b9769
The search results do not contain specific details about the b9769 release. Based on the available information about llama.cpp and its release patterns, here is a knowledge base entry: Release b9769 o
Knowledge catalogue
The search results do not contain specific details about the b9769 release. Based on the available information about llama.cpp and its release patterns, here is a knowledge base entry: Release b9769 o
Release b9770 of llama.cpp addresses server functionality by fixing remote preset handling and adding tests (PR #24938). The release includes pre-built binaries for multiple platforms including macOS,
llama.cpp release b9771 addresses Vulkan optimization by making mul_mm ALIGNED a spec constant, reducing shader variant explosion and binary size. This release is part of the ongoing development of ll
Release b9773 of llama.cpp adds Vulkan support for the GET_ROWS_BACK operation . The release includes pre-built binaries for multiple platforms including macOS, Linux, Windows, and Android with variou
The b9774 release of llama.cpp adds Vulkan backend support for multiple operations including SQR, SQRT, SIN, COS, CLAMP, LEAKY_RELU, and NORM functions, along with fixes for non-contiguous tensor hand
b9775 is a release of llama.cpp published on June 23, 2026 , featuring 'server: check draft context creation error' improvements . The release includes pre-built binaries across multiple platforms inc
arXiv:2606.22089v1 Announce Type: new Abstract: Breast arterial calcification (BAC) on screening mammograms is an emerging cardiovascular risk biomarker, but quantitative use requires reproducible seg
arXiv:2606.21525v1 Announce Type: new Abstract: Model-free reinforcement learning algorithms such as Proximal Policy Optimization (PPO) treat the environment as a black box, estimating policy gradient
arXiv:2606.21172v1 Announce Type: new Abstract: Video world models are increasingly used in autonomous driving to forecast future scene evolution and provide future-aware spatio-temporal representatio
arXiv:2606.21498v1 Announce Type: cross Abstract: Autoregressive text-to-image (T2I) generation has recently advanced rapidly, yet aligning generated images with human preferences remains challenging.
arXiv:2606.20701v1 Announce Type: cross Abstract: Learned communication improves coordination in cooperative multi-agent reinforcement learning, but it also creates a trust problem: a trained policy m
arXiv:2606.21092v1 Announce Type: new Abstract: We introduce BASIL, a user-friendly desktop application for process optimization. BASIL employs a Bayesian approach, incorporating special acquisition f
arXiv:2606.21712v1 Announce Type: cross Abstract: Batch inference has become a central mode of AI computation, yet existing inference engines still rely on execution models designed for interactive se
arXiv:2606.21014v1 Announce Type: new Abstract: Robots must generate trajectories that remain faithful to learned expert behavior while satisfying safety constraints and task-specific objectives speci
arXiv:2606.22188v1 Announce Type: new Abstract: Large multi-modal language models are increasingly deployed in high-stakes domains, making well-calibrated uncertainty essential. Traditional Bayesian m
arXiv:2606.21080v1 Announce Type: cross Abstract: Bayesian model averaging in support-indexed regression induces a posterior distribution over active predictor supports. Under predictor redundancy, po
arXiv:2606.21789v1 Announce Type: cross Abstract: Accurate 3D seismic velocity modeling through seismic travel-time tomography using both active- and passive-source data provides critical underpinning
Cloudflare Inc. and newsletter platform beehiiv Inc. today launched an integration that hands independent publishers a single toggle to decide whether artificial intelligence crawlers can reach their
Duncan Riley / SiliconANGLE: Beehiiv adds Cloudflare AI Crawl Control to writers' dashboards, allowing them to decide whether AI crawlers can scrape their work — Cloudflare Inc. and newsletter platfor
arXiv:2606.21645v1 Announce Type: cross Abstract: Large language models (LLMs) can readily reproduce conventional expressions, yet their ability to model gradient frequency distributions remains under
arXiv:2606.20909v1 Announce Type: new Abstract: Earth observation imagery plays a critical role in environmental monitoring, urban planning, disaster assessment, and climate analysis. While multi-spec
arXiv:2606.20668v1 Announce Type: cross Abstract: LLM supervision systems, namely input/output moderation filters and jailbreak detectors, are the primary safeguard against misuse in deployed AI appli
arXiv:2606.22338v1 Announce Type: cross Abstract: Robots deployed in realistic settings will accumulate experience across many sessions and tasks over their deployment. The robot's tasks may often req
arXiv:2606.22497v1 Announce Type: new Abstract: Microscopic imaging provides essential visual evidence for studying plant biology and pathology at the cellular and subcellular levels. However, existin
arXiv:2606.20859v1 Announce Type: cross Abstract: We present a family of conformal test martingales based on shifted Legendre polynomials, which extends the Simple Jumper martingale. The Simple Legend
arXiv:2606.22931v1 Announce Type: new Abstract: In this paper, we present a framework dubbed extbf{BEV-Denoise} that estimates and removes intrinsic noise from learned Bird's-Eye-View (BEV) features t
arXiv:2512.14200v2 Announce Type: replace Abstract: Recent advances in Neural Radiance Fields and 3D Gaussian Splatting have demonstrated strong potential for large-scale UAV-based 3D reconstruction t
arXiv:2606.15127v2 Announce Type: replace Abstract: Reasoning models are increasingly used in settings where the final answer is not the only object of review: educational tools may show students inte
arXiv:2606.21279v1 Announce Type: new Abstract: Nowadays, more and more disasters of different natures are appearing. Several disaster assessment approaches have been developed in order to identify da
arXiv:2606.20599v1 Announce Type: cross Abstract: Tree of Thought (ToT) search has become a promising direction for improving the reasoning capabilities of large language models, but deploying these m
arXiv:2606.21838v1 Announce Type: new Abstract: Multimodal contrastive learning has enabled zero-shot visual classification by aligning images with textual categories. However, in hierarchically struc
arXiv:2606.20678v1 Announce Type: cross Abstract: Mechanistic interpretability methods summarize a transformer component by a single importance score, conflating two distinct roles: a component may ma
arXiv:2606.20770v1 Announce Type: cross Abstract: Current Vision-Language Models (VLMs) are celebrated for their multilingual capabilities, yet they operate under a flawed assumption: that one languag
arXiv:2606.20680v1 Announce Type: new Abstract: A biometric verifier is often deployed with a strict false match budget, so only a narrow, low false match rate (FMR) slice of the score range is used.
