Claude Code v2.1.181 introduces support for setting config settings via prompt syntax like /config thinking=false, adds sandbox Apple Events support on macOS, and improves streaming, auto-retry, and subagent behavior. It also fixes numerous bugs related to startup, file handling, clipboard, and UI responsiveness across platforms.
Claude v2.1.178 introduces new permission rules using Tool(param:value) syntax, improved workflow and skill loading in nested directories, and enhanced auto mode and error messaging. It fixes critical issues including crashes, authentication errors, and UI behavior in Chrome and VSCode, while refining tool prompts and undo functionality.
Decoupled Search Grounding (DSG) separates search functionality from reasoning models, enabling vendor-agnostic, tunable, and reusable search grounding. DSG achieves near-native accuracy on SimpleQA with 91% lower search cost and 99.4% warm-cache hit rate, while reducing latency by 68% and preserving concise output contracts.
RTSGameBench addresses limitations in existing RTS benchmarks by offering diverse gameplay, targeted competency diagnosis, and self-evolving scenario generation. It evaluates vision-language models in strategic reasoning under uncertainty, revealing that state-of-the-art models struggle with multiagent coordination and large-scale tasks.
ThinkDeception introduces a progressive reinforcement learning framework that enables interpretable multimodal deception detection. It leverages a step-by-step annotated Chain of Thought dataset and proposes Visual-Audio Consistency Group Relative Policy Optimization with a dynamic curriculum, enhancing reasoning quality and outperforming existing methods on mainstream benchmarks.
TRAP evaluates how well models complete tasks using private data without leaking it. Across 22 models, all show non-trivial privacy leakage, with instruction-following ability linked to higher leakage. Structural private field isolation prevents leakage by replacing private fields with hash keys, maintaining task accuracy without sacrificing privacy.
Spotlight enables DiT RL post-training by leveraging idle spot GPUs, reducing costs by 1.4-6.4× while achieving superior image quality. It uses stale model weights in exploration and reconfigures sequence parallelism in real time, allowing efficient GPU utilization without breaking training pipelines.
RODS addresses sample depletion in multi-turn tool-use RL by using reward variance to detect capability boundaries. It synthesizes new data in real time, matching structural complexity of boundary samples, and maintains a dynamic replay buffer that co-evolves with the policy. RODS achieves performance comparable to a 17K-sample offline pipeline with 20x fewer trajectories.
AdsMind is a closed-loop multi-agent system that uses machine learning force fields and feedback to correct errors in adsorption configuration searches on catalyst surfaces. It achieves 100% and 98.8% success rates on AA20 and OCD-GMAE62 benchmarks, reduces energy dispersion by 14-fold compared to baselines, and maintains correct adsorption-energy signs in DFT validation, outperforming open-loop LLM agents.
The ggml-cpu project now conditionally enables the POWER11 backend in ggml based on compiler support for -mcpu=power11. This prevents build failures on current GCC/Clang toolchains while maintaining forward compatibility. Updates to CMakeLists.txt support this change, and -mcpu=power10 is used for both P10 and P11 architectures.
llama.cpp version b9692 introduces new binaries for macOS, Linux, Android, Windows, and openEuler across multiple architectures. The release includes updates to support Vulkan, ROCm, OpenVINO, SYCL, and HIP, with fixes to remove batch dim usage in llava_uhd.
The Metal backend in llama.cpp has been extended to support f16 and bf16 tensor types for the concat operator, in addition to existing f32 and i32 support. This update includes specialized kernel templates, updated pipeline getters, and improved type-based kernel dispatch, with assistance from pi:llama.cpp/Qwen3.6-27B.
llama.cpp version b9687 introduces new binaries for macOS, Linux, Android, Windows, and openEuler across multiple architectures. The release includes support for Vulkan, ROCm, OpenVINO, SYCL, and HIP, with updates to improve device validation and performance on available hardware.
llama.cpp releases version b9688, adding model management and SSE realtime updates APIs. The release includes prebuilt binaries for macOS, Linux, Android, Windows, and openEuler, supporting various architectures and acceleration frameworks like Vulkan, CUDA, OpenVINO, and SYCL.
LLaMA.cpp version b9685 introduces SYCL-based dev2dev memcpy functionality, moving GGML_SYCL_DEV2DEV_MEMCPY to runtime table and improving peer-to-peer communication detection. The release includes precompiled binaries for macOS, Linux, Android, Windows, and openEuler across multiple architectures and APIs including Vulkan, ROCm, OpenVINO, and SYCL (FP32/FP16).
LLaMA.cpp release b9684 introduces a new 3D convolution operation (conv_3d) and includes optimized implementations. The release provides prebuilt binaries for macOS, Linux, Android, Windows, and openEuler across various architectures and hardware acceleration options, including SYCL, Vulkan, CUDA, and OpenVINO.
llama.cpp version b9682 introduces Vulkan support for Linux and Windows, enabling GPU acceleration. The release includes binaries for macOS, Linux, Android, Windows, and openEuler across multiple architectures, with CPU and GPU options including CUDA, OpenVINO, SYCL, and ROCm.
llama.cpp version b9675 enables FP16 support for operations like SQR, SQRT, LOG, SIN, COS, and CLAMP. The release includes binaries for macOS, Linux, Android, Windows, and openEuler across multiple architectures, with support for Vulkan, ROCm, OpenVINO, SYCL (FP16 and FP32), and CUDA 12.4 and 13.3.
llama.cpp releases version b9680 with updated Vulkan support and new binaries for macOS, Linux, Android, Windows, and openEuler. The release includes CPU and GPU variants for multiple architectures, with support for Vulkan, CUDA, OpenVINO, SYCL, and ROCm.
LegalHalluLens introduces a framework to audit AI hallucinations in legal contexts by analyzing typed hallucination profiles across four claim categories. It reveals a 38-40 point gap between obligation/numeric and temporal claims, and shows two systems with identical 52% hallucination rates can have opposite risk directions. The framework uses a Risk Direction Index and calibrated debate pipelines to reduce fabricated detections by 45%, offering actionable diagnostics for trustworthy legal AI deployment.