The Small Claims Paper Determination Pilot: Filtering out the County Courts’ ‘Garbage Claims’
US decides SpaceX is like an airline, exempting it from Labor Relations Act
US labels SpaceX a common carrier by air, will regulate firm under railway law.
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Airbnb says a third of its customer support is now handled by AI in the US and Canada
Airbnb was poised to introduce an app that doesn't just search for you, but one that "knows you." CEO Brian Chesky said, "It will help guests plan their entire trip, help hosts better run their businesses, and help the company...
Agentic AI for Commercial Insurance Underwriting with Adversarial Self-Critique
arXiv:2602.13213v1 Announce Type: new Abstract: Commercial insurance underwriting is a labor-intensive process that requires manual review of extensive documentation to assess risk and determine policy pricing. While AI offers substantial efficiency improvements, existing solutions lack comprehensive reasoning capabilities and internal...
A Geometric Taxonomy of Hallucinations in LLMs
arXiv:2602.13224v1 Announce Type: new Abstract: The term "hallucination" in large language models conflates distinct phenomena with different geometric signatures in embedding space. We propose a taxonomy identifying three types: unfaithfulness (failure to engage with provided context), confabulation (invention of semantically...
Intelligence as Trajectory-Dominant Pareto Optimization
arXiv:2602.13230v1 Announce Type: new Abstract: Despite recent advances in artificial intelligence, many systems exhibit stagnation in long-horizon adaptability despite continued performance optimization. This work argues that such limitations do not primarily arise from insufficient learning, data, or model capacity, but...
AST-PAC: AST-guided Membership Inference for Code
arXiv:2602.13240v1 Announce Type: new Abstract: Code Large Language Models are frequently trained on massive datasets containing restrictively licensed source code. This creates urgent data governance and copyright challenges. Membership Inference Attacks (MIAs) can serve as an auditing mechanism to detect...
MAPLE: A Sub-Agent Architecture for Memory, Learning, and Personalization in Agentic AI Systems
arXiv:2602.13258v1 Announce Type: new Abstract: Large language model (LLM) agents have emerged as powerful tools for complex tasks, yet their ability to adapt to individual users remains fundamentally limited. We argue this limitation stems from a critical architectural conflation: current...
TemporalBench: A Benchmark for Evaluating LLM-Based Agents on Contextual and Event-Informed Time Series Tasks
arXiv:2602.13272v1 Announce Type: new Abstract: It is unclear whether strong forecasting performance reflects genuine temporal understanding or the ability to reason under contextual and event-driven conditions. We introduce TemporalBench, a multi-domain benchmark designed to evaluate temporal reasoning behavior under progressively...
DiffusionRollout: Uncertainty-Aware Rollout Planning in Long-Horizon PDE Solving
arXiv:2602.13616v1 Announce Type: new Abstract: We propose DiffusionRollout, a novel selective rollout planning strategy for autoregressive diffusion models, aimed at mitigating error accumulation in long-horizon predictions of physical systems governed by partial differential equations (PDEs). Building on the recently validated...
Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens
arXiv:2602.13517v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive reasoning capabilities by scaling test-time compute via long Chain-of-Thought (CoT). However, recent findings suggest that raw token counts are unreliable proxies for reasoning quality: increased generation length does...
LLM-Confidence Reranker: A Training-Free Approach for Enhancing Retrieval-Augmented Generation Systems
arXiv:2602.13571v1 Announce Type: new Abstract: Large language models (LLMs) have revolutionized natural language processing, yet hallucinations in knowledge-intensive tasks remain a critical challenge. Retrieval-augmented generation (RAG) addresses this by integrating external knowledge, but its efficacy depends on accurate document retrieval...
Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment
arXiv:2602.13575v1 Announce Type: new Abstract: Current alignment methods for Large Language Models (LLMs) rely on compressing vast amounts of human preference data into static, absolute reward functions, leading to data scarcity, noise sensitivity, and training instability. We introduce Elo-Evolve, a...
Tutoring Large Language Models to be Domain-adaptive, Precise, and Safe
arXiv:2602.13860v1 Announce Type: new Abstract: The overarching research direction of this work is the development of a ''Responsible Intelligence'' framework designed to reconcile the immense generative power of Large Language Models (LLMs) with the stringent requirements of real-world deployment. As...
Pre-Editorial Normalization for Automatically Transcribed Medieval Manuscripts in Old French and Latin
arXiv:2602.13905v1 Announce Type: new Abstract: Recent advances in Automatic Text Recognition (ATR) have improved access to historical archives, yet a methodological divide persists between palaeographic transcriptions and normalized digital editions. While ATR models trained on more palaeographically-oriented datasets such as...