arXiv:2606.20702v1 Announce Type: new Abstract: Vision-language models (VLMs) have sparked growing interest in zero-shot Earth Observation (EO) downstream tasks, with further gains enabled by remote-s
arXiv:2505.24160v3 Announce Type: replace-cross Abstract: Medical image challenges have played a transformative role in advancing the field, catalyzing innovation and establishing new performance benc
arXiv:2606.21775v1 Announce Type: new Abstract: Recently, world models have emerged as a promising paradigm for building intelligent agents by learning predictive models that estimate future environme
arXiv:2606.22171v1 Announce Type: new Abstract: Epidemic forecasting models typically rely on surveillance data reported over administrative regions, treating them as atomic units, thereby obscuring s
arXiv:2606.20812v1 Announce Type: new Abstract: EEG foundation models can learn generalizable representations from large-scale EEG corpora to enable single-backbone transfer across diverse clinical an
arXiv:2511.22973v2 Announce Type: replace Abstract: Long video generation is a critical step toward building realistic world models, requiring both high visual fidelity and long-range interaction cons
arXiv:2606.23531v1 Announce Type: new Abstract: Endoscopic retrograde cholangiopancreatography (ERCP) demands precise endoscopic navigation and stable biliary cannulation within a narrow monocular fie
arXiv:2606.22515v1 Announce Type: new Abstract: Sexual identification of decomposed cadavers challenges traditional methods dependent on visual anthropological analysis. This study evaluates state-of-
arXiv:2606.22138v1 Announce Type: cross Abstract: We present BioMatrix, the first multimodal foundation model that natively integrates sequences, structures, and natural language for both molecules an
arXiv:2606.21398v1 Announce Type: new Abstract: Vision-Language Models (VLMs) for embodied navigation rely on selecting a fixed number of frames from a growing trajectory history. As episodes extend,
arXiv:2606.22999v1 Announce Type: new Abstract: The rapid deployment of Vision-Language Models (VLMs) in dynamic environments necessitates the ability to learn continuously without forgetting. However
arXiv:2606.22456v1 Announce Type: new Abstract: Maintaining accurate navigation during GNSS outages remains a significant challenge for autonomous systems relying on low-cost inertial sensors. While c
Allium Inc., a startup that provides financial institutions with data about the cryptocurrency market, has raised 40 million in funding. Amplify Partners led the Series A round. Allium stated in its a
DFlash is an open source block diffusion model for speculative decoding that significantly accelerates LLM inference on NVIDIA Blackwell GPUs by drafting entire token blocks in parallel and verifying
arXiv:2601.22100v3 Announce Type: replace Abstract: Optimizing Conditional Value-at-risk (CVaR) using policy gradient (a.k.a CVaR-PG) faces significant challenges of sample inefficiency. This ineffici
arXiv:2606.23023v1 Announce Type: new Abstract: Although state-of-the-art neural video codecs (NVCs) have achieved remarkable performance, they suffer from limited generalization when encountering com
arXiv:2606.21594v1 Announce Type: new Abstract: Recent advances in large pre-trained models have led to remarkable progress in instance segmentation on general images. However, industrial scenarios re
arXiv:2606.23270v1 Announce Type: new Abstract: As instruction-based editing models and multimodal large language models advance, diverse image editing tasks have become feasible. However, achieving p
arXiv:2505.11495v2 Announce Type: replace Abstract: Push recovery during locomotion will facilitate the deployment of humanoid robots in human-centered environments. In this paper, we present a unifie
arXiv:2606.23494v1 Announce Type: new Abstract: Automated diagnosis of 3D brain CT scans is essential for critical care, yet it remains challenging due to the heavy reliance on manual annotations and
arXiv:2505.11129v2 Announce Type: replace Abstract: Representation learning is one of the key research topics in machine learning, and the framework of self-supervised learning (SSL) has revolutionize
arXiv:2606.22824v1 Announce Type: new Abstract: Speech-to-IPA transcription is useful when the desired output is pronunciation rather than orthographic text, but competitive multilingual systems are o
arXiv:2606.21497v1 Announce Type: new Abstract: Modern deep neural network architectures are trained via backpropagation, which requires errors to be sequentially propagated through all layers before
BREAKING: Starlink is helping power education for hundreds of girls in the Democratic Republic of the Congo. 🇨🇩 • School provides free education to 430 girls • Community programs reach 5,000+ people a
arXiv:2606.23199v1 Announce Type: new Abstract: Attributed graph clustering partitions nodes by jointly exploiting node attributes and graph topology. It remains challenging due to attribute heterogen
arXiv:2606.20644v1 Announce Type: cross Abstract: Multi-objective shortest-path (MOSP) algorithms traditionally rely on single-valued heuristics (SVHs), which associate each state with a single admiss