Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders
arXiv:2602.17941v1 Announce Type: new Abstract: Graph data is becoming increasingly prevalent due to the growing demand for relational insights in AI across various domains. Organizations regularly use graph data to solve complex problems involving relationships and connections. Causal learning is...
Court grapples with disputes over efforts to recover losses from Cuban confiscations
In a pair of oral arguments on Monday, the Supreme Court wrestled with disputes over whether U.S. companies can recover under U.S. law for losses resulting from the confiscation of […]The postCourt grapples with disputes over efforts to recover losses...
Birthright citizenship: under the flag
Brothers in Law is a recurring series by brothers Akhil and Vikram Amar, with special emphasis on measuring what the Supreme Court says against what the Constitution itself says. For more content from […]The postBirthright citizenship: under the flagappeared first...
Supreme Court agrees to hear case on Colorado dispute over climate change
Returning from its winter recess, the Supreme Court on Monday added just one new case to its oral argument docket. In a list of orders from the justices’ private conference […]The postSupreme Court agrees to hear case on Colorado dispute...
SCOTUStoday for Monday, February 23
Happy Monday! Although we here at SCOTUSblog are still recovering from a busy Friday analyzing the tariffs ruling, we have to turn our attention to the February argument session. It […]The postSCOTUStoday for Monday, February 23appeared first onSCOTUSblog.
AIs can generate near-verbatim copies of novels from training data
LLMs memorize more training data than previously thought.
With AI, investor loyalty is (almost) dead: At least a dozen OpenAI VCs now also back Anthropic
While some dual investors are understandable, others were more shocking, and signal the disregard of a longstanding ethical conflict-of-interest rule.
Anthropic accuses Chinese AI labs of mining Claude as US debates AI chip exports
Anthropic accuses DeepSeek, Moonshot, and MiniMax of using 24,000 fake accounts to distill Claude’s AI capabilities, as U.S. officials debate export controls aimed at slowing China’s AI progress.
Particle’s AI news app listens to podcasts for interesting clips so you you don’t have to
AI news app Particle can now pull in key moments from podcasts, letting readers instantly play short, relevant clips alongside related stories.
Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation
Trustworthy Artificial Intelligence (AI) is based on seven technical requirements sustained over three main pillars that should be met throughout the system’s entire life cycle: it should be (1) lawful, (2) ethical, and (3) robust, both from a technical and...
When Remembering and Planning are Worth it: Navigating under Change
arXiv:2602.15274v1 Announce Type: new Abstract: We explore how different types and uses of memory can aid spatial navigation in changing uncertain environments. In the simple foraging task we study, every day, our agent has to find its way from its...
AgriWorld:A World Tools Protocol Framework for Verifiable Agricultural Reasoning with Code-Executing LLM Agents
arXiv:2602.15325v1 Announce Type: new Abstract: Foundation models for agriculture are increasingly trained on massive spatiotemporal data (e.g., multi-spectral remote sensing, soil grids, and field-level management logs) and achieve strong performance on forecasting and monitoring. However, these models lack language-based reasoning...
Improving LLM Reliability through Hybrid Abstention and Adaptive Detection
arXiv:2602.15391v1 Announce Type: new Abstract: Large Language Models (LLMs) deployed in production environments face a fundamental safety-utility trade-off either a strict filtering mechanisms prevent harmful outputs but often block benign queries or a relaxed controls risk unsafe content generation. Conventional...
GenAI-LA: Generative AI and Learning Analytics Workshop (LAK 2026), April 27--May 1, 2026, Bergen, Norway
arXiv:2602.15531v1 Announce Type: new Abstract: This work introduces EduEVAL-DB, a dataset based on teacher roles designed to support the evaluation and training of automatic pedagogical evaluators and AI tutors for instructional explanations. The dataset comprises 854 explanations corresponding to 139...
RUVA: Personalized Transparent On-Device Graph Reasoning
arXiv:2602.15553v1 Announce Type: new Abstract: The Personal AI landscape is currently dominated by "Black Box" Retrieval-Augmented Generation. While standard vector databases offer statistical matching, they suffer from a fundamental lack of accountability: when an AI hallucinates or retrieves sensitive data,...
On inferring cumulative constraints
arXiv:2602.15635v1 Announce Type: new Abstract: Cumulative constraints are central in scheduling with constraint programming, yet propagation is typically performed per constraint, missing multi-resource interactions and causing severe slowdowns on some benchmarks. I present a preprocessing method for inferring additional cumulative...
CARE Drive A Framework for Evaluating Reason-Responsiveness of Vision Language Models in Automated Driving
arXiv:2602.15645v1 Announce Type: new Abstract: Foundation models, including vision language models, are increasingly used in automated driving to interpret scenes, recommend actions, and generate natural language explanations. However, existing evaluation methods primarily assess outcome based performance, such as safety and...
