GenAI4ELab/papercli-papers huggingface dataset:
Diffusion for Combating the Hallucination in Large Language Models (Student Abstract)
Authors: Hyojun Ahn; Joongheon Kim
Venue: AAAI 2026 (main)

Abstract: Large language models (LLMs) often generate
hallucinations—fluent yet factually incorrect
responses—that undermine reliability in knowledge-intensive
tasks.
Existing approaches for hallucination mitigation typically
rely on external retrieval modules or probability
heuristics, which either require additional resources or
lack interpretability. In this work, we propose a
diffusion-based hallucination detection framework (DHDF)
that leverages U-Net denoising to reconstruct consensus
answers from multiple LLM outputs. If the diffusion process
exhibits spurious convergence away from factual ground
truth, it provides a clear signal of hallucination. To
quantify factual correctness, we incorporate TruthfulQA
scores as a fact-grounded evaluation metric, distinguishing
well-aligned models (high scores) from hallucination-prone
models (low scores). Experimental results demonstrate that
convergence dynamics under diffusion, combined with
fact-grounded QA evaluation, offer an effective and
interpretable pathway for hallucination detection without
relying on external knowledge bases.
Robust Adaptive Multi-Step Predictive Shielding (Student Abstract)
Authors: Tanmay Ambadkar; Darshan Chudiwal; Greg Anderson; Abhinav Verma
Venue: AAAI 2026 (main)

Abstract: Ensuring safety in deep reinforcement learning is challenging, as formal methods that provide strong guarantees often fail to scale to complex, high-dimensional systems. We introduce RAMPS, a scalable shielding framework that pairs a general-purpose, learned linear dynamics model with a robust, multi-step Control Barrier Function (CBF) for real-time safety interventions. Experiments show RAMPS significantly reduces safety violations in high-dimensional environments compared to state-of-the-art methods, without sacrificing task performance.
Dynamics-Aware Planning Representation for Zero-Shot Reinforcement Learning (Student Abstract)
Authors: Jungho An; Taeyoung Kim; Haeun Kim; Dongsoo Har
Venue: AAAI 2026 (main)

Abstract: Offline Zero-Shot Reinforcement Learning requires an agent
to solve unseen tasks using only a fixed offline dataset
without explicit rewards. A central challenge is learning
representations that capture both high-level long-term
planning and low-level physical dynamics. We propose a
novel framework, Dynamics-Aware Planning Representation
(DAPR), which disentangles these two aspects via
complementary contrastive objectives. Specifically, DAPR
learns goal-oriented planning directions and local
dynamics-consistent directions in the latent space. By
jointly enforcing these constraints, DAPR yields
representations that balance “where to go” with “how to
move.” Experiments on standard locomotion benchmarks
(Walker, Cheetah, Quadruped) demonstrate that DAPR
consistently improves performance and generalization over
strong baselines, achieving substantial gains on precision
demanding tasks.
Weight Entropy-Maximised Evidential Metamodel for Uncertainty Quantification (Student Abstract)
Authors: Gouranga Bala; Abhimanyu Chauhan; Amit Sethi
Venue: AAAI 2026 (main)

Abstract: Reliable uncertainty quantification (UQ) is crucial for deploying deep learning models in safety-critical domains. Existing UQ methods often either rely on multi-pass inference, which increases computational cost, or restrict expressiveness by using only final-layer embeddings. In this work, we propose a lightweight evidential meta-model that leverages multi-layer feature fusion from a pretrained backbone, capturing both low-level features and high-level semantics to better estimate uncertainty. To further enhance epistemic fidelity, we integrate maximum weight-entropy (Max-WEnt) regularization, which encourages hypothesis diversity without altering the base network or adding test-time overhead. Experiments across two benchmark settings, medical (BACH, HAM10000, BreakHIS, DIV2K) and natural (ImageNet, SVHN, Fashion-MNIST, ImageNet-C) datasets, demonstrate consistent improvements in AUROC of out-of-distribution detection compared to prior post-hoc UQ methods. Our findings show that combining multi-layer evidential modeling with Max-WEnt provides a robust, efficient, and practical framework for trustworthy AI in high-stakes applications. The meta-model adds only ~0.8M parameters and trains in under four hours on a single 48GB GPU, making it practical for real-world deployment.
Fusing Time-Domain and Constellation Views: A Multimodal MAE for Wireless Signals (Student Abstract)
Authors: Agniva Banerjee; Arijit Sen
Venue: AAAI 2026 (main)

