The Observation Theory EncyclopediaFrom TSKAboutBy kindBy chapterBy Lean fileLedgerProvenance

the flip result

DefinitionAt matched bits, the outcome in which the code with the worse reconstruction error scores better on the consumer's task, while a code built to destroy the read subspace scores worst. The book's central empirical claim. Chapter 4. Also flip, the.
ExampleCodes diag(0.3, 1.7) and diag(1.7, 0.3) have the same total error 2.0, and a reader at 15 degrees pays 0.394 for one and 1.606 for the other.
BookData Mining as Observation, draft 0.2, commit f3914f0; entry id flip, kind result.
Statusmeasures [demonstrated]; measures [replicated] ×2; refutes or corrects [refuted]. Corrections: 6 item(s), see below.
Defining equation

Book equation 4.5.

Assumptions and scope
  • Matched bits, and reconstruction matched or reported, for every comparison of two codes.
  • A read operator misaligned with where the signal has its energy. The alignment of book equation 4.6 is the dial, and near one, the coupling null, no flip is possible.
  • The read operator is the average of local linearizations, and the ordering it predicts is the ordering on average over the workload.
Prior artConsumer-relative compression sits beside the information bottleneck of Tishby, Pereira, and Bialek and beside task-based quantization. The flip is the measured claim that at matched bits the code that reconstructs worse can serve the consumer better.
Evidencegeometric-observation/claims/LEDGER.md:63, geometric-observation/claims/LEDGER.md:64, geometric-observation/claims/LEDGER.md:66, geometric-observation/claims/LEDGER.md:92, geometric-observation/chapters/ch02_failure_of_observer_free_measurement.md:40-60, geometric-observation/chapters/ch16_honest_negatives.md, turboquant-pro/docs/KV_KEYS_FINDING.md:1-49, geometric-observation/chapters/ch08_value.md:40-70, geometric-observation/claims/LEDGER.md, geometric-observation/chapters/ch08_value.md:84-97, geometric-observation/chapters/ch08_value.md:108-116, geometric-observation/chapters/ch16_honest_negatives.md:73-81, geometric-observation/chapters/ch16_honest_negatives.md:28-30, geometric-observation/chapters/ch16_honest_negatives.md:32-34, geometric-observation/chapters/ch16_honest_negatives.md:36-50, geometric-observation/chapters/ch16_honest_negatives.md:56-61, geometric-observation/chapters/ch16_honest_negatives.md:106-112, lean/DataMiningAsObservation/Flip.lean
Reviewedsemantic review 2026-09-06; generated 2026-09-10 from records at the commits on the provenance page.
first codesecond codereaderfirst codesecond codesame trace
At matched bits, the code that reconstructs worse can serve the consumer better.

Equation

Book equation 4.5.

\[\text{flip}:\quad \mathrm{task}(O)>\mathrm{task}(R)\ \ \text{and}\ \ \operatorname{tr}M^{O}_\delta>\operatorname{tr}M^{R}_\delta,\qquad \mathrm{task}(\text{anti})<\mathrm{task}(R),\qquad \text{bits}(O)=\text{bits}(R).\]

Book equation 3.1.

\[\begin{gathered} d_O(u)=u^{\top}\Sigma\,u, \qquad u=(\cos15^\circ,\ \sin15^\circ), \\ \Sigma_1=\operatorname{diag}(0.3,1.7),\ \Sigma_2=\operatorname{diag}(1.7,0.3), \qquad d_O=0.394\ \text{vs}\ 1.606. \end{gathered}\]

Book equation 0.10.

\[d_O=\operatorname{tr}(P_C\,M_\delta)=\mathbb E\!\left[\delta^{\top}P_C\,\delta\right],\qquad M_\delta=\mathbb E\!\left[\delta\delta^{\top}\right],\qquad P_C=I\ \Rightarrow\ d_O=\operatorname{tr}M_\delta.\]

Conditions

Conditions are curated in entries.toml rather than read from a record.

Ledger

First stated

Volume 14, chapter 8, geometric-observation/chapters/ch08_value.md:1-30, DOI 10.5281/zenodo.21776291. The two-dimensional case is the cover and equation 3.1 of Data Mining as Observation.

