VALIDATION.md

Behavioral Validation

This records whether the released stimuli actually elicit the intended space-tracking behavior in capable open models. It is a check on the stimuli, not a reproduction of any result in the papers that study these constructions (arXiv:2607.10248, arXiv:2607.11945): those papers' numbers come from the project experiment scripts, and this corpus is a companion stimulus suite derived from the same constructions.

Method

Each row's prompt is a completion that ends on a copula, so the answer is the next token. For every prompt we read the next-token logits and take:

• forced accuracy (primary): argmax restricted to the item's in-play values (value_space_map). This is the minimal-pair question: does the model pick the correct space's value over the competing one?
• open accuracy (secondary): argmax over the full color vocabulary. The gap between open and forced surfaces the model's color prior and any off-vocabulary drift; it is a diagnostic, not the headline.

Answers are scored on the first token of {color} using the target model's own tokenizer; the single-token audit below reports whether each answer is one token.

Scoring is completion-style (no chat template), matching the corpus format. Instruction models may behave differently under their chat template; verify for your setup.

Caveats

• Two architectures, still provisional. These numbers are Qwen2.5-7B-Instruct and OLMo-2-1124-7B-Instruct. Agreement across two models is reassuring but not proof of generality. The cross-model pattern matters more than any single cell: a frame weak on one model may be fine on another, and only frames weak on both point to the stimulus.
• Accuracy below 1.0 is expected: two-space and nested constructions are genuinely hard, and the two-entity control is designed to leak (see below). The bar is that a capable model tracks the correct space well above chance, not perfectly.

Per-construction results

Completion scoring, all splits, forced accuracy (argmax restricted to the item's competing values). Overall 0.87 (Qwen2.5-7B-Instruct) and 0.90 (OLMo-2-1124-7B-Instruct).

construction	n	Qwen-7B	OLMo-2-7B
single_space_control	120	1.000	1.000
belief_reality	450	0.933	0.933
counterfactual_or_alternative_space	500	0.846	0.930
nested_depictive	360	0.825	0.892
two_entity_selectivity	240	0.858	0.854
nested_belief	240	0.821	0.817

Both models track the correct space well above the two-value chance line (0.50) on every construction. Forced accuracy is the metric that answers the space-tracking question; the open six-way argmax deflates the four-color constructions through the model's prior toward the two unused colors, so it stays a diagnostic rather than the headline.

The two-entity control leaks as designed (swap 0.125 Qwen, 0.142 OLMo-2): it separates entity-bound space assignment from a global image-versus-reality feature, and a model that copies the distractor's value scores as swap. Nested belief is the hardest construction on both models, driven by one frame (see below).

Per-template results

Forced accuracy per frame. This is the diagnostic for a weak surface realization.

construction	template	n	Qwen-7B	OLMo-2-7B
belief_reality	belief-thinks	150	0.967	0.860
belief_reality	belief-convinced	150	0.960	0.960
belief_reality	belief-believes	150	0.873	0.980
counterfactual	cf-hypothetical	100	0.940	0.940
counterfactual	cf-painting	100	0.810	0.910
counterfactual	cf-story	100	0.910	0.900
counterfactual	cf-dream	100	0.800	0.930
counterfactual	cf-movie	100	0.770	0.970
nested_belief	nb-imagines-confirms	80	0.975	0.900
nested_belief	nb-assumes-sees	80	0.800	0.838
nested_belief	nb-thinks-knows	80	0.688	0.713
nested_depictive	nd-reflection-portrait	120	0.867	0.933
nested_depictive	nd-photo-painting	120	0.825	0.900
nested_depictive	nd-snapshot-sketch	120	0.783	0.842
single_space_control	single-space-*	40 each	1.000	1.000
two_entity_selectivity	te-painting	80	0.850	0.912
two_entity_selectivity	te-photo	80	0.863	0.838
two_entity_selectivity	te-drawing	80	0.863	0.812

nb-thinks-knows is the hardest nested-belief frame (0.69 Qwen, 0.71 OLMo-2) but no longer near chance. An earlier build read 0.60 / 0.55: the readout then repeated a verb ({observer} thinks {believer} thinks ...), which let the model collapse the two belief levels. Changing the outer verb to believes lifted the frame on both models (+0.09 Qwen, +0.16 OLMo-2), so the cause was surface phrasing, not a defect in the scenario. It stays the softest nested-belief frame, consistent with genuine 2-deep difficulty on top of the phrasing effect.

Frame variance elsewhere is model-specific rather than stimulus-driven: cf-movie is Qwen's softest counterfactual (0.77) but OLMo-2's strongest (0.97).

Single-token audit

All answer colors are a single token under both models' tokenizers (Qwen2.5-7B-Instruct and OLMo-2-1124-7B-Instruct), with the leading space: blue, green, red, brown, yellow, purple. Re-check for any other model before interpreting token-level results.

Reproduce

The build and validation scripts live in the source repository, github.com/osteele/mental-spaces. With the model and a GPU available, the validation script scores every prompt (uv run installs torch and transformers from the lockfile):

uv run python scripts/exp_corpus_behavioral_audit.py --model Qwen/Qwen2.5-7B-Instruct