PERSONA: Dynamic and Compositional Inference-Time Personality Control via Activation Vector Algebra
arXiv:2602.15669v1 Announce Type: new Abstract: Current methods for personality control in Large Language Models rely on static prompting or expensive fine-tuning, failing to capture the dynamic and compositional nature of human traits. We introduce PERSONA, a training-free framework that achieves...
Recursive Concept Evolution for Compositional Reasoning in Large Language Models
arXiv:2602.15725v1 Announce Type: new Abstract: Large language models achieve strong performance on many complex reasoning tasks, yet their accuracy degrades sharply on benchmarks that require compositional reasoning, including ARC-AGI-2, GPQA, MATH, BBH, and HLE. Existing methods improve reasoning by expanding...
This human study did not involve human subjects: Validating LLM simulations as behavioral evidence
arXiv:2602.15785v1 Announce Type: new Abstract: A growing literature uses large language models (LLMs) as synthetic participants to generate cost-effective and nearly instantaneous responses in social science experiments. However, there is limited guidance on when such simulations support valid inference about...
Enhancing Building Semantics Preservation in AI Model Training with Large Language Model Encodings
arXiv:2602.15791v1 Announce Type: new Abstract: Accurate representation of building semantics, encompassing both generic object types and specific subtypes, is essential for effective AI model training in the architecture, engineering, construction, and operation (AECO) industry. Conventional encoding methods (e.g., one-hot) often...
CircuChain: Disentangling Competence and Compliance in LLM Circuit Analysis
arXiv:2602.15037v1 Announce Type: cross Abstract: As large language models (LLMs) advance toward expert-level performance in engineering domains, reliable reasoning under user-specified constraints becomes critical. In circuit analysis, for example, a numerically correct solution is insufficient if it violates established methodological...
Indic-TunedLens: Interpreting Multilingual Models in Indian Languages
arXiv:2602.15038v1 Announce Type: cross Abstract: Multilingual large language models (LLMs) are increasingly deployed in linguistically diverse regions like India, yet most interpretability tools remain tailored to English. Prior work reveals that LLMs often operate in English centric representation spaces, making...
GRACE: an Agentic AI for Particle Physics Experiment Design and Simulation
arXiv:2602.15039v1 Announce Type: cross Abstract: We present GRACE, a simulation-native agent for autonomous experimental design in high-energy and nuclear physics. Given multimodal input in the form of a natural-language prompt or a published experimental paper, the agent extracts a structured...
Beyond Context Sharing: A Unified Agent Communication Protocol (ACP) for Secure, Federated, and Autonomous Agent-to-Agent (A2A) Orchestration
arXiv:2602.15055v1 Announce Type: cross Abstract: In the artificial intelligence space, as we transition from isolated large language models to autonomous agents capable of complex reasoning and tool use. While foundational architectures and local context management protocols have been established, the...
PolyNODE: Variable-dimension Neural ODEs on M-polyfolds
arXiv:2602.15128v1 Announce Type: cross Abstract: Neural ordinary differential equations (NODEs) are geometric deep learning models based on dynamical systems and flows generated by vector fields on manifolds. Despite numerous successful applications, particularly within the flow matching paradigm, all existing NODE...
AIC CTU@AVerImaTeC: dual-retriever RAG for image-text fact checking
arXiv:2602.15190v1 Announce Type: new Abstract: In this paper, we present our 3rd place system in the AVerImaTeC shared task, which combines our last year's retrieval-augmented generation (RAG) pipeline with a reverse image search (RIS) module. Despite its simplicity, our system...
Extracting Consumer Insight from Text: A Large Language Model Approach to Emotion and Evaluation Measurement
arXiv:2602.15312v1 Announce Type: new Abstract: Accurately measuring consumer emotions and evaluations from unstructured text remains a core challenge for marketing research and practice. This study introduces the Linguistic eXtractor (LX), a fine-tuned, large language model trained on consumer-authored text that...
NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering
arXiv:2602.15353v1 Announce Type: new Abstract: Large pretrained language models and neural reasoning systems have advanced many natural language tasks, yet they remain challenged by knowledge-intensive queries that require precise, structured multi-hop inference. Knowledge graphs provide a compact symbolic substrate for...
Far Out: Evaluating Language Models on Slang in Australian and Indian English
arXiv:2602.15373v1 Announce Type: new Abstract: Language models exhibit systematic performance gaps when processing text in non-standard language varieties, yet their ability to comprehend variety-specific slang remains underexplored for several languages. We present a comprehensive evaluation of slang awareness in Indian...