The Sufficiency-Conciseness Trade-off in LLM Self-Explanation from an Information Bottleneck Perspective
arXiv:2602.14002v1 Announce Type: new Abstract: Large Language Models increasingly rely on self-explanations, such as chain of thought reasoning, to improve performance on multi step question answering. While these explanations enhance accuracy, they are often verbose and costly to generate, raising...
GRRM: Group Relative Reward Modeling for Machine Translation
arXiv:2602.14028v1 Announce Type: new Abstract: While Group Relative Policy Optimization (GRPO) offers a powerful framework for LLM post-training, its effectiveness in open-ended domains like Machine Translation hinges on accurate intra-group ranking. We identify that standard Scalar Quality Metrics (SQM) fall...
Context Shapes LLMs Retrieval-Augmented Fact-Checking Effectiveness
arXiv:2602.14044v1 Announce Type: new Abstract: Large language models (LLMs) show strong reasoning abilities across diverse tasks, yet their performance on extended contexts remains inconsistent. While prior research has emphasized mid-context degradation in question answering, this study examines the impact of...
Epistemic Traps: Rational Misalignment Driven by Model Misspecification
arXiv:2602.17676v1 Announce Type: new Abstract: The rapid deployment of Large Language Models and AI agents across critical societal and technical domains is hindered by persistent behavioral pathologies including sycophancy, hallucination, and strategic deception that resist mitigation via reinforcement learning. Current...
WorkflowPerturb: Calibrated Stress Tests for Evaluating Multi-Agent Workflow Metrics
arXiv:2602.17990v1 Announce Type: new Abstract: LLM-based systems increasingly generate structured workflows for complex tasks. In practice, automatic evaluation of these workflows is difficult, because metric scores are often not calibrated, and score changes do not directly communicate the severity of...
IRPAPERS: A Visual Document Benchmark for Scientific Retrieval and Question Answering
arXiv:2602.17687v1 Announce Type: cross Abstract: AI systems have achieved remarkable success in processing text and relational data, yet visual document processing remains relatively underexplored. Whereas traditional systems require OCR transcriptions to convert these visual documents into text and metadata, recent...
Robust Pre-Training of Medical Vision-and-Language Models with Domain-Invariant Multi-Modal Masked Reconstruction
arXiv:2602.17689v1 Announce Type: cross Abstract: Medical vision-language models show strong potential for joint reasoning over medical images and clinical text, but their performance often degrades under domain shift caused by variations in imaging devices, acquisition protocols, and reporting styles. Existing...
Agentic Unlearning: When LLM Agent Meets Machine Unlearning
arXiv:2602.17692v1 Announce Type: cross Abstract: In this paper, we introduce \textbf{agentic unlearning} which removes specified information from both model parameters and persistent memory in agents with closed-loop interaction. Existing unlearning methods target parameters alone, leaving two critical gaps: (i) parameter-memory...
EXACT: Explicit Attribute-Guided Decoding-Time Personalization
arXiv:2602.17695v1 Announce Type: cross Abstract: Achieving personalized alignment requires adapting large language models to each user's evolving context. While decoding-time personalization offers a scalable alternative to training-time methods, existing methods largely rely on implicit, less interpretable preference representations and impose...
"Everyone's using it, but no one is allowed to talk about it": College Students' Experiences Navigating the Higher Education Environment in a Generative AI World
arXiv:2602.17720v1 Announce Type: cross Abstract: Higher education students are increasingly using generative AI in their academic work. However, existing institutional practices have not yet adapted to this shift. Through semi-structured interviews with 23 college students, our study examines the environmental...
GeneZip: Region-Aware Compression for Long Context DNA Modeling
arXiv:2602.17739v1 Announce Type: cross Abstract: Genomic sequences span billions of base pairs (bp), posing a fundamental challenge for genome-scale foundation models. Existing approaches largely sidestep this barrier by either scaling relatively small models to long contexts or relying on heavy...
Impact of Artificial Intelligence on Dental Education: A Review and Guide for Curriculum Update
In this intellectual work, the clinical and educational aspects of dentistry were confronted with practical applications of artificial intelligence (AI). The aim was to provide an up-to-date overview of the upcoming changes and a brief analysis of the influential advancements...
Feedback-based Automated Verification in Vibe Coding of CAS Adaptation Built on Constraint Logic
arXiv:2602.18607v1 Announce Type: new Abstract: In CAS adaptation, a challenge is to define the dynamic architecture of the system and changes in its behavior. Implementation-wise, this is projected into an adaptation mechanism, typically realized as an Adaptation Manager (AM). With...