b8970
b8970 is a release of llama.cpp, a C/C++ project for large language model inference. The naming convention using a build hash (b8970) is typical of llama.cpp's continuous release pattern rather than t
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b8970 is a release of llama.cpp, a C/C++ project for large language model inference. The naming convention using a build hash (b8970) is typical of llama.cpp's continuous release pattern rather than t
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
arXiv:2604.25774v1 Announce Type: new Abstract: Accurate nutrient estimation from unstructured recipe text is an important yet challenging problem in dietary monitoring, due to ambiguous ingredient te
arXiv:2602.01433v2 Announce Type: replace Abstract: Multivariate time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-te
arXiv:2604.25300v1 Announce Type: new Abstract: Deploying tiny object perception on edge platforms is challenging because practical systems must satisfy both strict compute budgets and end-to-end late
arXiv:2604.25533v1 Announce Type: new Abstract: Worldwide image geo-localization aims to infer the geographic location of an image captured anywhere on Earth, spanning street, city, regional, national
arXiv:2604.24972v1 Announce Type: new Abstract: Clinical abnormality grounding for rare diseases is often hindered by data scarcity, making supervised fine-tuning impractical and single-pass inference
IMO DeepSeek v4 demonstrated utter confidence and competence by not benchmaxxing, not focusing on some BS final run cost, not even spending inference-optimal compute. just showed up, demonstrated SOTA
arXiv:2604.24827v1 Announce Type: new Abstract: Closed-source frontier labs do not disclose parameter counts, and the standard alternative -- inference economics -- carries 2imes+ uncertainty from har
arXiv:2604.24809v1 Announce Type: new Abstract: We present Nautile-370M, a 371-million-parameter small language model designed for efficient reasoning under strict parameter and inference budgets. Nau
arXiv:2604.25859v1 Announce Type: new Abstract: World action models jointly predict future video and action during training, raising an open question about what role the future-prediction branch actua
arXiv:2604.25781v1 Announce Type: new Abstract: Articulation modeling aims to infer movable parts and their motion parameters for a 3D object, enabling interactive animation, simulation, and shape edi
Dell Technologies’ AI Factory is emerging as a core building block for enterprise AI deployment, delivering full-stack infrastructure for training and inference since its 2024 launch. Yet as AI moves
arXiv:2601.12483v2 Announce Type: replace-cross Abstract: Quantum error correction is a key ingredient for large scale quantum computation, protecting logical information from physical noise by encodi
arXiv:2604.23666v1 Announce Type: cross Abstract: Line current differential relays (LCDRs) are measurement-driven relays that rely on time-synchronized multi-phase current waveforms to infer internal
arXiv:2512.09972v5 Announce Type: replace-cross Abstract: Navigating the capability--efficiency trade-off in Large Language Models (LLMs) requires approximating a high-quality Pareto set. Existing mod
arXiv:2604.22937v1 Announce Type: new Abstract: Verification is becoming central to both reinforcement-learning-based training and inference-time control of large language models (LLMs). Yet current v
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
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:2601.06352v2 Announce Type: replace Abstract: Adapting large language models to individual users remains challenging due to the tension between fine-grained personalization and scalable deployme
arXiv:2604.23112v1 Announce Type: new Abstract: Multimodal Federated Learning (MMFL) enables privacy-preserving collaborative training, but real-world clinical applications often suffer from within-mo
DeepSeek V4-Pro is now available on the Fireworks AI platform, likely offering accelerated inference speeds ('Zoooooom' suggests performance optimization). This deployment enables users to access Deep
Excited to support @NVIDIA Nemotron 3 Nano Omni, now available on Fireworks. It's the first open model that handles vision, audio, video, and text in a single inference loop. Built for multimodal sub-
arXiv:2604.23277v1 Announce Type: cross Abstract: Long-context large language models remain computationally expensive to run and often fail to reliably process very long inputs, which makes context co
GitHub is transitioning GitHub Copilot to a usage-based billing model to manage escalating AI inference costs. Effective June 1, subscribers will receive a set allotment of 'AI Credits'
* 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:2603.01581v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models build a token-domain robot control paradigm, yet suffer from low speed. Speculative Decoding (SD) is an op
arXiv:2604.24432v1 Announce Type: cross Abstract: Long-context ability, has become one of the most important iteration direction of next-generation Large Language Models, particularly in semantic unde
