b9470
The search results don't contain specific information about release b9470. Based on available context, b9470 is a build release from llama.cpp, a C/C++ implementation that enables LLM inference with m
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The search results don't contain specific information about release b9470. Based on available context, b9470 is a build release from llama.cpp, a C/C++ implementation that enables LLM inference with m
B9471 is a build release of llama.cpp, an open-source C/C++ implementation for LLM inference that enables running large language models on consumer hardware with optimized performance. This intermedia
B9473 is a release of llama.cpp, a C/C++ framework for large language model inference . The release represents a specific build commit from the ggml-org/llama.cpp repository. This entry likely documen
arXiv:2606.00144v1 Announce Type: cross Abstract: Speculative decoding speeds up autoregressive decoding by using a drafter to propose multiple tokens that a verifier validates in parallel. In resourc
arXiv:2606.01666v1 Announce Type: cross Abstract: The scaling of Large Language Models (LLMs) has driven significant performance gains but created substantial challenges in inference efficiency. While
arXiv:2602.03670v2 Announce Type: replace-cross Abstract: Equilibrium Propagation (EP) is a physics-inspired learning algorithm that uses stationary states of a dynamical system both for inference and
arXiv:2602.05293v2 Announce Type: replace Abstract: SAM3D enables scalable, open-world 3D reconstruction from complex scenes, yet its deployment is hindered by prohibitive inference latency. In this w
arXiv:2606.00309v1 Announce Type: new Abstract: Stochastic gradient Langevin dynamics combined with Gibbs updates (SGLD--Gibbs) provides a highly scalable approach to approximate Bayesian inference in
arXiv:2603.18016v2 Announce Type: replace-cross Abstract: Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to propose draft tokens that are subsequen
arXiv:2602.08585v2 Announce Type: replace-cross Abstract: Given the quadratic complexity of attention, KV cache eviction is vital to accelerate model inference. Current KV cache eviction methods typic
arXiv:2606.00757v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) suffer from overfitting and over-squashing of long-range information. Stochastic graph augmentations (e.g., edge deletion)
arXiv:2606.00851v1 Announce Type: cross Abstract: Empathetic spoken dialogue systems must infer a user's emotional state to respond appropriately, yet everyday speech often carries weak, neutral, or a
arXiv:2606.00496v1 Announce Type: new Abstract: Oscillatory neural signals such as electroencephalography (EEG) and local field potentials (LFPs) show phase relationships that coordinate communication
arXiv:2602.07223v2 Announce Type: replace Abstract: Long-context large language model (LLM) inference has become the norm for today's AI applications. However, it is severely bottlenecked by the incre
arXiv:2606.00310v1 Announce Type: new Abstract: Visual Autoregressive (VAR) models deliver high-quality image generation but suffer from significant inference latency at high resolutions. Recent accel
Release b9459 is a version of llama.cpp, the C/C++ implementation for LLM inference. The specific release would contain bug fixes, optimizations, or new features for the llama.cpp project, which enabl
arXiv:2605.30613v1 Announce Type: cross Abstract: Over the past year, prompt caching in Large Language Models (LLMs) has become increasingly more popular across inference APIs. Prompt caching helps sa
arXiv:2605.30635v1 Announce Type: new Abstract: Inferring dynamics from population snapshots is a fundamental challenge in machine learning and biology. In scRNA-sequencing (scRNA-seq), destructive me
arXiv:2605.31096v1 Announce Type: new Abstract: While visually grounded Chain-of-Thought (CoT) has emerged as a promising paradigm to enhance fine-grained perception in multimodal large language model
arXiv:2605.31279v1 Announce Type: cross Abstract: To make cross-band channel prediction practical for AI-native RAN, algorithms must generalize across diverse environments and support real-time infere
arXiv:2605.31408v1 Announce Type: cross Abstract: Skill documents provide procedural knowledge to large-language-model agents at inference time. This article studies whether the presentation granulari
arXiv:2602.06161v2 Announce Type: replace-cross Abstract: Parallel diffusion decoding can accelerate diffusion language model inference by unmasking multiple tokens per step, but aggressive parallelis
Release b9433 is a version update from the llama.cpp project, which provides LLM inference in C/C++. Based on the GitHub releases structure, this is a specific build iteration that would contain impro
B9434 is a release build identifier from the llama.cpp project, an open-source C/C++ implementation for large language model inference. Llama.cpp releases include updates, optimizations, and bug fixes
arXiv:2605.30150v1 Announce Type: new Abstract: LLMs are increasingly used to generate candidate-idea pools for creative tasks where broad exploration is valuable. Parallel inference can be attractive
B9387 is a release of llama.cpp, the main playground for developing features for the GGML library, with the goal of enabling LLM inference with minimal setup and state-of-the-art performance on a wide
b9401 is a release build of llama.cpp, an LLM inference framework in C/C++ that provides tools for running large language models locally. As an intermediate build in the llama.cpp project, it includes
B9403 is an intermediate build release of llama.cpp, an open-source project enabling LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware locally and in the cl
B9406 is a release build number from the llama.cpp project, a C/C++ implementation enabling LLM inference . Based on the release numbering pattern observed in the project's history, this represents an
