Historical source. Some claims in older records were subsequently corrected. The associated article states the adopted interpretation. This record preserves the original source alongside its rendered reading view.
Rendered archival Markdown
This reading view preserves headings, tables, lists, code fragments and mathematical notation from the local research record.
Flagship stories and visual direction
Positioning
Lead with consequential ML capabilities and the strongest qualified results: physical extrapolation beyond the tested direct KAN, efficient spline-layer execution, structure-exact physics representation, scoped zero-forgetting updates, and verification-guided improvement. The mathematical toolbox explains the result; it should not displace the result from the headline.
The publication map remains the evidence inventory. This brief changes its editorial hierarchy: zero-forgetting and self-improvement belong among the principal stories, not only in a reserve list. Boldness belongs in the question and demonstrated capability, with the comparator and conditions specified in the opening paragraph and figure captions.
Flagship lineup
These titles are proposed editorial framing, not newly validated experiments. Existing blog/manuscript IDs are retained where possible.
| Flagship | Proposed title | Evidence and claim boundary | Animated example | Manuscript connection |
|---|
| F01 | Beyond a direct KAN: learning the physical law that extrapolates | In the controlled oscillatory-law experiment, median amplitude-OOD rollout nRMSE is 9.29e-7 for the selected reproduction atlas versus 0.330 for the tested direct KAN. This uses known outer physics and a favorable candidate library; it is not architecture-wide KAN superiority. | Train inside a shaded amplitude range, then drive beyond it; display reference, direct KAN, polynomial control and operator-law rollouts with the recovered flux below. | B06 / M03 |
| F02 | 4.2× faster spline-layer evaluation through cardinal structure | Archived MPS batch-1024, 32-input, 64-output, 512-knot comparison: 4.23× forward and 2.95× forward/backward versus dense explicit-cardinal evaluation. Smaller grids can favor dense execution. This is not a 4.2× end-to-end training claim. | Reveal four active coefficients versus full basis materialization, then switch grid size to reveal the measured crossover. | B02 / M01 |
| F03 | When physics learning needs a solve, not a training loop | Matched synthetic Helmholtz/null-space reproduction reaches numerical precision under its stated sampling, operator and boundary assumptions. Exact satisfaction alone does not establish recovery of an arbitrary PDE solution. Classical direct/spectral methods are essential context. | Fit a wave from sufficient observations; reveal that every candidate already satisfies the homogeneous equation, then show what changes when the operator is misspecified. | Expanded B01 / M01, with M03 distinguishing unknown-law learning |
| F04 | Zero forgetting where it can be guaranteed | Protected-support coefficient updates preserve a declared continuous input region; immutable versions preserve their own programs. Pooled Gram accumulation alone does not preserve old-task predictions. | Learn a new local feature while the protected curve stays fixed; contrast this with a sample-only constraint that changes the curve between samples. | B12–B13 / M05 |
| F05 | Can a frozen language model improve without changing its weights? | The archived 120-problem multiplication stream gives 59/120 correct with execution-filtered exemplars, 37/120 without memory and 16/120 with unfiltered exemplars. This is an in-context memory/control result, not open-ended RSI or a spline-specific gain. | A problem enters three lanes: no memory, all prior answers, and verified answers only; show what enters the next prompt and final aggregate accuracy. | Promote R03 into the core series; candidate M21 below |
| F06 | Why self-improvement stalls—and what changes the outcome | Fixed-feature self-labeling, simulated verifier quality, trainable representations and teaching produce different outcomes in the archived panels. Do not convert these bounded experiments into a universal impossibility theorem. | Show separate curves for confidence filtering, an oracle-style checker and externally supplied teaching; label where each source of information comes from. | Companion to F05 / candidate M21 |
| F07 | Keep the loss function, discard the training stream | Compact summaries retain a fixed family of stable-filter loss and terminal-state queries; both Hermite and Chebyshev pass the recorded panel. Not arbitrary neural-network retraining from a capsule. | Discard the waveform, change a permitted time constant and compare the retained-query answer with the full-record reference. | B25–B26 / M14 |
| F08 | How small can a learned drone model become? | Strong measured storage/fitting advantages coexist with worse accuracy than the matched adapted neural comparator. No unmeasured drone flight, MCU or energy claim. | Recorded-flight trajectory replay next to a three-axis accuracy/storage/fitting-cost comparison, not staged footage implying new autonomous control. | B10 / M04 |
| F09 | Grow a spline network without disrupting its predictions | Exact nested transport preserves the represented function in the tested additive periodic model. Growth still requires validation; the smooth-target case worsens when capacity is increased unnecessarily. | Insert knots while the function remains stationary; compare with heuristic transfer and show the eventual validation gate. | B04 / M02 |
| F10 | Can a laptop hear you breathe? A contactless acoustic-sensing prototype | Consumer-audio code and a local report describe a short recording and a respiration-band peak. Independent reference agreement and robustness are not established; the existing medical-grade and uncertainty-limit claims are not adopted. | Show the physical speaker–reflector–microphone path beside a clearly labeled replay of the recorded signal; a future reference trace is added only after actual synchronized acquisition. | Promote B38 to a practical flagship; technical case study first |
| F11 | How much speech can a tiny dynamical model preserve? | Existing source–filter and codebook prototypes support an engineering walkthrough, not a verified competitive bitrate/quality result. | Switch between original and reconstructed audio while the excitation, poles and bit-accounting display explain what is retained. | B37 / M20 |
| F12 | A useful numerical service on one CPU thread | The recorded native calculator and verifier run on this workstation with measured request costs and process memory. This is a working engineering artifact, not proof of profitable trading or low-power-device performance. | A screen recording shows a request, native result, validation and real process measurements; visually distinguish the calculator from the verifier. | B32–B34 / M17–M18 |
F01, F02 and F03 are different claims. F01 is a learning/representation result; F02 is an implementation result; F03 is a structure-exact matched problem. Their numbers must not migrate into one another's headlines. In particular, the CPU Cox comparison is not a neural inner-product speed measurement.
