This is an illustrative reading of
`corpus/grok-nvidia-q4-fy24-summary/diff_with_nvidia-q4-fy24-press-release.json`.
The diff is computed automatically; this file teaches how to
READ it.
Q4 FY2024 earnings press release, 1847 characters, financial
genre. Authoritative source for its own numbers.
Reasoning's 200-word "neutral business-news register" summary
of that press release, 1160 characters, ai_response genre.
The transformation kind is `llm_summary`. The diff measures what
Grok did to decision-readiness when compressing the press
release into a summary.
Both source and summary address two of five analytical
perspectives (stakeholders + trends). The summary did NOT drop
any dimension the source had. Coverage held steady.
Substantive read: Grok preserved the press release's
structural coverage. This is not surprising for a summary that
keeps to the source's scope; it would be a finding if a summary
LOST coverage (e.g., "Grok dropped the stakeholders dimension
when compressing"). Here, coverage is not the load-bearing
finding of the diff.
Why coverage is only 2/5 in both: earnings press releases
are by genre narrow. Causes (why), risks (what could go wrong),
uncertainty (what is unknown) are typically absent from a
release whose job is to communicate facts about a quarter. This
is not a Grok problem; the source itself addresses 2 of 5.
This is the load-bearing finding of the diff. Three things to
read:
1. Hedge ratio is 0.00 on both sides. Neither the press
release nor the summary hedges its numerical claims. This is
genre-appropriate for a press release (record revenue,
reaching a record, etc.) and replicated faithfully by the
summary.
2. Claim count fell 12 -> 9. The summary dropped three
numerical claims in compression. The diff does not tell us
WHICH three; a researcher writing this up would need to
read both texts to identify the dropped claims and assess
whether they were load-bearing.
3. Confidence Imbalance pattern emerged in transformation.
This is the key finding. The press release did NOT trigger
FVS-002 (Confidence Imbalance); the summary did. Grok's
summary, by compressing 12 unhedged claims into 9 unhedged
claims, crossed the structural threshold for the pattern.
The pattern is the canon entry teaching that confident
prose with no hedging signals overconfidence; the SUMMARY
reads as more overconfident than the SOURCE on this
structural measure.
Substantive read: the LLM summary is structurally MORE
overconfident than the source it summarized. Not because Grok
added confident claims; because compression dropped enough
claims to cross a density threshold. A reader using the summary
for a decision would receive a structurally more decisive read
than the original source supports.
Sentence-attribution is the share of sentences carrying an
explicit source reference. The press release, written by NVIDIA
about itself, attributes nothing externally. The summary
attributes 10% — likely sentences that mention "NVIDIA reported"
or similar, where the LLM added attribution language that the
source did not have.
Substantive read: the summary is MORE attributed than the
source. A summary should not normally invent attribution; this
is worth a closer look. A researcher writing this up would
check whether Grok's added attribution is faithful (the source
IS NVIDIA) or invented (Grok wrote "according to NVIDIA" where
the source had no explicit attribution).
This dimension is non-comparable for THIS PAIR. The corpus
profiles were computed without Source Network (offline curation
for reproducibility), so neither side has verification data.
The diff field is honest about this: "neither side had claims
checked."
Substantive read: robustness is a corpus gap, not a
finding. A future Phase 2.5 corpus refresh with Source Network
enabled would fill this in. A researcher should NOT report
"robustness held steady" for this pair; they should report
"robustness not measured."
Both source and summary lack failure-framing markers and the
uncertainty dimension. Neither press releases nor LLM summaries
of them typically engage with counterfactuals — the genre
expectation is forward-looking optimism for the press release
and faithful compression for the summary.
Substantive read: counterfactual is held steady because both
sides have the same gap. Not a finding about the
transformation; a finding about the genre. A researcher
writing this up would not lead with "Grok lost counterfactual
engagement" because Grok had nothing to lose.
What Grok did to decision-readiness when summarizing the NVIDIA
press release:
1. Compressed the claim set (12 -> 9 numerical claims),
crossing the structural threshold for Confidence Imbalance.
The summary reads as MORE overconfident than the source on
the structural measure.
2. Added attribution (0% -> 10% sentence-attribution).
Worth verifying whether the added attribution is faithful or
invented.
3. Preserved coverage (2/5 in both). Compression did not
drop a dimension the source had.
4. No information about robustness (corpus profiles
computed offline) or counterfactual differences (genre-
shared gap).
This is one pair, with one model, summarizing one source.
Generalizing to "Grok summaries always introduce overconfidence"
would be a sample-size error. The aggregate findings page
will accumulate signal across more pairs as the corpus grows.
ones for decision-making (the diff is dimension-level, not
claim-level)
require comparing the surfaced attribution against the source
text)
structural; quality is a reader judgment
source (would require more transformation pairs in the
corpus)
The diff is a starting point for research on LLM summary
behavior, not an answer.
Grok 4.1 Fast Reasoning's 200-word summary of NVIDIA's Q4 FY2024 earnings press release shows a structural shift in calibration not visible in the source. The press release contains 12 unhedged numerical claims; Grok's summary compresses to 9 unhedged claims, crossing the density threshold for the Confidence Imbalance pattern (FVS-002). Coverage of analytical perspectives held steady (2 of 5 on both sides, both addressing only stakeholders and trends). Sentence-level attribution increased from 0% to 10%; whether the added attribution language is faithful to the source or introduced by the model is a follow-up investigation. Robustness and counterfactual dimensions are not informative for this pair (robustness pending Source Network re-run; counterfactual is genre-absent on both sides).
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Cited from the Frame Check decision-readiness corpus, revision `70e2a95a9d1f`.