Current LLM agent scaffolds hard-code reasoning structures (plan, execute, verify, etc.) in advance, making them brittle when tasks require adapting the reasoning structure itself. The authors propose Deep Reasoning, an inference-time approach that constructs task-specific scaffolds via meta-reasoning: a formal language represents meta-reasoning as executable decompositions over associative inference, formal computation, and recursive subproblem solving. They instantiate this in DOLORES, a general-purpose agent that distributes complex tasks across controlled reasoning threads. DOLORES outperforms state-of-the-art scaffolds by 24.8% on average across multi-hop reasoning, long-chain QA, long-context aggregation, and deep research tasks, and an 8B version beats all evaluated 32B baselines from the same family in over half the benchmarks.
Main takeaways:
- Hard-coded scaffolds are brittle when tasks require adapting reasoning structure, not just content
- Deep Reasoning uses a formal meta-reasoning language to construct task-specific scaffolds at inference time
- DOLORES distributes cognition across structured, lower-load reasoning threads (associative, formal, recursive)
- Outperforms state-of-the-art scaffolds by 24.8% on average across four hard benchmarks
- 8B version surpasses all 32B baselines from same family in >50% of benchmarks—bridging the scaling gap via better structure