b12114
Fixed get_max_size to return maxBufferSize and improved the memset shader to handle buffer sizes exceeding the maximum binding size, preventing issues with whole-buffer bindings in memset and clear operations.
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Agents & automation / 01Agents Are Going AsyncLangChain
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Agents Are Going Async
LangChainResearch & methods / 02Augmentation with Reinforcement Transformation for Low-textured Scientific Sketch Image RetrievalarXiv cs.CV
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Augmentation with Reinforcement Transformation for Low-textured Scientific Sketch Image Retrieval
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b12131
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MemoAIMusic & audioMemoAI is an AI-powered audio-to-text tool that supports localized processing, eliminating the need to upload data and ensuring privacy and security. It can efficiently transcribe YouTube videos, podcasts, and local audio/video files (such as MP4, MP3, AAC, M4A, etc.) into text, while offering core features like multilingual translation, speaker diarization, real-time subtitles, and floating notes—making it ideal for meeting minutes, interview transcription, content learning, and more.
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2026-11-05
Follow the timelineb12116
The llama.cpp release b12116 introduces support for the drama-3 text-to-speech model, includes cleanup improvements, and ensures the drama3 helper stops at the codebook tower limit. This update provides binaries for multiple platforms including macOS, Linux, Windows, Android, and iOS, with support for various backends such as CUDA, Vulkan, ROCm, OpenVINO, and SYCL. Some builds remain disabled or require additional setup.
b12118
The update enhances OpenCL performance for decode operations by optimizing workgroup usage for q4_K gate up and GLU, and adds support for Flash Attention (FA) in various configurations including GQA 2, 6, and 16 with different head sizes, improving efficiency on specific hardware setups.
b12121
A fix was implemented in llama.cpp to correct the calculation of on-device sequence state copy sizes for quantized key-value (KV) caches, ensuring accurate copying of data blocks and preventing crashes or incorrect state restoration. The update adjusts the element count computation using proper block size handling and removes redundant code elements.
b12122
The update introduces optimizations for CUDA by fusing NVFP4 gate/up operations and GLU within MMQ, honoring requested precision in fused gate/up MMQ, sharing scale checks for NVFP4, skipping unused FP4 kernels, testing with W4A8 configurations without scales and with batching, and adding checks for gate_scale before loading fused NVFP4 gate scale.
b12123
The update introduces DSV4_HC_PRE and DSV4_HC_POST support for OpenCL, along with improvements to dsv4_hc_post functionality, enhancing performance and compatibility across various platforms including macOS, Linux, Windows, Android, and embedded systems.
b12124
The release includes support for non-contiguous sigmoid operations in OpenCL, enhancing compatibility and performance across various hardware platforms. This update is part of a broader set of improvements and new binary distributions for multiple operating systems and backends, including macOS, Linux, Windows, Android, and openEuler, with support for CUDA, Vulkan, ROCm, OpenVINO, SYCL, and other accelerators.
b12125
The update enables f32 and q5_k multi-column GEMV by default for Adreno X2 on X2E devices, improving performance for specific computational tasks in the llama.cpp framework.
b12128
The fix addresses intermittent CI failures in TOPK_MOE tests caused by near ties in logits, particularly with uniform random inputs and narrow-range gating functions like sqrt_softplus. The solution involves redrawing logits until group selection and top-k decisions have a relative margin of 1e-4 in double precision, and constructing logits by inverting the gating function using a grid-based approach to ensure consistent expert selection across backends.
Call for Proposals: PyTorch Day China 2026
The Call for Proposals for PyTorch Day China 2026 closes November 10 at 23:59 China Standard Time. The event takes place in Beijing on December 12, bringing together PyTorch developers,... The post Call for Proposals: PyTorch Day China 2026 appeared first on PyTorch.
