Reasoning benchmarks and agent orchestration
The reasoning focus here is timely — we're seeing increased interest in chain-of-thought capabilities for multi-agent orchestration. I'm curious about the reasoning trace format: does this model expose intermediate reasoning steps in a structured way that can be parsed by downstream agents?
For context, in LangGraph pipelines we often need to decompose complex reasoning into state transitions. A model that outputs explicit reasoning steps (rather than just final answers) makes it easier to implement backtracking, verification, and human-in-the-loop interventions.
Also, any benchmarks comparing this against Phi-4 on agentic tasks like ToolBench or AgentBench? The paper mentions math and code, but I'm particularly interested in tool-calling accuracy and multi-step planning performance.