SCOTUStoday for Tuesday, February 24
On this day in 1803, the Supreme Court released its ruling in Marbury v. Madison, which established the principle of judicial review (or did it?). Mark the anniversary with us […]The postSCOTUStoday for Tuesday, February 24appeared first onSCOTUSblog.
In Defense of Substantive Due Process
Introduction Originalism has a branding and substance problem.[1] If originalism is what it purports to be—impartial and value-free enforcement of the Founders’ intention and “the only approach to text that is compatible with democracy”[2]—more Americans would have faith in the...
Chill
Introduction No concept is more pervasive in the law of freedom of speech than chill.[1] The chilled speech doctrine guards against self-censorship: it permits First Amendment challenges based on the allegation that a law deters the plaintiff or others from...
In a replay of 2019, Apple says a single desktop Mac will be manufactured in the US
Apple is still working to get favorable tariff treatment from the Trump administration.
India’s AI boom pushes firms to trade near-term revenue for users
ChatGPT and rivals are testing whether India's massive AI user boom can translate into paying customers as free offers wind down.
Meta strikes up to $100B AMD chip deal as it chases ‘personal superintelligence’
Meta is buying billions of dollars in AMD AI chips in a multiyear deal tied to a 160 million-share warrant, deepening its push to diversify beyond Nvidia and expand data center capacity.
Oura launches a proprietary AI model focused on women’s health
The model supports questions spanning the full reproductive health spectrum, from early menstrual cycles through menopause.
Final 4 days to save up to $680 on your TechCrunch Disrupt 2026 pass
Just 4 days left before savings of up to $680 on your TechCrunch Disrupt 2026 pass end on February 27 at 11:59 p.m. PT. Register to save at one of the most anticipated tech events of the year.
Nimble raises $47M to give AI agents access to real-time web data
Nimble uses AI agents to search the web, verify and validate the results, and then clean and structure the information into neat tables that can then be queried like a database.
QueryPlot: Generating Geological Evidence Layers using Natural Language Queries for Mineral Exploration
arXiv:2602.17784v1 Announce Type: cross Abstract: Mineral prospectivity mapping requires synthesizing heterogeneous geological knowledge, including textual deposit models and geospatial datasets, to identify regions likely to host specific mineral deposit types. This process is traditionally manual and knowledge-intensive. We present QueryPlot,...
Deep Learning for Dermatology: An Innovative Framework for Approaching Precise Skin Cancer Detection
arXiv:2602.17797v1 Announce Type: cross Abstract: Skin cancer can be life-threatening if not diagnosed early, a prevalent yet preventable disease. Globally, skin cancer is perceived among the finest prevailing cancers and millions of people are diagnosed each year. For the allotment...
Mind the Style: Impact of Communication Style on Human-Chatbot Interaction
arXiv:2602.17850v1 Announce Type: cross Abstract: Conversational agents increasingly mediate everyday digital interactions, yet the effects of their communication style on user experience and task success remain unclear. Addressing this gap, we describe the results of a between-subject user study where...
Enhancing Scientific Literature Chatbots with Retrieval-Augmented Generation: A Performance Evaluation of Vector and Graph-Based Systems
arXiv:2602.17856v1 Announce Type: cross Abstract: This paper investigates the enhancement of scientific literature chatbots through retrieval-augmented generation (RAG), with a focus on evaluating vector- and graph-based retrieval systems. The proposed chatbot leverages both structured (graph) and unstructured (vector) databases to...
Financial time series augmentation using transformer based GAN architecture
arXiv:2602.17865v1 Announce Type: cross Abstract: Time-series forecasting is a critical task across many domains, from engineering to economics, where accurate predictions drive strategic decisions. However, applying advanced deep learning models in challenging, volatile domains like finance is difficult due to...
MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies
arXiv:2602.17868v1 Announce Type: cross Abstract: Developing foundation models for time series classification is of high practical relevance, as such models can serve as universal feature extractors for diverse downstream tasks. Although early models such as Mantis have shown the promise...
Understanding Unreliability of Steering Vectors in Language Models: Geometric Predictors and the Limits of Linear Approximations
arXiv:2602.17881v1 Announce Type: cross Abstract: Steering vectors are a lightweight method for controlling language model behavior by adding a learned bias to the activations at inference time. Although effective on average, steering effect sizes vary across samples and are unreliable...
Games That Teach, Chats That Convince: Comparing Interactive and Static Formats for Persuasive Learning
arXiv:2602.17905v1 Announce Type: cross Abstract: Interactive systems such as chatbots and games are increasingly used to persuade and educate on sustainability-related topics, yet it remains unclear how different delivery formats shape learning and persuasive outcomes when content is held constant....
