The latest AI news, in focus.

b11887

tests : adjust default threads for test-llama-context (#31157) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11887/llama-b11887-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11887/llama-b11887-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11887/llama-b11887-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11887/llama-b11887-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11

b11886

qwen: merge tensors on convert (#31028) * qwen: merge tensors on convert * convert: use generator functions * qwen4exp: remove conts and pre-scale **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11886/llama-b11886-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11886/llama-b11886-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11886/llama-b11886-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11886/llama-b11886-bin-ubuntu-x64.tar.gz

b11884

llama: fix exhausting CPU and MEM caused by dry sequence breaker (#31035) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11884/llama-b11884-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11884/llama-b11884-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11884/llama-b11884-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11884/llama-b11884-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/d

b11883

llama-quant : fix 4D tensor quantization using ggml_nrows() and name-based expert detection (#30820) * llama-quant : fix quantizing 4D tensors nrows_total was ne[1]*ne[2], so for a 4D tensor only the first ne[2] slices were quantized and the rest of the rows were dropped. Use ggml_nrows(tensor) to count all rows, including ne[3]. Fixes #29916 * llama-quant : detect expert tensors by name and fix imatrix size for 4D tensors The MXFP4_MOE path used ne[2] > 1 to detect MoE expert tensors. This is wrong for non-expert tensors with 3 or more dims, e.g. MLA projections (BailingMoE3) and 4D conv kernels. Use the tensor name instead: all tensors used by GGML_OP_MUL_MAT_ID are named *_exps or *_chexps. This also removes the BailingMoE3 special case. The imatrix holds one vector of ne[0] values per

b11882

grammar: limit recursion depth to prevent stack overflow (#29495) (#29755) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11882/llama-b11882-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11882/llama-b11882-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11882/llama-b11882-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11882/llama-b11882-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/

Apollo’s chief economist says AI delivers “zero financial return” to U.S. companies outside the tech industry.

Thorsten Sløk, Chief Economist at Apollo Global Management, analyzed the S&P 493 (the S&P 500 excluding seven tech giants) and found that non-tech firms’ profit margins are on par with per-capita revenue, with no evidence that AI has boosted productivity. He noted that AI lacks an understanding of key business factors such as accountability, trust, and corporate culture, and therefore has yet to deliver financial returns.

Why it mattersThis view is based on empirical observations of the mainstream listed‑company universe (S&P 493), citing specific data ranges and limitations (excluding seven technology firms such as Alphabet and Amazon), and aligns with the findings of several recent studies, offering a verifiable analytical framework with clearly defined attribution boundaries.

b11881

llama : respect no_alloc in all memory caches (#31087) * llama : respect no_alloc in memory caches Assisted-by: dsh:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * llama : TODO: move no_alloc out of llama_hparams Assisted-by: dsh:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * llama : TODO: move no_alloc out of llama_hparams Assisted-by: dsh:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11881/llama-b11881-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS

b11880

tools : reject truncated GGUF tensor data in merge (#31044) Co-authored-by: Shreya Agrawal **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11880/llama-b11880-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11880/llama-b11880-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11880/llama-b11880-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11880/llama-b11880-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/lla

b11878

ggml-cuda: fuse qwen MRoPE + set-rows (#31025) **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11878/llama-b11878-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama.cpp/releases/download/b11878/llama-b11878-bin-macos-x64.tar.gz) - [iOS XCFramework](https://github.com/ggml-org/llama.cpp/releases/download/b11878/llama-b11878-xcframework.zip) **Linux:** - [Ubuntu x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11878/llama-b11878-bin-ubuntu-x64.tar.gz) - [Ubuntu arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/b11878/llama-b11878

b11877

vulkan: Add a larger tile size for conv3d (coopmat2 only) (#29920) * vulkan: Add a larger tile size for conv3d (coopmat2 only) This adds a larger tile size, in practice it is only for coopmat2 where we don't need shared memory to store the output (would use more than 64KB). Add some additional representative backend tests. At least, this is what codex came up with from looking at sd.cpp Wan VAE. * only apply large tile size for newer driver versions **Website:** - **Attestations:** - **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b11877/llama-b11877-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLED](https://github.com/ggml-org/llama.cpp/pull/23780) - [macOS Intel (x64)](https://github.com/ggml-org/llama

Research reveals the local geometric structure of semantic meaning in large language models

A paper published on October 24, 2026, on arXiv (ID 2610.30715v1) characterizes the local geometric properties of semantic meaning in embedding space across four decoder-only large language models—Gemma 4, Llama 3.2, Qwen 2.5, and Pythia—using cross-lingual probes and fine-grained probes.

Why it mattersThis study is the first to systematically characterize the invariance, variability, and refinement mechanisms within “the same semantic factor” in LLMs, proposing the “local anisotropic neighborhood” model. The approach has been validated through blind human evaluations, demonstrating rigorous methodology, broad model coverage, and cross-architecture consistency in its findings.

