b11486
hexagon: improved GELU accuracy (#30104) Assisted-by: OpenCode Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53797268 macOS/iOS: macOS Apple Silicon (arm6
Knowledge catalogue
hexagon: improved GELU accuracy (#30104) Assisted-by: OpenCode Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53797268 macOS/iOS: macOS Apple Silicon (arm6
hexagon: support tiled Q4_K and Q6_K GET_ROWS (#30115) hexagon: support tiled Q4_K GET_ROWS Assisted-by: OpenCode properly reject Q4_K views Assisted-by: OpenCode hexagon: support tiled Q6_K GET_ROWS
model : add LiquidAI/d1-omni-600M decision model (#30114) model : add LiquidAI/d1-omni-600M decision model Assisted-by: Claude Opus 5.5 mtmd : keep conformer GLU sigmoid on CUDA Assisted-by: Claude Op
hexagon: Q6_K weight dequant speedup (#30121) hexagon: Q6_K weight dequant speedup Assisted-by: OpenCode unroll by another factor of 2 Assisted-by: OpenCode Website: https://llama.app Attestations: ht
hexagon: enable alloc_buffer_n (#30126) hex-bufs: add support for alloc_buffer_n hex-bufs: add support for splitting large tensors into separate buffers hex-bufs: update GGML_HEXAGON_MBUF to accept th
sycl: remove duplicate block-size defines from op headers (#29507) All were duplicates of the values in ggml/src/ggml-sycl/presets.hpp Website: https://llama.app Attestations: https://github.com/ggml-
sycl: add grouped MoE XMX GEMM (#29245) Co-authored-by: cwriter cwriter@localhost Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53864714 macOS/iOS: macOS
sycl: fuse the delta-net alpha gate (add + unary + mul) (#29687) sycl: fuse the delta-net alpha gate (add + unary + mul) tests: cover the fused add + unary + mul chain sycl: give the fused alpha gate
CUDA: fix norm family kernels when ne[2]/ne[3] exceed grid dim limits (#28175) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53919465 macOS/iOS: macOS App
cuda : Use byte strides for roll to allow non-contiguous ROLL operations (#29547) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53926988 macOS/iOS: macOS
hex-mmadd: do not assume aligned read/write when bias-add is fused (#30133) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53932639 macOS/iOS: macOS Apple
sycl: FWHT optimizations (#29605) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53938516 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64
vendor : update cpp-httplib to 0.60.1 (#30134) Signed-off-by: Adrien Gallouët angt@huggingface.co Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53949333 m
vulkan : fix TOP_K for +inf/NaN inputs and k = 1 on negative values (#30107) The bucket search in topk_nary_search.comp started from the range [0, 0xFF800000), which ends just below the ordered-uint m
llama: support MoE cache over multiple GPUs (#30112) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/53988921 macOS/iOS: macOS Apple Silicon (arm64) macOS A
CUDA: fix CCCL version guard breaking on major version rollover (#29453) CUDA: fix CCCL version guard breaking on major version rollover The guard compared the major and minor components independently
CUDA : looped PAD kernel for more than 65535 rows or slices (#30147) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54006924 macOS/iOS: macOS Apple Silicon
CUDA: fix MMQ out-of-bounds reads (#29953) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54029251 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silic
model : fix DFlash output head sharing (#30111) llama : fix DFlash output head sharing Assisted-by: Codex dflash : read tied output weights from GGUF metadata Assisted-by: Codex llama : share tied wor
CUDA: improve top-k algorithm selection (#28713) CUDA: radix top-k for large row counts Replaces CUB's per-row DeviceTopKKernel with a grid-over-rows radix select, gated on GGML_CUDA_TOPK_RADIX_MIN_RO
Musa FWHT fix (#30167) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/54083178 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI
arXiv:2610.10160v1 Announce Type: new Abstract: Mishandled checked baggage remains a recurrent issue in airport operations, and current recovery workflows still largely rely on tag-based tracking, whi
arXiv:2610.09464v1 Announce Type: new Abstract: Political discourse often uses rhetorical and persuasive language to frame narratives, influence public opinion, and mobilize audiences. While Bangla na
arXiv:2610.09026v1 Announce Type: cross Abstract: We present BEACON-SP, an ontology-grounded Graph Retrieval-Augmented Generation (GraphRAG) framework for clinician-facing decision support in behavior
arXiv:2610.10198v1 Announce Type: new Abstract: Behavior Foundation Models (BFMs) are emerging as a paradigm for translating human intentions into executable humanoid behaviors. As these models evolve
There are way too many forks to keep track of, so I've gotten lost. As far as I can tell, Radiance VLLM is best for models that fit in GPUs while some form of llama.cpp is probably best for MOE RAM-sp
I'm developing a benchmark for a low resource language and I don't want it to be leaked and used for training when it is being used to get predictions. For locally run models it shouldn't be a problem
arXiv:2610.10163v1 Announce Type: new Abstract: Linking people's appearance and actions to character identities is essential for understanding video narratives. We present a framework for identity-awa
arXiv:2603.13357v2 Announce Type: replace Abstract: Bi-CamoDiffusion is introduced, an evolution of the CamoDiffusion framework for camouflaged object detection. It integrates edge priors into early-s
Big new paper nobody is talking about ‼️ 🌎 Robot world models now have scaling laws. Meta trained an 8B-parameter JEPA on 15,000 hours of robot video to prove it. RoboJEPA (FAIR at Meta + Mila) imagin
arXiv:2610.09193v1 Announce Type: cross Abstract: MemoryAgentBench's Conflict Resolution split is read as measuring 'selective forgetting'. We execute the benchmark's own rule - the newest statement a
