MiniMax Unveils M2.5 Model with 'Interleaved Thinking'
MiniMax has released its M2.5 model, built for agentic productivity in real-world tasks. The company touts a capability called “interleaved thinking” that allows the agent to plan, check, and adapt its actions mid-trajectory, requiring evaluation protocols that assess process quality, not just final outcomes.
The "interleaved thinking" in MiniMax's M2.5 model represents a broader industry shift pioneered by models like Anthropic's Claude 4. This technique allows an AI agent to alternate between reasoning and using tools, preserving its chain of thought across multiple steps. This method is crucial for complex, multi-turn tasks where an initial plan is insufficient, enabling the model to adapt its strategy based on the outputs of actions like web searches or code execution. Evaluating such agentic systems requires new benchmarks that go beyond final-answer accuracy. Frameworks like AgentBench, WebArena, and SWE-Bench assess an agent's entire process, including its planning, tool selection, and decision-making across tasks in web browsing, coding, and operating systems. For data labelers, this creates a demand for process-oriented evaluation, where the quality of the reasoning path is as important as the outcome. This push for more reliable agents is also reshaping alignment techniques. While Reinforcement Learning from Human Feedback (RLHF) was foundational, its reliance on human reviewers creates bottlenecks at scale. As a result, labs are increasingly adopting Constitutional AI, a method where the model critiques and refines its own outputs based on a set of codified principles, a process known as Reinforcement Learning from AI Feedback (RLAIF). The demand for training data is bifurcating between human and synthetic sources. Synthetic data offers unmatched speed and scale, but models trained on human-labeled data can outperform their synthetic counterparts by 12-18% on complex reasoning tasks. The most advanced AI labs now use a hybrid approach: synthetic data provides volume for baseline training, while high-quality human data is reserved for refining nuanced capabilities, pushing performance frontiers, and validating safety. For startups selling to these AI labs, the go-to-market strategy is also evolving. Traditional lead generation is being replaced by AI-driven "digital discovery" to identify intent signals before outreach. The focus is on building trust by demonstrating verifiable outcomes, with some modern GTM frameworks even using AI to simulate buyer objections and pricing sensitivity before the first sales call. The fundraising environment for AI infrastructure is robust, bucking broader market downturns. Infrastructure-focused fundraising more than doubled to over $250 billion in 2025, driven by the demand for data centers and AI-enabling technologies. However, capital is heavily concentrating in mega-rounds for established players like OpenAI, which recently raised $110 billion, and Anthropic, which closed a $30 billion round.
