GenAI4ELab/papercli-papers huggingface dataset:
Style-First Authorship Verification for Academic Integrity in the Generative AI Era (Student Abstract)
Authors: Jun Jang; Thai Le; Bo Wang
Venue: AAAI 2026 (main)

Abstract: With the rise of generative artificial intelligence
(GenAI), academic dishonesty in classrooms has skyrocketed,
yet the existing solutions for detecting such dishonesty
often fall short. Standard "AI detectors" merely analyze
one text at a time, failing to account for students'
previous writings, which risks erroneous predictions.
Meanwhile, existing token-based authorship verification
(AV) models fail to analyze the nuances in writing styles
that truly distinguish authorship. To fill this existing
gap, we propose a novel AV framework that combines
token-level stylometric features (e.g., POS tag patterns)
with handcrafted stylistic features (e.g., sentence
structure variation) to construct a comprehensive feature
set. Using both benchmark corpora and real-world high
school student essays, we trained multiple machine learning
classifiers using the proposed feature set. Our initial
experiments show that our approach outperforms the standard
token-only baselines by over 25%, while offering
interpretable, style-based insights. These preliminary
results highlight the importance of nuanced stylistic
features and suggest that a holistic AV system can provide
educators with more reliable and transparent detection
tools. Looking ahead, we plan to extend this work with
large language models and multi-agent approaches to further
enhance robustness and adaptability.
Self-Guided Planning and Repair Framework for Code Generation (Student Abstract)
Authors: Chun-Wei Kang; Chung-Chi Chen; An-Zi Yen
Venue: AAAI 2026 (main)

Abstract: Large Language Models (LLMs) demonstrate strong
capabilities in code generation but often lack adaptability
in planning and refinement. We propose Self-PR, a framework
that integrates adaptive plan selection and iterative
repair to improve correctness and generalization. Self-PR
constructs a reusable plan database via task clustering and
trains a selector to choose task-specific strategies.
Incorrect outputs are refined through multi-round feedback
until correctness. Trained only on HumanEval, Self-PR
generalizes well to out-of-distribution tasks (MBPP),
improving pass@1 by +4.9% on HumanEval and +5.5% on MBPP
compared to Modularization-of-Thought prompting.
Experiments across Llama-3 (8B, 70B) and GPT-4o-mini
confirm robustness and scalability. These findings suggest
that adaptive planning and feedback-driven repair are
essential for reliable LLM-based code generation.
CompRestacking: Capturing Channel Dependency in Highly Correlated Multivariate Time Series Data (Student Abstract)
Authors: Min Kim; Ohyun Jo
Venue: AAAI 2026 (main)

Abstract: The consideration of channel correlation is crucial for
improving the performance of multivariate time series
forecasting. However, existing models fail to capture it in
homogeneous and highly correlated channels. In this work,
we introduce CompRestacking (Compression Restacking), a
strikingly intuitive and effective method to address this
problem. The approach consists of three main components:
(1) PCC-Restacking for correlation-aware channel ordering,
(2) Temporal embedding for time encoding, and (3)
Aggregation compression for compact token generation.
CompRestacking consistently outperforms in experiment
results. The results demonstrate that CompRestacking
leverages strong channel correlations for improved
performance.
Spatially-Guided Self-Attention Refinement for Zero-Shot Hair Segmentation (Student Abstract)
Authors: Suin Kim; Jihoon Lee; Moonsung Kang; Doheun Cha; Sangtae Ahn
Venue: AAAI 2026 (main)

Abstract: Recent advances in diffusion-based models have significantly broadened their scope, extending well beyond image generation to encompass zero-shot segmentation tasks. In this work, we introduce a novel, training-free approach that harnesses both self- and cross-attention maps to achieve highly detailed hair segmentation. Our method demonstrates remarkable efficacy in producing fine-grained results without the need for additional training.
Privacy-Preserving Argumentative Explanations (Student Abstract)
Authors: Ungsik Kim; Minjae Lee; Jiho Bae; Minje Kim; Sang-Min Choi; Suwon Lee
Venue: AAAI 2026 (main)

