GenAI4ELab/papercli-papers huggingface dataset:
Knowledge Graph for Efficient Multi-hop Question Generation (Student Abstract)
Authors: Al Hasib Mahamud; Yllias Chali
Venue: AAAI 2026 (main)

Abstract: Question generation is the task of natural language processing where the goal is to generate fluent, grammatically correct, error-free questions based on a given input context and optionally an answer. Multi-hop question generation is a more complex task compared to traditional single-hop question generation, as it requires reasoning over multiple information from multiple input contexts in generating multi-hop questions. In this paper, we have addressed the challenge of building a multi-hop question generation system by combining the knowledge graphs with large language models. We have designed a framework KG4QG (Knowledge Graph for Question Generation), where knowledge graphs are generated from the input contexts. For the knowledge graph embedding, we have used Graph Attention Network, and for input text embedding, we have leveraged Sentence Transformer. Finally, we apply BART and T5 models as Large Language Models to generate multi-hop questions from our proposed model. Using HotpotQA dataset to evaluate the performance of our KG4QG framework, our proposed methodology has shown an enhancement of performance over the previous methodologies.
Towards Capable and Secure Autonomous Computer-Use Agents (Student Abstract)
Authors: Malak Mahdy; Carlos Rubio-Medrano
Venue: AAAI 2026 (main)

Abstract: Autonomous computer-use agents (ACUAs) enable end-to-end computer operation with human-like capabilities, executing commands across applications and making independent decisions. However, their real-world effectiveness and security remain largely untested. A systematic evaluation of ACUAs from Anthropic, OpenAI, and open-source projects categorized them into full computer access and browser-based agents. Findings reveal substantial limitations, with success rates dropping as low as 28% in some cases. Additionally, a 100% rate of unauthorized software installation was observed in certain tasks. The agents also demonstrated susceptibility to prompt injection attacks. The impact of varied prompting strategies on performance was also examined. In response to these weaknesses, a new agent framework designed to address these limitations is proposed. This work bridges agentic AI, human-computer interaction (HCI), and security to address the observed limitations of ACUAs, prioritizing both capability and safety.
NewsLensAI: NER-Guided Summarization for Mitigating Hallucination and Bias in LLM-Based News Summaries (Student Abstract)
Authors: Gaurank Maheshwari; Ambika Taploo; Ashiqur R. Khudabukhsh
Venue: AAAI 2026 (main)

Abstract: Automated news summarization using large language models (LLMs) offers great potential to enhance information accessibility. However, critical challenges, such as hallucinations, bias, and toxicity, threaten their reliability and societal acceptance. In this paper, we present NewsLensAI, a novel summarization framework explicitly designed to address these trustworthiness concerns through Named Entity Recognition (NER)-guided prompting. By anchoring summaries in key factual entities extracted from source articles, our method significantly reduces factual inaccuracies without altering model weights or architectures. We evaluated NewsLensAI on a dataset of 1,500 real-world news articles using open-source (LLaMA 3) and proprietary (Gemini 1.5) LLMs. Our analysis encompasses factual consistency, political bias shifts, sentiment preservation, and moderation of toxicity. Our results indicate substantial improvements in factual alignment, demonstrated by an average increase in the BERTScore from 0.80 (baseline) to 0.88 (NER-enhanced), and an approximately 60% reduction in hallucinated entities. To capture contextual terms that are relevant beyond the core entities, we use TF-IDF salience scoring to supplement standard NER categories, particularly for legislative terms and event identifiers. Furthermore, we identify and characterize a notable “centrist drift,” wherein summaries tend to moderate extreme biases present in source articles, along with a measurable reduction in toxic or emotionally charged language. Complementing our empirical findings, we introduce a real-time NewsLensAI demo that summarizes live news feeds from the Guardian API, providing dynamic bias and sentiment analysis. This practical implementation underscores the real-world applicability and potential societal benefit of our approach. Finally, we discuss critical ethical implications, including potential impacts on media literacy and information diversity. Our interdisciplinary approach, linking NLP, journalism, and ethical analysis, positions NewsLensAI as a meaningful step towards safer, fairer, and more trustworthy AI-generated news consumption.
Guided Latent Spaces for Controllable Multi-Scenario Generation in Autonomous Driving (Student Abstract)
Authors: Manasa Mariam Mammen; Zafer Kayatas; Stefan Wagner
Venue: AAAI 2026 (main)