b9412 is a release of llama.cpp, an open-source project for LLM inference in C/C++ . Build identifier b9412 represents a specific commit or version in the llama.cpp development lifecycle, following th
arXiv:2605.28919v1 Announce Type: cross Abstract: Large language models have achieved strong reasoning capabilities, though often at the cost of massive parameter counts and expensive inference. In th
arXiv:2511.05924v3 Announce Type: replace Abstract: Estimating probability density and its score from samples remains a core problem in generative modeling, Bayesian inference, and kinetic theory. Exi
arXiv:2605.29343v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference by pairing a target model with a lightweight draft model whose proposed tokens are verif
arXiv:2604.05446v2 Announce Type: replace-cross Abstract: Obtaining high-quality labels is costly, whereas unlabeled covariates are often abundant, motivating semi-supervised inference methods with re
arXiv:2605.29326v1 Announce Type: new Abstract: High-density electromyography (HD-EMG) has emerged as a powerful modality for decoding fine-grained neuromuscular activity, enabling real-time neural-ma
arXiv:2602.02909v2 Announce Type: replace Abstract: Inference-time scaling via chain-of-thought (CoT) reasoning is a major driver of state-of-the-art LLM performance, but it comes with substantial lat
arXiv:2605.29662v1 Announce Type: new Abstract: Real-time inference of vision-language-action (VLA) models is essential for robotic control. While visual token pruning has shown strong potential for a
arXiv:2605.30076v1 Announce Type: new Abstract: Activation-based control steers large language models (LLMs) by intervening on their internal representations during inference, and has emerged as an ef
arXiv:2605.30317v1 Announce Type: new Abstract: Autoregressive image and video generators are trained with teacher-forced histories but must sample from their own generated prefixes at inference time,
Based on the available information about llama.cpp, b9378 is likely an intermediate build release of the llama.cpp project. llama.cpp enables LLM inference in C/C++ , and the project releases multiple
arXiv:2605.28516v1 Announce Type: cross Abstract: Simulation-based inference with neural posterior estimation (NPE) often yields overconfident and unreliable posteriors under limited simulation budget
arXiv:2605.28302v1 Announce Type: cross Abstract: Modern large language model (LLM) inference has progressively disaggregated to keep pace with growing model sizes and tight TTFT and TPOT service-leve
arXiv:2511.20439v2 Announce Type: replace-cross Abstract: In Vision Language Models (VLMs), vision tokens are quantity-heavy yet information-dispersed compared with language tokens, thus consume too m
arXiv:2605.27993v1 Announce Type: new Abstract: Object hallucination remains a primary obstacle to the reliable deployment of Multimodal Large Language Models (MLLMs). Current inference-time mitigatio
arXiv:2605.28142v1 Announce Type: cross Abstract: Inference-time sampling can elicit strong reasoning abilities from language models without additional training. Existing power-sampling methods do so
arXiv:2605.26382v1 Announce Type: new Abstract: Deploying high-performing 3D medical image segmenters (e.g., nnU-Net) is often limited by memory footprint and inference latency. Compression is therefo
arXiv:2411.02355v4 Announce Type: replace-cross Abstract: Quantization is a powerful tool for accelerating large language model (LLM) inference, but the accuracy-performance trade-offs across differen
arXiv:2605.26908v1 Announce Type: new Abstract: Exploiting the indistinguishability of objects in a probabilistic graphical model such as a factor graph is key to lifted probabilistic inference algori
arXiv:2605.26582v1 Announce Type: cross Abstract: Discrete diffusion models achieve strong performance in text and image generation, but their inference remains slow and must inherently balance sampli
arXiv:2605.27128v1 Announce Type: new Abstract: Real-time semantic segmentation models offer an excellent balance between accuracy and inference speed. However, deploying these models in dynamic real
arXiv:2605.26632v1 Announce Type: new Abstract: Diffusion Transformers (DiT) achieve strong performance in image generation but incur substantial inference costs. While prior work has reduced this cos
arXiv:2605.27030v1 Announce Type: new Abstract: Test-Time Scaling (TTS) enhances the reasoning capabilities of large language models by allocating additional inference compute to explore the solution
arXiv:2605.27245v1 Announce Type: new Abstract: Symbolic regression (SR) seeks closed-form mathematical expressions that fit observed data. Neural SR methods amortize the search by training an encoder
arXiv:2505.11758v2 Announce Type: replace-cross Abstract: Few-shot adaptation of vision-language models remains fundamentally limited by how negative class signals are handled at inference. Existing m
arXiv:2502.15835v5 Announce Type: replace-cross Abstract: Pragmatic reasoning helps interlocutors infer intended meaning from ambiguous or underspecified messages by considering shared context and cou
arXiv:2603.06687v2 Announce Type: replace-cross Abstract: Geo-temporal understanding, the ability to infer location, time, and contextual properties from visual input alone, underpins applications suc
b9310 is a release build of llama.cpp, a C/C++ implementation for LLM inference . As an intermediate build number in the llama.cpp project's continuous release cycle, it represents incremental updates
Build b9315 is a release version of llama.cpp, a C/C++ implementation for LLM inference. As an intermediate build in the llama.cpp development sequence, b9315 likely includes bug fixes, performance im
llama.cpp is a C/C++ implementation for LLM inference . Build b9319 is a specific commit/version release from the llama.cpp project repository, representing a particular point in the software's develo
arXiv:2605.22883v1 Announce Type: new Abstract: Current AI energy benchmarks measure consumption at the granularity of a single model invocation or training run. For classical single-turn workloads th