Search: “llm”

everyone would have a deeper appreciation for Agent Products that rock because of great Context/Harness Engineering if they… talked to: - LLM Base models - Post-Trained models with no harness (no tool

arXiv:2605.01567v1 Announce Type: cross Abstract: Large language model (LLM) coding agents increasingly operate over repositories, terminals, tests, and execution traces across long software-engineeri

arXiv:2605.02364v1 Announce Type: new Abstract: Upweighting high-quality data in LLM pretraining often improves performance, but in datalimited regimes, especially under overtraining, stronger upweigh

arXiv:2605.01870v1 Announce Type: new Abstract: Large Language Models (LLMs) have substantially advanced the field of Natural Language Processing (NLP), achieving state-of-the-art performance across a

arXiv:2605.01167v1 Announce Type: new Abstract: Activation steering is a method for controlling Large Language Model (LLM) behavior by intervening in its internal representations to increase the align

arXiv:2605.01822v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly being used to support scientific discovery. In chemistry, tasks such as reaction prediction and structure

arXiv:2511.07328v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) methods enhance LLM performance by efficiently filtering relevant context for LLMs, reducing hallucinations and

arXiv:2605.00424v1 Announce Type: cross Abstract: Agent skills -- structured packages of instructions, scripts, and references that augment a large language model (LLM) without modifying the model its

arXiv:2605.02888v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model (LLM) inference by using a small draft model to propose candidate tokens that a larger target mo

arXiv:2605.02126v1 Announce Type: new Abstract: Ultrasound foundation models have achieved strong performance on structured prediction tasks but remain exclusively vision-based, limiting zero-shot and

arXiv:2602.21054v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) frequently hallucinate, limiting their safe deployment in real-world applications. Existing LLM self-eval

arXiv:2602.06932v3 Announce Type: replace Abstract: Speculative decoding can significantly accelerate LLM serving, yet most deployments today disentangle speculator training from serving, treating spe

You need feedback to help power the data flywheel! This 'feedback' can come from many sources: - direct user feedback (thumbs up/down) - indirect user feedback (code suggestions accepted) - llm as jud

llama.cpp enables LLM inference in C/C++ , and release b9014 represents a build revision or commit snapshot from the llama.cpp repository on GitHub. This release would include bug fixes, feature impro

Release b9016 is a build of llama.cpp, an LLM inference implementation in C/C++ . This build designation represents a specific intermediate version in the project's development cycle, similar to other

B9018 is the latest version of llama.cpp released on May 4, 2026. Llama.cpp is a project for LLM inference in C/C++ , providing efficient large language model execution with broad hardware support inc

b9019 is a release of llama.cpp, a C/C++ implementation for LLM inference. As a release tag from the ggml-org/llama.cpp repository, it represents a specific commit or version of the project that inclu

b9025 is a release from llama.cpp, a C/C++ project for LLM inference . The release uses a build numbering system for intermediate versions of the library. As a llama.cpp release, it likely includes up

arXiv:2602.02443v2 Announce Type: replace Abstract: Test-time scaling improves LLM performance by generating multiple candidate solutions, yet token-level sampling requires temperature tuning that tra

arXiv:2605.00610v1 Announce Type: new Abstract: SFT and RLVR represent two fundamental yet distinct paradigms for LLM post-training, each excelling in distinct dimensions. SFT expands knowledge breadt

arXiv:2512.01020v2 Announce Type: replace-cross Abstract: Evaluating the quality of LLM-generated reasoning traces in expert domains (e.g., law) is essential for ensuring credibility and explainabilit

This post documents a 10-day project to build a local AI system using open-source tools and models, specifically combining Ollama (a local LLM framework), Gemma 4 (a language model), Claude Cowork 3P,

arXiv:2408.11349v2 Announce Type: replace Abstract: Mercari is the largest C2C e-commerce marketplace in Japan, having more than 20 million active monthly users. Search being the fundamental way to di

arXiv:2605.00702v1 Announce Type: new Abstract: Large language model (LLM) agents require long-term user memory for consistent personalization, but limited context windows hinder tracking evolving pre