Abstract: This paper introduces a multi-modal masked autoencoder (MMAE) that jointly denoises and classifies signals by fusing time-domain IQ sequences and constellation diagrams within a cross-attentive transformer. This approach treats noise as a learnable modality to enhance robustness, a dynamic masking curriculum combined with domain regularization training and a hybrid loss function to promote domain-invariant features. Experimentation on the RadioML 2018.01A and RadioML22 datasets demonstrates superior accuracy across different SNR levels while using substantially less labeled data than state-of-the-art approaches.
Zero-Shot Vision Language Reasoning via Dual-layer Scene Graph Chain of Thoughts (Student Abstract)
Authors: Yash Bansal; Parshiv Kapoor; Agam Pandey
Venue: AAAI 2026 (main)

Abstract: Large Multimodal Models (LMMs) often hallucinate objects and struggle with compositional reasoning in complex visual scenes. Structured Scene Graph (SG) representations explicitly encoding objects, attributes, and relations can mitigate these issues, however finetuning risks catastrophic forgetting. Recent zero-shot approaches prompt LMMs with scene graphs, yet typically rely on a single SG generated in one step, limiting capture of holistic context and question-specific details. We introduce a Dual-Layer Scene Graph Chain-of-Thought DLSG-CoT framework that enriches reasoning by combining two structured SGs: a Global Scene Graph (G-SG) that offers comprehensive image context, and a Query-Specific Scene Graph (Q-SG) produced through a two-step process targeting information relevant to the input query. Extensive experiments demonstrate that DLSG-CoT substantially improves LMM performance on compositional and context-sensitive tasks.
3D Gaussian Splatting for Reconstructing Large Sparse Environments (Student Abstract)
Authors: Jonathan Boel Nielsen; Xuan Huy Pham; Erdal Kayacan; Andriy Sarabakha
Venue: AAAI 2026 (main)

Abstract: 3D Gaussian splatting (3DGS) has recently demonstrated significant potential in computer vision, enabling high-fidelity 3D scene reconstruction with real-time rendering and fast training times. However, existing methods struggle in large, visually sparse, geometric self-similarity environments due to heavy reliance on image-based feature matching and depth information. In this work, we propose a novel reconstruction pipeline that reduces the dependence on visual features by incorporating IMU and LiDAR data to generate accurate point clouds and robustly localize images within the scene. Global colorization is achieved through 3D-to-2D projections of the localized images, which are then used to supervise 3DGS training. Our results demonstrate that the proposed pipeline significantly enhances the quality of 3D reconstruction for large, sparse scenarios, opening up new opportunities for applications in remote mapping and autonomous inspection.
Network Restoration Games with Quotas (Student Abstract)
Authors: Philip Bogaars; Argyrios Deligkas; Eduard Eiben; Michail Fasoulakis
Venue: AAAI 2026 (main)

Abstract: In a game of Network Restoration Games With Quotas, there is an underlying graph where a subset of its edges have to be restored by a set of agents. Each agent has a creation cost for each such edge, a traversal cost for every edge of the graph, and in addition they have a quota on the number of edges they have to restore. Then, given a set of edges that fulfill the quota, the cost of an agent is the cost of creating these edges, plus the cost of reaching them, i.e., the traversal cost. We prove that any cost-minimizing allocation is swap-stable, i.e., there is no profitable exchange of edges between any pair of agents, but computing one is hard even on trees. We complement this by designing an algorithm that finds a swap-stable allocation on trees in polynomial time and we quantify its cost against the optimal one.
Always Refuse: Steering LLMs Against Jailbreaks with Contrastive Activations (Student Abstract)
Authors: Abhilekh Borah; Niranjan Chebrolu; Kokil Jaidka
Venue: AAAI 2026 (main)

