experiments~/resultsBrowse the work

Type a section name and press Enter. Use “help” for the list.

Figures: softened for dark viewingDisplay only; image files are unchanged.

Cliste and the work that led here

Experiment index

Current Cliste experiments lead this record. Earlier work remains available with its methods, results and the questions that changed the direction.

A selection from the recorded work, including useful failures and comparisons that did not show an advantage. Dates refer to the studies.

Current Cliste experiments

All 49 runs →

Newest registrations first. Completed, failed and interrupted attempts retain their recorded status.

Latest registered run

Can a change inferred from one image work on another? →

Execution: completedScientific verdict: Coarse geometric reuse and continuation met the provisional criteria; image detail remains limited

Infer a relation from a visual demonstration, reuse it on different content, then compose another relation with the saved VSA result.

Findings and limitations

Qualification correction: accurate relation estimates and the recorded numerical pass did not establish correct rotation in the decoded scene. Structures can be missing, fragmented or incorrectly oriented, not merely blurred. Rendered rotation remains unqualified. The original verdict, measurements and downloadable records are retained as historical evidence.

Fifteen of sixteen demonstrated changes were accepted. Median geometric displacement error, including the refusal, was 0.93% of the image short side. The particular rotation, scale and displacement were inferred from image pairs; the possible geometric family was supplied.

A second operation used only the saved first-stage VSA and a second inferred relation. After two operations, RGB mean squared error was 0.018995 (pixel values scaled from 0 to 1; lower is better) versus 0.057015 for no geometric change through the same number of passes, and 0.048183 when the first change was skipped. Those are reductions of 66.7% and 60.6%; all 32 queries improved against the unchanged comparison.

All five predefined numerical criteria passed, and independent checks reproduced all 416 image comparisons. The first-step comparison against wrong demonstrations remains statistically uncertain.

Supplying the true geometry still produced mean RGB error of 0.011179, compared with 0.000570 for directly encoding and decoding the desired photograph. A subsequent component diagnostic found local rotation failure even with original encoder features and exact geometry. Recovery from source VSA is therefore not required for the failure; this does not isolate every contribution from feature transformation, composition and decoding.

Rotation, uniform scaling and translation are a supplied family, and the two-step procedure is supplied. The system fits numeric relations without neural retraining or acquiring a new operation family. Refused fits use explicit identity fallbacks; the refused example remains in all totals. Desired query images and known geometry are observer-only references in this experiment.

The five historical numerical criteria passed and the first-stage wrong-demonstration confidence interval includes zero. Neither fact establishes intact geometry in the rendered result. Relation fitting and continuation through saved VSA remain separate evidence; the application path is not a qualified visual rotation mechanism. No semantic editing, invented detail, general imagination or runtime promotion is established. The continuing life is unchanged.

Open the run, figures and evidence →

Some learning transfers; large feature gains can yield small image gains →

Execution: completedScientific verdict: fixed training development diagnostic complete

Distinguish improvement in the learned feature objective from useful decoded predictions, on training examples and separate development videos.

Findings and limitations

Original-feature transport lowers training image error 6.09% but raises development error 5.14%. Recovered synthesis lowers training feature error 32.06%, mostly in the coarse overview, while image error improves about 1% on both training and development. The audit passed all 960 comparisons. Fixed-panel visual review finds recognizable scenes and persistent blur or overlapping moving contours, not consistently coherent middle frames.

Training examples were previously used for learning. Development videos are separate from constructor training but are existing diagnostic data, not a fresh test set. Original encoder features are privileged inputs. Feature error is not image quality or coherence; low error on dark or static regions is not temporal understanding. All outputs pass through VSA before the frozen renderer.

Open the run, figures and evidence →

Original features did not produce the required prediction gain →

Execution: completedScientific verdict: original feature learning effect below criterion

Test whether the limited temporal-construction result also occurs when endpoint features are supplied directly from the frozen encoder.

Findings and limitations

Original encoder features improve the untrained blend by 2.91% on whole-image error and 4.26% on changing pixels. Against their own original-feature blend, transport changes changing-pixel error by −0.60% and synthesis by +0.19%, with different effects between the two videos. Neither reaches the required improvement on each source. Complete visual review finds no consistent learned repair of doubled moving contours.

The same previously exposed test cases from two videos. Original encoder features are privileged inputs at fitting and evaluation; they do not qualify a VSA-only input architecture. All candidates still recompose VSA before the frozen renderer. This is a bounded diagnostic, not independent validation or a complete causal isolation of the learning limitation.

Open the run, figures and evidence →

Can a continuing mind perceive, remember, and compose experience in vectors?

How can perception, memory, disposition, and action develop into one continuing individual?

Can relations emerge from experience before they have names?

Can a discovered program become a useful building block for the next problem?

Can an agent pass on working methods that help its successor?

How can agent work be continued, checked, and improved across models?