Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numbers
arXiv:2603.12684v1 Announce Type: new Abstract: Federated Clustering (FC) is an emerging and promising solution in exploring data distribution patterns from distributed and privacy-protected data in an unsupervised manner. Existing FC methods implicitly rely on the assumption that clients are with...
How to use the new ChatGPT app integrations, including DoorDash, Spotify, Uber, and others
Learn how to use Spotify, Canva, Figma, Expedia, and other apps directly in ChatGPT.
Announcement of opinions for Friday, March 20
On Friday, March 20, we will be live blogging as the court potentially releases opinions in one or more argued cases from the current term. Click here for a list […]The postAnnouncement of opinions for Friday, March 20appeared first onSCOTUSblog.
Does legislative history have a judicial future?
Major Questions is a recurring series by Adam White, which analyzes the court’s approach to administrative law, agencies, and the lower courts. Does legislative history have a future in judicial […]The postDoes legislative history have a judicial future?appeared first onSCOTUSblog.
Is Justice Alito jumping the gun on voting rights?
Cases and Controversies is a recurring series by Carolyn Shapiro, primarily focusing on the effects of the Supreme Court’s rulings, opinions, and procedures on the law, on other institutions, and on […]The postIs Justice Alito jumping the gun on voting...
SCOTUStoday for Friday, March 13
President Chester A. Arthur nominated Justice Samuel Blatchford to the court on this day in 1882. According to Justia, Blatchford was a “precocious talent” who “enrolled in Columbia College (now […]The postSCOTUStoday for Friday, March 13appeared first onSCOTUSblog.
Another AT&T FirstNet user gets shocking $6,200 bill, at $2 per megabyte
Bizarre FirstNet charge nearly identical to one that hit different user in 2024.
Lawyer behind AI psychosis cases warns of mass casualty risks
AI chatbots have been linked to suicides for years. Now one lawyer says they are showing up in mass casualty cases too, and the technology is moving faster than the safeguards.
The biggest AI stories of the year (so far)
The AI industry is constantly churning out news, like major acquisitions, indie developer successes, public outcry, and existentially dangerous contract negotiations.
Training Is Everything: Artificial Intelligence, Copyright, and Fair Training
To learn how to behave, the current revolutionary generation of AIs must be trained on vast quantities of published images, written works, and sounds, many of which fall within the core subject matter of copyright law. To some, the use...
The Density of Cross-Persistence Diagrams and Its Applications
arXiv:2603.11623v1 Announce Type: new Abstract: Topological Data Analysis (TDA) provides powerful tools to explore the shape and structure of data through topological features such as clusters, loops, and voids. Persistence diagrams are a cornerstone of TDA, capturing the evolution of...
Social, Legal, Ethical, Empathetic and Cultural Norm Operationalisation for AI Agents
arXiv:2603.11864v1 Announce Type: new Abstract: As AI agents are increasingly used in high-stakes domains like healthcare and law enforcement, aligning their behaviour with social, legal, ethical, empathetic, and cultural (SLEEC) norms has become a critical engineering challenge. While international frameworks...
VisiFold: Long-Term Traffic Forecasting via Temporal Folding Graph and Node Visibility
arXiv:2603.11816v1 Announce Type: new Abstract: Traffic forecasting is a cornerstone of intelligent transportation systems. While existing research has made significant progress in short-term prediction, long-term forecasting remains a largely uncharted and challenging frontier. Extending the prediction horizon intensifies two critical...
Reversible Lifelong Model Editing via Semantic Routing-Based LoRA
arXiv:2603.11239v1 Announce Type: new Abstract: The dynamic evolution of real-world necessitates model editing within Large Language Models. While existing methods explore modular isolation or parameter-efficient strategies, they still suffer from semantic drift or knowledge forgetting due to continual updating. To...
STAIRS-Former: Spatio-Temporal Attention with Interleaved Recursive Structure Transformer for Offline Multi-task Multi-agent Reinforcement Learning
arXiv:2603.11691v1 Announce Type: new Abstract: Offline multi-agent reinforcement learning (MARL) with multi-task datasets is challenging due to varying numbers of agents across tasks and the need to generalize to unseen scenarios. Prior works employ transformers with observation tokenization and hierarchical...
Evaluating Explainable AI Attribution Methods in Neural Machine Translation via Attention-Guided Knowledge Distillation
arXiv:2603.11342v1 Announce Type: new Abstract: The study of the attribution of input features to the output of neural network models is an active area of research. While numerous Explainable AI (XAI) techniques have been proposed to interpret these models, the...
PACED: Distillation at the Frontier of Student Competence
arXiv:2603.11178v1 Announce Type: new Abstract: Standard LLM distillation wastes compute on two fronts: problems the student has already mastered (near-zero gradients) and problems far beyond its reach (incoherent gradients that erode existing capabilities). We show that this waste is not...