Abstract: Scenario-based testing is an important approach for the
development and validation of autonomous driving systems,
as it enables evaluation across different driving
situations. Safety-critical scenarios are especially
relevant, but they occur rarely in real-world data, which
creates the need for generation methods. In this paper, we
present a scalable AI-based approach based on a variational
autoencoder that unifies the generation of different types
of critical scenarios while introducing controllability
through a structured latent space. The integration of
unified generation and latent space control advances
AI-based scenario generation towards practical use, thereby
supporting the requirements of industrial validation
pipelines.
BRI-MH: Behavioral Risk Index for Mental Health — An Interpretable Multimodal LLM-Augmented Framework (Student Abstract)
Authors: Mahi Mann; Avinash Anand; Rajiv Ratn Shah
Venue: AAAI 2026 (main)

Abstract: Mental health monitoring faces challenges from fragmented
data and opaque risk scores. We present BRI-MH, an in-
terpretable multimodal framework combining behavioral sig-
nals with cognitive features from large language models to
produce a weekly Behavioral Risk Index. Unlike prior work
with isolated or black-box scores, BRI-MH offers transpar-
ent, actionable insights and links continuous monitoring to
adaptive feedback and therapeutic support, bridging digital
phenotyping and clinical care.
Deterministic Hyperdimensional Learning with Rank Refinement (Student Abstract)
Authors: Abu Kaisar Mohammad Masum; Sercan Aygun
Venue: AAAI 2026 (main)

Abstract: Hyperdimensional Computing (HDC) represents data as
high-dimensional
hypervectors that are robust and efficient for learning.
Existing methods often rely on pseudo-random hypervector
generation, which can suffer from poor orthogonality and
high variance across runs, ultimately slowing convergence.
These approaches typically require numerous iterations (20–
100) to achieve acceptable accuracy. We propose a method
that utilizes deterministic Sobol-based linear projections
and
rank-based retraining to construct more stable and
discriminative
hypervectors, thereby reducing class confusion. Unlike
pseudo-random initialization, our projections guarantee
reproducibility
and better coverage of the feature space. As a result,
our approach achieves up to 97% accuracy in only 5
iterations.
This makes our model up to 20× faster while simultaneously
improving accuracy.
Exposing DeepFakes via Hyperspectral Domain Mapping (Student Abstract)
Authors: Aditya Mehta; Swarnim Chaudhary; Pratik Narang; Jagat Sesh Challa
Venue: AAAI 2026 (main)

Abstract: Modern generative and diffusion models produce highly realistic images that can mislead human perception and even sophisticated automated detection systems. Most detection methods operate in RGB space and thus analyze only three spectral channels. We propose HSI-Detect, a two-stage pipeline that reconstructs a 31-channel hyperspectral image from a standard RGB input and performs detection in the hyperspectral domain. Expanding the input representation into denser spectral bands amplifies manipulation artifacts that are often weak or invisible in the RGB domain, particularly in specific frequency bands. We evaluate HSI-Detect across FaceForensics++ dataset and show the consistent improvements over RGB-only baselines, illustrating the promise of spectral-domain mapping for Deepfake detection.
New Metrics for Disambiguating Feature Overlap and Catastrophic Forgetting in Incremental Learning Contexts (Student Abstract)
Authors: Niklas M. Melton; Leonardo Enzo Brito da Silva; Donald C. Wunsch
Venue: AAAI 2026 (main)

