ReFlect: A Harness System for LLM Reasoning Error Recovery
A recent study published on arXiv presents ReFlect, a harness system aimed at enhancing the reasoning capabilities of LLMs for complex, multi-stage tasks. Existing methods, such as chain-of-thought and ReAct, tend to accumulate errors without detection. In contrast, ReFlect implements a deterministic wrapper that incorporates independent error detection and recovery mechanisms. Testing across six reasoning domains revealed that self-critique at the prompt level generates structured templates, successfully identifying issues in 90 out of 100 evaluated reflection blocks. Additionally, LLMs incorrectly accept erroneous answers in at least 76% of instances. ReFlect's task success rates range from 41% with GPT-4o-mini to 56% with Claude Sonnet 4.5 across six different models.
Key facts
- ReFlect is a harness system for LLM reasoning.
- It creates standalone error detection and recovery logic.
- Current paradigms fail on long-horizon, multi-stage tasks.
- Self-critique flagged no issues in 90 of 100 audited blocks.
- LLMs wrongly accept wrong answers in at least 76% of cases.
- ReFlect achieves 41% success on GPT-4o-mini.
- ReFlect achieves 56% success on Claude Sonnet 4.5.
- Experiments covered six reasoning domains.
Entities
Institutions
- arXiv