Inferring Chronic Treatment Onset from ePrescription Data: A Renewal Process Approach
arXiv:2602.23824v1 Announce Type: new Abstract: Longitudinal electronic health record (EHR) data are often left-censored, making diagnosis records incomplete and unreliable for determining disease onset. In contrast, outpatient prescriptions form renewal-based trajectories that provide a continuous signal of disease management. We...
A Theory of Random Graph Shift in Truncated-Spectrum vRKHS
arXiv:2602.23880v1 Announce Type: new Abstract: This paper develops a theory of graph classification under domain shift through a random-graph generative lens, where we consider intra-class graphs sharing the same random graph model (RGM) and the domain shift induced by changes...
Learning Generation Orders for Masked Discrete Diffusion Models via Variational Inference
arXiv:2602.23968v1 Announce Type: new Abstract: Masked discrete diffusion models (MDMs) are a promising new approach to generative modelling, offering the ability for parallel token generation and therefore greater efficiency than autoregressive counterparts. However, achieving an optimal balance between parallel generation...
MINT: Multimodal Imaging-to-Speech Knowledge Transfer for Early Alzheimer's Screening
arXiv:2602.23994v1 Announce Type: new Abstract: Alzheimer's disease is a progressive neurodegenerative disorder in which mild cognitive impairment (MCI) marks a critical transition between aging and dementia. Neuroimaging modalities, such as structural MRI, provide biomarkers of this transition; however, their high...
InfoNCE Induces Gaussian Distribution
arXiv:2602.24012v1 Announce Type: new Abstract: Contrastive learning has become a cornerstone of modern representation learning, allowing training with massive unlabeled data for both task-specific and general (foundation) models. A prototypical loss in contrastive training is InfoNCE and its variants. In...
pathsig: A GPU-Accelerated Library for Truncated and Projected Path Signatures
arXiv:2602.24066v1 Announce Type: new Abstract: Path signatures provide a rich representation of sequential data, with strong theoretical guarantees and good performance in a variety of machine-learning tasks. While signatures have progressed from fixed feature extractors to trainable components of machine-learning...
Justices to consider breadth of a federal defendant’s waiver of appeal
In Hunter v. United States, to be argued on Tuesday, March 3, the Supreme Court will address how broad federal defendants’ waivers of their right to appeal can be and […]The postJustices to consider breadth of a federal defendant’s waiver...
SCOTUStoday for Monday, March 2
If you are looking for a great introduction to this morning’s argument in United States v. Hemani, please check out this animated explainer, done in partnership with Briefly. Our live […]The postSCOTUStoday for Monday, March 2appeared first onSCOTUSblog.
Trump FCC's equal-time crackdown doesn't apply equally—or at all—to talk radio
FCC Chairman Brendan Carr's unequal enforcement of the equal-time rule.
Cursor has reportedly surpassed $2B in annualized revenue
The four-year-old startup saw its revenue run rate double over the past three months, according to one Bloomberg source.
No one has a good plan for how AI companies should work with the government
As OpenAI transitions from a wildly successful consumer startup into a piece of national security infrastructure, the company seems unequipped to manage its new responsibilities.
Tech workers urge DOD, Congress to withdraw Anthropic label as a supply-chain risk
Tech workers have signed an open letter urging the Department of Defense to withdraw its designation of Anthropic as a "supply chain risk" and instead to settle the matter quietly.
A married founder duo’s company, 14.ai, is replacing customer support teams at startups
14.ai also launched a consumer brand to understand how much AI can handle customer support tasks.
Anthropic’s Claude reports widespread outage
Anthropic's AI chatbot Claude experienced widespread service disruptions on Monday morning, with thousands of users reporting issues accessing the bot.
Investors spill what they aren’t looking for anymore in AI SaaS companies
TechCrunch spoke with VCs to learn what investors aren't looking for in AI SaaS startups anymore.
Expressive Association as Shield, not Sword: A Constitutional Defense of DEI
Introduction Diversity, equity, and inclusion (DEI)—an effort aimed at remedying historic inequality in opportunities—faces the chopping block. Its opposition claims it commits the very sin it aimed to rid: discrimination. DEI’s opposition has mobilized and attacked on all fronts, already...