Abstract: We propose a framework for privacy-preserving argumentative explanations using homomorphic encryption. This method applies the Cheon-Kim-Kim-Song scheme, along with a soft k-means adapted for encrypted computation, to generate explanations without exposing sensitive data. By leveraging GPU acceleration, speedups of approximately 470–670 times were achieved compared with CPU execution. Experimental results show that explanation fidelity is maintained for small- to medium-scale models, whereas significant degradation occurs in larger models. These findings suggest that our study provides an initial step toward enabling secure and trustworthy argumentative explanations under encryption while also highlighting the challenges that remain for generalizability to more complex models.
Strategic Tool Enhanced AI Agent for Multi-Issue Negotiation (Student Abstract)
Authors: Daiki Kitashima; Ryota Higa; Katsuhide Fujita
Venue: AAAI 2026 (main)

Abstract: Automated negotiation, a form of interaction among autonomous agents, plays a central role in multi-agent systems, yet the application of large language model (LLM) in this domain remains underexplored. An LLM can serve as a meta-strategist, adaptively selecting explicit strategies for execution by external strategic tools based on its capabilities. 
We propose a negotiation AI agent equipped with explicit strategic tools, including time-dependent and tit-for-tat negotiation strategies. Our results show that strategic tool enhanced negotiators achieve approximately 16% higher average utility compared with baseline, latest LLM negotiators.
Q-MoFusion: A Quantum Classifier for Masquito Species Classification (Student Abstract)
Authors: Vishesh Kumar; Ahana Chanda; Poulomi Bhattacharya; Akshay Agarwal
Venue: AAAI 2026 (main)

Abstract: Automated mosquito species identification is critical for combating vector-borne diseases. We introduce Q-MoFusion, a novel hybrid quantum-classical framework that fuses deep features from pre-trained Audio Spectrogram Transformer (AST) and Whisper models using a Variational Quantum Circuit (VQC). Our approach significantly outperforms individual backbones and prior state-of-the-art benchmarks, demonstrating superior accuracy and robustness, particularly on imbalanced classes. Q-MoFusion demonstrates the potential of hybrid quantum computing to enhance bioacoustic surveillance for addressing critical public health challenges.
zkQML: Verifiable and Privacy-Preserving Inference for Quantum Machine Learning (Student Abstract)
Authors: Seung Kwon Lee; Seok Bin Son; Joongheon Kim; Hoh Peter In
Venue: AAAI 2026 (main)

Abstract: Quantum machine learning (QML) has attracted growing
interest for their ability to achieve superior performance
with significantly fewer parameters.
However, the high cost and scarcity of current hardware
push inference to cloud-hosted quantum devices, creating a
tension between verifiability and confidentiality.
This work proposes a novel framework that converts quantum
neural network operations into classical arithmetic
circuits that faithfully approximate genuine quantum
computations. By encrypting these circuits with
zero-knowledge proofs, it ensures computational validity
while concealing internal parameters. Experimental results
show that our classical circuits achieve fidelity above
0.9996 and total variation distance below 1% compared to
actual quantum computations, verifying the practicality of
trustworthy and privacy-preserving quantum inference.
Tailored ViT Slimming: Budget-Aware Multi-Dimensional Sparsity Regularization for Vision Transformers Pruning (Student Abstract)
Authors: Suwoong Lee; Seungjae Lee; Yunho Jeon; Junmo Kim
Venue: AAAI 2026 (main)

Abstract: We propose Tailored ViT Slimming (TVS), a budget-aware multi-dimensional pruning framework for Vision Transformers. TVS injects learnable masks into MHSA and MLP modules and applies adaptive non-convex sparsity regularization to achieve maximal utilization of parameters under strict module-wise budgets. In addition, by retaining scaled masks after pruning, TVS avoids abrupt accuracy drops and provides stable initialization for fine-tuning. On ImageNet-1k with DeiT-S and DeiT-B, TVS consistently outperforms prior ViT compression methods. This result empirically shows that the non-convex sparsity regularizer is effective not only in CNNs but also in ViTs.
Hybrid PPO–DQN for Multi-Objective Adaptive Cruise Control in Eco-Driving: Reward Shaping Toward Safety and Sustainability (Student Abstract)
Authors: Tae Hoon Lee; Joongheon Kim
Venue: AAAI 2026 (main)

