can have far-reaching consequences. We introduce a novel dataset comprising publicly available UN Security Council (UNSC) records from 1994 to 2024, including draft resolutions, voting records, and diplomatic speeches. Using this dataset, we propose the United Nations Benchmark (UNBench), the first comprehensive benchmark designed to evaluate LLMs across four interconnected political science tasks: co-penholder judgment, representative voting simulation, draft adoption prediction, and representative statement generation. These tasks span the three stages of the UN decision-making process—drafting, voting, and discussing—and aim to assess LLMs' ability to understand and simulate political dynamics. Our experimental analysis demonstrates the potential and challenges of applying LLMs in this domain, providing insights into their strengths and limitations in political science. To the best of our knowledge, this is the first benchmark to systematically evaluate LLMs in UN decision-making, contributing to the growing intersection of AI and political science.
CellStream: Dynamical Optimal Transport Informed Embeddings for Reconstructing Cellular Trajectories from Snapshots Data
Authors: Yue Ling; Peiqi Zhang; Zhenyi Zhang; Peijie Zhou
Venue: AAAI 2026 (main)

Abstract: Single-cell RNA sequencing (scRNA-seq), especially temporally resolved datasets, enables genome-wide profiling of gene expression dynamics at single-cell resolution across discrete time points. However, current technologies provide only sparse, static snapshots of cell states and are inherently influenced by technical noise, complicating the inference and representation of continuous transcriptional dynamics. Although embedding methods can reduce dimensionality and mitigate technical noise, the majority of existing approaches typically treat trajectory inference separately from embedding construction, often neglecting temporal structure.  To address this challenge, here we introduce CellStream, a novel deep learning framework that jointly learns embedding and cellular dynamics from single-cell snapshots data by integrating an autoencoder with unbalanced dynamical optimal transport. Compared to existing methods, CellStream generates dynamics-informed embeddings that robustly capture temporal developmental processes while maintaining high consistency with the underlying data manifold. We demonstrate CellStream’s effectiveness on both simulated datasets and real scRNA-seq data, including spatial transcriptomics. Our experiments indicate significant quantitative improvements over state-of-the-art methods in representing cellular trajectories with enhanced temporal coherence and reduced noise sensitivity. Overall, CellStream provides a new tool for learning and representing continuous streams from the noisy, static snapshots of single-cell gene expression.
Multimodal Table Understanding with Difficulty-aware Reinforcement Learning
Authors: Chaohu Liu; Haoyu Cao; YongXiang Hua; Linli Xu
Venue: AAAI 2026 (main)

Abstract: Multimodal table understanding, which aims for a comprehensive grasp of table content by integrating cellular text, tabular structure, and visual presentation, remains a core yet challenging area of research.
We identify that the structural complexity of a table, quantifiable by intrinsic properties such as the ratio of merged cells and the total number of cells, presents a significant obstacle for existing models. 
Our empirical analysis reveals that the performance of leading Multimodal Large Language Models (MLLMs) deteriorates markedly as table complexity increases, exposing a critical vulnerability in their ability to perceive and reason over intricate tabular data.
To address this challenge, we propose MM-Table-R1, a model enhanced through difficulty-aware reinforcement learning (RL) post-training strategy. 
Specifically, we introduce both task-level and data-level curriculum learning. 
The task-level curriculum is designed to establish a capability ladder, where the model first learns basic perceptual and semantic alignment of table data, and then progresses to acquiring multi-step reasoning capabilities.
The data-level curriculum ensures that the model is not exposed to difficult samples prematurely, facilitating a more gradual and effective learning process.
Furthermore, we invest considerable effort in constructing a high-quality, large-scale training corpus by curating and processing data from diverse open-source table datasets, ensuring that each instance is paired with an objectively verifiable reward signal.
Demonstrating exceptional parameter efficiency, our 3B-parameter model sets a new benchmark by surpassing both established 3B and 7B models, including those specifically designed for table reasoning.
NOTAM-Evolve: A Knowledge-Guided Self-Evolving Optimization Framework with LLMs for NOTAM Interpretation
Authors: Maoqi Liu; Quan Fang; Yuhao Wu; Can Zhao; Yang Yang; Kaiquan Cai
Venue: AAAI 2026 (main)

