GenAI4ELab/papercli-papers huggingface dataset:
Graph-Theoretic Consistency for Robust and Topology-Aware Semi-Supervised Histopathology Segmentation (Student Abstract)
Authors: Ha-Hieu Pham; Minh Le; Han Huynh; Nguyen Quoc Khanh Le; Huy-Hieu Pham
Venue: AAAI 2026 (main)

Abstract: Semi-supervised semantic segmentation (SSSS) is vital in computational pathology, where dense annotations are costly and limited. Existing methods often rely on pixel-level consistency, which propagates noisy pseudo-labels and produces fragmented or topologically invalid masks. We propose Topology Graph Consistency (TGC), a framework that integrates graph-theoretic constraints by aligning Laplacian spectra, component counts, and adjacency statistics between prediction graphs and references. This enforces global topology and improves segmentation accuracy. Experiments on GlaS and CRAG demonstrate that TGC achieves state-of-the-art performance under 5–10% supervision and significantly narrows the gap to full supervision.
Federated Cross-Modal Style-Aware Prompt Generation (Student Abstract)
Authors: Suraj Prasad; Navyansh Mahla; Sunny Gupta; Amit Sethi
Venue: AAAI 2026 (main)

Abstract: Existing federated prompt learning methods for
vision-language models like CLIP rely solely on text-based
prompts and final-layer visual features, missing crucial
multiscale visual details and client-specific style
variations. This limits generalization across non-IID
distributions and novel classes. We introduce FedCSAP
(Federated Cross-Modal Style-Aware Prompt Generation),
which harnesses multiscale features from CLIP's vision
encoder alongside domain-aware style statistics from client
data. By fusing these visual representations with textual
context, FedCSAP generates adaptive, context-aware prompts
that enhance robustness across seen and unseen classes. Our
privacy-preserving approach operates through local training
and global aggregation, effectively handling heterogeneous
client distributions. Experiments on multiple image
classification datasets demonstrate that FedCSAP
significantly outperforms existing federated prompt
learning methods in both accuracy and generalization.
Q-ROAR: Outlier-Aware Rescaling for RoPE Position Interpolation in Quantized Long-Context LLMs (Student Abstract)
Authors: Ye Qiao; Sitao Huang
Venue: AAAI 2026 (main)

Abstract: Extending LLM context windows is key for long-range tasks. RoPE-based position interpolation (PI) scales input length without retraining, and post-training quantization (PTQ) enables efficient deployment; however, combining PI with PTQ degrades accuracy due to long-context aliasing, dynamic-range dilation, axis-grid anisotropy, and outlier shifts that induce position-dependent logit noise. We give the first systematic analysis of PI+PTQ and propose two diagnostics: Interpolation Pressure (per-band phase-scaling sensitivity) and Tail Inflation Ratio (outlier shift from short to long contexts). We then introduce Q-ROAR, a RoPE-aware, weight-only stabilization that bands RoPE dimensions and lightly searches per-band scales for W_Q,W_K, with an optional symmetric variant. Q-ROAR needs only a tiny long-context dev set and no fine-tuning or kernel changes, recovering up to 0.7% accuracy and more than 14% GovReport perplexity reduction while preserving short-context performance.
PEFT-DML: Parameter-Efficient Fine-Tuning Deep Metric Learning for Robust Multi-Modal 3D Object Detection in Autonomous Driving (Student Abstract)
Authors: Abdolazim Rezaei; Mehdi Sookhak
Venue: AAAI 2026 (main)

Abstract: This study introduces PEFT-DML, a parameter-efficient deep metric learning framework for robust multi-modal 3D object detection in autonomous driving. Unlike conventional models that assume fixed sensor availability, PEFT-DML maps diverse modalities (LiDAR, radar, camera, IMU, GNSS) into a shared latent space, enabling reliable detection even under sensor dropout or unseen modality–class combinations. By integrating Low-Rank Adaptation (LoRA) and adapter layers, PEFT-DML achieves significant training efficiency while enhancing robustness to fast motion, weather variability, and domain shifts. Experiments on benchmarks nuScenes demonstrate superior accuracy.
Semantic Embedding and Synthetic Augmentation for Longitudinal Survey Prediction (Student Abstract)
Authors: Julia Rezvani; Alina Hyk; Thuyen Pham; Leonardo Marciaga; Chunyang Liao; Raffaele Vardavas; Konstantinos Mitsopoulos
Venue: AAAI 2026 (main)

Abstract: Longitudinal surveys are a crucial component of behavioral research. Such surveys, however, face significant gaps in the data created by item and unit non-responses as well as semantic gaps resulting from questionnaires, assessed trends, and data collection methods evolving over time. Using 15 waves of vaccination surveys as a test-bed, we demonstrate how modern AI techniques can bridge both item and unit gaps, originating from non-response, and semantic gaps, originating from instrument evolution.