Abstract: In adaptive cruise control (ACC), balancing safety,
comfort, and sustainability still remains challenging.
Accordingly, we propose a hybrid reinforcement learning
framework combining proximal policy optimization (PPO) and
deep Q-network (DQN) with a multi-objective reward for
autonomous carbon-neutral eco-driving. Experimental results
revealed the contrasts between eco and non-eco modes,
underscoring how reward design shapes driving behaviors.
Synthetic-to-Real Transfer Learning for League of Legends Minimap Object Detection (Student Abstract)
Authors: Younsung Lee; Yujin Kim; Kyungjoong Kim
Venue: AAAI 2026 (main)

Abstract: Esports is growing rapidly, yet the data available to
researchers is limited due to the game company policies.
Consequently, vision-based approaches utilizing game
screens are gaining attention as a practical alternative.
We focus on the League of Legends minimap and address the
challenges of champion detection when extracting champion
information from the minimap. The challenges in this domain
include small objects, rapid movement, and frequent
occlusions.
We propose a transfer-learning-based object detection
pipeline that combines synthetic data with a subset of replay
data. Synthetic data enables the rapid generation of
diverse scenarios and improves training scalability, while
replay data reduces the data distribution gap. This approach
achieves 0.588 mean average precision, improving over
replay-only by 0.261 and synthetic-only by 0.312, with 6.4 ms
latency. Furthermore, we constructed a dataset
encompassing all champions, enabling comparative analysis
of detection models and supporting reproducible
benchmarking for various application studies.
Steering Sparse Autoencoder Latents to Control Dynamic Head Pruning in Vision Transformers (Student Abstract)
Authors: Yousung Lee; Dongsoo Har
Venue: AAAI 2026 (main)

Abstract: Dynamic head pruning in Vision Transformers (ViTs) improves
efficiency by removing redundant attention heads, but
existing pruning policies are often difficult to interpret
and control. In this work, we propose a novel framework by
integrating Sparse Autoencoders (SAEs) with dynamic
pruning, leveraging their ability to disentangle dense
embeddings into interpretable and controllable sparse
latents. Specifically, we train an SAE on the final-layer
residual embedding of the ViT and amplify the sparse
latents with different strategies to alter pruning
decisions. Among them, per-class steering reveals compact,
class-specific head subsets that preserve accuracy. For
example, bowl improves accuracy (76%→82%) while reducing
head usage (0.72→0.33) via heads h2 and h5. These results
show that sparse latent features enable class-specific
control of dynamic pruning, effectively bridging pruning
efficiency and mechanistic interpretability in ViTs.
HARK: Hierarchical Agentic Retrieval with Keyframing for Video Understanding (Student Abstract)
Authors: Jingcheng Li; Ye Qiao; Sitao Huang
Venue: AAAI 2026 (main)

Abstract: Current video understanding models struggle with temporal
reasoning and efficient processing while balancing detail
preservation with computational efficiency. We propose a
hierarchical memory system that segments videos into action
and scene units, combined with question-aware agentic
keyframe selection. Our method achieves 70.3% overall
accuracy on VideoMME short video benchmarks.
HyMoERec: Hybrid Mixture-of-Experts for Sequential Recommendation (Student Abstract)
Authors: Kunrong Li; Zhu Sun; Kwan Hui Lim
Venue: AAAI 2026 (main)

Abstract: We propose HyMoERec, a novel sequential recommendation framework that addresses the limitations of uniform Position-wise Feed-Forward Networks in existing models. Current approaches treat all user interactions and items equally, overlooking the heterogeneity in user behavior patterns and diversity in item complexity. HyMoERec initially introduces a hybrid mixture-of-experts architecture that combines shared and specialized expert branches with an adaptive expert fusion mechanism for the sequential recommendation task. This design captures diverse reasoning for varied users and items while ensuring stable training. Experiments on MovieLens-1M and Beauty datasets demonstrate that HyMoERec consistently outperforms state-of-the-art baselines.
Obedience or Vigilance? How Large Language Models React to Malicious Multiple-Choice Options (Student Abstract)
Authors: Yow-Fu Liou; Yu-Chien Tang; An-Zi Yen
Venue: AAAI 2026 (main)

