Why Are Linear RNNs More Parallelizable?
arXiv:2603.03612v1 Announce Type: new Abstract: The community is increasingly exploring linear RNNs (LRNNs) as language models, motivated by their expressive power and parallelizability. While prior work establishes the expressivity benefits of LRNNs over transformers, it is unclear what makes LRNNs...
Extending Neural Operators: Robust Handling of Functions Beyond the Training Set
arXiv:2603.03621v1 Announce Type: new Abstract: We develop a rigorous framework for extending neural operators to handle out-of-distribution input functions. We leverage kernel approximation techniques and provide theory for characterizing the input-output function spaces in terms of Reproducing Kernel Hilbert Spaces...
Freezing of Gait Prediction using Proactive Agent that Learns from Selected Experience and DDQN Algorithm
arXiv:2603.03651v1 Announce Type: new Abstract: Freezing of Gait (FOG) is a debilitating motor symptom commonly experienced by individuals with Parkinson's Disease (PD) which often leads to falls and reduced mobility. Timely and accurate prediction of FOG episodes is essential for...
A Stein Identity for q-Gaussians with Bounded Support
arXiv:2603.03673v1 Announce Type: new Abstract: Stein's identity is a fundamental tool in machine learning with applications in generative models, stochastic optimization, and other problems involving gradients of expectations under Gaussian distributions. Less attention has been paid to problems with non-Gaussian...
JANUS: Structured Bidirectional Generation for Guaranteed Constraints and Analytical Uncertainty
arXiv:2603.03748v1 Announce Type: new Abstract: High-stakes synthetic data generation faces a fundamental Quadrilemma: achieving Fidelity to the original distribution, Control over complex logical constraints, Reliability in uncertainty estimation, and Efficiency in computational cost -- simultaneously. State-of-the-art Deep Generative Models (CTGAN,...
LEA: Label Enumeration Attack in Vertical Federated Learning
arXiv:2603.03777v1 Announce Type: new Abstract: A typical Vertical Federated Learning (VFL) scenario involves several participants collaboratively training a machine learning model, where each party has different features for the same samples, with labels held exclusively by one party. Since labels...
Relational In-Context Learning via Synthetic Pre-training with Structural Prior
arXiv:2603.03805v1 Announce Type: new Abstract: Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or vision. A key obstacle is that high-quality RDBs are private, scarce and structurally heterogeneous, making...
Pretrained Vision-Language-Action Models are Surprisingly Resistant to Forgetting in Continual Learning
arXiv:2603.03818v1 Announce Type: new Abstract: Continual learning is a long-standing challenge in robot policy learning, where a policy must acquire new skills over time without catastrophically forgetting previously learned ones. While prior work has extensively studied continual learning in relatively...
Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation
arXiv:2603.03820v1 Announce Type: new Abstract: Interactive recommender systems (IRS) are increasingly optimized with Reinforcement Learning (RL) to capture the sequential nature of user-system dynamics. However, existing fairness-aware methods often suffer from a fundamental oversight: they assume the observed user state...
Structure-Aware Distributed Backdoor Attacks in Federated Learning
arXiv:2603.03865v1 Announce Type: new Abstract: While federated learning protects data privacy, it also makes the model update process vulnerable to long-term stealthy perturbations. Existing studies on backdoor attacks in federated learning mainly focus on trigger design or poisoning strategies, typically...
Believe Your Model: Distribution-Guided Confidence Calibration
arXiv:2603.03872v1 Announce Type: new Abstract: Large Reasoning Models have demonstrated remarkable performance with the advancement of test-time scaling techniques, which enhances prediction accuracy by generating multiple candidate responses and selecting the most reliable answer. While prior work has analyzed that...
HateMirage: An Explainable Multi-Dimensional Dataset for Decoding Faux Hate and Subtle Online Abuse
arXiv:2603.02684v1 Announce Type: new Abstract: Subtle and indirect hate speech remains an underexplored challenge in online safety research, particularly when harmful intent is embedded within misleading or manipulative narratives. Existing hate speech datasets primarily capture overt toxicity, underrepresenting the nuanced...
Graph-GRPO: Stabilizing Multi-Agent Topology Learning via Group Relative Policy Optimization
arXiv:2603.02701v1 Announce Type: new Abstract: Optimizing communication topology is fundamental to the efficiency and effectiveness of Large Language Model (LLM)-based Multi-Agent Systems (MAS). While recent approaches utilize reinforcement learning to dynamically construct task-specific graphs, they typically rely on single-sample policy...
OCR or Not? Rethinking Document Information Extraction in the MLLMs Era with Real-World Large-Scale Datasets
arXiv:2603.02789v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) enhance the potential of natural language processing. However, their actual impact on document information extraction remains unclear. In particular, it is unclear whether an MLLM-only pipeline--while simpler--can truly match the...
Faster, Cheaper, More Accurate: Specialised Knowledge Tracing Models Outperform LLMs
arXiv:2603.02830v1 Announce Type: new Abstract: Predicting future student responses to questions is particularly valuable for educational learning platforms where it enables effective interventions. One of the key approaches to do this has been through the use of knowledge tracing (KT)...