Condition-Gated Reasoning for Context-Dependent Biomedical Question Answering
arXiv:2602.17911v1 Announce Type: cross Abstract: Current biomedical question answering (QA) systems often assume that medical knowledge applies uniformly, yet real-world clinical reasoning is inherently conditional: nearly every decision depends on patient-specific factors such as comorbidities and contraindications. Existing benchmarks do...
MIRA: Memory-Integrated Reinforcement Learning Agent with Limited LLM Guidance
arXiv:2602.17930v1 Announce Type: cross Abstract: Reinforcement learning (RL) agents often suffer from high sample complexity in sparse or delayed reward settings due to limited prior structure. Large language models (LLMs) can provide subgoal decompositions, plausible trajectories, and abstract priors that...
Towards More Standardized AI Evaluation: From Models to Agents
arXiv:2602.18029v1 Announce Type: new Abstract: Evaluation is no longer a final checkpoint in the machine learning lifecycle. As AI systems evolve from static models to compound, tool-using agents, evaluation becomes a core control function. The question is no longer "How...
Click it or Leave it: Detecting and Spoiling Clickbait with Informativeness Measures and Large Language Models
arXiv:2602.18171v1 Announce Type: new Abstract: Clickbait headlines degrade the quality of online information and undermine user trust. We present a hybrid approach to clickbait detection that combines transformer-based text embeddings with linguistically motivated informativeness features. Using natural language processing techniques,...
Simplifying Outcomes of Language Model Component Analyses with ELIA
arXiv:2602.18262v1 Announce Type: new Abstract: While mechanistic interpretability has developed powerful tools to analyze the internal workings of Large Language Models (LLMs), their complexity has created an accessibility gap, limiting their use to specialists. We address this challenge by designing,...
PsihoRo: Depression and Anxiety Romanian Text Corpus
arXiv:2602.18324v1 Announce Type: new Abstract: Psychological corpora in NLP are collections of texts used to analyze human psychology, emotions, and mental health. These texts allow researchers to study psychological constructs, detect mental health issues and analyze emotional language. However, mental...
Predicting Contextual Informativeness for Vocabulary Learning using Deep Learning
arXiv:2602.18326v1 Announce Type: new Abstract: We describe a modern deep learning system that automatically identifies informative contextual examples (\qu{contexts}) for first language vocabulary instruction for high school student. Our paper compares three modeling approaches: (i) an unsupervised similarity-based strategy using...
Validating Political Position Predictions of Arguments
arXiv:2602.18351v1 Announce Type: new Abstract: Real-world knowledge representation often requires capturing subjective, continuous attributes -- such as political positions -- that conflict with pairwise validation, the widely accepted gold standard for human evaluation. We address this challenge through a dual-scale...
SPQ: An Ensemble Technique for Large Language Model Compression
arXiv:2602.18420v1 Announce Type: new Abstract: This study presents an ensemble technique, SPQ (SVD-Pruning-Quantization), for large language model (LLM) compression that combines variance-retained singular value decomposition (SVD), activation-based pruning, and post-training linear quantization. Each component targets a different source of inefficiency:...
RVR: Retrieve-Verify-Retrieve for Comprehensive Question Answering
arXiv:2602.18425v1 Announce Type: new Abstract: Comprehensively retrieving diverse documents is crucial to address queries that admit a wide range of valid answers. We introduce retrieve-verify-retrieve (RVR), a multi-round retrieval framework designed to maximize answer coverage. Initially, a retriever takes the...
Lost Before Translation: Social Information Transmission and Survival in AI-AI Communication
arXiv:2602.17674v1 Announce Type: cross Abstract: When AI systems summarize and relay information, they inevitably transform it. But how? We introduce an experimental paradigm based on the telephone game to study what happens when AI talks to AI. Across five studies...
Reducing Text Bias in Synthetically Generated MCQAs for VLMs in Autonomous Driving
arXiv:2602.17677v1 Announce Type: cross Abstract: Multiple Choice Question Answering (MCQA) benchmarks are an established standard for measuring Vision Language Model (VLM) performance in driving tasks. However, we observe the known phenomenon that synthetically generated MCQAs are highly susceptible to hidden...
LATMiX: Learnable Affine Transformations for Microscaling Quantization of LLMs
arXiv:2602.17681v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is a widely used approach for reducing the memory and compute costs of large language models (LLMs). Recent studies have shown that applying invertible transformations to activations can significantly improve quantization robustness...