We address these gaps through a two-component framework. We leverage LLM-generated semantic embeddings of survey questions to encode question meaning, enabling a Deep & Cross Network used for imputation to jointly model responses across item semantics, individual characteristics, and temporal dynamics. This structure directly addresses survey evolution by operating in learned semantic space. To overcome data scarcity, we use cluster-informed synthetic data generation via hierarchical prompting that produces synthetic responses preserving distributional properties and empirical cluster structure. Our approach achieves a strong improvement in semantic gap tasks and 80-90% synthetic data fidelity, providing practical solutions for evolving longitudinal studies.
Latent Representations of Land–Sea Boundaries and Extreme Temperature in Aurora’s Encoder (Student Abstract)
Authors: Benjamin Richards; Pushpa Kumar Balan
Venue: AAAI 2026 (main)

Abstract: Deep learning models are emerging as strong alternatives to numerical weather prediction, yet their
internal
representations remain poorly understood. We analyze the
latent
space of Microsoft’s Aurora model to test whether its embed-
dings align with known physical processes. First, we show
that
land–sea distinctions are strongly captured, with errors
mainly
at coastlines. Second, we examine extreme surface
temperatures
using percentile-based thresholds, finding that embeddings
reveal
a gradient from moderate to severe events, though recall
degrades
at the rarest percentiles. These results suggest that
Aurora’s
encoder encodes physically consistent features but
underestimates
rare extremes. Our study combines deep learning forecasting,
interpretable representation learning, and classical ML
probing,
illustrating how cross-disciplinary AI methods can yield
insight
into foundation models
Distractor-Based Jailbreaking Attacks in Language Models and Associated Changes in Chain-of-Thought Content (Student Abstract)
Authors: Tate Rowney; Xuning Ying
Venue: AAAI 2026 (main)

Abstract: We identify a jailbreaking vulnerability in multiple open-source LLMs: by augmenting dangerous requests using certain "distractors" to obfuscate their intent, we elicit specific, actionable responses on a wide variety of harmful topics. We find that such an attack noticeably alters the contents of these models' chains of thought, including changed frequencies of seemingly unrelated n-grams and heightened ethical scrutiny about harmful requests even when their response is ultimately jailbroken.
A Lightweight Safety Helmet Compliance Detection via Multimodal Fusion (Student Abstract)
Authors: Jeong Hwan Ryu; Azimjon Akhtamov; Md Azher Uddin; Aziz Nasridinov
Venue: AAAI 2026 (main)

Abstract: Ensuring proper use of personal protective equipment (PPE), especially helmets, is essential for workplace safety. Conventional object detectors often fail to distinguish whether a helmet is worn correctly, and existing approaches relying on single-model pipelines are prone to localization errors and false alarms. Moreover, most prior studies do not guarantee real-time performance. To resolve these challenges, we propose a lightweight multimodal approach that integrates a YOLO11-based object detector with a pose estimation model, achieving higher F1 scores and lower false alarm rates while maintaining real-time performance.
SciDataMAS: LLM-Driven MAS for Scientific Data Management (Student Abstract)
Authors: Alexander Sachuk; Vyacheslav Chukanov; Ekaterina Pchitskaya
Venue: AAAI 2026 (main)

Abstract: The management and annotation of complex, multi-modal scientific data remains a major obstacle for AI-driven research due to poor reusability and scalability of current solutions. We propose SciDataMAS, a novel LLM-powered multi-agent system (MAS), which automate scientific data management through a structured data lake with provenance-based organization and an adaptive metadata taxonomy. The system uses specialized workflows for automated dataset creation, data insertion and retrieval. Experiments show the system's proficiency, with modern LLMs like GPT-5 successfully generating rich metadata schemas and filling them with high accuracy. This work provides a foundational step towards fully automated, reusable, and scalable scientific data organization which may lead to generation and accumulation by scientific community well annotated AI-ready datasets.
CRIME: Community Rewiring for Influence and Masking Entities in Social Networks (Student Abstract)
Authors: Nilanjana Saha; Animesh Dutta
Venue: AAAI 2026 (main)

