Search: “llm”

arXiv:2601.12193v3 Announce Type: replace Abstract: Modern video retrieval systems are expected to handle diverse tasks ranging from corpus-level retrieval, fine-grained moment localization to flexibl

arXiv:2604.15613v1 Announce Type: cross Abstract: We present VoodooNet, a non-iterative neural architecture that replaces the stochastic gradient descent (SGD) paradigm with a closed-form analytic sol

We are excited to have @baseten as a day 0 launch partner for Kimi K2.6! Their inference stack brings KV-aware routing, NVFP4 on Blackwell, multi-modal hierarchical caching, and prefill-decode disaggr

arXiv:2510.09689v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been augmented with web search to overcome the limitations of the static knowledge boundary by accessing up-

b8841 is a release of llama.cpp dated April 18, 2026 , a C/C++ library for large language model inference. The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art

b8842 is a release of llama.cpp, a C/C++ implementation for LLM inference. The llama.cpp project publishes multiple releases in a single day as part of its active development cycle. This specific rele

b8848 is a release from llama.cpp, an open-source C/C++ project for LLM inference . This release builds on the rapidly-developing codebase with regular updates that include bug fixes, feature improvem

The llama.cpp project is the main playground for developing new features for the ggml library , and the main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art perfor

Human cognitive friction has long been acting as a regularizer for a lot of digital infrastructure. It made software APIs less terrible and codebases less complex. Now LLM disintermediation is causing

Release b8838 is a version of llama.cpp, a C/C++ implementation for LLM inference. The release likely contains bug fixes, performance improvements, and updates to the codebase for running large langua

Nous Research announced integration or collaboration between Hermes (their AI agent framework) and Ollama (a local LLM runtime platform), enabling users to run Hermes agents with locally-hosted langua

One of the premier journals in my field... I think there are very valid reasons to set rules on AI in peer review (including disclosure), but the idea that all AI models steal your data is very 2023.

arXiv:2604.14902v1 Announce Type: cross Abstract: Intelligent embodied agents should not simply follow instructions, as real-world environments often involve unexpected conditions and exceptions. Howe

arXiv:2604.13940v1 Announce Type: new Abstract: Scientific peer review faces mounting strain as submission volumes surge, making it increasingly difficult to sustain review quality, consistency, and t

arXiv:2601.13503v2 Announce Type: replace Abstract: Psychiatric narratives encode patient identity not only through explicit identifiers but also through idiosyncratic life events embedded in their cl

arXiv:2508.15396v2 Announce Type: replace Abstract: The increasing adoption of large language models (LLMs) has raised serious concerns about their reliability and trustworthiness. As a result, a grow

b8823 is a build release of llama.cpp, the C/C++ implementation for LLM inference that enables efficient language model execution locally and in the cloud. Based on the rapid development cycle of the

arXiv:2604.14251v1 Announce Type: new Abstract: Monitoring LLM safety at scale requires balancing cost and accuracy: a cheap latent-space probe can screen every input, but hard cases should be escalat

arXiv:2604.14210v1 Announce Type: new Abstract: A claim has been circulating on social media and practitioner forums that Chinese prompts are more token-efficient than English for LLM coding tasks, po

arXiv:2604.03920v2 Announce Type: replace Abstract: LLM-based social simulations can generate believable community interactions, enabling ``policy wind tunnels'' where governance interventions are tes

arXiv:2604.15037v1 Announce Type: cross Abstract: Recent advancements in LLM agents are gradually shifting from reactive, text-based paradigms toward proactive, multimodal interaction. However, existi

arXiv:2604.15134v1 Announce Type: new Abstract: Reliable procedural monitoring in video requires exposure to naturally occurring human errors and the recoveries that follow. In egocentric recordings,

arXiv:2604.08064v2 Announce Type: replace Abstract: Existing memory benchmarks for LLM agents evaluate explicit recall of facts, yet overlook implicit memory where experience becomes automated behavio

