Compact Prompting in Instruction-tuned LLMs for Joint Argumentative Component Detection
arXiv:2603.03095v1 Announce Type: new Abstract: Argumentative component detection (ACD) is a core subtask of Argument(ation) Mining (AM) and one of its most challenging aspects, as it requires jointly delimiting argumentative spans and classifying them into components such as claims and...
Evaluating Performance Drift from Model Switching in Multi-Turn LLM Systems
arXiv:2603.03111v1 Announce Type: new Abstract: Deployed multi-turn LLM systems routinely switch models mid-interaction due to upgrades, cross-provider routing, and fallbacks. Such handoffs create a context mismatch: the model generating later turns must condition on a dialogue prefix authored by a...
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...
BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing?
arXiv:2603.03194v1 Announce Type: new Abstract: Current benchmarks for code agents primarily assess narrow, repository-specific fixes, overlooking critical real-world challenges such as cross-repository reasoning, domain-specialized problem solving, dependency-driven migration, and full-repository generation. To address this gap, we introduce BeyondSWE, a comprehensive...
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....
Safety Training Persists Through Helpfulness Optimization in LLM Agents
arXiv:2603.02229v1 Announce Type: cross Abstract: Safety post-training has been studied extensively in single-step "chat" settings where safety typically refers to refusing harmful requests. We study an "agentic" (i.e., multi-step, tool-use) setting where safety refers to harmful actions directly taken by...
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...
StitchCUDA: An Automated Multi-Agents End-to-End GPU Programing Framework with Rubric-based Agentic Reinforcement Learning
arXiv:2603.02637v1 Announce Type: cross Abstract: Modern machine learning (ML) workloads increasingly rely on GPUs, yet achieving high end-to-end performance remains challenging due to dependencies on both GPU kernel efficiency and host-side settings. Although LLM-based methods show promise on automated GPU...
RxnNano:Training Compact LLMs for Chemical Reaction and Retrosynthesis Prediction via Hierarchical Curriculum Learning
arXiv:2603.02215v1 Announce Type: new Abstract: Chemical reaction prediction is pivotal for accelerating drug discovery and synthesis planning. Despite advances in data-driven models, current approaches are hindered by an overemphasis on parameter and dataset scaling. Some methods coupled with evaluation techniques...
ATPO: Adaptive Tree Policy Optimization for Multi-Turn Medical Dialogue
arXiv:2603.02216v1 Announce Type: new Abstract: Effective information seeking in multi-turn medical dialogues is critical for accurate diagnosis, especially when dealing with incomplete information. Aligning Large Language Models (LLMs) for these interactive scenarios is challenging due to the uncertainty inherent in...
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction
arXiv:2603.02221v1 Announce Type: new Abstract: In healthcare tabular predictions, classical models with feature engineering often outperform neural approaches. Recent advances in Large Language Models enable the integration of domain knowledge into feature engineering, offering a promising direction. However, existing approaches...
Characterizing and Predicting Wildfire Evacuation Behavior: A Dual-Stage ML Approach
arXiv:2603.02223v1 Announce Type: new Abstract: Wildfire evacuation behavior is highly variable and influenced by complex interactions among household resources, preparedness, and situational cues. Using a large-scale MTurk survey of residents in California, Colorado, and Oregon, this study integrates unsupervised and...
Subspace Geometry Governs Catastrophic Forgetting in Low-Rank Adaptation
arXiv:2603.02224v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has emerged as a parameter-efficient approach for adapting large pre-trained models, yet its behavior under continual learning remains poorly understood. We present a geometric theory characterizing catastrophic forgetting in LoRA through the...
Scaling Reward Modeling without Human Supervision
arXiv:2603.02225v1 Announce Type: new Abstract: Learning from feedback is an instrumental process for advancing the capabilities and safety of frontier models, yet its effectiveness is often constrained by cost and scalability. We present a pilot study that explores scaling reward...
Efficient Sparse Selective-Update RNNs for Long-Range Sequence Modeling
arXiv:2603.02226v1 Announce Type: new Abstract: Real-world sequential signals, such as audio or video, contain critical information that is often embedded within long periods of silence or noise. While recurrent neural networks (RNNs) are designed to process such data efficiently, they...
Neural Paging: Learning Context Management Policies for Turing-Complete Agents
arXiv:2603.02228v1 Announce Type: new Abstract: The proof that Large Language Models (LLMs) augmented with external read-write memory constitute a computationally universal system has established the theoretical foundation for general-purpose agents. However, existing implementations face a critical bottleneck: the finite and...
Generalized Discrete Diffusion with Self-Correction
arXiv:2603.02230v1 Announce Type: new Abstract: Self-correction is an effective technique for maintaining parallel sampling in discrete diffusion models with minimal performance degradation. Prior work has explored self-correction at inference time or during post-training; however, such approaches often suffer from limited...
Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction
arXiv:2603.02231v1 Announce Type: new Abstract: Large-scale wave field reconstruction requires precise solutions but faces challenges with computational efficiency and accuracy. The physics-based numerical methods like Finite Element Method (FEM) provide high accuracy but struggle with large-scale or high-frequency problems due...
Beyond Binary Preferences: A Principled Framework for Reward Modeling with Ordinal Feedback
arXiv:2603.02232v1 Announce Type: new Abstract: Reward modeling is crucial for aligning large language models with human preferences, yet current approaches lack a principled mathematical framework for leveraging ordinal preference data. When human annotators provide graded preferences on a Likert scale...
Adaptive Personalized Federated Learning via Multi-task Averaging of Kernel Mean Embeddings
arXiv:2603.02233v1 Announce Type: new Abstract: Personalized Federated Learning (PFL) enables a collection of agents to collaboratively learn individual models without sharing raw data. We propose a new PFL approach in which each agent optimizes a weighted combination of all agents'...
Talking with Verifiers: Automatic Specification Generation for Neural Network Verification
arXiv:2603.02235v1 Announce Type: new Abstract: Neural network verification tools currently support only a narrow class of specifications, typically expressed as low-level constraints over raw inputs and outputs. This limitation significantly hinders their adoption and practical applicability across diverse application domains...
Length Generalization Bounds for Transformers
arXiv:2603.02238v1 Announce Type: new Abstract: Length generalization is a key property of a learning algorithm that enables it to make correct predictions on inputs of any length, given finite training data. To provide such a guarantee, one needs to be...
High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approach
arXiv:2603.02265v1 Announce Type: new Abstract: In order to evaluate the invulnerability of networks against various types of attacks and provide guidance for potential performance enhancement as well as controllability maintenance, network controllability robustness (NCR) has attracted increasing attention in recent...
Boosting Meta-Learning for Few-Shot Text Classification via Label-guided Distance Scaling
arXiv:2603.02267v1 Announce Type: new Abstract: Few-shot text classification aims to recognize unseen classes with limited labeled text samples. Existing approaches focus on boosting meta-learners by developing complex algorithms in the training stage. However, the labeled samples are randomly selected during...
PRISM: Exploring Heterogeneous Pretrained EEG Foundation Model Transfer to Clinical Differential Diagnosis
arXiv:2603.02268v1 Announce Type: new Abstract: EEG foundation models are typically pretrained on narrow-source clinical archives and evaluated on benchmarks from the same ecosystem, leaving unclear whether representations encode neural physiology or recording-distribution artifacts. We introduce PRISM (Population Representative Invariant Signal...