Abstract: In social networks, revealing the structure of communities can expose sensitive groups to detection. Traditional approaches, such as DICE, attempt to hide these communities by randomly rewiring links, but this strategy is often inefficient and insecure. We propose an efficient heuristic method called CRIME (Community Rewiring for Influence and Masking Entities) to address this challenge. CRIME removes the most influential internal links, measured by edge-betweenness centrality, and adds external links with the least betweenness centrality. Experiments on real-world networks demonstrate that CRIME hides targeted communities more effectively than DICE, and also achieves faster execution and improves hiding effectiveness by up to 99.8%.
Bridging Machine Learning and Physics for Scalable Long-Term Building Temperature Prediction (Student Abstract)
Authors: Rohan Saha; Tushar Shinde
Venue: AAAI 2026 (main)

Abstract: Building temperature prediction is crucial for energy optimization and control in smart cities. We present a physics-enhanced XGBoost framework in a multi-stage sequential scaling approach. Starting from single-zone, single-day predictions, we progressively scale to multi-zone, multi-year forecasts using real-world data from Google's Smart Building Simulator. Our method incorporates physics-enhanced features, temporal encodings, and inter-zone interactions, achieving mean absolute errors (MAE) as low as 0.169°F for weekly multi-zone predictions. For longer horizons, we employ ensemble strategies, demonstrating robust performance up to 2.5 years. Compared to baseline models, our framework consistently improves long-term prediction fidelity. This work advances urban AI by enabling accurate long-term building dynamics modeling for downstream control tasks and bridges machine learning with physics-based modeling approaches.
Feasibility-Aware Masked Transformer for the Pickup-and-Delivery Problem with Time Windows (Student Abstract)
Authors: Kaede Saito; Ryota Higa; Hiromu Imura; Masaaki Kondo
Venue: AAAI 2026 (main)

Abstract: The Pickup-and-Delivery Problem with Time Windows (PDPTW)
is a time-constrained variant of the vehicle-routing
problem (VRP). Complex time constraints make it difficult
to solve using existing NCO methods. In this paper, we
present the Feasibility-Aware Masked Transformer
(FAM-Trans) specialized for PDPTW. FAM-Trans integrates a
lightweight side encoder with a context-aware embedding
scheme that effectively captures temporal dependencies. A
dynamic key-value module continuously updates node
embeddings as the route progresses. During inference, a
feasibility-guided post-inference filtering strategy
suppresses constraint violations without post-hoc repair.
Experiments on standard PDPTW benchmarks show that
FAM-Trans outperforms NCO baselines by 20~35% in solution
quality and constraint satisfaction.
When Equal Isn’t Fair: Mitigating Over-Normalization in Large Language Models (Student Abstract)
Authors: Ravada Satyadev; Aditya Ganesh Kumar; Avinash Anand; Rajiv Ratn Shah; Zhengkui Wang; Mukesh Prasad
Venue: AAAI 2026 (main)

Abstract: Bias in Large Language Models (LLMs) is increasingly
addressed through fairness-oriented techniques. However, in
some cases, these approaches may inadvertently remove
genuine cultural differences between groups, leading to
“over-normalization” or models losing important
socio-cultural distinctions. In this work, we introduce
OverNormEval, a benchmark designed to detect when an LLM
exhibits such over-normalization. We further explore the
use of Direct Preference Optimization (DPO) to mitigate
over-normalization.
MV-LLMRec: Multi-View Representation Learning with Large Language Models for Recommendation (Student Abstract)
Authors: Minkyung Song; Soyoung Park; Sungsu Lim
Venue: AAAI 2026 (main)