arXiv:2610.10383v1 Announce Type: new Abstract: Boosting converts weak hypotheses with a small edge over random guessing into highly accurate predictors, but the expressive power of the resulting clas
arXiv:2610.09804v1 Announce Type: new Abstract: Reinforcement learning is now central to eliciting reasoning in large language models, while in the popular algorithm Group Relative Policy Optimization
arXiv:2610.09467v1 Announce Type: cross Abstract: Contextual biasing supplies an ASR system with a list of expected words at inference time, but existing methods rely on word boundaries that Japanese
arXiv:2610.08815v1 Announce Type: new Abstract: Agentic AI-enabled automation cannot be safely deployed in high-stakes environments on probabilistic reasoning alone. A recurring risk is epistemic drif
arXiv:2610.09733v1 Announce Type: new Abstract: Multilingual LLMs answer the same multi-hop reasoning question across languages, but we lack a mechanistic account of whether they share an internal cir
arXiv:2610.10058v1 Announce Type: new Abstract: Repeated queries over shared documents incur redundant encoding, while caching model states introduces persistent storage costs. Building on CoMem's int
Opus5.5, Sol6.1, Fable, and Astra have all proven they can and the scene has exploded this past week. Part of that is from the tools and feedback loops maturing though. Are any open weight models (at
arXiv:2610.08917v1 Announce Type: new Abstract: While vision-language-action (VLA) models have advanced rapidly, running them at every control step remains expensive. Prior work accelerates VLA infere
arXiv:2610.10208v1 Announce Type: cross Abstract: Automatic audio scene description turns a recording into a text account of a situation. One difficulty is deciding which elements of the audio should
arXiv:2505.21468v2 Announce Type: replace Abstract: We present Causal Posterior Estimation (CPE), a novel method for Bayesian inference in simulator models, where evaluating the likelihood function is
arXiv:2610.09541v1 Announce Type: cross Abstract: Signals that predict whether a chain-of-thought (CoT) trace is correct are compared by AUC, but deploying one requires a threshold with a guarantee. W
arXiv:2610.09476v1 Announce Type: cross Abstract: Geometric and Spectral Alignment (GSA) characterizes trained networks through spectral concentration, physical-channel alignment, support structure, a
came across a pretty interesting technical breakdown of chatgpt's newly launched 'intelligent ui' feature, and thought this subreddit might find it interesting. for anyone unfamiliar with the concept,
arXiv:2610.09549v1 Announce Type: new Abstract: And-Inverter Graphs (AIGs) are fundamental representations for logic synthesis and verification in Electronic Design Automation (EDA). As structured rep
arXiv:2610.08862v1 Announce Type: cross Abstract: Household robots must accommodate new user instructions while executing ongoing tasks. Existing agents often regenerate or extensively revise the rema
arXiv:2610.09633v1 Announce Type: cross Abstract: The evaluation of coding agents generally strives to be as realistic as possible. In our study, we collect 4,782 agent sessions of real software engin
TL;DR: For my use case, mradermacher/Signal-3.8-27B-Terse-Coder.i1-Q4_K_M proved out. Ymmv based on your domain-specific tests. Time taken to solve problems compared to frontier models is massive; esp
arXiv:2610.10460v1 Announce Type: new Abstract: Multi-teacher on-policy distillation (MOPD) is used in two settings. In common-domain composition, several teachers score each student rollout from one
arXiv:2610.09229v1 Announce Type: new Abstract: LLM-as-judge is now a standard tool for scalable evaluation, but judge performance is still often summarized by a single accuracy number. This aggregate
arXiv:2610.10302v1 Announce Type: new Abstract: In Continual Multi-Agent Reinforcement Learning (CMARL), agents learn cooperative policies across sequences of tasks, aiming to adapt effectively to new
arXiv:2610.10379v1 Announce Type: new Abstract: Continual learning requires models to adapt to new domains and new classes while retaining prior knowledge. Many existing methods rely on continued opti
arXiv:2610.09597v1 Announce Type: new Abstract: Asynchronous RL accelerates large language model post-training by decoupling rollout generation from optimization, but trains on stale trajectories. Exi
arXiv:2610.09487v1 Announce Type: cross Abstract: Schema matching increasingly uses generative language models to rerank retrieved column candidates, although the underlying task is a bounded correspo
AI costs don’t always scale predictably with traffic. A retry loop, oversized context window, or unnecessarily expensive model can make a small number of traces disproportionately expensive. The post
Fraud prevention startup Darwinium UK Ltd. today launched two new capabilities that make intent intelligence the foundation of its platform. Journey Transition Probability flags unusual changes in the
arXiv:2610.10340v1 Announce Type: cross Abstract: We consider online learning in non-stationary environments, where the goal is to track an unknown parameter that switches abruptly between a finite se
arXiv:2610.09609v1 Announce Type: new Abstract: Semi-supervised learning (SSL) relies on two core mechanisms: self-training under the Teacher-Student (T-S) framework and joint optimization of labeled
arXiv:2610.09772v1 Announce Type: new Abstract: Small language-model agents on edge devices must hold a persona and reason correctly at once, inside one context window that fills with conversational h
arXiv:2609.32701v2 Announce Type: replace-cross Abstract: In security-sensitive applications, language-model agents are often required to coordinate without disclosing confidential information. Yet re