Detecting Intrinsic and Instrumental Self-Preservation in Autonomous Agents: The Unified Continuation-Interest Protocol
arXiv:2603.11382v1 Announce Type: new Abstract: Autonomous agents, especially delegated systems with memory, persistent context, and multi-step planning, pose a measurement problem not present in stateless models: an agent that preserves continued operation as a terminal objective and one that does...
ThReadMed-QA: A Multi-Turn Medical Dialogue Benchmark from Real Patient Questions
arXiv:2603.11281v1 Announce Type: new Abstract: Medical question-answering benchmarks predominantly evaluate single-turn exchanges, failing to capture the iterative, clarification-seeking nature of real patient consultations. We introduce ThReadMed-QA, a benchmark of 2,437 fully-answered patient-physician conversation threads extracted from r/AskDocs, comprising 8,204 question-answer...
Entropy Guided Diversification and Preference Elicitation in Agentic Recommendation Systems
arXiv:2603.11399v1 Announce Type: new Abstract: Users on e-commerce platforms can be uncertain about their preferences early in their search. Queries to recommendation systems are frequently ambiguous, incomplete, or weakly specified. Agentic systems are expected to proactively reason, ask clarifying questions,...
Automated Detection of Malignant Lesions in the Ovary Using Deep Learning Models and XAI
arXiv:2603.11818v1 Announce Type: new Abstract: The unrestrained proliferation of cells that are malignant in nature is cancer. In recent times, medical professionals are constantly acquiring enhanced diagnostic and treatment abilities by implementing deep learning models to analyze medical data for...
GPT4o-Receipt: A Dataset and Human Study for AI-Generated Document Forensics
arXiv:2603.11442v1 Announce Type: new Abstract: Can humans detect AI-generated financial documents better than machines? We present GPT4o-Receipt, a benchmark of 1,235 receipt images pairing GPT-4o-generated receipts with authentic ones from established datasets, evaluated by five state-of-the-art multimodal LLMs and a...
FinRule-Bench: A Benchmark for Joint Reasoning over Financial Tables and Principles
arXiv:2603.11339v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied to financial analysis, yet their ability to audit structured financial statements under explicit accounting principles remains poorly explored. Existing benchmarks primarily evaluate question answering, numerical reasoning, or anomaly...
Leveraging Large Language Models and Survival Analysis for Early Prediction of Chemotherapy Outcomes
arXiv:2603.11594v1 Announce Type: new Abstract: Chemotherapy for cancer treatment is costly and accompanied by severe side effects, highlighting the critical need for early prediction of treatment outcomes to improve patient management and informed decision-making. Predictive models for chemotherapy outcomes using...
LLMs can construct powerful representations and streamline sample-efficient supervised learning
arXiv:2603.11679v1 Announce Type: new Abstract: As real-world datasets become increasingly complex and heterogeneous, supervised learning is often bottlenecked by input representation design. Modeling multimodal data for downstream tasks, such as time-series, free text, and structured records, often requires non-trivial domain-specific...
CINDI: Conditional Imputation and Noisy Data Integrity with Flows in Power Grid Data
arXiv:2603.11745v1 Announce Type: new Abstract: Real-world multivariate time series, particularly in critical infrastructure such as electrical power grids, are often corrupted by noise and anomalies that degrade the performance of downstream tasks. Standard data cleaning approaches often rely on disjoint...
Counterweights and Complementarities: The Convergence of AI and Blockchain Powering a Decentralized Future
arXiv:2603.11299v1 Announce Type: new Abstract: This editorial addresses the critical intersection of artificial intelligence (AI) and blockchain technologies, highlighting their contrasting tendencies toward centralization and decentralization, respectively. While AI, particularly with the rise of large language models (LLMs), exhibits a...
Summarize Before You Speak with ARACH: A Training-Free Inference-Time Plug-In for Enhancing LLMs via Global Attention Reallocation
arXiv:2603.11067v1 Announce Type: new Abstract: Large language models (LLMs) achieve remarkable performance, yet further gains often require costly training. This has motivated growing interest in post-training techniques-especially training-free approaches that improve models at inference time without updating weights. Most training-free...
Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing
arXiv:2603.11433v1 Announce Type: new Abstract: In modern transportation networks, adversaries can manipulate routing algorithms using false data injection attacks, such as simulating heavy traffic with multiple devices running crowdsourced navigation applications, to mislead vehicles toward suboptimal routes and increase congestion....
Gender Bias in Generative AI-assisted Recruitment Processes
arXiv:2603.11736v1 Announce Type: new Abstract: In recent years, generative artificial intelligence (GenAI) systems have assumed increasingly crucial roles in selection processes, personnel recruitment and analysis of candidates' profiles. However, the employment of large language models (LLMs) risks reproducing, and in...