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.
Concatenating LLM-generated features to graph neural networks systematically reduces accuracy on homophilous benchmarks, with PubMed accuracy dropping by -17.0 ± 0.3 pp. This degradation is linked to LLM-alone discriminability (Delta_sig), which correlates strongly with concatenation cost (r² = 0.38) and shows a power law relationship with feature dimension and node count (r² = 0.97), particularly in low-Delta_sig, low-node scenarios.
OPD-Evolver introduces a slow-fast co-evolution framework that enables agents to select, act on, and reuse experience through on-policy self-distillation. It outperforms existing memory and training-based methods by up to 11.5% and 5.8% respectively, and demonstrates capability to challenge large-scale models like Qwen3.5-397B-A17B and Step-3.5-Flash.
SkillMigrator learns reusable web skills by matching layout structures instead of specific element references. It stores each skill as a transferable interaction pattern (TIP) with a structural sketch, enabling efficient skill reuse across sites. Compared to state-of-the-art methods, it reduces average LLM-action counts by 8-10% on WebArena and Mind2Web at matched success rates.
MambaCount introduces a spatial sparse state space duality block to enable efficient text-guided open-vocabulary object counting. It addresses causal modeling limitations and high entropy in spatial token responses, achieving state-of-the-art results on FSC-147 with a test MAE of 12.23 while maintaining linear complexity.
EnvRL introduces a framework that enhances agentic reinforcement learning by incorporating environment dynamics through state prediction and inverse dynamics objectives. It achieves significant gains in success rates on long-horizon benchmarks, improving Qwen-2.5-1.5B-Instruct performance from 72.8% to 77.4% on ALFWorld and from 56.8% to 67.0% on WebShop when trained with GRPO.
The LLM-as-Environment-Engineer framework uses LLMs to automatically redesign training environments in reinforcement learning by analyzing failure trajectories and contextual data. On the MAPF-FrozenLake testbed, it outperforms larger proprietary LLMs and fixed-environment baselines, with Qwen3-4B achieving the strongest aggregate performance. Analysis shows that failure evidence and preserved working configurations are key, and the current RL checkpoint performs better than the base model as an environment engineer.
SuCo introduces Minimal Sufficient CoT (MSC) as the shortest reasoning prefix adequate for correct answers. It employs a two-stage training framework—MSC-Aligned Fine-Tuning and Sufficiency-Aware Policy Optimization—to reduce reasoning length while maintaining or improving accuracy across math, code, and science tasks.
A causal audit shows that text-only models match multimodal models in chest radiography accuracy. Across nine systems, a text-only model performs within 5.7 points of the best multimodal model, and a 119-billion-parameter model is indistinguishable from a 7-billion-parameter text-only baseline. Grounding audits, not accuracy, should determine clinical deployment.
A new framework automates prompt refinement for LLM agents by splitting the observation-to-action pipeline into goal-conditioned and action selection modules. It uses an LLM-driven evolutionary loop to iteratively improve prompts based on environment feedback, achieving up to 72.5% success on PutNext where prior agents failed, without model fine-tuning.
Dynamic Rollout Editing (DRE) addresses overthinking in RL-trained reasoning models by modifying successful trajectories post-answer emergence. DRE preserves the correct reasoning prefix while editing unnecessary continuation, weakening the credit assigned to redundant thinking without penalizing valid reasoning. Experiments across diverse tasks demonstrate its effectiveness in reducing overthinking.
GLM-5.2 is the first open-weights model to achieve 80% accuracy on Terminal-Bench and outperforms all other available open models. It also surpasses Gemini, positioning it as a frontier-level model at a significantly lower cost.
llama.cpp version b9669 adds backend sampling support for Eagle3. The release includes binaries for macOS, Linux, Android, Windows, and openEuler across multiple architectures and hardware acceleration options, including Vulkan, CUDA, ROCm, OpenVINO, and SYCL.
llama.cpp release b9670 includes fixes for NVFP4 edge cases in llama-graph, such as moving post-GEMM MUL operations and restricting build_ffn to supported combinations. The release provides binaries for macOS, Linux, Android, Windows, and openEuler across multiple architectures and backend options, including CUDA, Vulkan, SYCL, and OpenVINO.
llama.cpp release b9667 introduces Vulkan support with S_v=16 via gated_delta_net. It includes binaries for macOS, Linux, Android, Windows, and openEuler across multiple architectures, with options for Vulkan, CUDA 12.4 and 13.3, ROCm, OpenVINO, and SYCL.
llama.cpp version b9665 introduces a new --offline flag for benchmarking. The release includes binary builds for macOS, Linux, Android, Windows, and openEuler across multiple architectures and hardware acceleration options, including Vulkan, CUDA, ROCm, OpenVINO, and SYCL.
LLaMA.cpp release b9663 adds support for OP EXPM1 and all unit test cases for FLOOR, TRUNC, and ROUND. It includes updated binaries for macOS, Linux, Android, Windows, and openEuler, with support for SYCL (FP32 and FP16), Vulkan, CUDA 12.4 and 13.3, and ROCm 7.2, along with an updated UI.
The sycl update extends support for reordered expert tensor handling in MoE MUL_MAT_ID to Q4_K, Q5_K, and Q6_K. Unsupported 3D reorder cases now fallback instead of aborting.
The llama.cpp release b9661 adds GGML_OP_COL2IM_1D support for Vulkan, using a bounded gather loop instead of full-K scan with modulo. It returns nullptr for unsupported types and includes builds for macOS, Linux, Android, Windows, and openEuler across CPU, Vulkan, CUDA, and SYCL.
LOGOS is a unified generative language model that represents scientific objects and their interactions as token sequences in a shared grammar. It achieves consistent or superior performance across diverse natural science tasks, demonstrating the feasibility of a single model serving multiple domains. The model scales positively with parameter count, and its design suggests that AI for Science should align deeply with large language models through shared architectures and training.