code-prompts-unsatisfiable-design-open-weight-code-reasoning-models-refuse CLM-0032On code prompts that are unsatisfiable by design, open-weight code and reasoning models refuse based on how suspicious the request looks rather than how deeply impossible it is: they refuse famous theoretical impossibilities far more often than requests for plausible-sounding nonexistent packages, and the stronger models in the set close the gap on theory while barely improving on fabricated ecosystem entities.
Capability: Stating false facts confidently
Observed on
Twelve open-weight code/reasoning models (7B-32B), 270 adversarial unsatisfiable prompts across six languages plus 91 matched solvable controls, temperature 0, graded by a two-tier.
Sources
- supportsRefusing the Impossible: A Taxonomy and Benchmark for Code Hallucination in Large Language ModelsMeasured: aggregate hallucination rate 0.60 and refusal 0.27 on adversarial prompts, 0% over-refusal on controls. Subcategory gradient from 0.98 (nonexistent npm) down to 0.08 (modified classics). Top-4 vs bottom-4 models: theory subcategories 0.23 vs 0.82, plausible-entity 0.87 vs 0.98. Prompt identity explained more outcome variance than model identity. Only open-weight models tested; no frontier commercial models, so the capability trend is extrapolated within a narrow band. Framing and per-ecosystem comparisons are observational, not fully crossed.
Disagreeing is the most useful thing you can do here. Nothing on this page waits for a reviewer: the counts above move when evidence lands, and every source stays attached with who brought it.
Related claims
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Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: A model set where refusal rates on plausible fabricated package prompts equal or exceed those on abstract theoretical-impossibility prompts, or where higher-capability models improve on both classes at similar rates. Proposed technique, not catalogued: Unsatisfiable-task refusal benchmark with matched solvable controls.