arXiv:2604.13849v1 Announce Type: cross Abstract: The rapid proliferation of Model Context Protocol (MCP)-based agentic systems has introduced a new category of security threats that existing framewor

arXiv:2604.14158v1 Announce Type: new Abstract: Current evaluations of long-term memory in LLMs are fundamentally static. By fixating on simple retrieval and short-context inference, they neglect the

arXiv:2603.27844v2 Announce Type: replace Abstract: Majority voting over multiple LLM attempts improves mathematical reasoning, but correlated errors limit the effective sample size. A natural fix is

arXiv:2604.13242v1 Announce Type: cross Abstract: Large language models (LLMs), particularly when integrated into agentic systems, have demonstrated human- and even superhuman-level performance across

arXiv:2604.14513v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used in scientific peer review, assisting with drafting, rewriting, expansion, and refinement. However, ex

arXiv:2509.21823v2 Announce Type: replace Abstract: Reward is critical to the evaluation and training of large language models (LLMs). However, existing rule-based or model-based reward methods strugg

arXiv:2604.13757v1 Announce Type: new Abstract: The next generation of autonomous AI systems will be constrained not only by model capability, but by how intelligence is structured across heterogeneou

arXiv:2604.14261v1 Announce Type: new Abstract: The rapid rise in AI conference submissions has driven increasing exploration of large language models (LLMs) for peer review support. However, LLM-base

arXiv:2507.02935v2 Announce Type: replace Abstract: Successful human-agent teaming relies on an agent being able to understand instructions given by a (human) principal. In many cases, an instruction

arXiv:2512.18436v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown impressive capability to understand and develop code. However, their capability to rigorously reason a

arXiv:2604.13440v1 Announce Type: new Abstract: Deploying Large Language Models (LLMs) on edge devices faces severe computational and memory constraints, limiting real-time processing and on-device in

Anthropic PBC today opened access to Claude Opus 4.7, the latest addition to its popular line of large language models. The company says that the LLM is significantly better than its predecessor at co

Build b8808 is an incremental release of **llama.cpp**, the open-source C/C++ library for running large language model (LLM) inference locally. Like all llama.cpp builds, it likely includes bug fixes,

b8811 is a release build from llama.cpp, a C/C++ library for LLM inference . The project releases multiple versions frequently as part of its rapid development cycle , with b8811 representing one of t

llama.cpp is a C/C++ library for LLM inference that enables running large language models on consumer hardware. Release b8814 is a specific version in the project's continuous release cycle, which fol

arXiv:2604.13583v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for legal reasoning requires workflows that span task design, expert annotation, model execution, and metric-bas

arXiv:2604.13705v1 Announce Type: new Abstract: Fairness in language models is typically studied as a property of a single, centrally optimized model. As large language models become increasingly agen

arXiv:2604.14121v1 Announce Type: new Abstract: LLM reasoning traces suffer from complex flaws -- *Step Internal Flaws* (logical errors, hallucinations, etc.) and *Step-wise Flaws* (overthinking, unde

arXiv:2604.14142v1 Announce Type: cross Abstract: While reinforcement learning with verifiable rewards (RLVR) significantly enhances LLM reasoning by optimizing the conditional distribution P(y|x), it

Had a great time at PyCon & PyData DE. Highly recommend it. Great open-source, community-focused conference with lots of builders in the Python AI, LLM and agent space. Taking a short family break, my

A community member shared a project on r/ollama (posted in Spanish) announcing the creation of a custom FUSE (Filesystem in Userspace) implementation designed to work with Ollama, the popular local LL

arXiv:2604.13064v1 Announce Type: new Abstract: Skill ecosystems have emerged as an increasingly important layer in Large Language Model (LLM) agent systems, enabling reusable task packaging, public d

arXiv:2512.10696v2 Announce Type: replace-cross Abstract: Procedural memory enables large language model (LLM) agents to internalize 'how-to' knowledge, theoretically reducing redundant trial-and-erro