Shedding light on the complex relationship between AI, art and copyright law
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....
Learning Personalized Agents from Human Feedback
arXiv:2602.16173v1 Announce Type: new Abstract: Modern AI agents are powerful but often fail to align with the idiosyncratic, evolving preferences of individual users. Prior approaches typically rely on static datasets, either training implicit preference models on interaction history or encoding...
EnterpriseGym Corecraft: Training Generalizable Agents on High-Fidelity RL Environments
arXiv:2602.16179v1 Announce Type: new Abstract: We show that training AI agents on high-fidelity reinforcement learning environments produces capabilities that generalize beyond the training distribution. We introduce \corecraft{}, the first environment in \textsc{EnterpriseGym}, Surge AI's suite of agentic RL environments. \corecraft{}...
Revolutionizing Long-Term Memory in AI: New Horizons with High-Capacity and High-Speed Storage
arXiv:2602.16192v1 Announce Type: new Abstract: Driven by our mission of "uplifting the world with memory," this paper explores the design concept of "memory" that is essential for achieving artificial superintelligence (ASI). Rather than proposing novel methods, we focus on several...
Verifiable Semantics for Agent-to-Agent Communication
arXiv:2602.16424v1 Announce Type: new Abstract: Multiagent AI systems require consistent communication, but we lack methods to verify that agents share the same understanding of the terms used. Natural language is interpretable but vulnerable to semantic drift, while learned protocols are...
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...
Who will pay for the Matrix? Simulation sponsors, AI governance, and the ethics and political economy of digital worlds
Institutionalizing trust in AI governance: from ethical principles to legal design
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...
Decoupling Strategy and Execution in Task-Focused Dialogue via Goal-Oriented Preference Optimization
arXiv:2602.15854v1 Announce Type: cross Abstract: Large language models show potential in task-oriented dialogue systems, yet existing training methods often rely on token-level likelihood or preference optimization, which poorly align with long-horizon task success. To address this, we propose Goal-Oriented Preference...
Kalman-Inspired Runtime Stability and Recovery in Hybrid Reasoning Systems
arXiv:2602.15855v1 Announce Type: cross Abstract: Hybrid reasoning systems that combine learned components with model-based inference are increasingly deployed in tool-augmented decision loops, yet their runtime behavior under partial observability and sustained evidence mismatch remains poorly understood. In practice, failures often...
Test-Time Adaptation for Tactile-Vision-Language Models
arXiv:2602.15873v1 Announce Type: cross Abstract: Tactile-vision-language (TVL) models are increasingly deployed in real-world robotic and multimodal perception tasks, where test-time distribution shifts are unavoidable. Existing test-time adaptation (TTA) methods provide filtering in unimodal settings but lack explicit treatment of modality-wise...
IT-OSE: Exploring Optimal Sample Size for Industrial Data Augmentation
arXiv:2602.15878v1 Announce Type: cross Abstract: In industrial scenarios, data augmentation is an effective approach to improve model performance. However, its benefits are not unidirectionally beneficial. There is no theoretical research or established estimation for the optimal sample size (OSS) in...
FUTURE-VLA: Forecasting Unified Trajectories Under Real-time Execution
arXiv:2602.15882v1 Announce Type: cross Abstract: General vision-language models increasingly support unified spatiotemporal reasoning over long video streams, yet deploying such capabilities on robots remains constrained by the prohibitive latency of processing long-horizon histories and generating high-dimensional future predictions. To bridge...
An order-oriented approach to scoring hesitant fuzzy elements
arXiv:2602.16827v1 Announce Type: new Abstract: Traditional scoring approaches on hesitant fuzzy sets often lack a formal base in order theory. This paper proposes a unified framework, where each score is explicitly defined with respect to a given order. This order-oriented...
Narrow fine-tuning erodes safety alignment in vision-language agents
arXiv:2602.16931v1 Announce Type: new Abstract: Lifelong multimodal agents must continuously adapt to new tasks through post-training, but this creates fundamental tension between acquiring capabilities and preserving safety alignment. We demonstrate that fine-tuning aligned vision-language models on narrow-domain harmful datasets induces...
RFEval: Benchmarking Reasoning Faithfulness under Counterfactual Reasoning Intervention in Large Reasoning Models
arXiv:2602.17053v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) exhibit strong performance, yet often produce rationales that sound plausible but fail to reflect their true decision process, undermining reliability and trust. We introduce a formal framework for reasoning faithfulness, defined...
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...
Texo: Formula Recognition within 20M Parameters
arXiv:2602.17189v1 Announce Type: new Abstract: In this paper we present Texo, a minimalist yet highperformance formula recognition model that contains only 20 million parameters. By attentive design, distillation and transfer of the vocabulary and the tokenizer, Texo achieves comparable performance...
BanglaSummEval: Reference-Free Factual Consistency Evaluation for Bangla Summarization
arXiv:2602.16843v1 Announce Type: new Abstract: Evaluating factual consistency is essential for reliable text summarization, particularly in high-stakes domains such as healthcare and news. However, most existing evaluation metrics overlook Bangla, a widely spoken yet under-resourced language, and often depend on...
Persona2Web: Benchmarking Personalized Web Agents for Contextual Reasoning with User History
arXiv:2602.17003v1 Announce Type: new Abstract: Large language models have advanced web agents, yet current agents lack personalization capabilities. Since users rarely specify every detail of their intent, practical web agents must be able to interpret ambiguous queries by inferring user...
ALPS: A Diagnostic Challenge Set for Arabic Linguistic & Pragmatic Reasoning
arXiv:2602.17054v1 Announce Type: new Abstract: While recent Arabic NLP benchmarks focus on scale, they often rely on synthetic or translated data which may benefit from deeper linguistic verification. We introduce ALPS (Arabic Linguistic & Pragmatic Suite), a native, expert-curated diagnostic...
Projective Psychological Assessment of Large Multimodal Models Using Thematic Apperception Tests
arXiv:2602.17108v1 Announce Type: new Abstract: Thematic Apperception Test (TAT) is a psychometrically grounded, multidimensional assessment framework that systematically differentiates between cognitive-representational and affective-relational components of personality-like functioning. This test is a projective psychological framework designed to uncover unconscious aspects of...
What Makes a Good Doctor Response? An Analysis on a Romanian Telemedicine Platform
arXiv:2602.17194v1 Announce Type: new Abstract: Text-based telemedicine has become a common mode of care, requiring clinicians to deliver medical advice clearly and effectively in writing. As platforms increasingly rely on patient ratings and feedback, clinicians face growing pressure to maintain...
Sam Altman would like remind you that humans use a lot of energy, too
"It also takes a lot of energy to train a human."
Microsoft’s new gaming CEO vows not to flood the ecosystem with ‘endless AI slop’
Is Microsoft's gaming division doubling down on AI?
Diverse Word Choices, Same Reference: Annotating Lexically-Rich Cross-Document Coreference
arXiv:2602.17424v1 Announce Type: new Abstract: Cross-document coreference resolution (CDCR) identifies and links mentions of the same entities and events across related documents, enabling content analysis that aggregates information at the level of discourse participants. However, existing datasets primarily focus on...
PEACE 2.0: Grounded Explanations and Counter-Speech for Combating Hate Expressions
arXiv:2602.17467v1 Announce Type: new Abstract: The increasing volume of hate speech on online platforms poses significant societal challenges. While the Natural Language Processing community has developed effective methods to automatically detect the presence of hate speech, responses to it, called...