Archive
Agent、MCP、coding agent、工作流自動化與評測。
代理工具
Brain Researcher constrains neuroimaging analysis agents with a knowledge graph, typed workflows, and review rules, while preserving hypotheses, evidence, and execution provenance. Tool routing improved substantially, but the rate of successfully grounding claims in verifiable evidence remained just 22.0%, and automated review also failed to catch a directional statistical error.
AI 安全
SafeBranch rolls back an agent’s hazardous trajectory to a critical decision point and compares safe and unsafe actions only within the same context. The trained model requires no external critic at deployment, but results are currently limited to simulated household environments, and data construction still relies on GPT-4o judgments.
最新模型
ConceptGuard no longer treats the forget and retain sets as unrelated facts. Instead, it requires models to suppress harmful uses of a technology while preserving legitimate ones. All four unlearning methods exhibit a trade-off between forgetting and utility, with highly inconsistent results across concepts.
推論系統
FormalTCS extracts research-grade problems from top theoretical computer science papers published over the past two years, measuring models stage by stage as they turn natural-language claims into machine-verifiable proofs. The best model achieves a Pass@8 of 28.6 when proving already-formalized theorems, but translating natural language into correct Lean theorem statements remains the primary bottleneck.
AI 安全
The open-source agent runtime Forge can now isolate MCP connections and credentials by user while automatically performing OAuth 2.1 metadata discovery and dynamic client registration. The release also adds granular egress controls and approval auditing, although MCP support currently remains focused on HTTP transport.
AI 安全
The new benchmark deduplicates agent skills from 13 public sources and splits them by structural family, preventing near-duplicate samples from appearing in both the training and test sets. The best text classifier performed strongly on a random split, but misclassified 62.4% of benign skills as malicious when evaluated on unseen sources.
代理工具
NVIDIA has updated its open-source agent execution stack, focusing on state drift across vLLM, llama.cpp, Ollama, and the model router. The new release also tightens sandbox lifecycle management, image provenance, and failure recovery, though it remains a patch release in a rapidly evolving project.
代理工具
SWE-bench Science uses 98 real-world scientific software repositories to test implicit contracts involving units, coordinate systems, numerical invariants, file formats, and more. Claude Code with Opus 5 currently ranks first, but its pass@1 remains below 50%, falling to just 27.78% on engineering integration tasks.
代理工具
Microsoft has open-sourced Thinkingbox, which uses isolated MCP tool environments and executable assertions to verify whether agents actually complete their work, rather than merely checking their responses or whether tool calls are valid. The best system achieves a 65.36% single-run success rate, but that falls to just 25.25% when the same task must succeed 20 consecutive times.
Task-CoEvolve dynamically selects validation tasks based on historical disagreement among candidate harnesses, then estimates overall performance using sampling probabilities. On Terminal-Bench 2.1, it approaches full-set search performance, but the paper uses the same tasks for search and evaluation, and the code has not yet been released.
推論系統
ReCache rewrites attention and positional encoding so that individual tools or skills can reuse their KV caches across requests. Qwen3 experiments show up to a 3.655× improvement in time to first token, but the method requires model fine-tuning and is not yet a drop-in inference plugin.
機器人／VLA 訓練
Google DeepMind has a vision-language model first break down long-horizon robotic tasks into subgoals executable by a VLA, then fine-tunes the original model on successful trajectories and adds residual off-policy reinforcement learning. The method improves exploration efficiency in simulated tasks, but has yet to demonstrate that it can replace human demonstrations on physical robots.