Abstract: Catastrophic forgetting remains a central challenge in lifelong learning, where newly acquired knowledge interferes with previously learned tasks, degrading performance over time. Mitigation strategies such as rehearsal and regularization have been proposed, but both introduce limitations, either by retaining old data or by constraining model updates in ways that may impair learning. Complicating matters, recent findings show that feature-space overlap between tasks can produce similar performance drops even in models that memorize data, making it difficult to distinguish true forgetting from representational interference. Current accuracy-based metrics fail to disentangle these effects, undermining diagnostic clarity.
In this work, we introduce the Overlap Index, an incremental cluster validity index adapted from the inter-cluster component of the iCONN index, which quantifies overlap between feature representations in input or latent space. We then introduce the Overshadowing and Forgetting Index, an online meta-metric that leverages the Overlap Index to attribute performance degradation to catastrophic forgetting, class overshadowing, or both. Our experimental results demonstrate that these tools enable more precise online and batch-mode evaluation of continual learning systems, paving the way for more targeted mitigation strategies.
Learning Droplet Dynamics on Rough Unstructured Surfaces Using Physics-Informed Neural Networks (Student Abstract)
Authors: Ganesh Sahadeo Meshram; Partha Pratim Chakrabarti; Suman Chakraborty
Venue: AAAI 2026 (main)

Abstract: This study develops a physics-informed neural network (PINN) framework to predict droplet spreading dynamics on unstructured rough surfaces. The trained model effectively captures temporal evolution of the droplet shape, contact line motion, and interfacial deformation. This integration of multiphase physics with neural networks provides a mesh-free and computationally efficient alternative to numerical solvers, enabling rapid analysis and design of wettability-controlled surfaces, microfluidic devices.
Detecting Citation Hallucinations in Large Language Model Outputs (Student Abstract)
Authors: Nipun Misra; Vikranth Udandarao
Venue: AAAI 2026 (main)

Abstract: Large Language Models (LLMs) are increasingly employed for literature reviews, academic drafting, and scholarly writing. While their fluency accelerates knowledge synthesis, they frequently produce fabricated or erroneous references, known as citation hallucinations (CHs). Recent studies report hallucination rates ranging from 18% in GPT-4 to over 70% in other frontier models, with domain-specific rates as high as 88% in legal contexts. Benchmarks such as CiteME further highlight the gap between LLMs (4.2–18.5% accuracy) and human annotators (69.7%), while retrieval-augmented systems like CiteAgent demonstrate partial progress. This study examines methods for automatically detecting hallucinated citations. We present a benchmark of machine-generated references labelled with three fine-grained categories (valid, partially valid, and hallucinated), and propose a hybrid detection pipeline combining bibliographic retrieval, fuzzy similarity, and LLM-based verification. Preliminary experiments indicate improvements over exact matching baselines. We argue that scalable, real-time citation verification is a crucial step toward developing trustworthy LLM-based scholarly assistants and generating reproducible scientific knowledge, and outline directions for multilingual and domain-specific extensions.
Social Influence-Based Mutual Acknowledgement Token Exchange (Student Abstract)
Authors: Shoma Mizuno; Keichi Namikoshi; Yuko Sakurai
Venue: AAAI 2026 (main)

Abstract: External incentive mechanisms have been studied as a method
to promote cooperation in sequential social dilemmas
involving multiple autonomous agents. Mutual Acknowledgment
Token Exchange (MATE) is one such approach: by enabling
agents to exchange acknowledgment tokens, it induces
cooperation without additional training. However, MATE’s
use of fixed, manually tuned token values limits
adaptability to nonstationary environments and can
constrain performance. To enable a dynamically adapted
token, we introduce Social Influence-based MATE (SI-MATE),
which allows agents to share their individual improvement
signals and to self-punishment in response to inequality.
Experiments in a four-agent environment show that SI-MATE
outperforms MATE across multiple metrics, including
learning speed.
Cumulant Attention in Vision Transformers (Student Abstract)
Authors: Yuto Morimoto; Zhipeng Wang; Koji Yasuda
Venue: AAAI 2026 (main)

