Urban Vibrancy Embedding and Application on Traffic Prediction
arXiv:2602.21232v1 Announce Type: cross Abstract: Urban vibrancy reflects the dynamic human activity within urban spaces and is often measured using mobile data that captures floating population trends. This study proposes a novel approach to derive Urban Vibrancy embeddings from real-time...
Scaling View Synthesis Transformers
arXiv:2602.21341v1 Announce Type: cross Abstract: Geometry-free view synthesis transformers have recently achieved state-of-the-art performance in Novel View Synthesis (NVS), outperforming traditional approaches that rely on explicit geometry modeling. Yet the factors governing their scaling with compute remain unclear. We present...
Representation Theorems for Cumulative Propositional Dependence Logics
arXiv:2602.21360v1 Announce Type: cross Abstract: This paper establishes and proves representation theorems for cumulative propositional dependence logic and for cumulative propositional logic with team semantics. Cumulative logics are famously given by System C. For propositional dependence logic, we show that...
Towards single-shot coherent imaging via overlap-free ptychography
arXiv:2602.21361v1 Announce Type: cross Abstract: Ptychographic imaging at synchrotron and XFEL sources requires dense overlapping scans, limiting throughput and increasing dose. Extending coherent diffractive imaging to overlap-free operation on extended samples remains an open problem. Here, we extend PtychoPINN (O....
Towards Controllable Video Synthesis of Routine and Rare OR Events
arXiv:2602.21365v1 Announce Type: cross Abstract: Purpose: Curating large-scale datasets of operating room (OR) workflow, encompassing rare, safety-critical, or atypical events, remains operationally and ethically challenging. This data bottleneck complicates the development of ambient intelligence for detecting, understanding, and mitigating rare...
Black-Box Reliability Certification for AI Agents via Self-Consistency Sampling and Conformal Calibration
arXiv:2602.21368v1 Announce Type: cross Abstract: Given a black-box AI system and a task, at what confidence level can a practitioner trust the system's output? We answer with a reliability level -- a single number per system-task pair, derived from self-consistency...
Small Language Models for Privacy-Preserving Clinical Information Extraction in Low-Resource Languages
arXiv:2602.21374v1 Announce Type: cross Abstract: Extracting clinical information from medical transcripts in low-resource languages remains a significant challenge in healthcare natural language processing (NLP). This study evaluates a two-step pipeline combining Aya-expanse-8B as a Persian-to-English translation model with five open-source...
MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation
arXiv:2602.21379v1 Announce Type: cross Abstract: We introduce MrBERT, a family of 150M-300M parameter encoders built on the ModernBERT architecture and pre-trained on 35 languages and code. Through targeted adaptation, this model family achieves state-of-the-art results on Catalan- and Spanish-specific tasks,...
The Headless Firm: How AI Reshapes Enterprise Boundaries
arXiv:2602.21401v1 Announce Type: cross Abstract: The boundary of the firm is determined by coordination cost. We argue that agentic AI induces a structural change in how coordination costs scale: in prior modular systems, integration cost grew with interaction topology (O(n^2)...
Multi-Level Causal Embeddings
arXiv:2602.22287v1 Announce Type: new Abstract: Abstractions of causal models allow for the coarsening of models such that relations of cause and effect are preserved. Whereas abstractions focus on the relation between two models, in this paper we study a framework...
Mirroring the Mind: Distilling Human-Like Metacognitive Strategies into Large Language Models
arXiv:2602.22508v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) often exhibit structural fragility in complex reasoning tasks, failing to produce correct answers even after successfully deriving valid intermediate steps. Through systematic analysis, we observe that these failures frequently stem not...
Strategy Executability in Mathematical Reasoning: Leveraging Human-Model Differences for Effective Guidance
arXiv:2602.22583v1 Announce Type: new Abstract: Example-based guidance is widely used to improve mathematical reasoning at inference time, yet its effectiveness is highly unstable across problems and models-even when the guidance is correct and problem-relevant. We show that this instability arises...
Generative Data Transformation: From Mixed to Unified Data
arXiv:2602.22743v1 Announce Type: new Abstract: Recommendation model performance is intrinsically tied to the quality, volume, and relevance of their training data. To address common challenges like data sparsity and cold start, recent researchs have leveraged data from multiple auxiliary domains...