NVIDIA cuOpt Agent Skills integrate LLM reasoning with GPU-accelerated solvers to enable AI agents to translate natural language supply chain problems into optimized mathematical models, with skills e

arXiv:2510.26020v2 Announce Type: replace Abstract: Multi-tool-integrated reasoning enables LLM-empowered tool-use agents to solve complex tasks by interleaving natural-language reasoning with calls t

arXiv:2501.00885v2 Announce Type: replace Abstract: The extraordinary success of recent Large Language Models (LLMs) on a diverse array of tasks has led to an explosion of scientific and philosophical

arXiv:2605.00528v1 Announce Type: cross Abstract: AI agents execute tens to hundreds of chained LLM calls per task, yet GPU schedulers treat each call as independent, discarding gigabytes of intermedi

arXiv:2605.00557v1 Announce Type: new Abstract: Scientific discovery is an extended process of ideation--surveying prior work, forming hypotheses, and refining reasoning--yet existing approaches treat

arXiv:2508.04086v2 Announce Type: replace Abstract: Prior work synthesizes tool-use LLM datasets by first generating a user query, followed by complex tool-use annotations like depth-first search (DFS

b9012 is a build version from the llama.cpp GitHub repository, which is an LLM inference implementation in C/C++ . The release likely contains updates, bug fixes, and improvements to the llama.cpp inf

that’s right. 😂 Finally, the great LeCun conversion arc is complete! “LLM agents = disaster” – said the guy who once defended Galactica like it was the Second Coming. Gary, you didn’t change… the time

b9000 is a build release of llama.cpp, a C/C++ implementation for LLM inference . The release follows the project's rapid development cycle where multiple releases can be published in a single day . A

B9002 is a build release of llama.cpp, a C/C++ implementation for LLM inference. The release includes compiled binaries and artifacts for multiple platforms and hardware configurations, as part of the

llama.cpp is an LLM inference framework in C/C++ that enables efficient local execution of large language models. Build b9004 is a recent release with optimizations and improvements for supporting var

B9008 is a build release of llama.cpp from May 2, 2026. Llama.cpp is a C/C++ implementation of LLM inference designed to enable large language model inference with minimal setup and high performance a

b9010 is a build release of llama.cpp, an open-source project for LLM inference in C/C++ . As a numbered build tag in the llama.cpp release system, it represents a specific development build containin

llama.cpp is a C/C++ implementation that enables LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware locally and in the cloud. Release b8995 is a build/versio

llama.cpp b8999 is a build release from the llama.cpp project, which provides LLM inference in C/C++ . This build represents an intermediate development version in the project's rapid release cycle. T

arXiv:2604.27045v1 Announce Type: cross Abstract: As Large Language Model (LLM) agents transition from single-session tools to persistent systems managing longitudinal healthcare journeys, their memor

arXiv:2604.27275v1 Announce Type: cross Abstract: Large language model (LLM) reading assistants are increasingly used in settings that require interpretation rather than simple retrieval. In these con

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely b

A Reddit discussion in the r/ollama community seeking recommendations for RAG (Retrieval-Augmented Generation) models, likely addressing model selection for local LLM-based document retrieval systems.

arXiv:2604.27820v1 Announce Type: new Abstract: Every document format in existence was designed for a human reader moving linearly through text. Autonomous LLM agents do not read - they retrieve. This

arXiv:2604.27233v1 Announce Type: new Abstract: Tool-calling agents are evaluated on tool selection, parameter accuracy, and scope recognition, yet LLM trajectory assessments remain inherently post-ho

arXiv:2604.27358v1 Announce Type: new Abstract: As large language model (LLM) agents are deployed in high-stakes environments, the question of how safely to delegate subtasks to specialized sub-agents