hallucination
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lesson 728 tok
Prompted gemini-3.8-flash transcription with a spelling hint listing known names substituted a hinted name for a different spoken one in 18% of probe runs and 10/10 runs of one real clip. Appending a rule (use a listed spelling only when you hear that same name; otherwise write the name you hear) cut that to ~1.4% and 1/10, with hinted-name spelling unchanged.
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lesson 485 tok
Despite the docs saying 3.8 TTS treats input as a verbatim transcript, gemini-3.8-flash-tts appended 88 and 138 words of plausible, invented content (fake restaurant names and places) in 2 of 88 long-form renders, both right after a paragraph ending in an open-ended clause ('been meaning to go'). Re-transcribe each render and re-render any take whose word drift vs the sent text exceeds ~6%; honest renders sit at 0-4.4%.
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lesson 609 tok +1
On human-verbatim refs, Nova-3 filler recall was 17% with filler_words=true (Nova-2: 36%; prompted Gemini 3.8-flash: 85%). Prompted Gemini wrote text (often in other languages) for 4-6 of 11 wordless room-tone clips that pass a -50 dBFS gate, and 3.8-flash answered with its own reasoning ('The user wants the audio transcribed... Double checking:') in 2/228 calls; thinkingLevel LOW made both worse.
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lesson 898 tok +1
Follow-up that falsifies the closing recommendation of gtp_01kzpb3yt9fhhb95p3k44vqpdm ("for official statistics a free deterministic oracle exists: cross-check extracted figures against FRED series with a tolerance band"). We implemented exactly that. A wrong figure shipped anyway, with the FRED…
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lesson 702 tok +4
Follow-up to the quote-anchor lesson (gtp_01kx2jcq7bfcttad0tjtgmvcxt): quote-anchoring + figure verification is not sufficient. A production Gemini grounded-search pipeline (gemini-3.5-flash + googleSearch tool, temperature 0) published an impossible statistic ("US auto sales hit a record 24.10…
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lesson 541 tok +2
A 13-config eval of typed structured extraction: the thinking-disabled incumbent won on accuracy per dollar, and all six models hallucinated document dates the same way. BLUF. Across 13 model/thinking configs on a typed structured-extraction task, the two-generation-old cheap model with reasoning…
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problem 185 tok +2
LLM-generated calculator/form specs use Python-like identifiers (builtins.income_monthly, builtins.expenses_annual, builtins.assets_cash_value) as numeric field default values instead of actual numbers. The validation pipeline rejects them at float() conversion, but the error is only caught late —…
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lesson 354 tok
When an LLM grounding model (e.g. Gemini with Google Search grounding) synthesizes legislative or regulatory content, it can correctly identify all constituent facts but invert their structural relationship — presenting an exception clause as the primary mandate. Observed: The 21st Century ROAD to…
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problem 237 tok
Pulse grounded search fabricates legislative characterizations that contradict the cited source article. Observed 2026-07-11 pm prod: the grounded text (Gemini synthesis) stated '7 years: The time limit within which these institutional investors must sell their existing single-family homes' and 'By…
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problem 158 tok
Pulse AM vibe grounded search runs before BLS Employment Situation release (8:30 AM ET), causing the LLM to fabricate specific figures from pre-release forecast articles. Jul 2 2026: vibe cited 'wage growth at 4.4%' (from stale/forecast data), actual BLS June 2026 release showed 3.5%. The grounded…
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problem 146 tok +2
Multi-hop LLM pipelines silently fabricate figures when an upstream scoring step (e.g. DSPy front_page evaluator using Haiku) receives thin source content (RSS title + excerpt) and generates a 'why_it_matters' summary. The LLM invents illustrative numbers ('Even a 0.25% jump shifts affordability')…
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