Abstract: Transformer models have achieved remarkable success across diverse deep learning fields, including natural language processing (NLP) and computer vision (CV). One drawback of these models is that the computational cost of the softmax attention, the core component of the transformer, exhibits quadratic complexity in both time and memory. As data scales up various attempts have been reported to overcome this bottleneck. The objective of this study is to propose a novel attention mechanism, "Cumulant Attention", that systematically balances efficiency and accuracy. This proposal introduces a statistical-mechanics perspective and a reliable approximation based on cumulant expansion into the attention layer. The low-order variant reduces computational complexity to linear order, similar to the linear attention, while keeping nonlinearity of the softmax attention. We evaluate several variants on CV tasks, including image classification with ViT on ImageNet-100 and video classification with ViViT on UCF-101. Experimental results demonstrate that the cumulant attention outperforms the linear attention and achieves accuracy comparable to the softmax attention. These findings validate the effectiveness of our approach and highlight future directions, including scaling to larger models, extending to other modalities, and optimizing implementations for GPU hardware.
iDT-diet: Toward Personalized Health Forecasting-An Intelligent Digital Twin Model for Diet-Influenced Biomarker Trajectories (Student Abstract)
Authors: Ashikur Nobel; Jacob Matos; Honggang Wang; Hua Fang
Venue: AAAI 2026 (main)

Abstract: We present iDT-diet, an intelligent digital twin prototype designed to model the long-term influence of diet quality on health biomarkers and chronic conditions. The system integrates three novel components: (i) a random forest learning model enhanced with Choquet LASSO feature selection for capturing complex, nonlinear interactions in temporal health data; (ii) a translation module that converts predictive outputs into natural language narratives of physical and biomarker states; and (iii) a generative 3D visualization engine that produces dynamic, personalized digital twins reflecting evolving health trajectories. This integration uniquely links advanced machine learning, interpretable communication, and immersive visualization within a single framework. While the current implementation focuses on retrospective digital twin generation, the system architecture supports real-time data integration, enabling continuous monitoring, predictive simulation, and personalized recommendation delivery for diet and lifestyle management.
Memory Based Advantage Shaping for LLM-Guided Reinforcement Learning (Student Abstract)
Authors: Narjes Nourzad; Carlee Joe-Wong
Venue: AAAI 2026 (main)

Abstract: In environments with sparse or delayed rewards,
reinforcement learning (RL) incurs high sample complexity
due to the large number of interactions needed for
learning. This limitation has motivated the use of large
language models (LLMs) for subgoal discovery and trajectory
guidance. While LLMs can support exploration, frequent
reliance on LLM calls raises concerns about scalability and
reliability. We address these challenges by constructing a
memory graph that encodes subgoals and trajectories from
both LLM guidance and the agent’s own successful rollouts.
From this graph, we derive a utility function that
evaluates how closely the agent’s trajectories align with
prior successful strategies. This utility shapes the
advantage function,  providing the critic with additional
guidance without altering the reward. Our method relies
primarily on offline input and only occasional online
queries, avoiding dependence on continuous LLM supervision.
Preliminary experiments in benchmark environments show
improved sample efficiency and faster early learning
compared to baseline RL methods, with final returns
comparable to methods that require frequent LLM interaction.
Behavioral-Similarity and Clustering-Based Methods for Static Graph Estimation in Hybrid GNNs (Student Abstract)
Authors: Ryusei Otani; Keichi Namikoshi; Yuko Sakurai; Mingyu Guo; Satoshi Oyama
Venue: AAAI 2026 (main)

Abstract: In this study, we propose two methods to estimate static graphs from a single dynamic graph and integrate them into hybrid Graph Neural Networks (GNNs), which combine long-term static structure with transient dynamic interactions. Since static graphs are often unavailable and attributes may be difficult to use at scale or under privacy constraints, we introduce: (i) a “behavioral similarity” estimator based on normalized co-occurrence, which requires no attributes, and (ii) an attribute-aware K-means + k-NN estimator that is more efficient than cosine similarity. Experiments on multiple real-world datasets show that both methods consistently improve predictive accuracy and training efficiency, underscoring the importance of static graph choice in hybrid GNNs.
ProRefine: Inference-Time Prompt Refinement with Textual Feedback (Student Abstract)
Authors: Deepak Pandita; Tharindu Cyril Weerasooriya; Ankit Shah; Isabelle Diana May-Xin Ng; Christopher M. Homan; Wei Wei
Venue: AAAI 2026 (main)