Abstract: Refusals must be resilient, not brittle.” Yet guarding refusals against adversarial phrasing and shifting user contexts remains difficult: large language models (LLMs) still yield to jailbreak prompts that evade safety filters and surface harmful content. We propose Refusal Activation Steering (RAS), a
training-free, inference-time method that uses contrastive activations to shift LLM responses, biasing generation trajectories toward refusals without altering model weights. The approach is modular and domain-targetable, avoiding collateral refusals on benign queries while strengthening activation-
space boundaries for unsafe content. On adversarial evaluations with an 8B instruction-tuned model, we find that steering improves refusal rate by ∼ 52% and reduces attack success rate by ∼ 40%, establishing a lightweight and interpretable safety layer for robust refusal consistency. To foster further research in this domain, we have made our implementation publicly available.
PT-DCFR: Accelerating and Improving Deep CFR Using Population Based Training (Student Abstract)
Authors: Dingzhong Cai; Huale Li; Hang Xiao; Shuhan Qi; Jiajia Zhang
Venue: AAAI 2026 (main)

Abstract: Deep CFR enables end-to-end approximation of Nash equilibria in imperfect-information games(IIGs) but is sensitive to hyperparameters, making manual tuning inefficient. To address this, we propose PT-DCFR, which integrates Population-Based Training(PBT) with Deep CFR to dynamically optimize hyperparameters during training. Building upon this, we further introduce P2T-DCFR, which decouples parameter selection from model performance.
AdaptDiff: Adaptive Guidance in Diffusion Models for Diverse and Identity-Consistent Face Synthesis (Student Abstract)
Authors: Eduarda Caldeira; Tahar Chettaoui; Naser Damer; Fadi Boutros
Venue: AAAI 2026 (main)

Abstract: Diffusion models conditioned on identity embeddings enable the generation of synthetic face images that consistently preserve identity across multiple samples. Recent work has shown that introducing an additional negative condition through classifier-free guidance during sampling provides a mechanism to suppress undesired attributes, thus improving inter-class separability. Building on this insight, we propose a dynamic weighting scheme for the negative condition that adapts throughout the sampling trajectory. This strategy leverages the complementary strengths of positive and negative conditions at different stages of generation, leading to more diverse yet identity-consistent synthetic data.
Bi-Level Preference Optimization for Retrieval-Augmented Generation (Student Abstract)
Authors: Sizhong Cao
Venue: AAAI 2026 (main)

Abstract: Retrieval-augmented generation (RAG) is the backbone of
knowledge-intensive NLP, yet its progress is hindered by a
long-standing asymmetry: Generators are refined while
retrievers remain static, and full end-to-end optimization
is prohibitively unstable. We present BPO-RAG, a bi-level
preference-learning framework that redefines the training
paradigm by jointly optimizing retrieval and generation
with a single supervision signal, pairwise preferences.
Stage~1 (Retrieval Preference Optimization) learns to
select superior evidence sets, while Stage~2 (Generation
Preference Optimization) aligns answer generation with the
same evidence, closing the gap between what to read and
what to write. This recipe without label requires no reward
model or online RL, integrates seamlessly with standard RAG
pipelines, and transforms preferences into a unifying
training currency. Across open-domain QA benchmarks,
BPO-RAG consistently advances retrieval quality and yields
more accurate, faithful answers, surpassing strong RAG
baselines with remarkable stability. By coupling retrieval
and generation under a unified preference framework,
BPO-RAG establishes a practical and principled path toward
the next generation of reliable, modular, and trustworthy
knowledge-intensive language models.
Discovering Linear Non-Gaussian Models for All Categories of Missing Data (Student Abstract)
Authors: Matteo Ceriscioli; Shohei Shimizu; Karthika Mohan
Venue: AAAI 2026 (main)