New research reveals that open-book assessments primarily measure reading behavior rather than knowledge retention.

An arXiv paper published on October 24, 2026 (2610.31017v1) points out that, in document-level LoRA adapter evaluations, open-book scoring relies heavily on the model’s ability to read on the fly, rather than on whether the adapter has truly encoded knowledge specific to the document; random adapters can reproduce 82% of the performance advantage of expert adapters on open-book tasks, but on cloze tasks involving unstructured text, they only replicate 2%—statistically indistinguishable from zero.

Why it mattersThis study, through systematic controlled experiments—comprising 150 Wikipedia articles, the Llama-3.1-8B-Instruct model, and a two-task comparison—reveals the fundamental limitations of the current open-book evaluation paradigm: it fails to effectively distinguish between “reading” and “remembering,” posing methodological challenges to the practical utility of knowledge-editing and retrieval‑oriented adapters. The paper also reports four silent errors that lead to “clean yet incorrect results,” highlighting risks to reproducibility.

FusionEval Released: A New Benchmark for Large Language Model Fusion

On October 24, 2026, researchers published the paper arXiv:2610.30162v1 on arXiv, introducing FusionEval—the first unified benchmark specifically designed to evaluate fusion methods for large language models. It covers vulnerable aspects such as reasoning chain length and tool invocation, and supports comparisons between fine-tuning-based and zero-shot fusion approaches.

Why it mattersThis benchmark fills the gap left by existing evaluations, which inadequately cover long-chain reasoning and tool usage; it clearly distinguishes between average performance and worst-case capability retention, provides a reproducible expert pool (based on Qwen3/Llama), multi-dimensional metrics (including GPU memory and runtime), and open-source code, offering strong practical guidance.

Research on Modulating the Capability Distribution of Large Language Models via Singular Value Spectrum Intervention: Empirical Analysis of Llama and Qwen Models

An AI study published on October 24, 2026, on arXiv (ID 2610.30676v1) applied post-hoc singular-value spectrum smoothing and spike perturbations to five open-source Llama and Qwen instruction-tuned models, finding that this lightweight intervention can systematically reshape the trade-offs in model performance across three capability domains: factual knowledge, language understanding, and deductive reasoning.

Why it mattersThis paper is the first to empirically demonstrate, within a multi‑model, multi‑task framework, the causal impact of the singular value spectrum of transformer feedforward layers on downstream capabilities, revealing structurally sensitive patterns in capability allocation and offering a new pathway for decoupling model capabilities and enabling controllable editing. All conclusions are based on publicly available models and standard benchmarks, with transparent methods and comprehensive evaluation dimensions.

Meta postpones the implementation of its new commercial terms, responding to publishers’ concerns about AI scraping.

Meta announced that, due to publishers’ concerns that its new commercial terms might authorize large-scale scraping of news website content for AI training, it would postpone the implementation of these terms—originally scheduled to take effect on October 30, 2026—to November 20, 2026.

Meta’s update to its commercial terms still fails to allay news organizations’ legal concerns about AI scraping.

Meta has revised its commercial account terms of service, removing language that explicitly permitted its AI models to be freely trained on news websites. However, legal experts note that the remaining broad wording—such as “related to your products or services”—could still be interpreted as authorizing large-scale web scraping.

Meta has restored the Muse sharing feature, which had previously been suspended due to DNS exposure of conversation links.

Meta suspended the feature after security researchers revealed that Muse’s shared links could be easily discovered due to naming conventions and public DNS exposure; it has now been re-enabled following adjustments.

Meta is vigorously promoting the Muse AI agent network: humans will primarily communicate with one another through AI agents.

Mark Zuckerberg is vigorously advancing a vision of the future built on autonomous AI agents—Muse will shop, work, and even engage in conversations with other AI agents on behalf of users. This vision gained attention following Meta’s Q3 2026 earnings call; Muse was made available to the public last month, though its actual capabilities and large-scale deployment remain in the early stages.

Jas Kaira, Head of AI Business at Blackstone: Concerns about AI infrastructure debt are exaggerated.

Jas Kaira, head of Blackstone’s AI business, stated at the TechCrunch Disrupt conference that market panic over massive debt in the AI infrastructure sector has been overly amplified. His key argument is that this debt is largely backed by long-term contracts with high‑credit‑rating clients—such as Microsoft and Meta—and that the underlying assets (e.g., GPUs) possess strong collateral value, meaning the risk profile differs fundamentally from that of the 2008 banking system.

Meta plans to issue a new class of non-voting shares to raise funds for large-scale AI investments.

Meta plans to create a new class of non-voting common stock, which will be distributed to existing shareholders in the form of stock dividends, to support its ongoing massive investments in AI models, services, and infrastructure, while maintaining the control structure held by founders such as Mark Zuckerberg.