The existing continuous-curvature penalty also belongs in the ML story: the additive-KAN refinement study already applies an analytic curvature Gram. Its recorded five-seed benefit is approximately a 1.10 geometric factor versus unregularized fine-grid training in that case. This supports an exact functional-regularization article; it does not establish a tenfold training speedup. See the main results, constitutive-edge subsection starting at line 1447.
Self-improvement: a concrete paper candidate and necessary corrections
Add M21 — Verification-Guided In-Context Improvement: Memory, Feedback and the Limits of Self-Training as a consolidation candidate, not a ready claim of recursive learning-rule improvement. It deserves a distinct question from M05's retention guarantees and M06's continual classification. The existing reserve post R03 becomes a core flagship rather than expanding the list merely to meet a quota.
The multiplication evidence was checked against the saved boolean outcomes and the current runner, not only the manuscript:
| Arm | Correct / 120 | Accuracy |
|---|
| No memory | 37 | 30.83% |
| Execution-filtered exemplars | 59 | 49.17% |
| Unfiltered exemplars | 16 | 13.33% |
The runner uses the latest four stored exemplars, not nearest-neighbor retrieval despite the older prose. Verification checks the parsed final answer against integer multiplication, not every intermediate reasoning step. The exemplar store is an ordinary growing list, not a demonstration of spline/Gram memory. It evaluates a frozen qwen2.5vl:7b model with temperature zero and an external execution signal; that signal is information, so “without any supervision” would be misleading.
The verified arm is 50.0% in the first half and 48.33% in the second half. Thus the aggregate lift does not demonstrate progressive compounding across this stream. A faithful animation must not draw a steadily rising curve that the record does not contain. There is also no learned change to the improvement algorithm itself. “Toward recursive self-improvement” is a motivation; “verification-guided in-context improvement” describes this experiment.
The runner catches request exceptions as empty responses, and the metric file does not retain full prompts/responses or explicit failure provenance. Before publishing a quantitative flagship, audit these details, problem duplication, ordering and control comparability, and locate the original run configuration. Do not silently rerun or rescue a revealed experiment. Any fresh confirmation needs a frozen protocol and should be distinguishable from this archive.
The self-training manuscripts also contain overly general claims such as “only teaching” can improve representations. Their observations should be stated as results of the tested protocols, not universal theorems about self-supervised learning. Likewise, an unchanged exemplar list does not establish invariant future answers from a language model.
Sources: runner, saved outcomes, scoreboard, main self-improvement section, and negative-result appendix.
Reference articles and visual lessons
The article structures were read; selected published assets were inspected visually. Full rendered-page layout and responsive behavior remain unreviewed because browser discovery returned no available browser. This is an asset and editorial review, not a claim to have watched every embedded video or inspected the live site's typography and spacing.
| Google Research reference | Observed material | Design lesson for this series |
|---|
| Learning Better Simulation Methods for Partial Differential Equations, July 23, 2019 | Article plus three frames of its 101-frame Burgers GIF: baseline and learned method share axes, colors and a visible simulation time. | A synchronized comparison can carry the result more directly than an architecture diagram. Borrow the explanatory principle, not the asset. |
| Titans + MIRAS: Helping AI have long-term memory, December 4, 2025 | Conceptual hero artwork and three-layer architecture diagram visually inspected; article exposes paper links and a looping-video section. That video was not played. | Give the hero, mechanism diagram and empirical figure different jobs. The hero attracts; the diagram explains; the results substantiate. |
| Image Compression with Neural Networks, September 29, 2016 | Article and magnified three-way reconstruction comparison inspected. | Use a meaningful close-up to make a technical tradeoff visible; explain both improved artifacts and lost detail. |
| An All-Neural On-Device Speech Recognizer, March 12, 2019 | Article and video/diagram captions read; its video was not played. | Lead with a user-observable capability, then connect it to model architecture and deployment constraints. |
These observations concern communication, not independent endorsement of every technical assertion in those posts. In particular, adopt the clarity and layering without copying overgeneralized biological or learning claims.