arXiv:2510.17590v2 Announce Type: replace Abstract: We present MERIT, an inference-time modular framework for multimodal misinformation detection that decomposes verification into four specialized mod
Laguna XS 2 is a compact language model available through Ollama's model library, designed for efficient inference on resource-constrained devices. The model represents an update or variant in the Lag
arXiv:2604.22881v1 Announce Type: cross Abstract: Generative recommendation (GR) offers superior modeling capabilities but suffers from prohibitive inference costs due to the repeated encoding of long
Open Models & a potential looming Haiku-apocalypse? 🚀 chart comparison UI courtesy of @OpenRouter there's a large class of problems where you don't need frontier intelligence. you want cheap, fast, an
arXiv:2604.23543v1 Announce Type: cross Abstract: Test-time alignment methods offer a promising alternative to fine-tuning by steering the outputs of large language models (LLMs) at inference time wit
arXiv:2604.23099v1 Announce Type: cross Abstract: Evaluating generative AI models is increasingly resource-intensive due to slow inference, expensive raters, and a rapidly growing landscape of models
arXiv:2604.22758v1 Announce Type: cross Abstract: Recently, at Xiaohongshu, the rapid expansion of e-commerce and advertising demands real-time business analytics with high accuracy and low latency. T
arXiv:2603.06165v2 Announce Type: replace-cross Abstract: The growing demand for text-to-image generation has led to rapid advances in generative modeling. Recently, text-to-image diffusion models tra
arXiv:2604.23442v1 Announce Type: new Abstract: Weeds compete with crops for light, water, and nutrients, reducing yield and crop quality. Efficient weed detection is essential for site-specific weed
arXiv:2512.23213v3 Announce Type: replace-cross Abstract: We propose LLM-PeerReview, an unsupervised LLM Ensemble method that selects the most ideal response from multiple LLM-generated candidates for
arXiv:2604.23263v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly utilized in various complex reasoning tasks due to their excellent instruction following capability. How
arXiv:2604.24003v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong reasoning performance by allocating substantial computation at inference time, often generating long and ver
arXiv:2604.22782v1 Announce Type: cross Abstract: Serving transformer language models with high throughput requires caching Key-Values (KVs) to avoid redundant computation during autoregressive genera
arXiv:2604.22852v1 Announce Type: cross Abstract: Cloud-hosted LLM inference for autonomous driving adds round-trip delay and depends on stable connectivity, while purely local edge models struggle un
arXiv:2604.23102v1 Announce Type: new Abstract: Standard evaluations of Bayesian deep learning methods assume that metric estimates are reliable, but we show this assumption fails under data scarcity.
Release b8944 was published on April 27, 2026 , representing a recent build of the llama.cpp project. Llama.cpp provides LLM inference in C/C++ for running large language models efficiently on consume
arXiv:2604.22056v1 Announce Type: new Abstract: Optimal wireless transmitter placement is a central task in radio-network planning, yet exhaustive search becomes prohibitively expensive at scale. This
arXiv:2604.22085v1 Announce Type: new Abstract: The transition from stateless language model inference to persistent, multi session autonomous agents has revealed memory to be a primary architectural
arXiv:2510.03248v3 Announce Type: replace-cross Abstract: Background: Traumatic brain injury modeling requires integrating volumetric neuroimaging, demographic parameters, and acquisition metadata. Fi
arXiv:2501.19277v4 Announce Type: replace-cross Abstract: We study online assortment optimization under stochastic choice when a decision maker simultaneously values cumulative revenue performance and
arXiv:2604.22577v1 Announce Type: new Abstract: Autonomous agent systems such as OpenClaw introduce significant efficiency challenges due to long-context inputs and multi-turn reasoning. This results
❤️ @ying11231, @BanghuaZ and @lmsysorg @sgl_project @radixark Let's go DeepSeek v4 Pro! The DeepSeek V4 garbled output bug in open source inference engine is fixed in SGLang. To everyone affected over
Sam Altman suggests the need for fundamental redesign of operating systems, user interfaces, and internet architecture, implying current designs may be outdated or inadequate for modern needs. The pos
A quantized version of DeepSeek-V4-Flash has been released on Hugging Face by the MLX community in 2-bit format with dynamic quantization, making the model more efficient for inference on resource-con
Sam Altman commented on a discussion about AI efficiency, noting that a particular capability compresses work that previously took approximately two weeks into a single day, indicating significant pro
Sam Altman praised OpenAI's o1 model (version 5.5) for its earnest, persistent approach to problem-solving, comparing it to the 'little engine that could' mentality. The comment reflects Altman's pers
llama.cpp is an LLM inference project in C/C++ that uses build identifiers like b8929 to track development releases. The project does not follow traditional release practices, as multiple releases can