Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching
arXiv:2602.22871v1 Announce Type: new Abstract: Reasoning with large language models often benefits from generating multiple chains-of-thought, but existing aggregation strategies are typically trajectory-level (e.g., selecting the best trace or voting on the final answer), discarding useful intermediate work from partial...
Modality Collapse as Mismatched Decoding: Information-Theoretic Limits of Multimodal LLMs
arXiv:2602.23136v1 Announce Type: new Abstract: Multimodal LLMs can process speech and images, but they cannot hear a speaker's voice or see an object's texture. We show this is not a failure of encoding: speaker identity, emotion, and visual attributes survive...
Fine-Tuning Without Forgetting In-Context Learning: A Theoretical Analysis of Linear Attention Models
arXiv:2602.23197v1 Announce Type: new Abstract: Transformer-based large language models exhibit in-context learning, enabling adaptation to downstream tasks via few-shot prompting with demonstrations. In practice, such models are often fine-tuned to improve zero-shot performance on downstream tasks, allowing them to solve...
Discourse-Aware Dual-Track Streaming Response for Low-Latency Spoken Dialogue Systems
arXiv:2602.23266v1 Announce Type: new Abstract: Achieving human-like responsiveness is a critical yet challenging goal for cascaded spoken dialogue systems. Conventional ASR-LLM-TTS pipelines follow a strictly sequential paradigm, requiring complete transcription and full reasoning before speech synthesis can begin, which results...
To Deceive is to Teach? Forging Perceptual Robustness via Adversarial Reinforcement Learning
arXiv:2602.22227v1 Announce Type: new Abstract: Despite their impressive capabilities, Multimodal Large Language Models (MLLMs) exhibit perceptual fragility when confronted with visually complex scenes. This weakness stems from a reliance on finite training datasets, which are prohibitively expensive to scale and...
Code World Models for Parameter Control in Evolutionary Algorithms
arXiv:2602.22260v1 Announce Type: new Abstract: Can an LLM learn how an optimizer behaves -- and use that knowledge to control it? We extend Code World Models (CWMs), LLM-synthesized Python programs that predict environment dynamics, from deterministic games to stochastic combinatorial...
Sustainable LLM Inference using Context-Aware Model Switching
arXiv:2602.22261v1 Announce Type: new Abstract: Large language models have become central to many AI applications, but their growing energy consumption raises serious sustainability concerns. A key limitation in current AI deployments is the reliance on a one-size-fits-all inference strategy where...
AutoQRA: Joint Optimization of Mixed-Precision Quantization and Low-rank Adapters for Efficient LLM Fine-Tuning
arXiv:2602.22268v1 Announce Type: new Abstract: Quantization followed by parameter-efficient fine-tuning has emerged as a promising paradigm for downstream adaptation under tight GPU memory constraints. However, this sequential pipeline fails to leverage the intricate interaction between quantization bit-width and LoRA rank....
CQSA: Byzantine-robust Clustered Quantum Secure Aggregation in Federated Learning
arXiv:2602.22269v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training without sharing raw data. However, shared local model updates remain vulnerable to inference and poisoning attacks. Secure aggregation schemes have been proposed to mitigate these attacks. In this...
Support Tokens, Stability Margins, and a New Foundation for Robust LLMs
arXiv:2602.22271v1 Announce Type: new Abstract: Self-attention is usually described as a flexible, content-adaptive way to mix a token with information from its past. We re-interpret causal self-attention transformers, the backbone of modern foundation models, within a probabilistic framework, much like...
Early Risk Stratification of Dosing Errors in Clinical Trials Using Machine Learning
arXiv:2602.22285v1 Announce Type: new Abstract: Objective: The objective of this study is to develop a machine learning (ML)-based framework for early risk stratification of clinical trials (CTs) according to their likelihood of exhibiting a high rate of dosing errors, using...
UpSkill: Mutual Information Skill Learning for Structured Response Diversity in LLMs
arXiv:2602.22296v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning abilities of large language models (LLMs) on mathematics and programming tasks, but standard approaches that optimize single-attempt accuracy can inadvertently suppress response diversity across repeated...
Predicting Multi-Drug Resistance in Bacterial Isolates Through Performance Comparison and LIME-based Interpretation of Classification Models
arXiv:2602.22400v1 Announce Type: new Abstract: The rise of Antimicrobial Resistance, particularly Multi-Drug Resistance (MDR), presents a critical challenge for clinical decision-making due to limited treatment options and delays in conventional susceptibility testing. This study proposes an interpretable machine learning framework...
A Learning-Based Hybrid Decision Framework for Matching Systems with User Departure Detection
arXiv:2602.22412v1 Announce Type: new Abstract: In matching markets such as kidney exchanges and freight exchanges, delayed matching has been shown to improve overall market efficiency. The benefits of delay are highly sensitive to participants' sojourn times and departure behavior, and...
