Large Language Models

Through a strategic partnership with the AWS Generative AI Innovation Center (GenAIIC), Rocket Close developed an intelligent document processing solution that

Explore the top agent skill marketplaces shaping how AI agents discover, install, and use reusable capabilities.

In the competitive landscape of AI agents, where businesses are closing investment deals everyday to build and expand their AI infrastructure and software, the

This article introduces Context-Augmented Generation (CAG) as an architectural refinement of RAG for enterprise systems. It shows how a Spring Boot-based contex

The tech industry spent the last two years convincing itself that co-pilots were the future. Tools that sit beside you, watch you work, and offer suggestions. I

You open ChatGPT, type out a prompt, and get back something generic. You tweak it. Still flat. You try again. Still not right. Sound familiar? Bad AI outputs ar

Claude Code is powerful but costs money. Every prompt burns API tokens and your code is sent to external servers. What if you could run the same workflow with a

On March 31, 2026, Anthropic shipped Claude Code v2.1.88 with a 59.8MB source map file still attached. The entire TypeScript source — 1,900 files, 512K+ lines —

Explore NVIDIA’s llama-nemotron-embed-1b-v2, a compact multilingual embedding model built for efficient retrieval across 26 languages.

Cognitive behavioral therapy and large language models might be the key to solving ADHD task paralysis. Most productivity software makes a core assumption: the

arXiv:2604.00005v1 Announce Type: new Abstract: Emotion plays an important role in human cognition and performance. Motivated by this, we investigate whether an

arXiv:2604.00085v1 Announce Type: new Abstract: Large language models applied to clinical prediction exhibit case-level heterogeneity: simple cases yield consis

arXiv:2604.00249v1 Announce Type: new Abstract: Single-agent large language model (LLM) systems struggle to simultaneously support diverse conversational functi

arXiv:2604.00281v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly embedded in computer science education through AI-assisted program

arXiv:2604.00362v1 Announce Type: new Abstract: No one has independently reproduced OpenAI's published scores for gpt-oss-20b with tools, because the original p

arXiv:2604.00414v1 Announce Type: new Abstract: LLM systems must make control decisions in addition to generating outputs: whether to answer, clarify, retrieve,

arXiv:2604.00421v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) layers increase model capacity by activating only a small subset of experts per token,

arXiv:2604.00442v1 Announce Type: new Abstract: Automating optimization modeling with LLMs is a promising path toward scalable decision intelligence, but existi

arXiv:2604.00445v1 Announce Type: new Abstract: Uncertainty estimation (UE) aims to detect hallucinated outputs of large language models (LLMs) to improve their

arXiv:2604.00477v1 Announce Type: new Abstract: LLM-based agent judges are an emerging approach to evaluating conversational AI, yet a fundamental uncertainty r

arXiv:2604.00478v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly prioritize user validation over epistemic accuracy-a phenomenon known

arXiv:2604.00510v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) is an effective test-time compute scaling (TTCS) method for improving the reasoni

arXiv:2604.00547v1 Announce Type: new Abstract: Unified Multimodal Large Models (UMLMs) integrate understanding and generation capabilities within a single arch

arXiv:2604.00550v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into life sciences has catalyzed the development of "AI Scientis

arXiv:2604.00555v1 Announce Type: new Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inabi

arXiv:2604.00594v1 Announce Type: new Abstract: As the focus in LLM-based coding shifts from static single-step code generation to multi-step agentic interactio

arXiv:2604.00716v1 Announce Type: new Abstract: Transformer language models contain localized reasoning circuits, contiguous layer blocks that improve reasoning

arXiv:2604.00790v1 Announce Type: new Abstract: While large language models (LLMs) have demonstrated strong performance on complex reasoning tasks such as compe

arXiv:2604.00890v1 Announce Type: new Abstract: Geometric Problem Solving (GPS) remains at the heart of enhancing mathematical reasoning in large language model

arXiv:2604.00901v1 Announce Type: new Abstract: Multi-agent Retrieval-Augmented Generation (RAG), wherein each agent takes on a specific role, supports hard que

arXiv:2604.00931v1 Announce Type: new Abstract: Existing methods for AI psychological counselors predominantly rely on supervised fine-tuning using static dialo

arXiv:2604.01007v1 Announce Type: new Abstract: AI agents increasingly operate over extended time horizons, yet their ability to retain, organize, and recall mu

arXiv:2604.01108v1 Announce Type: new Abstract: Evaluating the ethical robustness of large language models (LLMs) deployed in software systems remains challengi

arXiv:2604.01151v1 Announce Type: new Abstract: As LLM agents are increasingly deployed in multi-agent systems, they introduce risks of covert coordination that

arXiv:2604.01202v1 Announce Type: new Abstract: We consider the question: when a large language reasoning model makes a choice, did it think first and then deci

arXiv:2604.00001v1 Announce Type: cross Abstract: Gradient-based data selection offers a principled framework for estimating sample utility in large language mo

arXiv:2604.00002v1 Announce Type: cross Abstract: Here we introduce the Olfactory Perception (OP) benchmark, designed to assess the capability of large language

arXiv:2604.00003v1 Announce Type: cross Abstract: This study evaluates the reliability of information extraction approaches from KRS documents using three strat

arXiv:2604.00004v1 Announce Type: cross Abstract: The extension of context windows in Large Language Models is typically facilitated by scaling positional encod

arXiv:2604.00007v1 Announce Type: cross Abstract: We present Dynin-Omni, the first masked-diffusion-based omnimodal foundation model that unifies text, image, a

arXiv:2604.00008v1 Announce Type: cross Abstract: As qualitative researchers show growing interest in using automated tools to support interpretive analysis, a

arXiv:2604.00009v1 Announce Type: cross Abstract: We present the design rationale, implementation attempt, and failure analysis of Eyla, a proposed identity-anc

arXiv:2604.00010v1 Announce Type: cross Abstract: Large language models cannot estimate how long their own tasks take. We investigate this limitation through fo

arXiv:2604.00011v1 Announce Type: cross Abstract: The growing prominence of large language models (LLMs) in daily life has heightened concerns that LLMs exhibit

arXiv:2604.00012v1 Announce Type: cross Abstract: Despite the impressive performance of general-purpose large language models (LLMs), they often require fine-tu

arXiv:2604.00013v1 Announce Type: cross Abstract: Multimodal sentiment analysis aims to understand human emotions by integrating textual, auditory, and visual m

arXiv:2604.00016v1 Announce Type: cross Abstract: The validity of online behavioral research relies on study participants being human rather than machine. In th

arXiv:2604.00018v1 Announce Type: cross Abstract: Decoding strategies play a central role in shaping the reasoning ability of large language models (LLMs). Trad