The Importance of Being Smoothly Calibrated
arXiv:2603.16015v1 Announce Type: new Abstract: Recent work has highlighted the centrality of smooth calibration [Kakade and Foster, 2008] as a robust measure of calibration error. We generalize, unify, and extend previous results on smooth calibration, both as a robust calibration...
Noisy Data is Destructive to Reinforcement Learning with Verifiable Rewards
arXiv:2603.16140v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven recent capability advances of large language models across various domains. Recent studies suggest that improved RLVR algorithms allow models to learn effectively from incorrect annotations, achieving performance...
Apple can delist apps "with or without cause," judge says in loss for Musi app
Judge tosses Musi case against Apple, sanctions lawyers for "mak[ing] up facts."
Trump's plan to shut down weather and climate center triggers lawsuit
Suit: The National Center for Atmospheric Research is to be terminated for no rational reason.
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
A Critical Analysis Of Rap Shield Laws
For years, scholars have been sounding the alarm on “rap on trial,” or the use of rap as evidence in criminal proceedings, pointing out that the fundamental characteristics of rap music make it uniquely susceptible to misinterpretation and prejudice. Scholars...
Repetition Without Exclusivity: Scale Sensitivity of Referential Mechanisms in Child-Scale Language Models
arXiv:2603.13696v1 Announce Type: new Abstract: We present the first systematic evaluation of mutual exclusivity (ME) -- the bias to map novel words to novel referents -- in text-only language models trained on child-directed speech. We operationalise ME as referential suppression:...
LLM-MINE: Large Language Model based Alzheimer's Disease and Related Dementias Phenotypes Mining from Clinical Notes
arXiv:2603.13673v1 Announce Type: new Abstract: Accurate extraction of Alzheimer's Disease and Related Dementias (ADRD) phenotypes from electronic health records (EHR) is critical for early-stage detection and disease staging. However, this information is usually embedded in unstructured textual data rather than...
DOVA: Deliberation-First Multi-Agent Orchestration for Autonomous Research Automation
arXiv:2603.13327v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated remarkable capabilities in tool use, reasoning, and code generation, yet single-agent systems exhibit fundamental limitations when confronted with complex research tasks demanding multi-source synthesis, adversarial verification, and personalized...
Traffic and weather driven hybrid digital twin for bridge monitoring
arXiv:2603.14028v1 Announce Type: new Abstract: A hybrid digital twin framework is presented for bridge condition monitoring using existing traffic cameras and weather APIs, reducing reliance on dedicated sensor installations. The approach is demonstrated on the Peace Bridge (99 years in...
Early Rug Pull Warning for BSC Meme Tokens via Multi-Granularity Wash-Trading Pattern Profiling
arXiv:2603.13830v1 Announce Type: new Abstract: The high-frequency issuance and short-cycle speculation of meme tokens in decentralized finance (DeFi) have significantly amplified rug-pull risk. Existing approaches still struggle to provide stable early warning under scarce anomalies, incomplete labels, and limited interpretability....
Projection-Free Evolution Strategies for Continuous Prompt Search
arXiv:2603.13786v1 Announce Type: new Abstract: Continuous prompt search offers a computationally efficient alternative to conventional parameter tuning in natural language processing tasks. Nevertheless, its practical effectiveness can be significantly hindered by the black-box nature and the inherent high-dimensionality of the...
Can We Trust LLMs on Memristors? Diving into Reasoning Ability under Non-Ideality
arXiv:2603.13725v1 Announce Type: new Abstract: Memristor-based analog compute-in-memory (CIM) architectures provide a promising substrate for the efficient deployment of Large Language Models (LLMs), owing to superior energy efficiency and computational density. However, these architectures suffer from precision issues caused by...
GhanaNLP Parallel Corpora: Comprehensive Multilingual Resources for Low-Resource Ghanaian Languages
arXiv:2603.13793v1 Announce Type: new Abstract: Low resource languages present unique challenges for natural language processing due to the limited availability of digitized and well structured linguistic data. To address this gap, the GhanaNLP initiative has developed and curated 41,513 parallel...
Supervised Fine-Tuning versus Reinforcement Learning: A Study of Post-Training Methods for Large Language Models
arXiv:2603.13985v1 Announce Type: new Abstract: Pre-trained Large Language Model (LLM) exhibits broad capabilities, yet, for specific tasks or domains their attainment of higher accuracy and more reliable reasoning generally depends on post-training through Supervised Fine-Tuning (SFT) or Reinforcement Learning (RL)....
State Algebra for Probabilistic Logic
arXiv:2603.13574v1 Announce Type: new Abstract: This paper presents a Probabilistic State Algebra as an extension of deterministic propositional logic, providing a computational framework for constructing Markov Random Fields (MRFs) through pure linear algebra. By mapping logical states to real-valued coordinates...