From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review
arXiv:2602.22438v1 Announce Type: new Abstract: Despite frequent double-blind review, systemic biases related to author demographics still disadvantage underrepresented groups. We start from a simple hypothesis: if a post-review recommender is trained with an explicit fairness regularizer, it should increase inclusion...
ECHO: Encoding Communities via High-order Operators
arXiv:2602.22446v1 Announce Type: new Abstract: Community detection in attributed networks faces a fundamental divide: topological algorithms ignore semantic features, while Graph Neural Networks (GNNs) encounter devastating computational bottlenecks. Specifically, GNNs suffer from a Semantic Wall of feature over smoothing in...
Beyond performance-wise Contribution Evaluation in Federated Learning
arXiv:2602.22470v1 Announce Type: new Abstract: Federated learning offers a privacy-friendly collaborative learning framework, yet its success, like any joint venture, hinges on the contributions of its participants. Existing client evaluation methods predominantly focus on model performance, such as accuracy or...
Efficient Continual Learning in Language Models via Thalamically Routed Cortical Columns
arXiv:2602.22479v1 Announce Type: new Abstract: Continual learning is a core requirement for deployed language models, yet standard training and fine-tuning pipelines remain brittle under non-stationary data. Online updates often induce catastrophic forgetting, while methods that improve stability frequently increase latency,...
TEFL: Prediction-Residual-Guided Rolling Forecasting for Multi-Horizon Time Series
arXiv:2602.22520v1 Announce Type: new Abstract: Time series forecasting plays a critical role in domains such as transportation, energy, and meteorology. Despite their success, modern deep forecasting models are typically trained to minimize point-wise prediction loss without leveraging the rich information...
Copyright Protection for AI-Generated Works
Since the 2010s, artificial intelligence (AI) has quickly grown from another subset of machine learning (ie deep learning) in particular with recent advances in generative AI, such as ChatGPT. The use of generative AI has gone beyond leisure purposes. It...
Precision Medicine and Data Privacy: Balancing Innovation with Patient Rights
The rapid advancement of precision medicine creates unprecedented opportunities for personalized treatment while raising complex data privacy and consent challenges.
Breakthrough in Quantum-Resistant Cryptography: Preparing for the Post-Quantum Era
NIST has finalized post-quantum cryptography standards, but the transition to quantum-resistant systems presents immense technical and organizational challenges.
MixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification
arXiv:2602.21608v1 Announce Type: new Abstract: Bangla-English code-mixing is widespread across South Asian social media, yet resources for implicit meaning identification in this setting remain scarce. Existing sentiment and sarcasm models largely focus on monolingual English or high-resource languages and struggle...
D-COT: Disciplined Chain-of-Thought Learning for Efficient Reasoning in Small Language Models
arXiv:2602.21786v1 Announce Type: new Abstract: Chain-of-Thought (CoT) distillation from Large Language Models (LLMs) often induces "overthinking" in Small Language Models (SLMs), leading to performance degradation and excessive token consumption. In this study, we propose Disciplined Chain-of-Thought (D-CoT), a novel framework...
Personalized Graph-Empowered Large Language Model for Proactive Information Access
arXiv:2602.21862v1 Announce Type: new Abstract: Since individuals may struggle to recall all life details and often confuse events, establishing a system to assist users in recalling forgotten experiences is essential. While numerous studies have proposed memory recall systems, these primarily...
Small Wins Big: Comparing Large Language Models and Domain Fine-Tuned Models for Sarcasm Detection in Code-Mixed Hinglish Text
arXiv:2602.21933v1 Announce Type: new Abstract: Sarcasm detection in multilingual and code-mixed environments remains a challenging task for natural language processing models due to structural variations, informal expressions, and low-resource linguistic availability. This study compares four large language models, Llama 3.1,...
DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain
arXiv:2602.22045v1 Announce Type: new Abstract: We introduce DLT-Corpus, the largest domain-specific text collection for Distributed Ledger Technology (DLT) research to date: 2.98 billion tokens from 22.12 million documents spanning scientific literature (37,440 publications), United States Patent and Trademark Office (USPTO)...
SymTorch: A Framework for Symbolic Distillation of Deep Neural Networks
arXiv:2602.21307v1 Announce Type: new Abstract: Symbolic distillation replaces neural networks, or components thereof, with interpretable, closed-form mathematical expressions. This approach has shown promise in discovering physical laws and mathematical relationships directly from trained deep learning models, yet adoption remains limited...
HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models
arXiv:2602.21340v1 Announce Type: new Abstract: Representing the past in a compressed, efficient, and informative manner is a central problem for systems trained on sequential data. The HiPPO framework, originally proposed by Gu & Dao et al., provides a principled approach...
Benchmarking State Space Models, Transformers, and Recurrent Networks for US Grid Forecasting
arXiv:2602.21415v1 Announce Type: new Abstract: Selecting the right deep learning model for power grid forecasting is challenging, as performance heavily depends on the data available to the operator. This paper presents a comprehensive benchmark of five modern neural architectures: two...