Court sides with parents in dispute over California policies on transgender students
The Supreme Court on Monday night granted a request from a group of California parents to reinstate a ruling by a federal district court that prohibits schools in that state […]The postCourt sides with parents in dispute over California policies...
Supreme Court grants Republicans’ request to pause order to redraw New York congressional map
The Supreme Court on Monday night cleared the way for New York to go forward with the 2026 elections using the state’s existing congressional map. Over the objections of the […]The postSupreme Court grants Republicans’ request to pause order to...
Court turns down several cases, including on filing fees for indigent prisoners and ability of felons to possess guns
Over the objections of the court’s three Democratic appointees, the Supreme Court on Monday morning declined to hear a case involving the payment of filing fees by indigent prisoners. The […]The postCourt turns down several cases, including on filing fees...
Birthright citizenship: A note on foundlings and comments on four complementary amicus briefs
Foundlings – babies born of unknown parentage – loomed large in the imagination of mid-19th century Americans, who dutifully read their Bibles and thought about baby Moses in a basket. […]The postBirthright citizenship: A note on foundlings and comments on...
Supreme Court skeptical of law banning drug users from possessing firearms
The Supreme Court on Monday was skeptical that the indictment of a Texas man on charges that he violated a federal law prohibiting the possession of a gun by the […]The postSupreme Court skeptical of law banning drug users from...
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...
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.
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.
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.
Uncovering Context Reliance in Unstructured Knowledge Editing
arXiv:2602.19043v1 Announce Type: new Abstract: Editing Large language models (LLMs) with real-world, unstructured knowledge is essential for correcting and updating their internal parametric knowledge. In this work, we revisit the fundamental next-token prediction (NTP) as a candidate paradigm for unstructured...
First Amendment Inversion
Introduction A new arrangement of First Amendment positions has upturned constitutional discourse in key areas. Familiar perspectives have transposed not only in Supreme Court opinions but also in policymaking and public debate—and some are reverting back. Inversion on important questions...
Academic Freedom by Other Names: Historical Foundations for the First Amendment Right
Introduction The Supreme Court has stated that academic freedom is a “special concern” of the First Amendment.[1] Yet before 1957, there were no American legal precedents that recognized academic freedom as a component of the First Amendment. But these protections...
An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models
arXiv:2602.20324v1 Announce Type: new Abstract: Phenotyping is fundamental to rare disease diagnosis, but manual curation of structured phenotypes from clinical notes is labor-intensive and difficult to scale. Existing artificial intelligence approaches typically optimize individual components of phenotyping but do not...
DMCD: Semantic-Statistical Framework for Causal Discovery
arXiv:2602.20333v1 Announce Type: new Abstract: We present DMCD (DataMap Causal Discovery), a two-phase causal discovery framework that integrates LLM-based semantic drafting from variable metadata with statistical validation on observational data. In Phase I, a large language model proposes a sparse...
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...
Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use
arXiv:2602.20426v1 Announce Type: new Abstract: The performance of LLM-based agents depends not only on the agent itself but also on the quality of the tool interfaces it consumes. While prior work has focused heavily on agent fine-tuning, tool interfaces-including natural...
PreScience: A Benchmark for Forecasting Scientific Contributions
arXiv:2602.20459v1 Announce Type: new Abstract: Can AI systems trained on the scientific record up to a fixed point in time forecast the scientific advances that follow? Such a capability could help researchers identify collaborators and impactful research directions, and anticipate...
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...
From Logs to Language: Learning Optimal Verbalization for LLM-Based Recommendation in Production
arXiv:2602.20558v1 Announce Type: new Abstract: Large language models (LLMs) are promising backbones for generative recommender systems, yet a key challenge remains underexplored: verbalization, i.e., converting structured user interaction logs into effective natural language inputs. Existing methods rely on rigid templates...
CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation
arXiv:2602.20571v1 Announce Type: new Abstract: Many benchmarks for automated causal inference evaluate a system's performance based on a single numerical output, such as an Average Treatment Effect (ATE). This approach conflates two distinct steps in causal analysis: identification-formulating a valid...
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...
Online Algorithms with Unreliable Guidance
arXiv:2602.20706v1 Announce Type: new Abstract: This paper introduces a new model for ML-augmented online decision making, called online algorithms with unreliable guidance (OAG). This model completely separates between the predictive and algorithmic components, thus offering a single well-defined analysis framework...
ICON: Indirect Prompt Injection Defense for Agents based on Inference-Time Correction
arXiv:2602.20708v1 Announce Type: new Abstract: Large Language Model (LLM) agents are susceptible to Indirect Prompt Injection (IPI) attacks, where malicious instructions in retrieved content hijack the agent's execution. Existing defenses typically rely on strict filtering or refusal mechanisms, which suffer...
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...
Balancing Multiple Objectives in Urban Traffic Control with Reinforcement Learning from AI Feedback
arXiv:2602.20728v1 Announce Type: new Abstract: Reward design has been one of the central challenges for real world reinforcement learning (RL) deployment, especially in settings with multiple objectives. Preference-based RL offers an appealing alternative by learning from human preferences over pairs...
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...
Pipeline for Verifying LLM-Generated Mathematical Solutions
arXiv:2602.20770v1 Announce Type: new Abstract: With the growing popularity of Large Reasoning Models and their results in solving mathematical problems, it becomes crucial to measure their capabilities. We introduce a pipeline for both automatic and interactive verification as a more...
POMDPPlanners: Open-Source Package for POMDP Planning
arXiv:2602.20810v1 Announce Type: new Abstract: We present POMDPPlanners, an open-source Python package for empirical evaluation of Partially Observable Markov Decision Process (POMDP) planning algorithms. The package integrates state-of-the-art planning algorithms, a suite of benchmark environments with safety-critical variants, automated hyperparameter...