Modeling Expert AI Diagnostic Alignment via Immutable Inference Snapshots
arXiv:2602.22973v1 Announce Type: new Abstract: Human-in-the-loop validation is essential in safety-critical clinical AI, yet the transition between initial model inference and expert correction is rarely analyzed as a structured signal. We introduce a diagnostic alignment framework in which the AI-generated...
Learning-based Multi-agent Race Strategies in Formula 1
arXiv:2602.23056v1 Announce Type: new Abstract: In Formula 1, race strategies are adapted according to evolving race conditions and competitors' actions. This paper proposes a reinforcement learning approach for multi-agent race strategy optimization. Agents learn to balance energy management, tire degradation,...
A Fusion of context-aware based BanglaBERT and Two-Layer Stacked LSTM Framework for Multi-Label Cyberbullying Detection
arXiv:2602.22449v1 Announce Type: new Abstract: Cyberbullying has become a serious and growing concern in todays virtual world. When left unnoticed, it can have adverse consequences for social and mental health. Researchers have explored various types of cyberbullying, but most approaches...
Iterative Prompt Refinement for Dyslexia-Friendly Text Summarization Using GPT-4o
arXiv:2602.22524v1 Announce Type: new Abstract: Dyslexia affects approximately 10% of the global population and presents persistent challenges in reading fluency and text comprehension. While existing assistive technologies address visual presentation, linguistic complexity remains a substantial barrier to equitable access. This...
Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA
arXiv:2602.22584v1 Announce Type: new Abstract: Industrial advertising question answering (QA) is a high-stakes task in which hallucinated content, particularly fabricated URLs, can lead to financial loss, compliance violations, and legal risk. Although Retrieval-Augmented Generation (RAG) is widely adopted, deploying it...
Enhancing Persuasive Dialogue Agents by Synthesizing Cross-Disciplinary Communication Strategies
arXiv:2602.22696v1 Announce Type: new Abstract: Current approaches to developing persuasive dialogue agents often rely on a limited set of predefined persuasive strategies that fail to capture the complexity of real-world interactions. We applied a cross-disciplinary approach to develop a framework...
The Poly Problem in Zoning: Redefining “Family” for a Changing Society lawreview - Minnesota Law Review
By ARIC SHORT & TANYA PIERCE. Full Text. Single-family zoning has long dictated not only where people may live but also with whom. Although extensively critiqued for perpetuating racial and economic exclusion, these laws also privilege relationships defined by blood,...
Volume 110 – Issue 3 - Minnesota Law Review
The trap Anthropic built for itself
Anthropic, OpenAI, Google DeepMind and others have long promised to govern themselves responsibly. Now, in the absence of rules, there's not a lot to protect them.
Anthropic’s Claude rises to No. 2 in the App Store following Pentagon dispute
Anthropic’s chatbot Claude seems to have benefited from the attention around the company’s fraught negotiations with the Pentagon.
The billion-dollar infrastructure deals powering the AI boom
Here's everything we know about the biggest AI infrastructure projects, including major spending from Meta, Oracle, Microsoft, Google, and OpenAI.
OpenAI’s Sam Altman announces Pentagon deal with ‘technical safeguards’
OpenAI's CEO claims its new defense contract includes protections addressing the same issues that became a flashpoint for Anthropic.
Imagination Helps Visual Reasoning, But Not Yet in Latent Space
arXiv:2602.22766v1 Announce Type: new Abstract: Latent visual reasoning aims to mimic human's imagination process by meditating through hidden states of Multimodal Large Language Models. While recognized as a promising paradigm for visual reasoning, the underlying mechanisms driving its effectiveness remain...
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)...
MTRAG-UN: A Benchmark for Open Challenges in Multi-Turn RAG Conversations
arXiv:2602.23184v1 Announce Type: new Abstract: We present MTRAG-UN, a benchmark for exploring open challenges in multi-turn retrieval augmented generation, a popular use of large language models. We release a benchmark of 666 tasks containing over 2,800 conversation turns across 6...
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...
SPARTA: Scalable and Principled Benchmark of Tree-Structured Multi-hop QA over Text and Tables
arXiv:2602.23286v1 Announce Type: new Abstract: Real-world Table-Text question answering (QA) tasks require models that can reason across long text and source tables, traversing multiple hops and executing complex operations such as aggregation. Yet existing benchmarks are small, manually curated -...