Search: “llm”

arXiv:2603.16496v2 Announce Type: replace Abstract: Large language model (LLM) agents increasingly rely on external memory to support long-horizon interaction, personalized assistance, and multi-step

arXiv:2604.26103v1 Announce Type: cross Abstract: All current LLM serving systems place the GPU at the center, from production-level attention-FFN disaggregation to NVIDIA's Rubin GPU-LPU heterogeneou

b8983 is a release of llama.cpp, a C/C++ implementation for LLM inference. The release represents a specific build/commit version in the active development of the llama.cpp project, which supports run

B8984 is a build/release version of llama.cpp, an open-source C/C++ library for large language model inference. Llama.cpp enables LLM inference with minimal setup and state-of-the-art performance on a

llama.cpp b8990 is a release version of a C/C++ LLM inference tool from the ggml-org project. The release likely contains bug fixes, performance improvements, or new features for local language model

llama.cpp is a C/C++ implementation for LLM inference , and b8991 is a specific release version from the project's ongoing development cycle. The search results indicate that the project doesn't follo

b8992 is a build release from the llama.cpp project, which publishes multiple releases in a single day . llama.cpp enables LLM inference with minimal setup and state-of-the-art performance on a wide r

Building a document processing pipeline at scale is hard, and is one of the reasons that it's hard to DIY your own document OCR solution by relying on LLM APIs. Your orchestration pipeline needs to ha

arXiv:2604.26256v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a critical paradigm for LLM post-training, yet the rollout phase -- accounting for 50--80% of total step time --

arXiv:2604.26235v1 Announce Type: cross Abstract: We introduce LATTICE, a benchmark for evaluating the decision support utility of crypto agents in realistic user-facing scenarios. Prior crypto agent

arXiv:2604.26622v1 Announce Type: new Abstract: Autonomous LLM agents increasingly operate in long-horizon, interactive settings where success depends on reusing experience accumulated over extended h

arXiv:2604.26573v1 Announce Type: new Abstract: Improving large language model (LLM) reasoning requires supervision that is both aligned with the model's own test-time states and informative at the to

arXiv:2603.04337v2 Announce Type: replace-cross Abstract: Constructing computer-aided design (CAD) models is labor-intensive but essential for engineering and manufacturing. Recent advances in Large L

arXiv:2603.15262v2 Announce Type: replace Abstract: Modern e-commerce search is evolving to resolve complex user intents. While Large Language Models (LLMs) offer strong reasoning, existing LLM-based

arXiv:2604.26412v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference, but SOTA hidden-state-based drafters suffer from long-range decay: draft accuracy degrades as the specul

b8969 is a release build of llama.cpp (commit bdc9c74), published on April 29, 2026. Llama.cpp is a C/C++ implementation designed to enable LLM inference with minimal setup and state-of-the-art perfor

b8971 is a build release of llama.cpp, an open-source C/C++ project for LLM inference . As a numbered build identifier from the llama.cpp repository, this release likely contains bug fixes, performanc

b8972 is a release of llama.cpp, a C/C++ implementation for LLM inference . The release identifier follows llama.cpp's development versioning scheme with frequent builds published to the GitHub releas

b8973 is a release of llama.cpp that added SVE tuned code for the gemm_q8_0_4x8_q8_0() kernel and changed arrays to static const in repack.cpp . The llama.cpp project enables LLM inference with minima

b8978 is a release of llama.cpp, a project designed to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware. The llama.cpp project uses sequential build

b8981 is a release version of llama.cpp, a C/C++ implementation for LLM inference . Build versions in llama.cpp releases typically contain updates to the underlying ggml library, performance optimizat

arXiv:2604.25120v1 Announce Type: new Abstract: We study clinical trial table reasoning, where answers are not directly stored in visible cells but must be reasoned from semantic understanding through

arXiv:2604.24770v1 Announce Type: new Abstract: Despite recent progress in automatic speech recognition (ASR), elderly ASR (EASR) remains challenging due to limited training data and the distinct acou

arXiv:2604.25031v1 Announce Type: new Abstract: When an LLM formalizes natural language, how do we know the output is faithful? We propose a roundtrip verification approach which does not require grou

arXiv:2601.02078v2 Announce Type: replace Abstract: The development of robust and generalizable robot learning models is critically contingent upon the availability of large-scale, diverse training da

arXiv:2604.25860v1 Announce Type: new Abstract: Machine-generated text (MGT) detection requires identifying structurally invariant signals across generation models, rather than relying on model-specif

arXiv:2604.25152v1 Announce Type: cross Abstract: We present MGTEVAL, an extensible platform for systematic evaluation of Machine-Generated Text (MGT) detectors. Despite rapid progress in MGT detectio