Measurements

Where the book states it Numbers, as the book’s sources table records them Source
chapter 1 section 1.4 cosine 0.995 and perplexity of order ten thousand, the recalibration negative geometric-observation/chapters/ch02_failure_of_observer_free_measurement.md:40-60; geometric-observation/chapters/ch16_honest_negatives.md NEG-2 and NEG-4; turboquant-pro/docs/KV_KEYS_FINDING.md:1-49
chapter 1 section 1.4 the flip sealed in twelve domains and three physics, held in at least five domains and all three geometric-observation/chapters/ch08_value.md:40-70; geometric-observation/claims/LEDGER.md rows GO-2, GO-B-AV163, D3, 038
chapter 2 section 2.5 whitened code wins at every budget geometric-observation/chapters/ch08_value.md:84-97
chapter 3 section 3.3 the self-refuted v0.8 claim, NEG-1 the-angular-observer/README.md:170-175; geometric-observation/chapters/ch16_honest_negatives.md NEG-1
chapter 4 section 4.3 the flip definition, flip versus (A2) verdict geometric-observation\chapters\ch08_value.md:1-30,100-108
chapter 4 section 4.3 GO-2 0.0934 vs 0.0938, 2.53 times, 12 of 12, anti 21 times; retrieval 0.0964, negative 4.70 and positive 4.65, recon 0.40 geometric-observation/claims/LEDGER.md rows GO-2 negative and positive halves
chapter 4 section 4.3 acoustic 148 of 201, 201 of 201, 152 of 201; seismic 13 of 17, 17 of 17, 13 of 17; whale 0.934 vs 0.883, 2 times, 300 of 300; at least 5 domains and 3 physics; battery prediction met geometric-observation/chapters/ch08_value.md:40-70; geometric-observation/claims/LEDGER.md rows GO-B-AV163, D3, 038
chapter 4 section 4.3 whitened code on whale 0.83, 0.85, 0.97 vs 0.41, 0.80, 0.89 geometric-observation/chapters/ch08_value.md:84-97
chapter 4 section 4.4 gradient compression anti 300 of 300, flip 27 percent, coupling boundary geometric-observation/chapters/ch08_value.md:108-116
chapter 4 section 4.4 GO-4 budget inversion, fixed m 10 rises, matched m 121, 126, 159 collapses, 3 seeds geometric-observation/claims/LEDGER.md row GO-4
chapter 4 section 4.6 NEG-4 through NEG-10, the 25 percent codebook confound, Spearman 0.80, the recon-matched precondition geometric-observation/chapters/ch16_honest_negatives.md NEG-4 to NEG-10
chapter 6 section 6.2 real model, median 0.567 vs bar 0.60, about 4.5 times chance, heads at 0.815 and 0.958, sixteen of sixteen both, two of three triggers, NEG-12, later rematch four of four geometric-observation/claims/LEDGER.md rows GO-B-Llama and NEG-12; geometric-observation/chapters/ch16_honest_negatives.md:73-81
chapter 6 section 6.2 whale clan classifier, 8718 codas, 0.934 vs 0.883, reconstructs twice as well, 300 of 300 geometric-observation/chapters/ch08_value.md:40-70; geometric-observation/claims/LEDGER.md row 038
chapter 8 section 8.2 cosine 0.995, perplexity near 1e4 turboquant-pro/docs/KV_KEYS_FINDING.md:1-49; geometric-observation/chapters/ch16_honest_negatives.md NEG-2
chapter 8 section 8.2 recalibration improves reconstruction and worsens the consumer geometric-observation/chapters/ch16_honest_negatives.md NEG-4

Failures and corrections

Invariance envelope

none declared

Machine checked

lean/DataMiningAsObservation/Flip.lean, theorems trace_eq, reading_difference, reader_swap_reverses, flip, cos_sq_pi_div_twelve, sin_sq_pi_div_twelve, reading_15_first, reading_15_second, sqrt_three_bounds, readings_15_to_three_decimals, reader_75_is_swap, four_to_one_flip, at observation-data-mining f3914f0; what the check covers is stated in the book’s appendix C.

Used in

Data Mining as Observation primer S, chapters 2, 3, 4, 6, 7, 11, 12, 13, 14.

Related

read distortion; alignment; anti arm; rank certificate; coupling null.

See also

none

Status

Generated 2026-09-10 by encyclopedia/generate.py; book at observation-data-mining f3914f0; the commit of every record is listed in the encyclopedia’s provenance.

← finite differencefloor, ceiling →