Towards Efficient Constraint Handling in Neural Solvers for Routing Problems
arXiv:2602.16012v1 Announce Type: new Abstract: Neural solvers have achieved impressive progress in addressing simple routing problems, particularly excelling in computational efficiency. However, their advantages under complex constraints remain nascent, for which current constraint-handling schemes via feasibility masking or implicit feasibility...
Improving Interactive In-Context Learning from Natural Language Feedback
arXiv:2602.16066v1 Announce Type: new Abstract: Adapting one's thought process based on corrective feedback is an essential ability in human learning, particularly in collaborative settings. In contrast, the current large language model training paradigm relies heavily on modeling vast, static corpora....
Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learning
arXiv:2602.16435v1 Announce Type: new Abstract: Automated feature engineering (AFE) enables AI systems to autonomously construct high-utility representations from raw tabular data. However, existing AFE methods rely on statistical heuristics, yielding brittle features that fail under distribution shift. We introduce CAFE,...
Framework of Thoughts: A Foundation Framework for Dynamic and Optimized Reasoning based on Chains, Trees, and Graphs
arXiv:2602.16512v1 Announce Type: new Abstract: Prompting schemes such as Chain of Thought, Tree of Thoughts, and Graph of Thoughts can significantly enhance the reasoning capabilities of large language models. However, most existing schemes require users to define static, problem-specific reasoning...
Creating a digital poet
arXiv:2602.16578v1 Announce Type: new Abstract: Can a machine write good poetry? Any positive answer raises fundamental questions about the nature and value of art. We report a seven-month poetry workshop in which a large language model was shaped into a...
Redefining boundaries in innovation and knowledge domains: Investigating the impact of generative artificial intelligence on copyright and intellectual property rights
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...
A Lightweight Explainable Guardrail for Prompt Safety
arXiv:2602.15853v1 Announce Type: cross Abstract: We propose a lightweight explainable guardrail (LEG) method for the classification of unsafe prompts. LEG uses a multi-task learning architecture to jointly learn a prompt classifier and an explanation classifier, where the latter labels prompt...
State Design Matters: How Representations Shape Dynamic Reasoning in Large Language Models
arXiv:2602.15858v1 Announce Type: cross Abstract: As large language models (LLMs) move from static reasoning tasks toward dynamic environments, their success depends on the ability to navigate and respond to an environment that changes as they interact at inference time. An...
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...
Evidence for Daily and Weekly Periodic Variability in GPT-4o Performance
arXiv:2602.15889v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in research both as tools and as objects of investigation. Much of this work implicitly assumes that LLM performance under fixed conditions (identical model snapshot, hyperparameters, and prompt)...
Simple Baselines are Competitive with Code Evolution
arXiv:2602.16805v1 Announce Type: new Abstract: Code evolution is a family of techniques that rely on large language models to search through possible computer programs by evolving or mutating existing code. Many proposed code evolution pipelines show impressive performance but are...
Fundamental Limits of Black-Box Safety Evaluation: Information-Theoretic and Computational Barriers from Latent Context Conditioning
arXiv:2602.16984v1 Announce Type: new Abstract: Black-box safety evaluation of AI systems assumes model behavior on test distributions reliably predicts deployment performance. We formalize and challenge this assumption through latent context-conditioned policies -- models whose outputs depend on unobserved internal variables...
Conv-FinRe: A Conversational and Longitudinal Benchmark for Utility-Grounded Financial Recommendation
arXiv:2602.16990v1 Announce Type: new Abstract: Most recommendation benchmarks evaluate how well a model imitates user behavior. In financial advisory, however, observed actions can be noisy or short-sighted under market volatility and may conflict with a user's long-term goals. Treating what...
Predictive Batch Scheduling: Accelerating Language Model Training Through Loss-Aware Sample Prioritization
arXiv:2602.17066v1 Announce Type: new Abstract: We introduce Predictive Batch Scheduling (PBS), a novel training optimization technique that accelerates language model convergence by dynamically prioritizing high-loss samples during batch construction. Unlike curriculum learning approaches that require predefined difficulty metrics or hard...
Toward Trustworthy Evaluation of Sustainability Rating Methodologies: A Human-AI Collaborative Framework for Benchmark Dataset Construction
arXiv:2602.17106v1 Announce Type: new Abstract: Sustainability or ESG rating agencies use company disclosures and external data to produce scores or ratings that assess the environmental, social, and governance performance of a company. However, sustainability ratings across agencies for a single...
Owen-based Semantics and Hierarchy-Aware Explanation (O-Shap)
arXiv:2602.17107v1 Announce Type: new Abstract: Shapley value-based methods have become foundational in explainable artificial intelligence (XAI), offering theoretically grounded feature attributions through cooperative game theory. However, in practice, particularly in vision tasks, the assumption of feature independence breaks down, as...