Abstract: When evaluating large language models (LLMs) for question
answering tasks, a common protocol is multiple-choice
question-answering (MCQA), where the model selects from a
fixed set of choices.
In contemporary robustness testing, researchers typically
perturb instructions or introduce confusion into factual
statements; however, model behavior also hinges on choice
compliance: whether models remain within the canonical set
A-D.
We formalize this setting by asking whether the model
continues to respect the interface's rules when the problem
presents a tempting alternative.
Our approach is interface-preserving: we append a single
selectable option E while keeping the question and A-D
unchanged.
Then, we introduce three types of malicious option
injection to assess LLMs' robustness.
Experimental results highlight the vulnerability of LLMs on
contradict type content of the additional option E.
Our evaluation framework can effectively serve as a
low-cost audit of rule adherence on existing datasets and
black-box models, surfaces off-policy items, and supports
interpretable model comparison for deployment.
HiPrune: Training-Free Visual Token Pruning via Hierarchical Attention in Vision-Language Models (Student Abstract)
Authors: Jizhihui Liu; Guangdao Zhu; Feiyi Du
Venue: AAAI 2026 (main)

Abstract: Vision-Language Models (VLMs) encode images into lengthy
sequences of visual tokens, leading to excessive
computational overhead and limited inference efficiency. In
this paper, we study the hierarchical attention pattern in
vision encoders and propose HiPrune, a training-free and
model-agnostic token Pruning framework for VLMs. We
identify that middle layers in the vision encoder attend to
object-centric regions, while deep layers capture global
contextual features. Based on this observation, HiPrune
selects tokens based on the attention score from the middle
and deep layers. Our method requires no retraining and
integrates seamlessly with any ViT-based VLM. Experiments
demonstrate that HiPrune achieves outstanding pruning
performance, maintaining a balance between efficiency and
efficacy.
Meta-Normalizing Flow for Data-Limited Offline Meta-Reinforcement Learning (Student Abstract)
Authors: Lianghui Liu; Zongzhang Zhang
Venue: AAAI 2026 (main)

Abstract: Offline Meta-Reinforcement Learning (OMRL) leverages pre-collected data to adapt to new tasks. Context-based methods learn task representations from contexts. However, the context is influenced by both the task and the behavior policy. The mismatch between the behavior policy and the testing policy causes a context distribution shift problem, which results in poor task representations and degraded performance. This problem is exacerbated in settings with data limitations. To address this, we propose a novel approach called Meta-Normalizing Flow (Meta-NF). First, it employs a highly expressive and sample-efficient normalizing flow policy. Second, it incorporates a metric for testing-time task representation selection to effectively mitigate the context shift problem. Empirical results demonstrate that Meta-NF outperforms existing OMRL methods, with both components contributing to its strong performance.
Efficient Preference Alignment via Pareto Exploration (Student Abstract)
Authors: Pengfei Liu; Rui Kong; Zongzhang Zhang
Venue: AAAI 2026 (main)

Abstract: Hand-craft reward engineering requires domain knowledge
with numerous trials and errors, while Preference-based
Reinforcement Learning (PbRL) avoids manual reward design
but often suffers from limited interpretability and
unstable training. To address these issues, we propose a
novel preference alignment framework. Our approach
leverages large language models to generate sub-reward
functions informed by prior knowledge and further align
human preferences by optimizing the weights combining these
sub-rewards. For policy learning, we introduce Policy
Optimization via Pareto Regularization (POPR) which
regularizes updates along Pareto-optimal directions.
Experiments show that our framework improves reward quality
and policy stability, achieving superior performance to
expert-designed rewards across most tasks.
CAPO: A Unified Policy Gradient Approach for Reward and Cost Optimization in Safe Reinforcement Learning (Student Abstract)
Authors: Xiaotao Liu; Prashant Mohit; Arvind Easwaran
Venue: AAAI 2026 (main)

Abstract: In safe reinforcement learning (SRL), there exists an
inherent conflict between maximizing reward and minimizing
cost. We propose a novel approach that effectively resolve
the conflict between maximizing reward and minimizing cost
in joint optimization.When the cost exceeds the threshold,
we perform cost-reducing updates. Otherwise, we compute
policy gradients that maximize expected rewards, while
using second-order Taylor approximation to evaluate whether
these reward-maximizing gradients would violate the cost
constraint. If constraint violation is detected, we adjust
the gradient direction to maintain safety compliance;
otherwise, we execute standard reward-increasing policy
updates. This approach helps ensure that reward-seeking
updates do not inadvertently increase costs, thereby
reducing the likelihood of constraint violations. Empirical
tests show our framework successfully manages reward-cost
trade-offs through reward augmentation and cost shaping,
improving both performance and safety without switching
optimization strategies. Results demonstrate that
concurrent treatment of both objectives in one policy
gradient update is viable for improving safe reinforcement
learning methods.
VLHSA: Vision-Language Hierarchical Semantic Alignment for Jigsaw Puzzle Solving with Eroded Gaps (Student Abstract)
Authors: Xinyan Liu; Zhuoning Xu
Venue: AAAI 2026 (main)