Abstract: Traditional recommenders often fail to disentangle the motivations behind user choices. To address this, we propose MV-LLMRec, a framework that models interactions through three views: Structural, Intent, and Conformity. MV-LLMRec leverages LLMs to generate rich semantic representations for intent and conformity, which are refined through graph propagation and dynamically fused via an attention mechanism. We evaluate MV-LLMRec on the Amazon-Movie and Amazon-Book datasets and show that it significantly outperforms state-of-the-art baselines, validating our approach.
Shortcut Learning Susceptibility in Vision Classifiers (Student Abstract)
Authors: Pirzada Suhail; Vrinda Goel; Amit Sethi
Venue: AAAI 2026 (main)

Abstract: Shortcut learning, where machine learning models exploit
spurious correlations in data instead of capturing
meaningful features, poses a significant challenge to
building generalizable models. Vision classifiers based on
Convolutional Neural Networks (CNNs), Multi-Layer
Perceptrons (MLPs), and Vision Transformers (ViTs) leverage
distinct architectural principles to process spatial and
structural information, making them differently susceptible
to shortcut learning. In this study, we systematically
evaluate these architectures by introducing deliberate
shortcuts into the dataset that are correlated with class
labels both positionally and via intensity, creating a
controlled setup to assess whether models rely on these
artificial cues or learn actual distinguishing features. We
perform both quantitative evaluation by training on the
shortcut-modified dataset and testing on two different test
sets—one containing the same shortcuts and another without
them—to determine the extent of reliance on shortcuts.
Additionally, qualitative evaluation is performed using
network inversion-based reconstruction techniques to
analyze what the models internalize in their weights,
aiming to reconstruct the training data as perceived by the
classifiers. Further, we evaluate susceptibility to
shortcut learning across different learning rates. Our
analysis reveals that CNNs at lower learning rates tend to
be more reserved against entirely picking up shortcut
features, while ViTs, particularly those without positional
encodings, almost entirely ignore the distinctive image
features in the presence of shortcuts.
Network Inversion for Uncertainty-Aware Out-of-Distribution Detection (Student Abstract)
Authors: Pirzada Suhail; Rehna Afroz Shaik; Gouranga Bala; Amit Sethi
Venue: AAAI 2026 (main)

Abstract: Out-of-distribution (OOD) detection and uncertainty
estimation (UE) are critical components for building safe
machine learning systems. In this work, we propose a novel
framework that combines network inversion with classifier
training to simultaneously address both OOD detection and
uncertainty estimation. We extend a standard n-class
classifier by adding an (n+1)-th "garbage" class to capture
outliers, initially populated with random Gaussian noise.
After each training epoch, we use network inversion to
reconstruct inputs for all classes; incoherent
reconstructions are assigned to the garbage class for
retraining. This iterative cycle of training, inversion,
and exclusion continues until inverted samples resemble
in-distribution data and uncertainty drops, indicating
learned decision boundaries and cleaner class manifolds. At
inference, the model detects OOD inputs by classifying them
as garbage, with confidence scores estimating uncertainty.
Unlike prior methods, this approach requires no external
OOD data or post-hoc calibration, providing a simple,
unified solution for robust classification, OOD detection,
and uncertainty estimation.
RESPOND: Realistic Environment Simulation of Population and Natural Disasters with LLM-Driven Agents (Student Abstract)
Authors: Roman Sultimov; Mikhail Mozikov; Dmitrii Abramov; Mariia Kovalchuk; Maksim Malykh; Aleksandr Volkov; Ilya Makarov; Andrei Osiptsov; Yury Maximov
Venue: AAAI 2026 (main)

Abstract: Climate change is driving more frequent and severe disasters, putting people and infrastructure at risk. Protecting communities requires models that capture both natural disasters dynamics and how people behave under extreme conditions. This demo presents RESPOND, a multi-agent LLM-enhanced platform that jointly simulates natural hazards and human response. RESPOND couples high-fidelity flood AI forecasting an agent-based model of
human behavior. LLM modules improve each agent decision-making, enabling context-aware reasoning over alerts, road closures, social signals, and changing water levels. The system simulates evacuation flows, resource seeking, and communication patterns producing actionable outputs for emergency management, urban planning, and policy. In the live demo one can run what-if or predicted scenarios, adjust assumptions, and observe emergent population behavior and risk hot spots in real time. By tightly coupling dynamic hazards with LLM-driven multi-agent behavior, RESPOND moves beyond fragmented tools and offers a practical, integrated platform for disaster preparedness and response.
Efficient Contextual Bandit Learning via Reward-Space Sampling and Online Optimization (Student Abstract)
Authors: Egor Suraveikin; Dastan Omirzak; Roman Sultimov; Yury Maximov
Venue: AAAI 2026 (main)