Epistemology of Generative AI: The Geometry of Knowing
arXiv:2602.17116v1 Announce Type: new Abstract: Generative AI presents an unprecedented challenge to our understanding of knowledge and its production. Unlike previous technological transformations, where engineering understanding preceded or accompanied deployment, generative AI operates through mechanisms whose epistemic character remains obscure,...
Web Verbs: Typed Abstractions for Reliable Task Composition on the Agentic Web
arXiv:2602.17245v1 Announce Type: new Abstract: The Web is evolving from a medium that humans browse to an environment where software agents act on behalf of users. Advances in large language models (LLMs) make natural language a practical interface for goal-directed...
Evaluating Monolingual and Multilingual Large Language Models for Greek Question Answering: The DemosQA Benchmark
arXiv:2602.16811v1 Announce Type: new Abstract: Recent advancements in Natural Language Processing and Deep Learning have enabled the development of Large Language Models (LLMs), which have significantly advanced the state-of-the-art across a wide range of tasks, including Question Answering (QA). Despite...
ReIn: Conversational Error Recovery with Reasoning Inception
arXiv:2602.17022v1 Announce Type: new Abstract: Conversational agents powered by large language models (LLMs) with tool integration achieve strong performance on fixed task-oriented dialogue datasets but remain vulnerable to unanticipated, user-induced errors. Rather than focusing on error prevention, this work focuses...
The Emergence of Lab-Driven Alignment Signatures: A Psychometric Framework for Auditing Latent Bias and Compounding Risk in Generative AI
arXiv:2602.17127v1 Announce Type: new Abstract: As Large Language Models (LLMs) transition from standalone chat interfaces to foundational reasoning layers in multi-agent systems and recursive evaluation loops (LLM-as-a-judge), the detection of durable, provider-level behavioral signatures becomes a critical requirement for safety...
ABCD: All Biases Come Disguised
arXiv:2602.17445v1 Announce Type: new Abstract: Multiple-choice question (MCQ) benchmarks have been a standard evaluation practice for measuring LLMs' ability to reason and answer knowledge-based questions. Through a synthetic NonsenseQA benchmark, we observe that different LLMs exhibit varying degrees of label-position-few-shot-prompt...
Small LLMs for Medical NLP: a Systematic Analysis of Few-Shot, Constraint Decoding, Fine-Tuning and Continual Pre-Training in Italian
arXiv:2602.17475v1 Announce Type: new Abstract: Large Language Models (LLMs) consistently excel in diverse medical Natural Language Processing (NLP) tasks, yet their substantial computational requirements often limit deployment in real-world healthcare settings. In this work, we investigate whether "small" LLMs (around...
Sink-Aware Pruning for Diffusion Language Models
arXiv:2602.17664v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) incur high inference cost due to iterative denoising, motivating efficient pruning. Existing pruning heuristics largely inherited from autoregressive (AR) LLMs, typically preserve attention sink tokens because AR sinks serve as stable...
DeepVision-103K: A Visually Diverse, Broad-Coverage, and Verifiable Mathematical Dataset for Multimodal Reasoning
arXiv:2602.16742v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has been shown effective in enhancing the visual reflection and reasoning capabilities of Large Multimodal Models (LMMs). However, existing datasets are predominantly derived from either small-scale manual construction or...
Low-Dimensional and Transversely Curved Optimization Dynamics in Grokking
arXiv:2602.16746v1 Announce Type: new Abstract: Grokking -- the delayed transition from memorization to generalization in small algorithmic tasks -- remains poorly understood. We present a geometric analysis of optimization dynamics in transformers trained on modular arithmetic. PCA of attention weight...
Machine Learning Argument of Latitude Error Model for LEO Satellite Orbit and Covariance Correction
arXiv:2602.16764v1 Announce Type: new Abstract: Low Earth orbit (LEO) satellites are leveraged to support new position, navigation, and timing (PNT) service alternatives to GNSS. These alternatives require accurate propagation of satellite position and velocity with a realistic quantification of uncertainty....
Escaping the Cognitive Well: Efficient Competition Math with Off-the-Shelf Models
arXiv:2602.16793v1 Announce Type: new Abstract: In the past year, custom and unreleased math reasoning models reached gold medal performance on the International Mathematical Olympiad (IMO). Similar performance was then reported using large-scale inference on publicly available models but at prohibitive...
Position: Why a Dynamical Systems Perspective is Needed to Advance Time Series Modeling
arXiv:2602.16864v1 Announce Type: new Abstract: Time series (TS) modeling has come a long way from early statistical, mainly linear, approaches to the current trend in TS foundation models. With a lot of hype and industrial demand in this field, it...