Abstract: Causal discovery is the task of learning causal models, encoding causal relationships, from a source of information, such as a dataset containing observational data. While many algorithms have been developed to discover causal models under varied sets of assumptions, the case in which the dataset is affected by missing data remains significantly underexplored. Naively applying standard causal discovery algorithms to listwise, test-wise, or regression-wise deleted datasets, or imputing the missing data, can introduce spurious associations between variables and bias function estimation in functional causal models. This issue arises when the data is missing at random or not at random. It ultimately invalidates the theoretical guarantees of these algorithms and prevents finding the true underlying causal model, even in the large-sample limit. An established family of causal models is the Linear Non-Gaussian Acyclic Model (LiNGAM), which assumes linear functional relationships and non-Gaussian independent noise terms. We propose a new causal discovery algorithm for LiNGAM, capable of recovering the underlying causal structure and providing unbiased estimates of the model’s parameters, even when the data is affected by MNAR missingness.
WingBeats and Snapshots: Fusing Sound and Vision for Mosquito Monitoring (Student Abstract)
Authors: Ahana Chanda; Akshay Agarwal
Venue: AAAI 2026 (main)

Abstract: Accurate identification of mosquito species is crucial for controlling vector-borne diseases, yet visual or acoustic methods alone are often insufficient. We propose a multimodal deep-learning framework that combines high-resolution images with wingbeat audio using a SwinV2 vision transformer and an Audio Spectrogram Transformer, thereby capturing complementary cues. On a six-species dataset, it achieves 97% accuracy, comparable to the best single-modality baseline, and is designed to improve robustness under noise or environmental variation, demonstrating the value of integrating multiple data sources for reliable mosquito surveillance.
AEFGL: Reverse Auction and Value Evaluation-Based Federated Graph Learning Incentive Mechanism (Student Abstract)
Authors: Xin Chang; Lixin Liu; Jingyu Wang; Jinling Yu; Xiaolin Zhang
Venue: AAAI 2026 (main)

Abstract: 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.
IMPACT: Integrated Multimodal Pipeline for Rapid Accident Causality Tracking (Student Abstract)
Authors: Vashu Chauhan; Avinash Anand; Manisha Luthra; Uelison Jean Lopes dos Santos; Carsten Binnig; Rajiv Ratn Shah
Venue: AAAI 2026 (main)

Abstract: Traffic accidents pose a significant societal challenge, with many fatalities being avoidable through timely emergency response. We introduce IMPACT (Integrated Multimodal Pipeline for Rapid Accident Causality Tracking), a scalable AI framework designed for autonomous, rapid traffic incident analysis using existing urban CCTV infrastructure. IMPACT combines a low-latency CPU-based vision module for real-time key-frame filtering (24 FPS) with the causal reasoning capabilities of MLLMs, reducing costly MLLM calls by over 92% compared to naive sparse sampling. We further present TRACE10K, a dataset featuring three-tier textual annotations that describe accident dynamics at the frame-sequence level.
Sleep-Like Replay Reduces Loss-Landscape Sharpness to Improve Generalization (Student Abstract)
Authors: Krishi Chawda; Jean Erik Delanois; Giri P Krishnan; Maksim Bazhenov
Venue: AAAI 2026 (main)

Abstract: One of the central challenges in deep learning is that
models trained on new tasks often overfit and lose the
ability to generalize. This issue arises because gradient
descent often converges to solutions in regions of the loss
landscape that are sharp near their minima. High sharpness
leads to rapid performance loss when test data are
perturbed or statistically shifted. Although sharpness has
been linked to generalization, few methods directly target
it to improve generalization. Here we demonstrate that an
unsupervised, sleep-like replay algorithm identifies low
loss regions with lower sharpness leading to improvement in
generalization to distortions, including Gaussian and
salt-and-pepper noise. Our study identifies loss-function
sharpness as a unifying measure for generalizable learning
and robustness, and points to new principles for designing
resilient AI systems.
Multi-Modal Interactive Control of Robotic Arm Based on Offline Large Language Models (Student Abstract)
Authors: Hanxiao Chen
Venue: AAAI 2026 (main)

Abstract: Large Language Models (LLMs) have revolutionized the modern society significantly with the numerous advanced interactions between humans and AI agents, whereas the usage of most large language models including ChatGPT are not friendly open-sourced and must require users paying a lot for such AI service continuously. Therefore, deploying open-sourced large language models on local servers can be considered as an efficient method to design and implement creative embodied AI algorithms with lower cost and more stable free usage. Inspired by this ordinary motivation, we originally propose and implement the “Socratic Models-ChatGLM”, which is a well-performed algorithm for multi-modal interactive control of robotic arm based on offline large language models via the facile PyBullet platform, even presents extraordinary potential to address complicated text-image multi-step long-horizon robotic manipulation tasks.
Fine-Tuning Sample Order Matters in Propositional Logical Question-Answering (Student Abstract)
Authors: Fengxiang Cheng; Chuan Zhou; Fenrong Liu; Robert van Rooij
Venue: AAAI 2026 (main)