A Systematic Evaluation Protocol of Graph-Derived Signals for Tabular Machine Learning
arXiv:2603.13998v1 Announce Type: new Abstract: While graph-derived signals are widely used in tabular learning, existing studies typically rely on limited experimental setups and average performance comparisons, leaving the statistical reliability and robustness of observed gains largely unexplored. Consequently, it remains...
AutoTool: Automatic Scaling of Tool-Use Capabilities in RL via Decoupled Entropy Constraints
arXiv:2603.13348v1 Announce Type: new Abstract: Tool use represents a critical capability for AI agents, with recent advances focusing on leveraging reinforcement learning (RL) to scale up the explicit reasoning process to achieve better performance. However, there are some key challenges...
DeceptGuard :A Constitutional Oversight Framework For Detecting Deception in LLM Agents
arXiv:2603.13791v1 Announce Type: new Abstract: Reliable detection of deceptive behavior in Large Language Model (LLM) agents is an essential prerequisite for safe deployment in high-stakes agentic contexts. Prior work on scheming detection has focused exclusively on black-box monitors that observe...
Think First, Diffuse Fast: Improving Diffusion Language Model Reasoning via Autoregressive Plan Conditioning
arXiv:2603.13243v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) generate text via iterative denoising but consistently underperform on multi-step reasoning. We hypothesize this gap stems from a coordination problem: AR models build coherence token-by-token, while diffusion models must coordinate...
Learning When to Trust in Contextual Bandits
arXiv:2603.13356v1 Announce Type: new Abstract: Standard approaches to Robust Reinforcement Learning assume that feedback sources are either globally trustworthy or globally adversarial. In this paper, we challenge this assumption and we identify a more subtle failure mode. We term this...
Agent-Based User-Adaptive Filtering for Categorized Harassing Communication
arXiv:2603.13288v1 Announce Type: new Abstract: We propose an agent-based framework for personalized filtering of categorized harassing communication in online social networks. Unlike global moderation systems that apply uniform filtering rules, our approach models user-specific tolerance levels and preferences through adaptive...
Knowledge Distillation for Large Language Models
arXiv:2603.13765v1 Announce Type: new Abstract: We propose a resource-efficient framework for compressing large language models through knowledge distillation, combined with guided chain-of-thought reinforcement learning. Using Qwen 3B as the teacher and Qwen 0.5B as the student, we apply knowledge distillation...
LLM Routing as Reasoning: A MaxSAT View
arXiv:2603.13612v1 Announce Type: new Abstract: Routing a query through an appropriate LLM is challenging, particularly when user preferences are expressed in natural language and model attributes are only partially observable. We propose a constraint-based interpretation of language-conditioned LLM routing, formulating...
Slang Context-based Inference Enhancement via Greedy Search-Guided Chain-of-Thought Prompting
arXiv:2603.13230v1 Announce Type: new Abstract: Slang interpretation has been a challenging downstream task for Large Language Models (LLMs) as the expressions are inherently embedded in contextual, cultural, and linguistic frameworks. In the absence of domain-specific training data, it is difficult...
Intelligent Materials Modelling: Large Language Models Versus Partial Least Squares Regression for Predicting Polysulfone Membrane Mechanical Performance
arXiv:2603.13834v1 Announce Type: new Abstract: Predicting the mechanical properties of polysulfone (PSF) membranes from structural descriptors remains challenging due to extreme data scarcity typical of experimental studies. To investigate this issue, this study benchmarked knowledge-driven inference using four large language...
PA-Net: Precipitation-Adaptive Mixture-of-Experts for Long-Tail Rainfall Nowcasting
arXiv:2603.13818v1 Announce Type: new Abstract: Precipitation nowcasting is vital for flood warning, agricultural management, and emergency response, yet two bottlenecks persist: the prohibitive cost of modeling million-scale spatiotemporal tokens from multi-variate atmospheric fields, and the extreme long-tailed rainfall distribution where...
Optimizing LLM Annotation of Classroom Discourse through Multi-Agent Orchestration
arXiv:2603.13353v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly positioned as scalable tools for annotating educational data, including classroom discourse, interaction logs, and qualitative learning artifacts. Their ability to rapidly summarize instructional interactions and assign rubric-aligned labels has...
Multimodal Emotion Regression with Multi-Objective Optimization and VAD-Aware Audio Modeling for the 10th ABAW EMI Track
arXiv:2603.13760v1 Announce Type: new Abstract: We participated in the 10th ABAW Challenge, focusing on the Emotional Mimicry Intensity (EMI) Estimation track on the Hume-Vidmimic2 dataset. This task aims to predict six continuous emotion dimensions: Admiration, Amusement, Determination, Empathic Pain, Excitement,...
ManiBench: A Benchmark for Testing Visual-Logic Drift and Syntactic Hallucinations in Manim Code Generation
arXiv:2603.13251v1 Announce Type: new Abstract: Traditional benchmarks like HumanEval and MBPP test logic and syntax effectively, but fail when code must produce dynamic, pedagogical visuals. We introduce ManiBench, a specialized benchmark evaluating LLM performance in generating Manim CE code, where...