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Ye Qiao, Sitao Huang

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.

Suraj Prasad, Navyansh Mahla, Sunny Gupta, Amit Sethi

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.

Ha-Hieu Pham, Minh Le, Han Huynh, Nguyen Quoc Khanh Le, Huy-Hieu Pham

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.

Manali Patel, Krupa Jariwala, Chiranjoy Chattopadhyay

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.

Iñigo Parra, Mano Bharathi M, Mayank Kumar, Pushpa Kumar Balan, Priyadarsi Mishra

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.

Beomdo Park, Hyeonseok Jang, Junseong Park, Minu Baek, Gihun Gil, Minsung Jung, Woohyeon Kwon, Harin Jang, Yeojin Jang, Hoon Jeong 等

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.

Deepak Pandita, Tharindu Cyril Weerasooriya, Ankit Shah, Isabelle Diana May-Xin Ng, Christopher M. Homan, Wei Wei

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.

Ryusei Otani, Keichi Namikoshi, Yuko Sakurai, Mingyu Guo, Satoshi Oyama

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.

Narjes Nourzad, Carlee Joe-Wong

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.

Ashikur Nobel, Jacob Matos, Honggang Wang, Hua Fang

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.

Yuto Morimoto, Zhipeng Wang, Koji Yasuda

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.

Shoma Mizuno, Keichi Namikoshi, Yuko Sakurai

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.

Nipun Misra, Vikranth Udandarao

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.

Ganesh Sahadeo Meshram, Partha Pratim Chakrabarti, Suman Chakraborty

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.

Niklas M. Melton, Leonardo Enzo Brito da Silva, Donald C. Wunsch

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.

Aditya Mehta, Swarnim Chaudhary, Pratik Narang, Jagat Sesh Challa

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.

Abu Kaisar Mohammad Masum, Sercan Aygun

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.

Mahi Mann, Avinash Anand, Rajiv Ratn Shah

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.

Manasa Mariam Mammen, Zafer Kayatas, Stefan Wagner

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.

Gaurank Maheshwari, Ambika Taploo, Ashiqur R. Khudabukhsh

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.