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...
Training-Free Agentic AI: Probabilistic Control and Coordination in Multi-Agent LLM Systems
arXiv:2603.13256v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems enable complex, long-horizon reasoning by composing specialized agents, but practical deployment remains hindered by inefficient routing, noisy feedback, and high interaction cost. We introduce REDEREF, a lightweight and training-free...
Prompt Complexity Dilutes Structured Reasoning: A Follow-Up Study on the Car Wash Problem
arXiv:2603.13351v1 Announce Type: new Abstract: In a previous study [Jo, 2026], STAR reasoning (Situation, Task, Action, Result) raised car wash problem accuracy from 0% to 85% on Claude Sonnet 4.5, and to 100% with additional prompt layers. This follow-up asks:...
FLUX: Data Worth Training On
arXiv:2603.13972v1 Announce Type: new Abstract: Modern large language model training is no longer limited by data availability, but by the inability of existing preprocessing pipelines to simultaneously achieve massive scale and high data quality. Current approaches are forced to sacrifice...
CMHL: Contrastive Multi-Head Learning for Emotionally Consistent Text Classification
arXiv:2603.14078v1 Announce Type: new Abstract: Textual Emotion Classification (TEC) is one of the most difficult NLP tasks. State of the art approaches rely on Large language models (LLMs) and multi-model ensembles. In this study, we challenge the assumption that larger...
Selective Fine-Tuning of GPT Architectures for Parameter-Efficient Clinical Text Classification
arXiv:2603.14183v1 Announce Type: new Abstract: The rapid expansion of electronic health record (EHR) systems has generated large volumes of unstructured clinical narratives that contain valuable information for disease identification, patient cohort discovery, and clinical decision support. Extracting structured knowledge from...
Automatic Inter-document Multi-hop Scientific QA Generation
arXiv:2603.14257v1 Announce Type: new Abstract: Existing automatic scientific question generation studies mainly focus on single-document factoid QA, overlooking the inter-document reasoning crucial for scientific understanding. We present AIM-SciQA, an automated framework for generating multi-document, multi-hop scientific QA datasets. AIM-SciQA extracts...
Motivation in Large Language Models
arXiv:2603.14347v1 Announce Type: new Abstract: Motivation is a central driver of human behavior, shaping decisions, goals, and task performance. As large language models (LLMs) become increasingly aligned with human preferences, we ask whether they exhibit something akin to motivation. We...
PARSA-Bench: A Comprehensive Persian Audio-Language Model Benchmark
arXiv:2603.14456v1 Announce Type: new Abstract: Persian poses unique audio understanding challenges through its classical poetry, traditional music, and pervasive code-switching - none captured by existing benchmarks. We introduce PARSA-Bench (Persian Audio Reasoning and Speech Assessment Benchmark), the first benchmark for...
PREBA: Surgical Duration Prediction via PCA-Weighted Retrieval-Augmented LLMs and Bayesian Averaging Aggregation
arXiv:2603.13275v1 Announce Type: new Abstract: Accurate prediction of surgical duration is pivotal for hospital resource management. Although recent supervised learning approaches-from machine learning (ML) to fine-tuned large language models (LLMs)-have shown strong performance, they remain constrained by the need for...
Enhanced Atrial Fibrillation Prediction in ESUS Patients with Hypergraph-based Pre-training
arXiv:2603.13297v1 Announce Type: new Abstract: Atrial fibrillation (AF) is a major complication following embolic stroke of undetermined source (ESUS), elevating the risk of recurrent stroke and mortality. Early identification is clinically important, yet existing tools face limitations in accuracy, scalability,...
Evidence-based Distributional Alignment for Large Language Models
arXiv:2603.13305v1 Announce Type: new Abstract: Distributional alignment enables large language models (LLMs) to predict how a target population distributes its responses across answer options, rather than collapsing disagreement into a single consensus answer. However, existing LLM-based distribution prediction is often...
Preventing Curriculum Collapse in Self-Evolving Reasoning Systems
arXiv:2603.13309v1 Announce Type: new Abstract: Self-evolving reasoning frameworks let LLMs improve their reasoning capabilities by iteratively generating and solving problems without external supervision, using verifiable rewards. Ideally, such systems are expected to explore a diverse problem space and propose new...
AI-Driven Predictive Maintenance with Real-Time Contextual Data Fusion for Connected Vehicles: A Multi-Dataset Evaluation
arXiv:2603.13343v1 Announce Type: new Abstract: Most vehicle predictive maintenance systems rely exclusively on internal diagnostic signals and are validated on deterministic synthetic data, limiting the credibility of reported metrics. This paper presents a simulation-validated proof-of-concept framework for V2X-augmented predictive maintenance,...
