Search: “llm”

arXiv:2604.11201v1 Announce Type: cross Abstract: LLM agents now perform strongly in software engineering, deep research, GUI automation, and various other applications, while recent agent scaffolds a

arXiv:2604.01687v2 Announce Type: replace Abstract: Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address. A tool

arXiv:2410.21316v2 Announce Type: replace-cross Abstract: Transformers and large language models~(LLMs) have seen rapid adoption in all domains. Their sizes have exploded to hundreds of billions of pa

arXiv:2604.11703v1 Announce Type: new Abstract: People experiencing homelessness (PEH) face substantial barriers to accessing timely, accurate information about community services. DreamKG addresses t

arXiv:2604.11094v1 Announce Type: cross Abstract: Contemporary microservice systems continue to grow in scale and complexity, leading to increasingly frequent and costly failures. While recent LLM-bas

arXiv:2510.06965v2 Announce Type: replace-cross Abstract: Math word problems (MWPs) are critical K-12 educational tools, and customizing them to students' interests and ability levels can enhance lear

arXiv:2604.10189v1 Announce Type: new Abstract: Large Language Models (LLMs) can generate factually inaccurate content even if they have corresponding knowledge, which critically undermines their reli

arXiv:2604.06788v2 Announce Type: replace-cross Abstract: We present a solver-agnostic framework in which coordinated large language model (LLM) agents autonomously execute the complete computational

arXiv:2601.04932v2 Announce Type: replace Abstract: Large language models (LLM) often hallucinate, and while adding citations is a common solution, it is frequently insufficient for accountability as

arXiv:2604.09937v1 Announce Type: new Abstract: Healthcare administration accounts for over $1 trillion in annual spending, making it a promising target for LLM-based computer-use agents (CUAs). While

arXiv:2604.10271v1 Announce Type: cross Abstract: In what way could a data breach involving government-issued IDs such as passports, driver's licenses, etc., rival a random voluntary disclosure on a n

arXiv:2604.11749v1 Announce Type: new Abstract: Language change both reflects and shapes social processes, and the semantic evolution of foundational concepts provides a measurable trace of historical

arXiv:2604.09670v1 Announce Type: cross Abstract: Intelligent systems must maintain and manipulate task-relevant information online to adapt to dynamic environments and changing goals. This capacity,

arXiv:2604.09960v1 Announce Type: new Abstract: The rapid adoption of large language models has introduced a new class of AI-generated fake news that coexists with traditional human-written misinforma

A community-shared open source CLI agent project posted to r/ollama, designed specifically for use with 8K token context windows in local Ollama-based LLM setups. Version 0.3 focuses on improving Olla

arXiv:2406.13621v2 Announce Type: replace Abstract: Commonsense reasoning often requires both textual and visual knowledge, yet Large Language Models (LLMs) trained solely on text lack visual groundin

arXiv:2510.01152v2 Announce Type: replace Abstract: LLMs cannot reliably recognize their parametric knowledge boundaries and often hallucinate answers to outside-of-boundary questions. In this paper,

arXiv:2604.10815v1 Announce Type: cross Abstract: MeloTune is an iPhone-deployed music agent that instantiates the Mesh Memory Protocol (MMP) and Symbolic-Vector Attention Fusion (SVAF) as a productio

arXiv:2509.22403v2 Announce Type: replace Abstract: Mobility Foundation Models (MFMs) have advanced the modeling of human movement patterns, yet they face a ceiling due to limitations in data scale an

arXiv:2604.11543v1 Announce Type: cross Abstract: Novelty is a core requirement in academic publishing and a central focus of peer review, yet the growing volume of submissions has placed increasing p