CaRe-KT: Continuity-Aware Retrieval-Augmented Reasoning for Knowledge Tracing
arXiv:2611.03744v1 Announce Type: new Abstract: Knowledge tracing (KT) estimates learners' evolving knowledge states from interaction histories to support adaptive learning. Retrieval-augmented KT improves these estimates using cross-learner behavioral evidence, but existing approaches typically separate evidence retrieval from downstream prediction. This creates a retrieval-inference alignment gap because evidence relevance varies with the learner's evolving state and prediction context. We propose CaRe-KT, a query-adaptive framework that maintains relevance continuity between evidence acquisition and inference without equating retrieval relevance with predictive utility. CaRe-KT introduces an Evidence-Graph Cache (EGC) that combines persistent question-level relational compatibility with interaction-level temporal validity to construct query-specific evidence subgraphs. Historical learning trajectories retrieved through these subgraphs are encoded alongside the target learner's history in a shared representation space using outcome-supervised contrastive learning. An attention mechanism combines acquisition relevance with learned query-evidence compatibility, allowing relevance to inform, rather than determine, each trajectory's predictive influence. Experiments across five benchmarks show that CaRe-KT outperforms strong KT baselines and remains robust under sparse-history and cold-start conditions. These findings support relevance continuity as a design principle for retrieval-augmented knowledge tracing.
Certinia brings AI into professional services workflows
Certinia Inc. is bringing AI into the software firms use to sell projects, assign staff, deliver work and bill clients. AI’s ability to instantaneously draw from a deep well of information and provide guidance in practically any field of expertise has changed the paradigm for professional services. For Certinia Inc., whose business facilitates 7.1 billion […] The post Certinia brings AI into professional services workflows appeared first on SiliconANGLE.
Chasing that 32 ms tail latency in ClickHouse
A customer saw an extra ~32 ms on simple SELECT queries after idle periods. We traced it to unbatched Native protocol metadata packets overflowing a reset TCP congestion window, reproduced it with network namespaces, and fixed it by batching them.
CHROMA-MILL: Joint Low-Light Enhancement and White Balance under Colored Illumination
arXiv:2611.04092v1 Announce Type: new Abstract: Capturing an image in a low-light environment produces a weak, noisy sensor signal. Low-light image enhancement (LLIE) aims to restore it. Auto white balance (AWB) is also required for every image, and the same noisy signal degrades its ability to infer the scene illuminant. LLIE is typically performed first, followed by AWB, but this strategy remains largely unexamined because existing low-light datasets span a narrow range of illuminants and lack illuminant ground truth for evaluating AWB. We address this with CHROMA-MILL, a RAW dataset of 50 scenes captured with a DSLR and smartphone under four calibrated illuminants at eleven intensity levels. Because light is additive, we blend these captures in the linear RAW domain to synthesize physically valid illuminant mixtures with exact chromaticity ground truth. Benchmarking on this data, we find joint correction is best, and introduce CTI-Retinexformer, which supervises an illumination map, a chromaticity prediction, and an illumination-invariant scene embedding, outperforming the best cascade by 0.7 dB PSNR$_L$ and 1.1 $\Delta E_{00}$.
Clearing up a robot’s fuzzy memory
A new system identifies and corrects unreliable object descriptions stored in a robot’s memory, improving the robot’s ability to interact with its environment.
Co-Occurrence Priors Override Visual Spatial Evidence: Understanding False Alarms in Vision-Language Models for Construction Struck-By Hazard Detection
arXiv:2611.04112v1 Announce Type: new Abstract: Vision-language models (VLMs) increasingly support construction hazard monitoring, yet their frequent false alarms remain poorly understood. This study investigates whether false struck-by hazard warnings arise from failures in object recognition, spatial relation encoding, or hazard decisions dominated by learned co-occurrence priors. We evaluated Qwen2.5-VL-7B and InternVL3.5-8B using 355 counterfactual worker-excavator scene pairs with controlled spatial proximity and worker-removal variants, combining behavioral analysis, internal representation probing, causal interventions, and decision correction. Both models recognized the entities and encoded their spatial relations (out-of-fold AUROC: 0.82 and 0.91), assigning higher hazard scores to near than far scenes in 79% and 73% of pairs. Nevertheless, 74% and 100% of far scenes were falsely classified as hazardous. Removing the worker reduced Qwen's hazard rate to 11%, while worker presence exerted 2.4- and 3.2-times greater influence on hazard scores than spatial proximity in the respective models. Attention-head interventions, neuron-level interventions, and activation steering failed to eliminate this co-occurrence bias. However, conditioning hazard decisions on the model's own spatial-relation representation improved Qwen's specificity from 0.36 to 0.58 at matched sensitivity, outperforming threshold recalibration. These findings reveal that VLM struck-by false alarms stem primarily from decision-making failures rather than deficiencies in object recognition or spatial encoding, suggesting that future efforts should prioritize conditioning hazard decisions on encoded spatial evidence rather than further improving visual recognition alone.