Abstract: Agentic workflows, where multiple AI agents collaborate to accomplish complex tasks like reasoning or planning, play a substantial role in many cutting-edge commercial applications. These workflows depend critically on the prompts used to provide the roles models play in such workflows. Poorly designed prompts that fail even slightly to guide individual agents can lead to sub-optimal performance that may snowball within a system of agents, limiting their reliability and scalability. To address this important problem of inference-time prompt optimization, we introduce ProRefine, an innovative inference-time optimization method that uses an agentic loop of LLMs to generate and apply textual feedback. ProRefine dynamically refines prompts for multi-step reasoning tasks without additional training or ground truth labels. Evaluated on five benchmark mathematical reasoning datasets, ProRefine significantly surpasses zero-shot Chain-of-Thought baselines by 3 to 37 percentage points. This approach not only boosts accuracy but also allows smaller models to approach the performance of their larger counterparts. This highlights its potential for building cost-effective and powerful hybrid AI systems, thereby democratizing access to high-performing AI.
Multi-Stage Reinforcement Learning for Robust Charging of Quantum Batteries (Student Abstract)
Authors: Beomdo Park; Hyeonseok Jang; Junseong Park; Minu Baek; Gihun Gil; Minsung Jung; Woohyeon Kwon; Harin Jang; Yeojin Jang; Hoon Jeong; Taewook Heo; Sangkeum Lee
Venue: AAAI 2026 (main)

Abstract: Quantum batteries have emerged as a next-generation energy
storage solution, leveraging quantum phenomena such as
superabsorption to overcome the limitations of conventional
energy technologies.
However, noise arising from interactions with the external
environment degrades the charging efficiency and stability
of the battery by disrupting the system's quantum coherence.
To address this challenge, this study proposes a robust
charging framework for a single-qubit quantum battery based
on the Jaynes-Cummings (JC) model.
The proposed framework combines the Proximal Policy
Optimization (PPO) algorithm with a multi-stage
reinforcement learning structure.
The agent first learns fundamental control principles in a
noise-free, ideal environment and subsequently performs
robust learning in progressively noisier and more complex
settings.
Simulation results demonstrate that the trained agent
navigates a stable charging trajectory on the Bloch sphere,
thereby achieving high ergotropy even in the presence of
noise.
These findings suggest that multi-stage reinforcement
learning is an effective solution for control problems in
noisy quantum systems and provides a theoretical foundation
for designing charging protocols for multi-qubit systems.
Adaptive Compute Efficient Learning via Conceptual-Criticality (Student Abstract)
Authors: Iñigo Parra; Mano Bharathi M; Mayank Kumar; Pushpa Kumar Balan; Priyadarsi Mishra
Venue: AAAI 2026 (main)

Abstract: The computational cost of large language models (LLMs) is
a primary obstacle to sustainable deployment. Static
resource
allocation is inefficient, as not all inputs require the
same
depth of processing. We propose a framework for adaptive,
compute-efficient learning via conceptual criticality, which
dynamically tailors computation to the assessed difficulty
of an input. A lightweight criticality prediction module es-
timates conceptual complexity on a continuous scale, and
this score governs the LLM’s inference pathway, selectively
activating token pruning, layer skipping, and quantization.
Simple inputs are processed with minimal FLOPs and la-
tency, while complex inputs use the model’s full capacity
to preserve accuracy. We benchmark our framework and in-
troduce metrics to quantify sensitivity to input criticality
and per-sample computational savings. Results demonstrate
an improved accuracy-efficiency trade-off, paving the way
for more resource-aware systems.
An Approach Towards Developing Relationally Intelligent Multimodal Framework for Stock Movement Prediction (Student Abstract)
Authors: Manali Patel; Krupa Jariwala; Chiranjoy Chattopadhyay
Venue: AAAI 2026 (main)

Abstract: The dependency of stock prices on a multitude of factors
makes the task of prediction exceedingly challenging. Given
the volatile nature of stock data, it is imperative to
integrate multiple sources of information to accurately
encompass the various factors that influence market trends.
To capture these complex dynamics, several multimodal
methodologies have been proposed, integrating market data,
technical indicators, and textual information. However, it
is claimed that these coarse-grained information sources do
not offer a holistic view of the market. Furthermore, these
sources are stock-specific and do not elucidate the
interconnections between various stocks. To address this
deficiency, we propose a multimodal approach that
incorporates this relational aspect alongside fine-grained
information sources. The applicability of our framework is
underscored by empirical results, which demonstrate the
superiority of our approach.