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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.

AAAI 2026

Knowledge Graph for Efficient Multi-hop Question Generation (Student Abstract)

poster

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Federated Graph Learning enables multiple clients to
collaboratively train graph models while protecting local
private data. However, most studies have assumed that all
clients contribute data voluntarily and actively. Without
reasonable incentives, clients are often reluctant to
contribute personal data for model training. Furthermore,
the budget for incentives is limited, and if clients with
low-quality graph data are incentivized to participate in
training, it will negatively impact the training
performance of all parties in the system. To address this,
we propose AEFGL, a Reverse Auction and Value
Evaluation-Based Incentive Mechanism for Federated Graph
Learning. First, we design a reverse auction mechanism
combining graph structural attribute motifs with client
production value. Then, we propose a method for evaluating
client production value based on the comparison of the
client's expected reward and actual value. This mechanism
can incentivize clients with high-quality graph data to
participate in training within budget constraints, thereby
improving the model quality. Experimental results validate
the superiority of the AEFGL mechanism and the economic
properties it satisfies.

AEFGL: Reverse Auction and Value Evaluation-Based Federated Graph Learning Incentive Mechanism (Student Abstract)

The two-dimensional (2D) graph structure of a molecule encodes abundant latent property information. A well-designed molecular graph encoder can capture informative low-dimensional dense representations of molecules, which can subsequently be applied to a widerange of downstream tasks. To achieve fine-grained anddiscriminative molecular representations that capture localized structural information, we propose an novel atom-level adaptive receptive field encoder, enabling each atomic node in the molecular graph to dynamically adjust its receptive field size. To the best of our knowledge, we are the first to introduce an effective rank-guided pruning
strategy for 2D molecular graphs.

Atom-level Adaptive Receptive Fields: A Pruning-Based Encoder for 2D Molecular Graphs (Student 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.

Domain-Specific Retrieval for Retrieval-Augmented Generation: A Case Study on Pertussis Research (Student 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.

Multi-Stage Reinforcement Learning for Robust Charging of Quantum Batteries (Student Abstract)

What if the next generation of human-computer interaction
is not a screen... but a conversation? Large Language
Models (LLMs) offer a new paradigm for interacting with
computers through text, but they lack shape reasoning
capabilities. We introduce Textual Anatomy Encoding (TAE),
a workflow that connects LLMs with 3D anatomies. TAE
employs clinician-validated semantic annotations and
rule-based prompts to achieve deterministic and
interpretable landmark localization. The results indicate
that TAE enables LLMs to move beyond text knowledge,
achieving accurate anatomy shape understanding. This
framework opens opportunities for diagnosis, surgical
planning, and scalable medical annotation, positioning LLMs
as a foundation for next-generation human–computer
interaction in healthcare.

Can Large Language Models Grasp 3D Medical Anatomy Shapes? (Student Abstract)

Radiotherapy (RT) is a cornerstone of cancer treatment, utilizing high doses of radiation to kill cancer cells and shrink tumors. Following RT, patient-reported outcomes (PROs) collected via standardized questionnaires are crucial for monitoring quality of life and side effects. However, traditional statistical and machine learning methods, which rely on structured numerical data, often fail to capture the nuanced emotions and semantic meaning within patients' health status. To address this, we developed a novel framework using zero- and few-shot Large Language Models (LLMs) to identify patients experiencing mild to severe depression. Furthermore, we enhanced classification performance through parameter-efficient fine-tuning with Low-Rank Adaptation (LoRA). The key is to freeze the pre-trained model's weights and add trainable low-rank matrices to each Transformer layer, drastically reducing the number of parameters in few-shot LLMs that need to be trained. Experiments on a prostate cancer PRO dataset for depression demonstrated that our fine-tuned LLMs consistently outperformed other baseline methods across key metrics, including AUC, AUCPR, Precision, and F1-score.

Fine-tuning Zero-shot Large Language Models for Patient-reported Outcomes (Student Abstract)

Large language models (LLMs) often perform better when
prompted to explain their reasoning, but it remains unclear
how well such gains persist as reasoning depth increases.
In this work, we propose a depth-aware evaluation framework
alongside the performance results on two structured
datasets: CLUTRR (kinship reasoning) and ProofWriter
(logical entailment), comparing direct vs. reasoning
(reasoning depth = number of inference steps required)
prompts across five models. Reasoning gave small gains at
shallow depths but quickly weakened and often reversed as
tasks grew more complex. In ProofWriter, GPT-5 reached 90%
accuracy at depth four in direct model, yet its reasoning
accuracy fell below baseline after depth two. Smaller
open-source models showed only unstable or negligible
gains, underscoring that reasoning in LLMs remains brittle
with increased depth.

When Reasoning Collapses: A Depth-Aware Probe into LLM Reasoning (Student Abstract)

We tackle the challenge of setting smart prices and advertising budgets for dairy products sold through three retail channels—General Trade (GT), Modern Trade (MT), and E-commerce (EC)—in markets where data is scarce. Traditional economic models can capture the complex relationships between price, trust, and advertising, but solving these models every time a manager wants to ask a question is slow and impractical. Our solution is to turn a game-theoretic market simulator into training data: we generate 10,000 market scenarios, solve for the best pricing and ad strategies, and train an AI to imitate those decisions. On unseen scenarios, the AI remains accurate (price RMSE ≤ 0.047, ad RMSE ≤ 0.031) and economically sound (Ratio of Means = 1.0010, Regret = 0.40%). To make it easy to use, we add a simple natural-language interface: users can say things like “trust is low, ad cost is high,” and the system returns price and ad suggestions along with confidence ranges. This creates a practical bridge between AI and economic rigor—delivering defensible decisions in seconds, even when data is limited.

Game-Theoretic Simulations Meet AI: Fast Policy Recommendations Under Data Scarcity (Student 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.

When Equal Isn’t Fair: Mitigating Over-Normalization in Large Language Models (Student 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

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AEFGL: Reverse Auction and Value Evaluation-Based Federated Graph Learning Incentive Mechanism (Student Abstract)

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