Transcending Classical Neural Network Boundaries: A Quantum-Classical Synergistic Paradigm for Seismic Data Processing
arXiv:2603.23984v1 Announce Type: new Abstract: In recent years, a number of neural-network (NN) methods have exhibited good performance in seismic data processing, such as denoising, interpolation, and frequency-band extension. However, these methods rely on stacked perceptrons and standard activation functions,...
Diet Your LLM: Dimension-wise Global Pruning of LLMs via Merging Task-specific Importance Score
arXiv:2603.23985v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities, but their massive scale poses significant challenges for practical deployment. Structured pruning offers a promising solution by removing entire dimensions or layers, yet existing methods face critical...
Can we generate portable representations for clinical time series data using LLMs?
arXiv:2603.23987v1 Announce Type: new Abstract: Deploying clinical ML is slow and brittle: models that work at one hospital often degrade under distribution shifts at the next. In this work, we study a simple question -- can large language models (LLMs)...
Understanding the Challenges in Iterative Generative Optimization with LLMs
arXiv:2603.23994v1 Announce Type: new Abstract: Generative optimization uses large language models (LLMs) to iteratively improve artifacts (such as code, workflows or prompts) using execution feedback. It is a promising approach to building self-improving agents, yet in practice remains brittle: despite...
i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data
arXiv:2603.24025v1 Announce Type: new Abstract: Unsupervised learning of high-dimensional data is challenging due to irrelevant or noisy features obscuring underlying structures. It's common that only a few features, called the influential features, meaningfully define the clusters. Recovering these influential features...
Lagrangian Relaxation Score-based Generation for Mixed Integer linear Programming
arXiv:2603.24033v1 Announce Type: new Abstract: Predict-and-search (PaS) methods have shown promise for accelerating mixed-integer linear programming (MILP) solving. However, existing approaches typically assume variable independence and rely on deterministic single-point predictions, which limits solution diversityand often necessitates extensive downstream search...
Birthright citizenship: more on Pete Patterson’s claims
Attorney Pete Patterson’s latest post on birthright citizenship repeats the biggest mistakes of his original post and also makes some new mistakes, chasing irrelevances and mangling the key legal issues. […]The postBirthright citizenship: more on Pete Patterson’s claimsappeared first onSCOTUSblog.
Court to consider ability of federal courts to confirm arbitration awards
Next week’s argument in Jules v Andre Balazs Properties considers a technical question about the jurisdiction of federal courts to enforce an arbitration award. It is the immediate successor of […]The postCourt to consider ability of federal courts to confirm...
Justices dubious about “harsh” rules for omissions by bankrupt debtors
Yesterday’s argument in Keathley v. Buddy Ayers Construction displayed a bench almost uniformly skeptical of a lower court’s absolute standard for responding to the failure of a debtor in bankruptcy […]The postJustices dubious about “harsh” rules for omissions by bankrupt...
Justices to hear argument on whether a crime’s “contemplated effects” can expand venue beyond where offense was committed
The Supreme Court will hear oral argument on Monday in Abouammo v. United States, in which it will consider whether federal prosecutors can try a defendant not only in the […]The postJustices to hear argument on whether a crime’s “contemplated...
The Supreme Court and voting identification
Courtly Observations is a recurring series by Erwin Chemerinsky that focuses on what the Supreme Court’s decisions will mean for the law, for lawyers and lower courts, and for people’s lives. […]The postThe Supreme Court and voting identificationappeared first onSCOTUSblog.
SCOTUStoday for Wednesday, March 25
It’s going to be another busy day at the Supreme Court, and it’s expected to start with opinion announcements.The postSCOTUStoday for Wednesday, March 25appeared first onSCOTUSblog.
The AI skills gap is here, says AI company, and power users are pulling ahead
Anthropic finds AI isn’t replacing jobs yet, but early data shows growing inequality as experienced users gain an edge, raising concerns about future displacement and workforce divides.
Lucid Bots raises $20M to keep up with demand for its window-washing drones
Lucid Bots has seen demand accelerate over the last year for its window-cleaning drones and power-washing robots.
Optimizing Small Language Models for NL2SQL via Chain-of-Thought Fine-Tuning
arXiv:2603.22942v1 Announce Type: new Abstract: Translating Natural Language to SQL (NL2SQL) remains a critical bottleneck for democratization of data in enterprises. Although Large Language Models (LLMs) like Gemini 2.5 and other LLMs have demonstrated impressive zero-shot capabilities, their high inference...
MedCausalX: Adaptive Causal Reasoning with Self-Reflection for Trustworthy Medical Vision-Language Models
arXiv:2603.23085v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have enabled interpretable medical diagnosis by integrating visual perception with linguistic reasoning. Yet, existing medical chain-of-thought (CoT) models lack explicit mechanisms to represent and enforce causal reasoning, leaving them vulnerable to spurious...