Abstract: Accurate interpretation of Notices To Airmen (NOTAMs) is critical for aviation safety, yet their condensed and cryptic language poses significant challenges to both manual and automated processing. Existing automated systems are typically limited to "Shallow Parsing," failing to extract the actionable intelligence needed for operational decisions. We formalize the complete interpretation task as "Deep Parsing," a dual-reasoning challenge requiring both dynamic knowledge grounding (linking the NOTAM to evolving real-world aeronautical data) and schema-based inference (applying static domain rules to deduce operational status). To tackle this challenge, we propose NOTAM-Evolve, a self-evolving framework that enables a Large Language Model (LLM) to autonomously master complex NOTAM interpretation. Leveraging a knowledge graph-enhanced retrieval module for data grounding, the framework introduces a crucial closed-loop learning process where the LLM progressively improves from its own outputs, minimizing the need for extensive human-annotated reasoning traces. In conjunction with this framework, we introduce a new benchmark dataset of 10,000 expert-annotated NOTAMs. Our experiments demonstrate that NOTAM-Evolve achieves a 30.4% absolute accuracy improvement over the base LLM, establishing a new state-of-the-art on the task of structured NOTAM interpretation.
ProtSAE: Disentangling and Interpreting Protein Language Models via Semantically-Guided Sparse Autoencoders
Authors: Xiangyu Liu; Haodi Lei; Yi Liu; Yang Liu; Wei Hu
Venue: AAAI 2026 (main)

Abstract: Sparse Autoencoder (SAE) has emerged as a powerful tool for mechanistic interpretability of large language models. Recent works apply SAE to protein language models (PLMs), aiming to extract and analyze biologically meaningful features from their latent spaces.  However, SAE suffers from semantic entanglement, where individual neurons often mix multiple nonlinear concepts, making it difficult to reliably interpret or manipulate model behaviors. In this paper, we propose a semantically-guided SAE, called ProtSAE. Unlike existing SAE which requires annotation datasets to filter and interpret activations, we guide semantic disentanglement during training using both annotation datasets and domain knowledge to mitigate the effects of entangled attributes. We design interpretability experiments showing that ProtSAE learns more biologically relevant and interpretable hidden features compared to previous methods. Performance analyses further demonstrate that ProtSAE maintains high reconstruction fidelity while achieving better results in interpretable probing. We also show the potential of ProtSAE in steering PLMs for downstream generation tasks.
Predict and Resist: Long-Term Accident Anticipation Under Sensor Noise
Authors: Xingcheng Liu; Bin Rao; Yanchen Guan; Chengyue Wang; Haicheng Liao; Jiaxun Zhang; Chengyu Lin; Meixin Zhu; Zhenning Li
Venue: AAAI 2026 (main)

Abstract: Accident anticipation is essential for proactive and safe autonomous driving, where even a brief advance warning can enable critical evasive actions. However, two key challenges hinder real-world deployment: (1) noisy or degraded sensory inputs from weather, motion blur, or hardware limitations, and (2) the need to issue timely yet reliable predictions that balance early alerts with false-alarm suppression. We propose a unified framework that integrates diffusion-based denoising with a time-aware actor-critic model to address these challenges. The diffusion module reconstructs noise-resilient image and object features through iterative refinement, preserving critical motion and interaction cues under sensor degradation. In parallel, the actor-critic architecture leverages long-horizon temporal reasoning and time-weighted rewards to determine the optimal moment to raise an alert, aligning early detection with reliability. Experiments on three benchmark datasets (DAD, CCD, A3D) demonstrate state-of-the-art accuracy and significant gains in mean time-to-accident, while maintaining robust performance under Gaussian and impulse noise. Qualitative analyses further show that our model produces earlier, more stable, and human-aligned predictions in both routine and highly complex traffic scenarios, highlighting its potential for real-world, safety-critical deployment.
CLM-Access: A Specialized Foundation Model for High-Dimensional Single-Cell ATAC-Seq Analysis
Authors: Ziqiang Liu; Bowen Li; Zhenyu Xu; Yantao Li; Junwei Zhang; Chulin Sha; Xiaolin Li
Venue: AAAI 2026 (main)

