Archive
Agent、MCP、coding agent、工作流自動化與評測。
最新模型
Z.ai says GLM-5.3 was not pretrained from scratch, with improvements in coding and cybersecurity capabilities coming entirely from larger-scale post-training. The model reports an 84.5% success rate on CyberGym, but its weights will not be released until a dual-use risk assessment is complete.
最新模型
SciFigBench uses more than 34,000 scientific-figure test configurations to evaluate visual understanding separately from the ability to acknowledge insufficient evidence. GPT-5.2 leads in description quality, while Gemini 3.1 Pro more often refuses to draw conclusions from ambiguous or misleading evidence.
代理工具
Agent Behavioral Contracts II analyzed 18,000 preregistered task runs and found highly correlated failures between two agents using the same model. The study instead uses linear programming over joint distributions to calculate finite-sample lower bounds, though the current evidence remains limited to two-agent handoff settings.
最新模型
DreamX-Phi injects the rigid-body transformations of two robotic arms directly into attention and constrains video generation using depth, object masks, and V-JEPA features. It scored 60.65 on a WorldArena 2.0 snapshot, but its weights and inference code will not be released until the challenge concludes, and generalization to real robots remains unverified.
GitHub Repo
The open-source MARC v1 uses YAML to define extraction, reasoning, and output agents, with support for the Gemini API or local deployment through Ollama. It improves error localization and data control, but presents only three types of use cases and does not demonstrate that a multi-agent approach is more accurate than a single prompt.
最新模型
A new study finds that sparse autoencoder ablation evaluations typically allow each dictionary to select its own highest-activation token, meaning ostensibly identical latents are tested at different positions. After fixing a shared position, the 7.6% and 11.9% of variance previously attributed to dictionary differences in controlled experiments nearly disappeared.
代理工具
VALG breaks machine learning theory research into problem formalization, proof dependency graphs, local derivations, and multi-perspective review, then determines whether to repair a proof or open a separate relaxed branch based on the level of failure. Across nine COLT 2026 open subproblems, the system produced 22 internally finalized candidates, but only two runs yielded results within the scope of the original problems.
代理工具
CAPRI separates “the proof is accepted by Isabelle” from “the model modified only authorized regions” into two independent checks. Across 180 repair experiments, 6 of 144 buildable candidates altered protected text; no further violations occurred after the model was restricted to returning only the proof body.
最新模型
PlayWorld uses an agent that observes generated frames and dynamically adjusts control commands, preventing differences in movement magnitude across world models from making fixed trajectories incomparable. Results from 171 scenarios show that although current models can generate fluid visuals, they still struggle to preserve global spatial structure and states that continue evolving outside the field of view.
AI 基礎設施
Instead of replicating an entire model, OpScale scales only the operators currently creating bottlenecks—such as attention and MLP—based on traffic and sequence length. The authors replayed 929,000 requests on A100 and GB200 clusters, using up to 36.3% fewer GPUs while meeting the same latency targets, but the prototype code has not yet been released.
最新模型
Using 600 verifiable Go life-and-death problems, TsuGO measures whether models allocate reasoning resources to the correct branches rather than merely counting correct answers and tokens. After four multiple-choice candidates are removed, both accuracy and search efficiency fall sharply across several reasoning models, leaving a clear gap with the neural-network-guided KataGo.
代理工具
Late in the Linux 7.2-rc7 release cycle, more than 230 contributors submitted over 400 fixes, some stemming from issues identified by AI review tools. AI has not replaced the core maintenance workflow, but agents such as Sashiko have pushed bug-finding speed beyond humans’ capacity to triage and validate reports.