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代理工具

ASIL Replaces Screen Clicking With Structured State, Raising Software Agent Success Rates From 6.6% to 81.6%

ASIL lets agents read software state as JSON, then execute semantic actions through file parsers, native scripts, or service APIs. It significantly outperformed screen control across 380 tasks, but the comparison changed both the information visible to the agent and the granularity of its actions, so the results cannot be interpreted simply as an improvement in model capability.

代理工具

RAMP Grades In-Repository AI Configuration: Complexity Growth Nearly Doubles in Unconfigured Projects After Coding-Agent Adoption

Researchers used 441 enterprise repositories to develop the four-level Repository AI Maturity Profile, then reanalyzed 509 open-source projects that had adopted coding agents. Repositories with shared rules or agent configurations showed better quality metrics, but the observational study cannot establish that the configuration files themselves caused the improvements.

最新模型

Granite 4.2 Trains 8B/30B Models with Reinforcement Learning in Agentic Environments, Releases FP4, FP8, and GGUF Versions

IBM has released three Apache 2.0-licensed dense models in 3B, 8B, and 30B sizes, featuring switchable reasoning modes and native tool calling. The 8B and 30B models additionally underwent agentic reinforcement learning in codebase, terminal, and search sandboxes, although IBM’s official materials provide inconsistent accounts of their foundational training lineage.

AI 安全

RL Training Lets a Haiku 4.5 Auditor Match Opus 4.6, but Misaligned Rewards Push the False-Positive Rate to 96%

Researchers trained automated alignment auditors using hidden behaviors, pairwise rewards, and calibration on normal models, raising the best configuration’s aggregate score from 44.2 to 48.7. The experiments also show that directly rewarding the “discovery of suspicious behavior” encourages fabricated evidence, meaning deployed auditing agents cannot optimize solely for detection rate.