Nodes Are Early, Edges Are Late: Probing Diagram Representations in Large Vision-Language Models
arXiv:2603.02865v1 Announce Type: new Abstract: Large vision-language models (LVLMs) demonstrate strong performance on diagram understanding benchmarks, yet they still struggle with understanding relationships between elements, particularly those represented by nodes and directed edges (e.g., arrows and lines). To investigate the...
LaTeX Compilation: Challenges in the Era of LLMs
arXiv:2603.02873v1 Announce Type: new Abstract: As large language models (LLMs) increasingly assist scientific writing, limitations and the significant token cost of TeX become more and more visible. This paper analyzes TeX's fundamental defects in compilation and user experience design to...
Eval4Sim: An Evaluation Framework for Persona Simulation
arXiv:2603.02876v1 Announce Type: new Abstract: Large Language Model (LLM) personas with explicit specifications of attributes, background, and behavioural tendencies are increasingly used to simulate human conversations for tasks such as user modeling, social reasoning, and behavioural analysis. Ensuring that persona-grounded...
Learning to Generate and Extract: A Multi-Agent Collaboration Framework For Zero-shot Document-level Event Arguments Extraction
arXiv:2603.02909v1 Announce Type: new Abstract: Document-level event argument extraction (DEAE) is essential for knowledge acquisition, aiming to extract participants of events from documents.In the zero-shot setting, existing methods employ LLMs to generate synthetic data to address the challenge posed by...
PrivMedChat: End-to-End Differentially Private RLHF for Medical Dialogue Systems
arXiv:2603.03054v1 Announce Type: new Abstract: Large language models are increasingly used for patient-facing medical assistance and clinical decision support, but adapting them to clinical dialogue often requires supervision derived from doctor-patient conversations that may contain sensitive information. Conventional supervised fine-tuning...
TAO-Attack: Toward Advanced Optimization-Based Jailbreak Attacks for Large Language Models
arXiv:2603.03081v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable success across diverse applications but remain vulnerable to jailbreak attacks, where attackers craft prompts that bypass safety alignment and elicit unsafe responses. Among existing approaches, optimization-based attacks have...
UniSkill: A Dataset for Matching University Curricula to Professional Competencies
arXiv:2603.03134v1 Announce Type: new Abstract: Skill extraction and recommendation systems have been studied from recruiter, applicant, and education perspectives. While AI applications in job advertisements have received broad attention, deficiencies in the instructed skills side remain a challenge. In this...
APRES: An Agentic Paper Revision and Evaluation System
arXiv:2603.03142v1 Announce Type: new Abstract: Scientific discoveries must be communicated clearly to realize their full potential. Without effective communication, even the most groundbreaking findings risk being overlooked or misunderstood. The primary way scientists communicate their work and receive feedback from...
Learning When to Act or Refuse: Guarding Agentic Reasoning Models for Safe Multi-Step Tool Use
arXiv:2603.03205v1 Announce Type: new Abstract: Agentic language models operate in a fundamentally different safety regime than chat models: they must plan, call tools, and execute long-horizon actions where a single misstep, such as accessing files or entering credentials, can cause...
Using Learning Progressions to Guide AI Feedback for Science Learning
arXiv:2603.03249v1 Announce Type: new Abstract: Generative artificial intelligence (AI) offers scalable support for formative feedback, yet most AI-generated feedback relies on task-specific rubrics authored by domain experts. While effective, rubric authoring is time-consuming and limits scalability across instructional contexts. Learning...
Self-Play Only Evolves When Self-Synthetic Pipeline Ensures Learnable Information Gain
arXiv:2603.02218v1 Announce Type: cross Abstract: Large language models (LLMs) make it plausible to build systems that improve through self-evolving loops, but many existing proposals are better understood as self-play and often plateau quickly. A central failure mode is that the...
Routing Absorption in Sparse Attention: Why Random Gates Are Hard to Beat
arXiv:2603.02227v1 Announce Type: cross Abstract: Can a transformer learn which attention entries matter during training? In principle, yes: attention distributions are highly concentrated, and a small gate network can identify the important entries post-hoc with near-perfect accuracy. In practice, barely....
HELIOS: Harmonizing Early Fusion, Late Fusion, and LLM Reasoning for Multi-Granular Table-Text Retrieval
arXiv:2603.02248v1 Announce Type: cross Abstract: Table-text retrieval aims to retrieve relevant tables and text to support open-domain question answering. Existing studies use either early or late fusion, but face limitations. Early fusion pre-aligns a table row with its associated passages,...
MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models
arXiv:2603.02482v1 Announce Type: cross Abstract: Safety evaluation and red-teaming of large language models remain predominantly text-centric, and existing frameworks lack the infrastructure to systematically test whether alignment generalizes to audio, image, and video inputs. We present MUSE (Multimodal Unified Safety...
FlashEvaluator: Expanding Search Space with Parallel Evaluation
arXiv:2603.02565v1 Announce Type: cross Abstract: The Generator-Evaluator (G-E) framework, i.e., evaluating K sequences from a generator and selecting the top-ranked one according to evaluator scores, is a foundational paradigm in tasks such as Recommender Systems (RecSys) and Natural Language Processing...