Abstract: Inspired by the success of large language models (LLMs) in natural language processing, cell language models (CLMs) have emerged as a promising paradigm to learn cell representations from high-dimensional single-cell data—particularly transcriptomic profiles from scRNA-seq. These foundation models have shown remarkable potential across a variety of downstream applications. However, there remains a lack of foundation models for scATAC-seq data, which measures chromatin accessibility at single-cell level and is critical for decoding epigenetic regulation. Developing such model is considerably more challenging due to the unique characteristics of scATAC-seq data, including the vast number of chromatin regions, lack of standardized annotations, extreme sparsity, and near-binary distributions. To address these challenges, we systematically explore various strategies and propose CLM-Access, a specialized foundation model for scATAC-seq data. CLM-Access incorporates three main innovations: (1) an unified data processing pipeline that maps 2.8 million cells onto an unified reference of over 1 million chromatin regions; (2) a specialized patching and embedding strategy to effectively manage high-dimensional inputs; and (3) a tailored masking and loss function design that preserves fine-grained regional information while enhancing training efficiency and representation quality. With comprehensive benchmarks, we show that CLM-Access significantly outperforms existing methods in key downstream tasks, including batch effect correction, cell type annotation, RNA expression prediction, and multi-modal integration. This work establishes a scalable and interpretable foundation model for single-cell epigenomic analysis and expands the application of CLMs in single-cell research.
When Genes Speak: A Semantic-Guided Framework for Spatially Resolved Transcriptomics Data Clustering
Authors: Jiangkai Long; Yanran Zhu; Chang Tang; Kun Sun; Yuanyuan Liu; Xuesong Yan
Venue: AAAI 2026 (main)

Abstract: Spatial transcriptomics enables gene expression profiling with spatial context, offering unprecedented insights into the tissue microenvironment. However, most computational models treat genes as isolated numerical features, ignoring the rich biological semantics encoded in their symbols. This prevents a truly deep understanding of critical biological characteristics.
To overcome this limitation, we present SemST, a semantic-guided deep learning framework for spatial transcriptomics data clustering. SemST leverages Large Language Models (LLMs) to enable genes to "speak" through their symbolic meanings, transforming gene sets within each tissue spot into biologically informed embeddings. These embeddings are then fused with the spatial neighborhood relationships captured by Graph Neural Networks (GNNs), achieving a coherent integration of biological function and spatial structure.
We further introduce the Fine-grained Semantic Modulation (FSM) module to optimally exploit these biological priors. The FSM module learns spot-specific affine transformations that empower the semantic embeddings to perform an element-wise calibration of the spatial features, thus dynamically injecting high-order biological knowledge into the spatial context.
Extensive experiments on public spatial transcriptomics datasets show that SemST achieves state-of-the-art clustering performance. Crucially, the FSM module exhibits plug-and-play versatility, consistently improving the performance when integrated into other baseline methods.
LLMs Unleashed: Generating Protocol Code from RFC Specifications
Authors: Junfeng Long; Jinshu Su; Biao Han
Venue: AAAI 2026 (main)

Abstract: RFC (Request for Comments) documents constitute the foundation of network protocol standardization. However, they are expressed in natural language, they tend to be lengthy and ambiguous, forcing protocol implementers to rely on extensive manual parsing and coding—a process that is both labor-intensive and prone to errors. This makes the automated parsing and comprehension of RFC documents a major challenge in network protocol research. 
To address this gap, we introduce large language models (LLMs) into the task of automatic network protocol code generation from RFC documents (RFC2Code) and propose a comprehensive evaluation framework to quantitatively assess LLM performance. We develop an end-to-end automated protocol generation system, APG (Automated Protocol-Generation), which supports implementations of ICMP, IGMP, NTP, and TCP. Compared to prior NLP (Natural language processing) methods, APG achieves a fully automated workflow with approximately 3.17× faster processing, 95% compile success and behavioral correctness for stateless protocols like ICMP, and 90% interoperability for complex stateful protocols such as TCP, requiring only minimal manual intervention.
From Blind Transfer to Wise Selection: Prototype-Driven Neighbor-Domain Adaptation for Fake News Detection
Authors: Wayne Lu; Yiheng Li
Venue: AAAI 2026 (main)

