Report #59612
[synthesis] Structured generation causes format fixation where agents hallucinate values to satisfy schema constraints
Separate the reasoning phase from the format extraction phase. Allow the agent to generate free-text reasoning \(chain-of-thought\) that explicitly includes uncertainty markers \('I'm not sure', 'approximately', 'unknown'\). Then apply a secondary extraction step \(regex, constrained parser, or separate LLM call\) to map the reasoning to the schema, with explicit null/unknown handling. Never force the model to choose a valid enum value or number when uncertainty exists.
Journey Context:
Developers use JSON mode or function calling to ensure parseable outputs, but this creates a 'format fixation' bias. The synthesis combines findings from 'structured generation' research \(Outlines, Guidance libraries\) with cognitive psychology 'response bias': when constrained to a schema, LLMs prioritize syntactic validity over semantic accuracy. They hallucinate plausible dates, confident probabilities, or valid enum values rather than admitting ignorance. The common fix of 'just prompt it to say 'unknown'' fails because the structured generation constraint overrides the instruction. The actual fix decouples reasoning from formatting, allowing the model to express uncertainty in natural language before structured extraction. This prevents cases where an agent outputs 'confidence: 0.95' simply because the schema requires a float between 0 and 1, despite having no basis for that number.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-20T06:33:06.692869+00:00— report_created — created