Thermal Robustness of Retrieval in Dense Associative Memories: LSE vs LSR Kernels
arXiv:2603.13350v1 Announce Type: new Abstract: Understanding whether retrieval in dense associative memories survives thermal noise is essential for bridging zero-temperature capacity proofs with the finite-temperature conditions of practical inference and biological computation. We use Monte Carlo simulations to map the...
Elon Musk's xAI sued for turning three girls' real photos into AI CSAM
Discord user led cops to Grok-generated CSAM of real girls, lawsuit says.
No accountability: Bills would ban liability lawsuits for climate change
This is the latest front in the battle over climate lawsuits.
Elon Musk’s xAI faces child porn lawsuit from minors Grok allegedly undressed
The three plaintiffs are seeking to represent anyone who had real images of them as a minor altered into sexual content by Grok.
The dictionary sues OpenAI
Encyclopedia Britannica and Merriam-Webster say that OpenAI violated the copyright of almost 100,000 articles by using them for LLM training.
An ethical framework for conversational AI in higher education: toward an evidence-based ethical governance
Developing and evaluating a chatbot to support maternal health care
arXiv:2603.13168v1 Announce Type: new Abstract: The ability to provide trustworthy maternal health information using phone-based chatbots can have a significant impact, particularly in low-resource settings where users have low health literacy and limited access to care. However, deploying such systems...
SPARROW: Learning Spatial Precision and Temporal Referential Consistency in Pixel-Grounded Video MLLMs
arXiv:2603.12382v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have advanced from image-level reasoning to pixel-level grounding, but extending these capabilities to videos remains challenging as models must achieve spatial precision and temporally consistent reference tracking. Existing video MLLMs...
Shattering the Shortcut: A Topology-Regularized Benchmark for Multi-hop Medical Reasoning in LLMs
arXiv:2603.12458v1 Announce Type: cross Abstract: While Large Language Models (LLMs) achieve expert-level performance on standard medical benchmarks through single-hop factual recall, they severely struggle with the complex, multi-hop diagnostic reasoning required in real-world clinical settings. A primary obstacle is "shortcut...
The Perfection Paradox: From Architect to Curator in AI-Assisted API Design
arXiv:2603.12475v1 Announce Type: cross Abstract: Enterprise API design is often bottlenecked by the tension between rapid feature delivery and the rigorous maintenance of usability standards. We present an industrial case study evaluating an AI-assisted design workflow trained on API Improvement...
TRACE: Temporal Rule-Anchored Chain-of-Evidence on Knowledge Graphs for Interpretable Stock Movement Prediction
arXiv:2603.12500v1 Announce Type: cross Abstract: We present a Temporal Rule-Anchored Chain-of-Evidence (TRACE) on knowledge graphs for interpretable stock movement prediction that unifies symbolic relational priors, dynamic graph exploration, and LLM-guided decision making in a single end-to-end pipeline. The approach performs...
LLM BiasScope: A Real-Time Bias Analysis Platform for Comparative LLM Evaluation
arXiv:2603.12522v1 Announce Type: cross Abstract: As large language models (LLMs) are deployed widely, detecting and understanding bias in their outputs is critical. We present LLM BiasScope, a web application for side-by-side comparison of LLM outputs with real-time bias analysis. The...
Continual Learning in Large Language Models: Methods, Challenges, and Opportunities
arXiv:2603.12658v1 Announce Type: new Abstract: Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge and sequential tasks while mitigating catastrophic forgetting-a critical limitation of the static pre-training paradigm...
Experimental evidence of progressive ChatGPT models self-convergence
arXiv:2603.12683v1 Announce Type: new Abstract: Large Language Models (LLMs) that undergo recursive training on synthetically generated data are susceptible to model collapse, a phenomenon marked by the generation of meaningless output. Existing research has examined this issue from either theoretical...
ESG-Bench: Benchmarking Long-Context ESG Reports for Hallucination Mitigation
arXiv:2603.13154v1 Announce Type: new Abstract: As corporate responsibility increasingly incorporates environmental, social, and governance (ESG) criteria, ESG reporting is becoming a legal requirement in many regions and a key channel for documenting sustainability practices and assessing firms' long-term and ethical...
Generalist Large Language Models for Molecular Property Prediction: Distilling Knowledge from Specialist Models
arXiv:2603.12344v1 Announce Type: new Abstract: Molecular Property Prediction (MPP) is a central task in drug discovery. While Large Language Models (LLMs) show promise as generalist models for MPP, their current performance remains below the threshold for practical adoption. We propose...