Verify as You Go: An LLM-Powered Browser Extension for Fake News Detection
arXiv:2603.05519v1 Announce Type: new Abstract: The rampant spread of fake news in the digital age poses serious risks to public trust and democratic institutions, underscoring the need for effective, transparent, and user-centered detection tools. Existing browser extensions often fall short...
Attention Meets Reachability: Structural Equivalence and Efficiency in Grammar-Constrained LLM Decoding
arXiv:2603.05540v1 Announce Type: new Abstract: We study grammar-constrained decoding (GCD) as a coupling between an autoregressive next-token distribution and a reachability oracle over a pushdown system compiled from a context-free grammar (CFG). We prove an oracle invariance theorem: language-equivalent grammars...
FreeTxt-Vi: A Benchmarked Vietnamese-English Toolkit for Segmentation, Sentiment, and Summarisation
arXiv:2603.05690v1 Announce Type: new Abstract: FreeTxt-Vi is a free and open source web based toolkit for creating and analysing bilingual Vietnamese English text collections. Positioned at the intersection of corpus linguistics and natural language processing NLP it enables users to...
PVminerLLM: Structured Extraction of Patient Voice from Patient-Generated Text using Large Language Models
arXiv:2603.05776v1 Announce Type: new Abstract: Motivation: Patient-generated text contains critical information about patients' lived experiences, social circumstances, and engagement in care, including factors that strongly influence adherence, care coordination, and health equity. However, these patient voice signals are rarely available...
Tutor Move Taxonomy: A Theory-Aligned Framework for Analyzing Instructional Moves in Tutoring
arXiv:2603.05778v1 Announce Type: new Abstract: Understanding what makes tutoring effective requires methods for systematically analyzing tutors' instructional actions during learning interactions. This paper presents a tutor move taxonomy designed to support large-scale analysis of tutoring dialogue within the National Tutoring...
RouteGoT: Node-Adaptive Routing for Cost-Efficient Graph of Thoughts Reasoning
arXiv:2603.05818v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at multi-step reasoning, yet increasing the structural complexity of inference does not consistently improve system-level returns. Methods such as Tree of Thoughts (ToT), Graph of Thoughts (GoT), and Adaptive Graph...
Lost in Stories: Consistency Bugs in Long Story Generation by LLMs
arXiv:2603.05890v1 Announce Type: new Abstract: What happens when a storyteller forgets its own story? Large Language Models (LLMs) can now generate narratives spanning tens of thousands of words, but they often fail to maintain consistency throughout. When generating long-form narratives,...
Building an Ensemble LLM Semantic Tagger for UN Security Council Resolutions
arXiv:2603.05895v1 Announce Type: new Abstract: This paper introduces a new methodology for using LLM-based systems for accurate and efficient semantic tagging of UN Security Council resolutions. The main goal is to leverage LLM performance variability to build ensemble systems for...
InfoGatherer: Principled Information Seeking via Evidence Retrieval and Strategic Questioning
arXiv:2603.05909v1 Announce Type: new Abstract: LLMs are increasingly deployed in high-stakes domains such as medical triage and legal assistance, often as document-grounded QA systems in which a user provides a description, relevant sources are retrieved, and an LLM generates a...
Learning Next Action Predictors from Human-Computer Interaction
arXiv:2603.05923v1 Announce Type: new Abstract: Truly proactive AI systems must anticipate what we will do next. This foresight demands far richer information than the sparse signals we type into our prompts -- it demands reasoning over the entire context of...
Implicit Style Conditioning: A Structured Style-Rewrite Framework for Low-Resource Character Modeling
arXiv:2603.05933v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities in role-playing (RP); however, small Language Models (SLMs) with highly stylized personas remains a challenge due to data scarcity and the complexity of style disentanglement. Standard Supervised...
ViewFusion: Structured Spatial Thinking Chains for Multi-View Reasoning
arXiv:2603.06024v1 Announce Type: new Abstract: Multi-view spatial reasoning remains difficult for current vision-language models. Even when multiple viewpoints are available, models often underutilize cross-view relations and instead rely on single-image shortcuts, leading to fragile performance on viewpoint transformation and occlusion-sensitive...
Evaluating Austrian A-Level German Essays with Large Language Models for Automated Essay Scoring
arXiv:2603.06066v1 Announce Type: new Abstract: Automated Essay Scoring (AES) has been explored for decades with the goal to support teachers by reducing grading workload and mitigating subjective biases. While early systems relied on handcrafted features and statistical models, recent advances...
Experiences Build Characters: The Linguistic Origins and Functional Impact of LLM Personality
arXiv:2603.06088v1 Announce Type: new Abstract: Human problem-solving is enriched by a diversity of styles and personality traits, yet the development of Large Language Models (LLMs) has largely prioritized uniform performance benchmarks that favour specific behavioural tendencies such as assertiveness. To...
Making Implicit Premises Explicit in Logical Understanding of Enthymemes
arXiv:2603.06114v1 Announce Type: new Abstract: Real-world arguments in text and dialogues are normally enthymemes (i.e. some of their premises and/or claims are implicit). Natural language processing (NLP) methods for handling enthymemes can potentially identify enthymemes in text but they do...