Reliable Classroom AI via Neuro-Symbolic Multimodal Reasoning
arXiv:2603.22793v1 Announce Type: new Abstract: Classroom AI is rapidly expanding from low-level perception toward higher-level judgments about engagement, confusion, collaboration, and instructional quality. Yet classrooms are among the hardest real-world settings for multimodal vision: they are multi-party, noisy, privacy-sensitive, pedagogically...
Less is More: Adapting Text Embeddings for Low-Resource Languages with Small Scale Noisy Synthetic Data
arXiv:2603.22290v1 Announce Type: new Abstract: Low-resource languages (LRLs) often lack high-quality, large-scale datasets for training effective text embedding models, hindering their application in tasks like retrieval-augmented generation (RAG) and semantic search. In this work, we challenge the prevailing assumption that...
ABSTRAL: Automatic Design of Multi-Agent Systems Through Iterative Refinement and Topology Optimization
arXiv:2603.22791v1 Announce Type: new Abstract: How should multi-agent systems be designed, and can that design knowledge be captured in a form that is inspectable, revisable, and transferable? We introduce ABSTRAL, a framework that treats MAS architecture as an evolving natural-language...
Explanation Generation for Contradiction Reconciliation with LLMs
arXiv:2603.22735v1 Announce Type: new Abstract: Existing NLP work commonly treats contradictions as errors to be resolved by choosing which statements to accept or discard. Yet a key aspect of human reasoning in social interactions and professional domains is the ability...
Beyond Preset Identities: How Agents Form Stances and Boundaries in Generative Societies
arXiv:2603.23406v1 Announce Type: new Abstract: While large language models simulate social behaviors, their capacity for stable stance formation and identity negotiation during complex interventions remains unclear. To overcome the limitations of static evaluations, this paper proposes a novel mixed-methods framework...
Between Rules and Reality: On the Context Sensitivity of LLM Moral Judgment
arXiv:2603.23114v1 Announce Type: new Abstract: A human's moral decision depends heavily on the context. Yet research on LLM morality has largely studied fixed scenarios. We address this gap by introducing Contextual MoralChoice, a dataset of moral dilemmas with systematic contextual...
Functional Component Ablation Reveals Specialization Patterns in Hybrid Language Model Architectures
arXiv:2603.22473v1 Announce Type: new Abstract: Hybrid language models combining attention with state space models (SSMs) or linear attention offer improved efficiency, but whether both components are genuinely utilized remains unclear. We present a functional component ablation framework applied to two...
MERIT: Memory-Enhanced Retrieval for Interpretable Knowledge Tracing
arXiv:2603.22289v1 Announce Type: new Abstract: Knowledge Tracing (KT) models students' evolving knowledge states to predict future performance, serving as a foundation for personalized education. While traditional deep learning models achieve high accuracy, they often lack interpretability. Large Language Models (LLMs)...
SAiW: Source-Attributable Invisible Watermarking for Proactive Deepfake Defense
arXiv:2603.23178v1 Announce Type: new Abstract: Deepfakes generated by modern generative models pose a serious threat to information integrity, digital identity, and public trust. Existing detection methods are largely reactive, attempting to identify manipulations after they occur and often failing to...
Online library learning in human visual puzzle solving
arXiv:2603.23244v1 Announce Type: new Abstract: When learning a novel complex task, people often form efficient reusable abstractions that simplify future work, despite uncertainty about the future. We study this process in a visual puzzle task where participants define and reuse...
Continuous Optimization for Satisfiability Modulo Theories on Linear Real Arithmetic
arXiv:2603.22877v1 Announce Type: new Abstract: Efficient solutions for satisfiability modulo theories (SMT) are integral in industrial applications such as hardware verification and design automation. Existing approaches are predominantly based on conflict-driven clause learning, which is structurally difficult to parallelize and...
Improving Safety Alignment via Balanced Direct Preference Optimization
arXiv:2603.22829v1 Announce Type: new Abstract: With the rapid development and widespread application of Large Language Models (LLMs), their potential safety risks have attracted widespread attention. Reinforcement Learning from Human Feedback (RLHF) has been adopted to enhance the safety performance of...
Understanding LLM Performance Degradation in Multi-Instance Processing: The Roles of Instance Count and Context Length
arXiv:2603.22608v1 Announce Type: new Abstract: Users often rely on Large Language Models (LLMs) for processing multiple documents or performing analysis over a number of instances. For example, analysing the overall sentiment of a number of movie reviews requires an LLM...
RelayS2S: A Dual-Path Speculative Generation for Real-Time Dialogue
arXiv:2603.23346v1 Announce Type: new Abstract: Real-time spoken dialogue systems face a fundamental tension between latency and response quality. End-to-end speech-to-speech (S2S) models respond immediately and naturally handle turn-taking, backchanneling, and interruption, but produce semantically weaker outputs. Cascaded pipelines (ASR ->...