We’re excited to introduce KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI, accepted at #ICASSP2026! 🐢 Blog https://pub.sakana.ai/kame/ Paper https://

arXiv:2604.24175v1 Announce Type: cross Abstract: Large language models have demonstrated strong reasoning capabilities in general knowledge question answering. However, their ability to handle tempor

arXiv:2604.23483v1 Announce Type: new Abstract: Multi-component natural language processing (NLP) pipelines are increasingly deployed for high-stakes decisions, yet no existing adversarial method can

arXiv:2604.24657v1 Announce Type: cross Abstract: Autonomous AI agents extend large language models into full runtime systems that load skills, ingest external content, maintain memory, plan multi-ste

arXiv:2604.24076v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly deployed in high-stakes and operational settings, evaluation strategies based solely on aggregate accur

llama.cpp is a C/C++ implementation for LLM inference. Build b8954 is a release version from the ggml-org/llama.cpp repository that likely includes bug fixes, optimizations, or new features added sinc

b8957 is a release of llama.cpp that includes spec parameter refactoring changes , featuring modifications to parameter handling and model specifications. The llama.cpp project enables LLM inference w

B8963 is a build release of llama.cpp, the C/C++ implementation of LLM inference. Llama.cpp is an open-source project that enables efficient language model execution on consumer hardware with minimal

b8964 is a release of llama.cpp, a C/C++ library for LLM inference . The release likely includes bug fixes, performance optimizations, or new features as part of the project's continuous development c

b8966 is one of the latest releases in the llama.cpp project , a C/C++ implementation for efficient LLM inference. This release represents an intermediate development build in the llama.cpp repository

arXiv:2604.24512v1 Announce Type: new Abstract: As LLM agents transition to autonomous digital coworkers, maintaining deterministic goal-directedness in non-linear multi-turn conversations emerged as

arXiv:2602.17513v2 Announce Type: replace Abstract: Clinical free-text notes contain vital patient information. They are structured into labelled sections; recognizing these sections has been shown to

arXiv:2510.13312v2 Announce Type: replace Abstract: We present ChatR1, a reasoning framework based on reinforcement learning (RL) for conversational question answering (CQA). Reasoning plays an import

arXiv:2604.22989v1 Announce Type: cross Abstract: Recent medical multimodal foundation models are built as multimodal LLMs (MLLMs) by connecting a CLIP-pretrained vision encoder to an LLM using LLaVA-

arXiv:2604.24361v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance in general machine translation, yet their ability in culture-aware scenarios remains poorl

* 본 아티클의 원문은 2026년 4월 23일 Google Cloud 블로그(영문)에 게재되었습니다. AI는 질문에 답하는 수준을 넘어 추론하고 행동하는 단계로 진화하고 있습니다. 오늘날의 에이전틱 시대(agentic era)를 선도하고자 하는 기업에는 이러한 새로운 요구사항에 맞춰 설계되고 최적화된 컴퓨팅 인프라가 필요합니다. 오늘 Google Cloud

arXiv:2604.24558v1 Announce Type: new Abstract: Recent work in hierarchical reinforcement learning has shown success in scaling to billions of timesteps when learning over a set of predefined option r

arXiv:2604.24074v1 Announce Type: new Abstract: Safety benchmarks such as HarmBench rely on LLM judges to classify model responses as harmful or safe, yet the judge configuration, namely the combinati

arXiv:2604.23257v1 Announce Type: cross Abstract: Software engineering (SE) organizations operate in a knowledge-intensive domain where critical assets -- architectural expertise, design rationale, an