A Causal Graph Approach to Oppositional Narrative Analysis
arXiv:2603.06135v1 Announce Type: new Abstract: Current methods for textual analysis rely on data annotated within predefined ontologies, often embedding human bias within black-box models. Despite achieving near-perfect performance, these approaches exploit unstructured, linear pattern recognition rather than modeling the structured...
CRIMSON: A Clinically-Grounded LLM-Based Metric for Generative Radiology Report Evaluation
arXiv:2603.06183v1 Announce Type: new Abstract: We introduce CRIMSON, a clinically grounded evaluation framework for chest X-ray report generation that assesses reports based on diagnostic correctness, contextual relevance, and patient safety. Unlike prior metrics, CRIMSON incorporates full clinical context, including patient...
MAPO: Mixed Advantage Policy Optimization for Long-Horizon Multi-Turn Dialogue
arXiv:2603.06194v1 Announce Type: new Abstract: Subjective multi-turn dialogue tasks, such as emotional support, require conversational policies that adapt to evolving user states and optimize long-horizon interaction quality. However, reinforcement learning (RL) for such settings remains challenging due to the absence...
LIT-RAGBench: Benchmarking Generator Capabilities of Large Language Models in Retrieval-Augmented Generation
arXiv:2603.06198v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is a framework in which a Generator, such as a Large Language Model (LLM), produces answers by retrieving documents from an external collection using a Retriever. In practice, Generators must integrate evidence...
Transparent AI for Mathematics: Transformer-Based Large Language Models for Mathematical Entity Relationship Extraction with XAI
arXiv:2603.06348v1 Announce Type: new Abstract: Mathematical text understanding is a challenging task due to the presence of specialized entities and complex relationships between them. This study formulates mathematical problem interpretation as a Mathematical Entity Relation Extraction (MERE) task, where operands...
Evaluation of Deontic Conditional Reasoning in Large Language Models: The Case of Wason's Selection Task
arXiv:2603.06416v1 Announce Type: new Abstract: As large language models (LLMs) advance in linguistic competence, their reasoning abilities are gaining increasing attention. In humans, reasoning often performs well in domain specific settings, particularly in normative rather than purely formal contexts. Although...
Speak in Context: Multilingual ASR with Speech Context Alignment via Contrastive Learning
arXiv:2603.06505v1 Announce Type: new Abstract: Automatic speech recognition (ASR) has benefited from advances in pretrained speech and language models, yet most systems remain constrained to monolingual settings and short, isolated utterances. While recent efforts in context-aware ASR show promise, two...
IntSeqBERT: Learning Arithmetic Structure in OEIS via Modulo-Spectrum Embeddings
arXiv:2603.05556v1 Announce Type: new Abstract: Integer sequences in the OEIS span values from single-digit constants to astronomical factorials and exponentials, making prediction challenging for standard tokenised models that cannot handle out-of-vocabulary values or exploit periodic arithmetic structure. We present IntSeqBERT,...
Autocorrelation effects in a stochastic-process model for decision making via time series
arXiv:2603.05559v1 Announce Type: new Abstract: Decision makers exploiting photonic chaotic dynamics obtained by semiconductor lasers provide an ultrafast approach to solving multi-armed bandit problems by using a temporal optical signal as the driving source for sequential decisions. In such systems,...
FuseDiff: Symmetry-Preserving Joint Diffusion for Dual-Target Structure-Based Drug Design
arXiv:2603.05567v1 Announce Type: new Abstract: Dual-target structure-based drug design aims to generate a single ligand together with two pocket-specific binding poses, each compatible with a corresponding target pocket, enabling polypharmacological therapies with improved efficacy and reduced resistance. Existing approaches typically...
A Novel Hybrid Heuristic-Reinforcement Learning Optimization Approach for a Class of Railcar Shunting Problems
arXiv:2603.05579v1 Announce Type: new Abstract: Railcar shunting is a core planning task in freight railyards, where yard planners need to disassemble and reassemble groups of railcars to form outbound trains. Classification tracks with access from one side only can be...
Identifying Adversary Characteristics from an Observed Attack
arXiv:2603.05625v1 Announce Type: new Abstract: When used in automated decision-making systems, machine learning (ML) models are vulnerable to data-manipulation attacks. Some defense mechanisms (e.g., adversarial regularization) directly affect the ML models while others (e.g., anomaly detection) act within the broader...
Reinforcement Learning for Power-Flow Network Analysis
arXiv:2603.05673v1 Announce Type: new Abstract: The power flow equations are non-linear multivariate equations that describe the relationship between power injections and bus voltages of electric power networks. Given a network topology, we are interested in finding network parameters with many...
Warm Starting State-Space Models with Automata Learning
arXiv:2603.05694v1 Announce Type: new Abstract: We prove that Moore machines can be exactly realized as state-space models (SSMs), establishing a formal correspondence between symbolic automata and these continuous machine learning architectures. These Moore-SSMs preserve both the complete symbolic structure and...
Bridging Domains through Subspace-Aware Model Merging
arXiv:2603.05768v1 Announce Type: new Abstract: Model merging integrates multiple task-specific models into a single consolidated one. Recent research has made progress in improving merging performance for in-distribution or multi-task scenarios, but domain generalization in model merging remains underexplored. We investigate...