Where Vision Becomes Text: Locating the OCR Routing Bottleneck in Vision-Language Models
arXiv:2602.22918v1 Announce Type: new Abstract: Vision-language models (VLMs) can read text from images, but where does this optical character recognition (OCR) information enter the language processing stream? We investigate the OCR routing mechanism across three architecture families (Qwen3-VL, Phi-4, InternVL3.5)...
CiteLLM: An Agentic Platform for Trustworthy Scientific Reference Discovery
arXiv:2602.23075v1 Announce Type: new Abstract: Large language models (LLMs) have created new opportunities to enhance the efficiency of scholarly activities; however, challenges persist in the ethical deployment of AI assistance, including (1) the trustworthiness of AI-generated content, (2) preservation of...
Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding?
arXiv:2602.23225v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) are often advertised as enabling parallel token generation, yet practical fast DLMs frequently converge to left-to-right, autoregressive (AR)-like decoding dynamics. In contrast, genuinely non-AR generation is promising because it removes AR's...
Enriching Taxonomies Using Large Language Models
arXiv:2602.22213v1 Announce Type: cross Abstract: Taxonomies play a vital role in structuring and categorizing information across domains. However, many existing taxonomies suffer from limited coverage and outdated or ambiguous nodes, reducing their effectiveness in knowledge retrieval. To address this, we...
WaveSSM: Multiscale State-Space Models for Non-stationary Signal Attention
arXiv:2602.22266v1 Announce Type: new Abstract: State-space models (SSMs) have emerged as a powerful foundation for long-range sequence modeling, with the HiPPO framework showing that continuous-time projection operators can be used to derive stable, memory-efficient dynamical systems that encode the past...
Early Risk Stratification of Dosing Errors in Clinical Trials Using Machine Learning
arXiv:2602.22285v1 Announce Type: new Abstract: Objective: The objective of this study is to develop a machine learning (ML)-based framework for early risk stratification of clinical trials (CTs) according to their likelihood of exhibiting a high rate of dosing errors, using...
When Should a Model Change Its Mind? An Energy-Based Theory and Regularizer for Concept Drift in Electrocardiogram (ECG) Signals
arXiv:2602.22294v1 Announce Type: new Abstract: Models operating on dynamic physiologic signals must distinguish benign, label-preserving variability from true concept change. Existing concept-drift frameworks are largely distributional and provide no principled guidance on how much a model's internal representation may move...
Training Agents to Self-Report Misbehavior
arXiv:2602.22303v1 Announce Type: new Abstract: Frontier AI agents may pursue hidden goals while concealing their pursuit from oversight. Alignment training aims to prevent such behavior by reinforcing the correct goals, but alignment may not always succeed and can lead to...
Reinforcement-aware Knowledge Distillation for LLM Reasoning
arXiv:2602.22495v1 Announce Type: new Abstract: Reinforcement learning (RL) post-training has recently driven major gains in long chain-of-thought reasoning large language models (LLMs), but the high inference cost of such models motivates distillation into smaller students. Most existing knowledge distillation (KD)...
Space Syntax-guided Post-training for Residential Floor Plan Generation
arXiv:2602.22507v1 Announce Type: new Abstract: Pre-trained generative models for residential floor plans are typically optimized to fit large-scale data distributions, which can under-emphasize critical architectural priors such as the configurational dominance and connectivity of domestic public spaces (e.g., living rooms...
Coarse-to-Fine Learning of Dynamic Causal Structures
arXiv:2602.22532v1 Announce Type: new Abstract: Learning the dynamic causal structure of time series is a challenging problem. Most existing approaches rely on distributional or structural invariance to uncover underlying causal dynamics, assuming stationary or partially stationary causality. However, these assumptions...
The legal protection of artificial intelligence-generated work: The argument for sui generis over copyright
Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. As with other elements of society, the modern economy has become more reliant on AI, indicating the potentially great influence it has on innovation. Many...
ChatGPT reaches 900M weekly active users
OpenAI shared the new numbers as part of its announcement that it has raised $110 billion in private funding.
OpenAI raises $110B in one of the largest private funding rounds in history
The new funding consists of a $50 billion investment from Amazon as well as $30 billion each from Nvidia and SoftBank, against a $730 billion valuation.
Structured Prompt Language: Declarative Context Management for LLMs
arXiv:2602.21257v1 Announce Type: new Abstract: We present SPL (Structured Prompt Language), a declarative SQL-inspired language that treats large language models as generative knowledge bases and their context windows as constrained resources. SPL provides explicit WITH BUDGET/LIMIT token management, an automatic...
ToolMATH: A Math Tool Benchmark for Realistic Long-Horizon Multi-Tool Reasoning
arXiv:2602.21265v1 Announce Type: new Abstract: We introduce \ToolMATH, a math-grounded benchmark that evaluates tool-augmented language models in realistic multi-tool environments where the output depends on calling schema-specified tools and sustaining multi-step execution. It turns math problems into a controlled, correctness-checkable...
