Interleaved thinking is essential for LLM agents: it means alternating between explicit reasoning and tool use, while carrying that reasoning forward between steps.This process significantly enhances planning, self‑correction, and reliability in long workflows. [...]
From community feedback, we've often observed failures to preserve prior-round thinking state across multi-turn interactions with M2. The root cause is that the widely-used OpenAI Chat Completion API does not support passing reasoning content back in subsequent requests. Although the Anthropic API natively supports this capability, the community has provided less support for models beyond Claude, and many applications still omit passing back the previous turns' thinking in their Anthropic API implementations. This situation has resulted in poor support for Interleaved Thinking for new models. To fully unlock M2's capabilities, preserving the reasoning process across multi-turn interactions is essential.
— MiniMax, Interleaved Thinking Unlocks Reliable MiniMax-M2 Agentic Capability
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