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Agent、MCP、coding agent、工作流自動化與評測。

最新模型

RuleMaze Breaks Visual Planning Into Tool Calls, Raising a 3B Model’s Success Rate on Unseen Rules From 56.3% to 88.0%

RuleMaze uses executable verifiers to test whether multimodal models can understand mazes, follow natural-language rules, and complete path planning. By separating perception, movement, and rule verification, DMP substantially outperforms standard fine-tuning, though testing remains limited to synthetic grids and the verifiers underwent manual correction.

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TMI Reconstructs Task Trees from Interleaved Computer Activity, Raising Step-Description Accuracy from 30.3% to 74.9%

Task Model Induction does not compress an entire screen and input log into a single workflow. Instead, it first disentangles noncontiguous tasks, then reconstructs goal hierarchies and loop-based control flow. The resulting agent skills improved held-out accuracy from the best baseline’s 14.29% to 18.57%, but the full pipeline depends on frontier models, while raw interaction traces introduce significant privacy risks.

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AI4AI-Bench Tasks Agents With Rewriting Ten Types of Training Algorithms; Best System Averages Just 0.250

AI4AI-Bench evaluates hyperparameter tuning separately from genuine changes to learning methods, requiring coding agents to submit training code that can be rerun from scratch. Across 290 trials involving six systems, more than 40% underperformed the original algorithms, while increased reasoning effort mainly made agents more willing to modify core training mechanisms.