Abstract: The contextual multi-armed bandit problem underlies applications in recommendations, e-commerce, finance, and healthcare, where balancing exploration and exploitation is critical. While algorithms such as Upper Confidence Bound (UCB) and Thompson Sampling (TS) achieve strong theoretical
guarantees, they often incur heavy computational cost from high-dimensional parameter estimation. We propose a new approach that combines reward sampling with online stochastic optimization. At each round, the algorithm samples hypothetical rewards for all actions and selects the action with the largest draw; the observed reward then updates the model via stochastic optimization. This design is both simple and efficient, preserving exploration while avoiding the pitfalls of greedy behavior on near-duplicate arms. Across synthetic and real-world datasets, our method attains near-optimal reward more quickly and with substantially lower computation than TS and UCB, demonstrating that sampling directly in reward space can improve both statistical efficiency and scalability.
Guarding Digital Identity: Attention-Guided Fusion for Detecting Forged ID Documents (Student Abstract)
Authors: Gargi Surendra Yeole; Poulomi Bhattacharya; Akshay Agarwal
Venue: AAAI 2026 (main)

Abstract: Government verification systems are increasingly relying on internet-based platforms, where users authenticate their identities by uploading images captured with ordinary mobile devices. However, the rapid advancements in generative algorithms have enabled the creation of highly realistic forged ID cards that can easily bypass such verification pipelines. These forgeries are not restricted to a single modality; they may target facial imagery, textual content, or both, posing significant challenges to existing detection approaches. We present a framework that analyzes visual features for ID forgery detection by integrating feature fusion with attention mechanisms, leveraging both convolutional neural network (CNN) architectures, such as ResNet-50 and EfficientNet, and transformer-based models, including ViT-16 and Swin Transformer. This study emphasises the significance of feature fusion and attention-driven representation learning in developing robust and trustworthy ID forgery detection systems for real-world deployment.
Domain-Specific Retrieval for Retrieval-Augmented Generation: A Case Study on Pertussis Research (Student Abstract)
Authors: Hiroki Takabatake; Niken Prasasti Martono; Asaomi Kuwae; Toshihiko Iuchi; Hayato Ohwada
Venue: AAAI 2026 (main)

Abstract: Integrating knowledge from scientific literature is essential in biomedical research. However, the rapid growth of scientific literature makes staying up to date increasingly challenging. Retrieval-Augmented Generation (RAG) offers a promising framework, but its effectiveness in specialized biomedical domains remains unclear. In this work, we propose a two-stage retrieval pipeline for RAG, with a focus on Bordetella pertussis as a case study. Our method first applies hard filtering with synonym expansion to eliminate irrelevant passages, and then performs hybrid search, followed by reranking. We evaluate our approach using a dataset of 58 pertussis-related queries with automatic relevance judgments from multiple large language models (LLMs). Experimental results show that our pipeline improves MAP@10 by 13.4-20.4 points compared with existing methods and achieves the highest MRR@10. Furthermore, consistent improvements across different LLMs highlight the effectiveness of our approach.
CtoD-MAT: Bridging Centralized and Decentralized Execution in Multi-Agent Reinforcement Learning (Student Abstract)
Authors: Shota Takayama; Katsuhide Fujita
Venue: AAAI 2026 (main)

Abstract: Although centralized training with centralized execution
(CTCE) excels at multi-agent coordination, its reliance on
global information limits its use in the real world.
Conversely, the practical decentralized execution (CTDE)
paradigm often struggles with complex coordination. This
paper bridges this critical gap by introducing the
Centralized-to-Decentralized (CtoD) learning concept: a
novel framework for transferring the knowledge of a
powerful centralized policy into a robust, practical
decentralized policy. Our method, CtoD-MAT, realizes this
transition through a curriculum that gradually shifts
agents from centralized to decentralized control. A key
innovation is our dynamic scheduling mechanism, featuring a
mediator module, which ensures a robust and effective
knowledge transfer. Using challenging SMAC benchmarks, we
demonstrate that CtoD-MAT successfully produces competitive
decentralized policies, notably solving complex
coordination tasks that are difficult for standard CTDE
methods.