Models Recall What They Violate: Constraint Adherence in Multi-Turn LLM Ideation
When researchers iteratively refine ideas with large language models, do the models preserve fidelity to the original objective? We introduce DriftBench, a benchmark for evaluating constraint adherence in multi-turn LLM-assisted scientific ideation. Across 2,146 scored benchmark runs spanning seven models from five providers (including two open-weight), four interaction conditions, and 38 research briefs from 24 scientific domains, we find that iterative pressure reliably increases structural complexity and often reduces adherence to original constraints. A restatement probe reveals a dissociation between declarative recall and behavioral adherence, as models accurately restate constraint...
| Authors | Garvin Kruthof |
|---|---|
| Primary category | cs.CL |
| Published | 30 April 2026 |
| Last updated at source | 4 May 2026 |
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