Which numbers are comparable

This repo holds runs made under three different configurations. Averaging or ranking across them is invalid. Two axes vary independently:

Examiner — examiner/dyna_conv_v19.py:863-864 uses TWO models: llm_chat_context = LLMChat("gpt-5") for grounded contexts and llm_chat_conv = LLMChat("gpt-4o") for the question turns. "GPT-4o backbone" refers only to the question turns.
SG parser — all published rows were parsed with gpt-4o-mini (run_all_metrics.sh sets GED_MODEL=gpt-4o-mini; other graders use local Qwen3).

tree	examiner	SG parser	comparable to published table?
`svg100_controlled/gemini-2.5-pro/`	gpt-5 + gpt-4o	gpt-4o-mini	YES
`svg500/gemini-2.5-pro/`	gpt-5.4-mini (both roles)	gpt-5.4-mini	no — both axes wrong
`svg500_gpt54_backbone/sg/*`	gpt-5.4-mini backbone	gpt-5.4-mini	parser axis wrong
`parser_ratio/`	n/a (fixed transcripts)	both, for measurement	n/a

Why svg500/ is not usable as a Gemini row

utils/llm.py applied its Azure branch AFTER the OpenAI branch and overwrote both the client and the requested model with AZURE_OPENAI_DEPLOYNAME. Because the module calls load_dotenv(".env") at import and .env contains AZURE_OPENAI_KEY, this could not be avoided by shell hygiene -- both examiner roles silently collapsed into one deployment. Fixed: Azure is now an elif fallback and logs any substitution.

Measured effect, same 100 images, paired:

metric	controlled (gpt-5 + gpt-4o)	collapsed (gpt-5.4-mini)
rounds	2239	4012
avg rounds/conv	11.2	20.1
regular	29.4%	34.3%
follow-up	25.0%	47.5%
adversarial	33.6%	17.5%
unanswerable	12.0%	0.7%

Ground truth was intact in both (adversarial gt=="No" 753/753 and 703/703; unanswerable canonical 268/268 and 29/29) -- the transcripts are not corrupt, the question mix is simply different, and the collapsed run also ran ~2x longer conversations.

Parser effect (parser_ratio/, 60 fixed images)

Same transcripts, same prompt and post-processing, only the parser model varies:

triples extracted: gpt-4o-mini 5008 vs gpt-5.4 2793 (ratio 0.5577)
GED: 122.20 vs 71.35 (ratio 1.7127; against the stored column, 1.7225)
per-image GED ratio: median 1.6887, range 1.07-3.14

Validation: recomputing GED from the stored gpt-4o-mini parse reproduced the published column to within 0.57% (122.20 vs 122.90), 23/60 images exact.

Caveat: GED is an anytime approximation against a 300s budget. 49/60 gpt-4o-mini and 42/60 gpt-5.4 calls hit that cap, so both means are upper bounds. The ratio is more trustworthy than either absolute number.

Do not apply the 1.71 parser ratio to svg100_controlled/ -- it is already parsed with gpt-4o-mini and would be double-corrected.

Headline numbers

run	GED	DeltaCon	images
gemini-2.5-pro, controlled + gpt-4o-mini parse	119.03	4.5600	100
gemini-2.5-pro, collapsed examiner + gpt-5.4 parse	137.08	4.5100	500
llava-1.5-7b, gpt-5.4 backbone + gpt-5.4 parse	93.34	4.2540	286
Qwen2.5-VL-7B, gpt-5.4 backbone + gpt-5.4 parse	108.29	4.2919	217

Only the first row is on the published footing. The llava/Qwen2.5 rows share each other's configuration but not the published one.