Code with CodeQwen1.5
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction The advent of advanced programming tools, which harnesses the power of large language models (LLMs), has significantly enhanced programmer productivity and accuracy. Notwithstanding these advancements, dominant coding assistants like Github Copilot, built upon proprietary LLMs, pose notable challenges in terms of cost, privacy, security, and potential copyright infringement. Recognizing the imperative for a more transparent and accessible alternative, the open-source community has embarked on a concerted endeavor to develop open codeLLMs.
Customers are pulling data back into the Oracle database as agents demand real-time access
A shared semantic layer is fast becoming the dividing line between lakehouses built for reporting and those built for artificial intelligence. As AI agents join human analysts in querying corporate data, enterprises need platforms that make sense of information scattered across clouds, formats and rival systems. Oracle Corp. has spent the past year rebuilding its […] The post Customers are pulling data back into the Oracle database as agents demand real-time access appeared first on SiliconANGLE.
Dharma Field Extractor: Any Document In, Structured Fields in JSON Out
The Dharma Field Extractor, a 2-billion-parameter model, specializes in extracting specific fields from diverse document types—such as invoices, IDs, and medical forms—into structured JSON format. It outperforms general-purpose frontier LLMs in accuracy and reduces cost by up to 147 times when self-hosted, demonstrating that domain-specialized small models can surpass large general-purpose models on well-defined tasks. The model supports both open extraction (detecting fields without a schema) and schema-directed extraction (returning exact field names and types), with training data spanning English, Brazilian Portuguese, and multilingual documents. Benchmarks show superior F1 scores and valid JSON output across real-world datasets like SROIE and FUNSD, reinforcing the thesis that specialization beats scale for enterprise document processing.
Do Pretrained Backbones Transfer to Physical Sensors? A Systematic Study of Multimodal Initialization Across the Sensor Stack
arXiv:2611.04034v1 Announce Type: new Abstract: Transfer learning from pretrained backbones has become standard practice in multi-sensor computer vision---yet two questions remain open: which pretrained weights transfer best to multi-sensor setups, and can in-domain pretraining outperform off-the-shelf solutions? Frameworks like MultiMAE demonstrate the utility of masked autoencoding in a multi-modal context, but rely entirely on dense, model-derived modalities. It is unclear how robustly these methods translate to real-world applications that use physical sensors, where the aggregation of diverse public datasets for large-scale pretraining inherently introduces modality sparsity. To investigate this systematically, we curate a multi-sensor autonomous driving collection of $2.82M$ images from 16 publicly available datasets, incorporating both physical hardware modalities (RGB, LWIR, NIR, Event) and model-derived modalities (Stereo Depth, Segmentation). We use this collection for in-domain pretraining based on the MultiMAE approach. We adapt MultiMAE's loss formulation to accommodate sparse samples, and study the effect of an enforced encoder bottleneck for modality alignment and of additional losses specific to dense reconstruction (SSIM and FFT). We then systematically compare against common pretrained backbones (ViT, MAE, DINO and MultiMAE) across downstream sensor fusion tasks. We find that MultiMAE is a surprisingly strong out-of-the-box baseline, transferring effectively even to modalities it has never seen, while our targeted modifications yield incremental but consistent improvements on multi-sensor transfer and overall robustness.
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