Archetypal Graph Generative Models: Explainable and Identifiable Communities via Anchor-Dominant Convex Hulls
arXiv:2602.21342v1 Announce Type: new Abstract: Representation learning has been essential for graph machine learning tasks such as link prediction, community detection, and network visualization. Despite recent advances in achieving high performance on these downstream tasks, little progress has been made...
D-Flow SGLD: Source-Space Posterior Sampling for Scientific Inverse Problems with Flow Matching
arXiv:2602.21469v1 Announce Type: new Abstract: Data assimilation and scientific inverse problems require reconstructing high-dimensional physical states from sparse and noisy observations, ideally with uncertainty-aware posterior samples that remain faithful to learned priors and governing physics. While training-free conditional generation is...
Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences
arXiv:2602.21585v1 Announce Type: new Abstract: Many applications seek to optimize LLM outputs at test time by iteratively proposing, scoring, and refining candidates over a discrete output space. Existing methods use a calibrated scalar evaluator for the target objective to guide...
The Beginnings Of The One Big Beautiful Bill Act: Placing The 2017 Tax Cuts And Jobs Act In Historical Perspective
On July 4, 2025, President Donald J. Trump signed into law the One Big Beautiful Bill Act (OBBBA). This new law was built on the foundations of its immediate predecessor,the 2017 Tax Cuts and Jobs Act (TCJA). This Essay examines...
Semantic Novelty at Scale: Narrative Shape Taxonomy and Readership Prediction in 28,606 Books
arXiv:2602.20647v1 Announce Type: new Abstract: I introduce semantic novelty--cosine distance between each paragraph's sentence embedding and the running centroid of all preceding paragraphs--as an information-theoretic measure of narrative structure at corpus scale. Applying it to 28,606 books in PG19 (pre-1920...
Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimization
arXiv:2602.20743v1 Announce Type: new Abstract: Anonymizing textual documents is a highly context-sensitive problem: the appropriate balance between privacy protection and utility preservation varies with the data domain, privacy objectives, and downstream application. However, existing anonymization methods rely on static, manually...
Explicit Grammar Semantic Feature Fusion for Robust Text Classification
arXiv:2602.20749v1 Announce Type: new Abstract: Natural Language Processing enables computers to understand human language by analysing and classifying text efficiently with deep-level grammatical and semantic features. Existing models capture features by learning from large corpora with transformer models, which are...
MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Elastic LLMs
arXiv:2602.20191v1 Announce Type: cross Abstract: Changing runtime complexity on cloud and edge devices necessitates elastic large language model (LLM) deployment, where an LLM can be inferred with various quantization precisions based on available computational resources. However, it has been observed...
Protein Language Models Diverge from Natural Language: Comparative Analysis and Improved Inference
arXiv:2602.20449v1 Announce Type: cross Abstract: Modern Protein Language Models (PLMs) apply transformer-based model architectures from natural language processing to biological sequences, predicting a variety of protein functions and properties. However, protein language has key differences from natural language, such as...
Controllable Exploration in Hybrid-Policy RLVR for Multi-Modal Reasoning
arXiv:2602.20197v1 Announce Type: new Abstract: Reinforcement Learning with verifiable rewards (RLVR) has emerged as a primary learning paradigm for enhancing the reasoning capabilities of multi-modal large language models (MLLMs). However, during RL training, the enormous state space of MLLM and...
QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
arXiv:2602.20309v1 Announce Type: new Abstract: Vision-language-action (VLA) models unify perception, language, and control for embodied agents but face significant challenges in practical deployment due to rapidly increasing compute and memory demands, especially as models scale to longer horizons and larger...
cc-Shapley: Measuring Multivariate Feature Importance Needs Causal Context
arXiv:2602.20396v1 Announce Type: new Abstract: Explainable artificial intelligence promises to yield insights into relevant features, thereby enabling humans to examine and scrutinize machine learning models or even facilitating scientific discovery. Considering the widespread technique of Shapley values, we find that...
$\kappa$-Explorer: A Unified Framework for Active Model Estimation in MDPs
arXiv:2602.20404v1 Announce Type: new Abstract: In tabular Markov decision processes (MDPs) with perfect state observability, each trajectory provides active samples from the transition distributions conditioned on state-action pairs. Consequently, accurate model estimation depends on how the exploration policy allocates visitation...
A Long-Short Flow-Map Perspective for Drifting Models
arXiv:2602.20463v1 Announce Type: new Abstract: This paper provides a reinterpretation of the Drifting Model~\cite{deng2026generative} through a semigroup-consistent long-short flow-map factorization. We show that a global transport process can be decomposed into a long-horizon flow map followed by a short-time terminal...