Using AI in Dance Notation and Copyright Infringement Prevention: Enhancing Creative Economy and Cultural Entrepreneurship in South Asia
Diffusion Modulation via Environment Mechanism Modeling for Planning
arXiv:2602.20422v1 Announce Type: new Abstract: Diffusion models have shown promising capabilities in trajectory generation for planning in offline reinforcement learning (RL). However, conventional diffusion-based planning methods often fail to account for the fact that generating trajectories in RL requires unique...
Implicit Intelligence -- Evaluating Agents on What Users Don't Say
arXiv:2602.20424v1 Announce Type: new Abstract: Real-world requests to AI agents are fundamentally underspecified. Natural human communication relies on shared context and unstated constraints that speakers expect listeners to infer. Current agentic benchmarks test explicit instruction-following but fail to evaluate whether...
KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning
arXiv:2602.20494v1 Announce Type: new Abstract: Driven by the increasingly complex and decision-oriented demands of time series analysis, we introduce the Semantic-Conditional Time Series Reasoning task, which extends conventional time series analysis beyond purely numerical modeling to incorporate contextual and semantic...
Inner Speech as Behavior Guides: Steerable Imitation of Diverse Behaviors for Human-AI coordination
arXiv:2602.20517v1 Announce Type: new Abstract: Effective human-AI coordination requires artificial agents capable of exhibiting and responding to human-like behaviors while adapting to changing contexts. Imitation learning has emerged as one of the prominent approaches to build such agents by training...
When can we trust untrusted monitoring? A safety case sketch across collusion strategies
arXiv:2602.20628v1 Announce Type: new Abstract: AIs are increasingly being deployed with greater autonomy and capabilities, which increases the risk that a misaligned AI may be able to cause catastrophic harm. Untrusted monitoring -- using one untrusted model to oversee another...
Identifying two piecewise linear additive value functions from anonymous preference information
arXiv:2602.20638v1 Announce Type: new Abstract: Eliciting a preference model involves asking a person, named decision-maker, a series of questions. We assume that these preferences can be represented by an additive value function. In this work, we query simultaneously two decision-makers...
Recursive Belief Vision Language Model
arXiv:2602.20659v1 Announce Type: new Abstract: Current vision-language-action (VLA) models struggle with long-horizon manipulation under partial observability. Most existing approaches remain observation-driven, relying on short context windows or repeated queries to vision-language models (VLMs). This leads to loss of task progress,...
How Foundational Skills Influence VLM-based Embodied Agents:A Native Perspective
arXiv:2602.20687v1 Announce Type: new Abstract: Recent advances in vision-language models (VLMs) have shown promise for human-level embodied intelligence. However, existing benchmarks for VLM-driven embodied agents often rely on high-level commands or discretized action spaces, which are non-native settings that differ...
Buffer Matters: Unleashing the Power of Off-Policy Reinforcement Learning in Large Language Model Reasoning
arXiv:2602.20722v1 Announce Type: new Abstract: Traditional on-policy Reinforcement Learning with Verifiable Rewards (RLVR) frameworks suffer from experience waste and reward homogeneity, which directly hinders learning efficiency on difficult samples during large language models post-training. In this paper, we introduce Batch...
Modality-Guided Mixture of Graph Experts with Entropy-Triggered Routing for Multimodal Recommendation
arXiv:2602.20723v1 Announce Type: new Abstract: Multimodal recommendation enhances ranking by integrating user-item interactions with item content, which is particularly effective under sparse feedback and long-tail distributions. However, multimodal signals are inherently heterogeneous and can conflict in specific contexts, making effective...
PyVision-RL: Forging Open Agentic Vision Models via RL
arXiv:2602.20739v1 Announce Type: new Abstract: Reinforcement learning for agentic multimodal models often suffers from interaction collapse, where models learn to reduce tool usage and multi-turn reasoning, limiting the benefits of agentic behavior. We introduce PyVision-RL, a reinforcement learning framework for...
Diagnosing Causal Reasoning in Vision-Language Models via Structured Relevance Graphs
arXiv:2602.20878v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) achieve strong performance on visual question answering benchmarks, yet often rely on spurious correlations rather than genuine causal reasoning. Existing evaluations primarily assess the correctness of the answers, making it unclear...
Motivation is Something You Need
arXiv:2602.21064v1 Announce Type: new Abstract: This work introduces a novel training paradigm that draws from affective neuroscience. Inspired by the interplay of emotions and cognition in the human brain and more specifically the SEEKING motivational state, we design a dual-model...