Large Language Models

You open Claude Code. You work for an hour — refactoring, debugging, building something real. You close the terminal. Next morning you type claude and... it has

Claude Code Is Trending — Here's the AI API Stack Developers Are Building With in 2026 Claude Code just got a massive upgrade. With /loop scheduled tasks, Compu

The most dangerous idea in AI isn't superintelligence. It's the quiet, creeping assumption that creativity was never real to begin with. I've been thinking abou

On March 25, 2026, Callum McMahon at futuresearch.ai published a minute-by-minute incident transcript of his team's response to the LiteLLM supply chain comprom

The regulatory landscape for AI has never been more complex. The EU AI Act is now in active enforcement for high-risk AI systems. Data privacy regulators across

OpenClaw is a powerful AI chat tool that lets you control your agent from the command line. The latest version of OpenClaw includes new features that make it ev

Lawmakers are making mistakes when creating new AI laws. They often fail to distinguish purpose-built AI vs general-purpose AI. Here's the problems. An AI Insid

This article explores why the enterprise orchestration layer may become the most valuable position in AI and what it means for the future of work, software &amp

arXiv:2603.24621v1 Announce Type: new Abstract: We introduce ARC-AGI-3, an interactive benchmark for studying agentic intelligence through novel, abstract, turn

arXiv:2603.24676v1 Announce Type: new Abstract: Multi-agent systems powered by large language models (LLMs) are increasingly deployed in settings that shape con

arXiv:2603.24736v1 Announce Type: new Abstract: In the design and safety analysis of advanced reactor systems, constructing input files for system-level thermal

arXiv:2603.24742v1 Announce Type: new Abstract: AI safety is an increasingly urgent concern as the capabilities and adoption of AI systems grow. Existing evolut

arXiv:2603.24747v1 Announce Type: new Abstract: The emergence of large language model agents capable of invoking external tools has created urgent need for form

arXiv:2603.24768v1 Announce Type: new Abstract: The engineering design research community has studied agentic AI systems that use Large Language Model (LLM) age

arXiv:2603.24787v1 Announce Type: new Abstract: Routing has emerged as a promising strategy for balancing performance and cost in large language model (LLM) sys

arXiv:2603.24856v1 Announce Type: new Abstract: Emergency response systems generate data from many agencies and systems. In practice, correlating and updating t

arXiv:2603.24866v1 Announce Type: new Abstract: The physical world is not merely visual; it is governed by rigorous structural and procedural constraints. Yet,

arXiv:2603.24904v1 Announce Type: new Abstract: We prove that platform-deterministic inference is necessary and sufficient for trustworthy AI. We formalize this

arXiv:2603.24929v1 Announce Type: new Abstract: Understanding and quantifying uncertainty in large language model (LLM) outputs is critical for reliable deploym

arXiv:2603.24943v1 Announce Type: new Abstract: This paper introduces \textbf{FinMCP-Bench}, a novel benchmark for evaluating large language models (LLMs) in so

arXiv:2603.24947v1 Announce Type: new Abstract: This paper provides some of the first large-scale descriptive evidence on how consumers adopt and use platform-e

arXiv:2603.24961v1 Announce Type: new Abstract: Assessing student handwritten scratchwork is crucial for personalized educational feedback but presents unique c

arXiv:2603.24963v1 Announce Type: new Abstract: Modern computational advertising platforms typically rely on recommendation systems to predict user responses, s

arXiv:2603.24967v1 Announce Type: new Abstract: Understanding why a large language model (LLM) is uncertain about the response is important for their reliable d

arXiv:2603.25001v1 Announce Type: new Abstract: Failure attribution is essential for diagnosing and improving multi-agent systems (MAS), yet existing benchmarks

arXiv:2603.25022v1 Announce Type: new Abstract: Knowledge distillation, model extraction, and behavior transfer have become central concerns in frontier AI. The

arXiv:2603.25031v1 Announce Type: new Abstract: In psychological support and emotional companionship scenarios, the core limitation of large language models (LL

arXiv:2603.25035v1 Announce Type: new Abstract: Compressed vision-language models (VLMs) are widely used to reduce memory and compute costs, making them a suita

arXiv:2603.25075v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) improve interpretability in multimodal models, but it remains unclear whether SAE fea

arXiv:2603.25097v1 Announce Type: new Abstract: Large Language Model based agents increasingly operate in high stakes, multi turn settings where factual groundi

arXiv:2603.25115v1 Announce Type: new Abstract: Few-Shot Class-Incremental Learning (FSCIL) can be particularly susceptible to acquisition contexts with only a

arXiv:2603.25133v1 Announce Type: new Abstract: Rubric-based evaluation has become a prevailing paradigm for evaluating instruction following in large language

arXiv:2603.25152v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) systems face significant challenges in complex reasoning, multi-hop queries

arXiv:2603.25158v1 Announce Type: new Abstract: Equipping Large Language Model (LLM) agents with domain-specific skills is critical for tackling complex tasks.

arXiv:2603.25197v1 Announce Type: new Abstract: As AI assistants become integrated into safety engineering workflows for Physical AI systems, a critical questio

arXiv:2603.25266v1 Announce Type: new Abstract: Probabilistic abstract interpretation is a theory used to extract particular properties of a computer program wh

arXiv:2603.25284v1 Announce Type: new Abstract: In this paper, we address post-training quantization (PTQ) for large language models (LLMs) from an overlooked p

arXiv:2603.25326v1 Announce Type: new Abstract: Interest in the concept of AI-driven harmful manipulation is growing, yet current approaches to evaluating it ar

arXiv:2603.25328v1 Announce Type: new Abstract: Automated Vehicle (AV) control in mixed traffic, where AVs coexist with human-driven vehicles, poses significant

arXiv:2603.25334v1 Announce Type: new Abstract: Distributed intelligence in industrial networks increasingly integrates sensing, communication, and computation

arXiv:2603.25356v1 Announce Type: new Abstract: Arithmetic puzzle games provide a controlled setting for studying difficulty in mathematical reasoning tasks, a

arXiv:2603.25412v1 Announce Type: new Abstract: Large language models (LLMs) increasingly rely on explicit chain-of-thought (CoT) reasoning to solve complex tas

arXiv:2603.25415v1 Announce Type: new Abstract: Semantic world models enable embodied agents to reason about objects, relations, and spatial context beyond pure

arXiv:2603.25450v1 Announce Type: new Abstract: Detecting when a language model is wrong without ground truth labels is a fundamental challenge for safe deploym

arXiv:2603.25480v1 Announce Type: new Abstract: Model retraining is usually treated as an ongoing maintenance task. But as Harrison Katz now argues, retraining

arXiv:2603.25498v1 Announce Type: new Abstract: As the Web transitions from static retrieval to generative interaction, the escalating environmental footprint o

arXiv:2603.25633v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used in math education not only as problem solvers but also as ass

arXiv:2603.25720v1 Announce Type: new Abstract: Robust perception and reasoning require consistency across sensory modalities. Yet current multimodal models oft