This r/ollama post discusses a tool for easily linking models between LM Studio and Ollama without duplicating disk storage. Both Ollama and LM Studio are popular local LLM tools, but they store their

arXiv:2504.06307v2 Announce Type: replace-cross Abstract: The rapid adoption of large language models (LLMs) has led to significant energy consumption and carbon emissions, posing a critical challenge

arXiv:2509.21882v2 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is a practical, scalable way to improve large language models on math, code, and other s

arXiv:2412.04272v5 Announce Type: replace-cross Abstract: In recent years, table reasoning has garnered substantial research interest, particularly regarding its integration with Large Language Models

arXiv:2604.11805v1 Announce Type: cross Abstract: We have witnessed remarkable advances in LLM reasoning capabilities with the advent of DeepSeek-R1. However, much of this progress has been fueled by

arXiv:2604.10516v1 Announce Type: new Abstract: Selecting the right knowledge is critical when using large language models (LLMs) to solve domain-specific data analysis tasks. However, most retrieval-

arXiv:2604.11563v1 Announce Type: cross Abstract: Providing AI agents with reliable long-term memory that does not hallucinate remains an open problem. Current approaches to memory for LLM agents -- s

arXiv:2604.10180v1 Announce Type: cross Abstract: Disaggregation maps parts of an AI workload to different types of GPUs, offering a path to utilize modern heterogeneous GPU clusters. However, existin

arXiv:2604.10261v1 Announce Type: new Abstract: Existing tool-use benchmarks for LLM agents are overwhelmingly linear: our analysis of six benchmarks shows 55 to 100% of instances are simple chains of

arXiv:2604.10135v1 Announce Type: cross Abstract: Researchers have explored different ways to improve large language models (LLMs)' capabilities via dummy token insertion in contexts. However, existin

arXiv:2604.11465v1 Announce Type: new Abstract: Large language model (LLM) agents show promise on realistic tool-use tasks, but deploying capable agents on modest hardware remains challenging. We stud

arXiv:2604.10386v1 Announce Type: new Abstract: Accurate estimation of cancer risk from longitudinal electronic health records (EHRs) could support earlier detection and improved care, but modeling su

Two major shifts will be seen in Agentic AI after Harness and YOU MUST KNOW. 1. Workflow design of your agents matters a lot more than any frontier model selection. Till now we have mostly focused on

arXiv:2604.10441v1 Announce Type: new Abstract: Medical large language models (LLMs) achieve impressive performance on standardized benchmarks, yet these evaluations fail to capture the complexity of

arXiv:2604.10827v1 Announce Type: new Abstract: Compute scaling for LLM reasoning requires allocating budget between exploring solution approaches (breadth) and refining promising solutions (depth). M

arXiv:2604.08588v1 Announce Type: cross Abstract: Effective automation hinges on deciding when to act and when to escalate. We model this as a decision under uncertainty: an LLM forms a prediction, es

arXiv:2604.05529v2 Announce Type: replace Abstract: Human mobility modeling is indispensable for diverse urban applications. However, existing data-driven methods often suffer from data scarcity, limi

**b8771** is a build release of [llama.cpp](https://github.com/ggml-org/llama.cpp), the open-source C/C++ library for running large language model (LLM) inference locally. Like other incremental build

Build **b8772** is a recent incremental release of [llama.cpp](https://github.com/ggml-org/llama.cpp), the open-source C/C++ library for local LLM inference. Based on surrounding release activity, it

Build b8775 is a specific incremental release of llama.cpp, the open-source C/C++ library for local LLM inference maintained under the ggml-org GitHub organization. Like other builds in its rapid, com

Build **b8777** is an incremental tagged release of the [llama.cpp](https://github.com/ggml-org/llama.cpp) project, a C/C++ library focused on enabling efficient LLM inference across a wide range of l

Build **b8778** is an incremental automated release of [llama.cpp](https://github.com/ggml-org/llama.cpp), the open-source C/C++ framework for running LLM inference locally and in the cloud. Like othe

llama.cpp build b8781 is the latest release of the open-source C/C++ LLM inference framework, published on April 13, 2026. Its primary change introduces a dedicated DeepSeek v3.2 chat parser along wit

arXiv:2507.04736v2 Announce Type: replace Abstract: Large Language Models have emerged as powerful tools for automating Register-Transfer Level (RTL) code generation, yet they face critical limitation

**cuba-memorys v0.7.0** is a persistent memory library for AI agents, shared in the r/ollama community, designed to give local LLM-powered agents the ability to retain information across sessions. It

arXiv:2604.08603v1 Announce Type: new Abstract: Existing LLM-based agent systems share a common architectural failure: they answer from the unrestricted knowledge space without first simulating how ac