Common Belief Revisited
arXiv:2602.15403v1 Announce Type: new Abstract: Contrary to common belief, common belief is not KD4. If individual belief is KD45, common belief does indeed lose the 5 property and keep the D and 4 properties -- and it has none of...
Quantifying construct validity in large language model evaluations
arXiv:2602.15532v1 Announce Type: new Abstract: The LLM community often reports benchmark results as if they are synonymous with general model capabilities. However, benchmarks can have problems that distort performance, like test set contamination and annotator error. How can we know...
Recursive Concept Evolution for Compositional Reasoning in Large Language Models
arXiv:2602.15725v1 Announce Type: new Abstract: Large language models achieve strong performance on many complex reasoning tasks, yet their accuracy degrades sharply on benchmarks that require compositional reasoning, including ARC-AGI-2, GPQA, MATH, BBH, and HLE. Existing methods improve reasoning by expanding...
This human study did not involve human subjects: Validating LLM simulations as behavioral evidence
arXiv:2602.15785v1 Announce Type: new Abstract: A growing literature uses large language models (LLMs) as synthetic participants to generate cost-effective and nearly instantaneous responses in social science experiments. However, there is limited guidance on when such simulations support valid inference about...
Developing AI Agents with Simulated Data: Why, what, and how?
arXiv:2602.15816v1 Announce Type: new Abstract: As insufficient data volume and quality remain the key impediments to the adoption of modern subsymbolic AI, techniques of synthetic data generation are in high demand. Simulation offers an apt, systematic approach to generating diverse...
EduResearchBench: A Hierarchical Atomic Task Decomposition Benchmark for Full-Lifecycle Educational Research
arXiv:2602.15034v1 Announce Type: cross Abstract: While Large Language Models (LLMs) are reshaping the paradigm of AI for Social Science (AI4SS), rigorously evaluating their capabilities in scholarly writing remains a major challenge. Existing benchmarks largely emphasize single-shot, monolithic generation and thus...
Indic-TunedLens: Interpreting Multilingual Models in Indian Languages
arXiv:2602.15038v1 Announce Type: cross Abstract: Multilingual large language models (LLMs) are increasingly deployed in linguistically diverse regions like India, yet most interpretability tools remain tailored to English. Prior work reveals that LLMs often operate in English centric representation spaces, making...
Combining scEEG and PPG for reliable sleep staging using lightweight wearables
arXiv:2602.15042v1 Announce Type: cross Abstract: Reliable sleep staging remains challenging for lightweight wearable devices such as single-channel electroencephalography (scEEG) or photoplethysmography (PPG). scEEG offers direct measurement of cortical activity and serves as the foundation for sleep staging, yet exhibits limited...
CLOT: Closed-Loop Global Motion Tracking for Whole-Body Humanoid Teleoperation
arXiv:2602.15060v1 Announce Type: cross Abstract: Long-horizon whole-body humanoid teleoperation remains challenging due to accumulated global pose drift, particularly on full-sized humanoids. Although recent learning-based tracking methods enable agile and coordinated motions, they typically operate in the robot's local frame and...
An effective Genetic Programming Hyper-Heuristic for Uncertain Agile Satellite Scheduling
arXiv:2602.15070v1 Announce Type: cross Abstract: This paper investigates a novel problem, namely the Uncertain Agile Earth Observation Satellite Scheduling Problem (UAEOSSP). Unlike the static AEOSSP, it takes into account a range of uncertain factors (e.g., task profit, resource consumption, and...
Structure-Aware Piano Accompaniment via Style Planning and Dataset-Aligned Pattern Retrieval
arXiv:2602.15074v1 Announce Type: cross Abstract: We introduce a structure-aware approach for symbolic piano accompaniment that decouples high-level planning from note-level realization. A lightweight transformer predicts an interpretable, per-measure style plan conditioned on section/phrase structure and functional harmony, and a retriever...
StrokeNeXt: A Siamese-encoder Approach for Brain Stroke Classification in Computed Tomography Imagery
arXiv:2602.15087v1 Announce Type: cross Abstract: We present StrokeNeXt, a model for stroke classification in 2D Computed Tomography (CT) images. StrokeNeXt employs a dual-branch design with two ConvNeXt encoders, whose features are fused through a lightweight convolutional decoder based on stacked...
PolyNODE: Variable-dimension Neural ODEs on M-polyfolds
arXiv:2602.15128v1 Announce Type: cross Abstract: Neural ordinary differential equations (NODEs) are geometric deep learning models based on dynamical systems and flows generated by vector fields on manifolds. Despite numerous successful applications, particularly within the flow matching paradigm, all existing NODE...
MB-DSMIL-CL-PL: Scalable Weakly Supervised Ovarian Cancer Subtype Classification and Localisation Using Contrastive and Prototype Learning with Frozen Patch Features
arXiv:2602.15138v1 Announce Type: cross Abstract: The study of histopathological subtypes is valuable for the personalisation of effective treatment strategies for ovarian cancer. However, increasing diagnostic workloads present a challenge for UK pathology departments, leading to the rise in AI approaches....
