Tag: #agentic-ai513 results
Step 3.5 Flash is a 196B MoE model with 11B active params for agentic tasks. Optimized with sliding-window attention and MTP-3 for low-latency inference. Matches frontier models on math, code, and agent benchmarks.
CLI-Gym generates 1,655 CLI tasks via agentic environment inversion from Dockerfiles. It simulates histories to create buggy states and derives tasks with error messages. Fine-tuned LiberCoder boosts Terminal-Bench scores by 21.1%.
Introduces authenticated workflows as a complete trust layer for enterprise agentic AI, protecting prompts, tools, data, and context. Enforces intent and integrity via cryptography and MAPL policy language. Integrates with nine AI frameworks for deterministic security.