Abstract: Large language models (LLMs) have achieved impressive progress in natural language processing tasks but still struggle with complex logical reasoning. We observe that in propositional logic question-answering (QA), LLMs' performance varies with the order of training samples during fine-tuning. Motivated by this, we propose a data-driven approach to automatically determine the fine-tuning sample order, enhancing the logical QA performance of LLMs. Specifically, we first quantify the logical reasoning complexity of propositional reasoning samples and then stratify the training data into several subsets of ascending complexity. Subsequently, we fine-tune the LLMs on these subsets, progressing from low to high reasoning complexity. Experimental results demonstrate that our approach outperforms single-stage fine-tuning baselines across diverse reasoning benchmarks.
Multimodal Coarse-to-Local Transformer for End-to-End Autonomous Driving (Student Abstract)
Authors: Yeryeong Cho; Joongheon Kim
Venue: AAAI 2026 (main)

Abstract: End-to-end (E2E) autonomous driving must maintain global consistency while preserving local precision. However, existing E2E approaches rarely achieve both goals simultaneously. Therefore, we propose a multimodal coarse-to-local transformer (MC2L-Transformer), which is composed of a hierarchical transformer architecture. Multimodal inputs are fused into a shared embedding, and global waypoints are produced. Local refinement is then utilized to capture fine interactions around the vehicle. Furthermore, a temporal encoder summarizes recent context, and navigation target and velocity are embedded to guide route- and speed-aware decoding. We evaluate in CARLA, and the results show lower collision and off-route rates even under sudden events. These results indicate that combining a coarse-to-local hierarchical transformer with a lightweight temporal context provides a practical step toward reliable E2E autonomous driving.
Constraint-Augmented Mongolian-Chinese Neural Machine Translation Based on Dynamic Feedback Alignment (Student Abstract)
Authors: Shuting Dai; Yatu Ji; Yanli Wang; Lei Shi; Qing-Dao-Er-Ji Ren; Nier Wu; Na Liu
Venue: AAAI 2026 (main)

Abstract: The scarcity of parallel corpora for Mongolian and Chinese constrains the performance of Mongolian-Chinese neural machine translation (NMT), particularly manifesting in
inadequate accuracy in translating specialized terminology. To address this limitation, this study adopts a lexically constrained augmentation strategy that constructs pseudo-source
sentences by appending Chinese constraint words to Mongolian source texts, while enforcing the inclusion of these constraints in the output to improve translation accuracy. However, this approach presents two inherent drawbacks: processing pseudo-sentences with a single encoder tends to induce semantic interference, while the introduced constraint words may exacerbate alignment errors during decoding. To overcome these limitations, this paper propose a Constraint-Augmented Mongolian-Chinese NMT method (CANMT) based on dynamic feedback alignment. The method employs a dual-encoder architecture to isolate bilingual representations, coupled with a dynamic feedback alignment module that progressively reduces alignment errors through iterative reffnement, thereby enhancing overall translation performance.
TWiST: Temporal Weakly-Supervised Triplets Recognition in Surgical Videos (Student Abstract)
Authors: Pranshu Danani; Yash Bansal; Parshiv Kapoor
Venue: AAAI 2026 (main)

Abstract: Deep learning is increasingly applied to intraoperative and
surgical video analysis to enable real-time workflow
recognition, and decision support for improved surgical
precision. A key direction is modeling surgical activity as
triplets of instrument, action, and target, which provide a
richer representation of procedures. However, existing
approaches often depend on bounding-box annotations or lack
temporal context. We propose TWiST (Temporal Weakly
Supervised Triplet detection), a framework that combines
weakly supervised instrument localization, temporal
attention for triplet prediction, and grounding of triplets
with detected instruments. Our experiments show that TWiST
outperforms prior weakly supervised baselines.