Extracting Consumer Insight from Text: A Large Language Model Approach to Emotion and Evaluation Measurement
arXiv:2602.15312v1 Announce Type: new Abstract: Accurately measuring consumer emotions and evaluations from unstructured text remains a core challenge for marketing research and practice. This study introduces the Linguistic eXtractor (LX), a fine-tuned, large language model trained on consumer-authored text that...
NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering
arXiv:2602.15353v1 Announce Type: new Abstract: Large pretrained language models and neural reasoning systems have advanced many natural language tasks, yet they remain challenged by knowledge-intensive queries that require precise, structured multi-hop inference. Knowledge graphs provide a compact symbolic substrate for...
Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Reddit
arXiv:2602.15504v1 Announce Type: new Abstract: Patients' expectations towards their treatment have a substantial effect on the treatments' success. While primarily studied in clinical settings, online patient platforms like medical subreddits may hold complementary insights: treatment expectations that patients feel unnecessary...
Fine-Refine: Iterative Fine-grained Refinement for Mitigating Dialogue Hallucination
arXiv:2602.15509v1 Announce Type: new Abstract: The tendency for hallucination in current large language models (LLMs) negatively impacts dialogue systems. Such hallucinations produce factually incorrect responses that may mislead users and undermine system trust. Existing refinement methods for dialogue systems typically...
ExpertWeaver: Unlocking the Inherent MoE in Dense LLMs with GLU Activation Patterns
arXiv:2602.15521v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) effectively scales model capacity while preserving computational efficiency through sparse expert activation. However, training high-quality MoEs from scratch is prohibitively expensive. A promising alternative is to convert pretrained dense models into sparse MoEs....
STAPO: Stabilizing Reinforcement Learning for LLMs by Silencing Rare Spurious Tokens
arXiv:2602.15620v1 Announce Type: new Abstract: Reinforcement Learning (RL) has significantly improved large language model reasoning, but existing RL fine-tuning methods rely heavily on heuristic techniques such as entropy regularization and reweighting to maintain stability. In practice, they often experience late-stage...
How Uncertain Is the Grade? A Benchmark of Uncertainty Metrics for LLM-Based Automatic Assessment
arXiv:2602.16039v1 Announce Type: new Abstract: The rapid rise of large language models (LLMs) is reshaping the landscape of automatic assessment in education. While these systems demonstrate substantial advantages in adaptability to diverse question types and flexibility in output formats, they...
GPSBench: Do Large Language Models Understand GPS Coordinates?
arXiv:2602.16105v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in applications that interact with the physical world, such as navigation, robotics, or mapping, making robust geospatial reasoning a critical capability. Despite that, LLMs' ability to reason about...
What Persona Are We Missing? Identifying Unknown Relevant Personas for Faithful User Simulation
arXiv:2602.15832v1 Announce Type: cross Abstract: Existing user simulations, where models generate user-like responses in dialogue, often lack verification that sufficient user personas are provided, questioning the validity of the simulations. To address this core concern, this work explores the task...
Language Model Representations for Efficient Few-Shot Tabular Classification
arXiv:2602.15844v1 Announce Type: cross Abstract: The Web is a rich source of structured data in the form of tables, from product catalogs and knowledge bases to scientific datasets. However, the heterogeneity of the structure and semantics of these tables makes...
Artificial intelligence in nursing: Priorities and opportunities from an international invitational think‐tank of the Nursing and Artificial Intelligence Leadership Collaborative
Abstract Aim To develop a consensus paper on the central points of an international invitational think‐tank on nursing and artificial intelligence (AI). Methods We established the Nursing and Artificial Intelligence Leadership (NAIL) Collaborative, comprising interdisciplinary experts in AI development, biomedical...
Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives
Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex...
Preference Optimization for Review Question Generation Improves Writing Quality
arXiv:2602.15849v1 Announce Type: cross Abstract: Peer review relies on substantive, evidence-based questions, yet existing LLM-based approaches often generate surface-level queries, drawing over 50\% of their question tokens from a paper's first page. To bridge this gap, we develop IntelliReward, a...
Narrative Theory-Driven LLM Methods for Automatic Story Generation and Understanding: A Survey
arXiv:2602.15851v1 Announce Type: cross Abstract: Applications of narrative theories using large language models (LLMs) deliver promising use-cases in automatic story generation and understanding tasks. Our survey examines how natural language processing (NLP) research engages with fields of narrative studies, and...
Building Safe and Deployable Clinical Natural Language Processing under Temporal Leakage Constraints
arXiv:2602.15852v1 Announce Type: cross Abstract: Clinical natural language processing (NLP) models have shown promise for supporting hospital discharge planning by leveraging narrative clinical documentation. However, note-based models are particularly vulnerable to temporal and lexical leakage, where documentation artifacts encode future...
Playing With AI: How Do State-Of-The-Art Large Language Models Perform in the 1977 Text-Based Adventure Game Zork?
arXiv:2602.15867v1 Announce Type: cross Abstract: In this positioning paper, we evaluate the problem-solving and reasoning capabilities of contemporary Large Language Models (LLMs) through their performance in Zork, the seminal text-based adventure game first released in 1977. The game's dialogue-based structure...