Abstract: Multimodal fake news detection across different domains is hampered by the critical challenge of negative transfer, which arises from the indiscriminate fusion of knowledge from all available source domains. Existing methods attempt to learn domain-invariant features or leverage external knowledge but often aggregate information from all domains equally. However, these approaches largely ignore the asymmetric relationships between domains, leading to performance degradation when irrelevant or conflicting knowledge is introduced. To address this, we propose a novel PANDA (Prototype-driven Asymmetric Neighbor-Domain Adaptation) framework that dynamically selects and integrates knowledge from only the most beneficial domains. Initially, PANDA employs a Domain-aware Modal Prompt Generation (DMPG) module to learn transferable knowledge representations for each domain. We then introduce a novel Prototype-based Asymmetric Distance (PAD) to quantify directional domain transferability, which guides a Gumbel-based Neighbor Selector (GNS) to identify the most relevant neighbor domains. Subsequently, a Domain-Collaborative Attention (DCA) module adaptively fuses the selected knowledge to enhance the target domain's representation. Extensive experiments on three benchmarks demonstrate PANDA's superiority, outperforming state-of-the-art baselines with an F1-score improvement of 1.5% on the Weibo-21 dataset.
PHPFND: Detecting Fake News via Post-Hoc Processing of LLMs Hallucination
Authors: Jinke Ma; Jiachen Ma; Wei Zhang; Yong Liu
Venue: AAAI 2026 (main)

Abstract: Large Language Models (LLMs) perform excellently in fake news detection tasks, but their outputs are often accompanied by hallucinations, i.e., generated content that is contradictory to facts. Previous studies have mostly mitigated hallucinations through prompt design. However, this paper reveals that regions in news articles which easily induce hallucinations in LLMs correspond closely to the most challenging regions for fake news detectors. In this paper, we propose a fake news detection framework (PHPFND) based on post-hoc processing of LLMs hallucination. Specifically, our framework includes a hallucination detection module (ISHD) based on information structuring that detects three types of hallucinations in LLMs in a targeted manner, and a hallucination-driven feature enhancement mechanism (HDFE) that incorporates hallucination signals as explicit features into sentence-level encoding and feature fusion to guide the model’s attention toward high-risk regions. Experimental results on two mainstream fake news datasets show that our proposed method significantly outperforms LLM-based baselines.
DynamicRTL: RTL Representation Learning for Dynamic Circuit Behavior
Authors: Ruiyang Ma; Yunhao Zhou; Yipeng Wang; Yi Liu; Zhengyuan Shi; Ziyang Zheng; Kexin Chen; Zhiqiang He; Lingwei Yan; Gang Chen; Qiang Xu; Guojie Luo
Venue: AAAI 2026 (main)

Abstract: There is a growing body of work on using Graph Neural Networks (GNNs) to learn representations of circuits, focusing primarily on their static characteristics. However, these models fail to capture circuit runtime behavior, which is crucial for tasks like circuit verification and optimization. To address this limitation, we introduce DR-GNN (DynamicRTL-GNN), a novel approach that learns RTL circuit representations by incorporating both static structures and multi-cycle execution behaviors. DR-GNN leverages an operator-level Control Data Flow Graph (CDFG) to represent Register Transfer Level (RTL) circuits, enabling the model to capture dynamic dependencies and runtime execution. To train and evaluate DR-GNN, we build the first comprehensive dynamic circuit dataset, comprising over 6,300 Verilog designs and 63,000 simulation traces. Our results demonstrate that DR-GNN outperforms existing models in branch hit prediction and toggle rate prediction. Furthermore, its learned representations transfer effectively to related dynamic circuit tasks, achieving strong performance in power estimation and assertion prediction.
MedLA: A Logic-Driven Multi-Agent Framework for Complex Medical Reasoning with Large Language Models
Authors: Siqi Ma; Jiajie Huang; Fan Zhang; Jinlin Wu; Yue Shen; Guohui Fan; Zhu Zhang; Zelin Zang
Venue: AAAI 2026 (main)

Abstract: Answering complex medical questions requires not only domain expertise and patient-specific information, but also structured and multi-perspective reasoning. Existing multi-agent approaches often rely on fixed roles or shallow interaction prompts, limiting their ability to detect and resolve fine-grained logical inconsistencies. To address this, we propose MedLA, a logic-driven multi-agent framework built on large language models. Each agent organizes its reasoning process into an explicit logical tree based on syllogistic triads (major premise, minor premise, and conclusion), enabling transparent inference and premise-level alignment. Agents engage in a multi-round, graph-guided discussion to compare and iteratively refine their logic trees, achieving consensus through error correction and contradiction resolution. We demonstrate that MedLA consistently outperforms both static role-based systems and single-agent baselines on challenging benchmarks such as MedDDx and standard medical QA tasks. Furthermore, MedLA scales effectively across both open-source and commercial LLM backbones, achieving state-of-the-art performance and offering a generalizable paradigm for trustworthy medical reasoning.