Abstract: Jigsaw puzzle solving remains difficult because models must
reconcile local fragment cues with global structure. Most
prior work leans solely on visual signals (edge or texture
coherence) and rarely exploits natural-language
descriptions, which are especially helpful for puzzles with
eroded gaps. We introduce a vision–language framework that
uses textual context to guide assembly. At its core, the
Vision–Language Hierarchical Semantic Alignment (VLHSA)
module aligns image patches with text via multi-level
matching—from local tokens to global summaries—within a
multimodal design that couples dual visual encoders with
language features for cross-modal reasoning. Across
multiple datasets, the method surpasses the state of the
art, including a 14.2 percentage point gain in piece
accuracy; ablations identify VLHSA as the principal source
of improvement. These results suggest a practical shift for
jigsaw solving: augmenting vision with language to resolve
ambiguous placements
A Foundation Model for Brain MRI with Dynamic Modality Integration (Student Abstract)
Authors: Minh Sao Khue Luu; Bair N. Tuchinov
Venue: AAAI 2026 (main)

Abstract: We introduce a single–backbone foundation model for brain MRI that supports dynamic modality integration: it operates with arbitrary, possibly unseen, combinations of MRI sequences at pretrain and transfer. The encoder is conditioned by text-derived modality embeddings via conditional layer normalization, while a variance–covariance penalty discourages feature collapse. Unlike expert-based designs that grow with each new sequence, our approach scales without adding modality-specific branches. Pretrained self-supervised on ∼60,000 heterogeneous MRIs, the model learns modality-aware yet modality-agnostic features. We outline evaluation on segmentation and classification under missing/unseen modalities and cross-center shifts, and present early feasibility on multiple sclerosis lesion segmentation under limited data. This work moves toward robust, protocol-agnostic MRI foundation models suited to real clinical variability.
BDI-based Opponent Modeling and Strategy Generation for Multi-Issue Negotiation (Student Abstract)
Authors: Tianzi Ma; Yulin Wu; Hang Ren; Xiaozhen Sun; Shuhan Qi; Xuan Wang
Venue: AAAI 2026 (main)

Abstract: Accurately modeling opponent behaviors and integrating strategy are key challenges for multi-issue automated negotiation. Existing approaches often isolate preference learning or trend prediction and lack a unified cognitive structure with coordinated reasoning. This paper proposes a BDI (Belief-Desire-Intention)-based opponent modeling and strategy generation framework. The framework analyzes opponent responses (Belief), predicts preference weights and the utility function (Desire), and infers utilities of future offers (Intention). Building on these predictions, we design a responsive strategy, enabling gradual concessions and balanced outcomes. Our main contributions are: D-MBUE in the Desire module, I-DABI in the Intention module, and the BDI Negotiator on top of the modeling modules. Experiments on 45 standard negotiation domains and against 12 representative opponents demonstrate the effectiveness of our BDI framework.
Generative AI-Driven Data Transformation for Enhanced Machine Learning Performance (Student Abstract)
Authors: Christopher MacDowell; Sarah Setiawan; Carol Jim; Ahmed Salem
Venue: AAAI 2026 (main)

Abstract: Machine Learning (ML) models have significant potential
across research and industry to enable data-driven insights
and decision-making. Their performance relies on input data
quality, but real-world datasets often contain
imperfections, making data preprocessing essential yet
time-consuming. Our research proposes a proof-of-concept
model using Generative Artificial Intelligence (GenAI) to
analyze and transform data for supervised ML
classification. The results from the GenAI models will be
compared with traditionally preprocessed data to evaluate
effectiveness. Preliminary results indicate that
incorporating GenAI models into the preprocessing pipeline
show potential in improving ML's classification performance.