Proposed visual language
This is an original design recommendation, not a measured specification of Google's site.
The editorial mix has three equal purposes: research results that change how a model learns; engineering results that change its resource cost; and practical demonstrations that make the capability tangible. A breathing-sensing prototype or audible vocoder can be more immediately understandable than a benchmark plot. Practical work should not be relegated to a miscellaneous section merely because it is signal processing rather than a large neural network.
- Light neutral background, dark readable text, generous whitespace and strong
typographic hierarchy. Keep body lines around 65–75 characters as an initial design target; let figures extend wider than the prose.
- One distinctive hero composition per story: waves becoming an operator,
a model growing around a protected region, or verified examples entering memory. Avoid a generic glowing brain on every post.
- A small consistent palette: blue/teal for the proposed method, charcoal for
reference, amber for a competing method/error. Supplement color with line patterns and labels; do not encode success only as green versus red.
- Flat, precise diagrams with a restrained use of depth. Do not reproduce
Google's logos, exact artwork or branding. The research should have its own recognizable visual identity.
- A concise title, one-sentence explanatory deck, author/date, and visible
Paper / Code / Results links. The reader should understand the result and its scope before encountering detailed mathematics.
- Prefer a single purposeful visual per section over dense dashboard layouts.
Each caption should state the comparison, observation and relevant condition.
Media package for each flagship
- Hero image: an original conceptual graphic that still works as a social
preview or static poster, with no unsupported performance number embedded.
- Short silent loop: roughly 8–15 seconds explaining one mechanism or
comparison; provide play/pause and a useful static fallback.
- Result figure: real archived data with units, comparator and conditions;
include unfavorable cases or the crossover where relevant.
- Optional 30–60-second video: for a trajectory, multi-stage workflow or
narrated capability demonstration. Recorded-data visualization is labeled as such, not presented as a new physical deployment.
- Optional interaction: one meaningful control, such as grid resolution,
protected interval, time constant or query amplitude. Do not add interaction merely to decorate the page.
SVG/Canvas is appropriate for explanatory diagrams and small interactive models; prerecorded video can keep heavy result replays cheap for readers. Use MP4/WebM with a poster and reduced-motion fallback for longer loops rather than making large GIF downloads the production default. These are proposed delivery choices; no page or media implementation has been created yet.
Scientific animations must obey the same accounting as figures. Synchronize simulation time when comparing predictions; label separately if comparing wall-clock runtime. Do not make one method look slow by animating it slowly. Keep axes and error scales comparable, expose train/test ranges, distinguish schematic motion from measured results, and do not turn selected examples into a claim about the complete cohort.
First storyboards
F01: physical extrapolation beyond the tested direct KAN
- Opening: two models agree inside the observed state-amplitude band.
- Transition: an input crosses outside the band; show the archived competing
rollouts and a reference on common scales.
- Mechanism: reveal the known PDE structure and the learned flux curve.
- Result: show all registered seeds, the polynomial control and the unmatched
law/noise limitations, not only the most dramatic oscillatory example.
- Closing: paper link and an explicit sentence on supplied physics and library.
The comparison should not insinuate that a KAN could not receive the same physics. The key scientific question is where learning is placed, not whether one architecture has universal inferiority.
F04: zero forgetting within a protected region
- Opening: a learned function and a clearly shaded protected interval.
- Update: new observations arrive outside it; only permitted coefficients move.
- Contrast: an ordinary update changes old predictions; a sample-nullspace
update hides drift between samples; support protection preserves the region.
- Boundary: conflicting demands inside the protected region require rejection,
an explicit version or a changed retention policy.
Do not imply unlimited plasticity, automatic context routing or retention of all downstream behavior. The restriction is the mechanism, not fine print.
F05: verified memory for a frozen LLM
- Opening: a multiplication prompt enters a frozen model.
- Branch: a final-answer checker admits a correct outcome to exemplar memory
or rejects an incorrect one; label the checker as external computation.
- Contrast: unfiltered memory also retains wrong answers and can contaminate
later prompts. Show stored content versus the last four examples actually used.
- Result: show 37/120, 59/120 and 16/120, plus an honest chronological plot.
- Boundary: weights and learning algorithm are unchanged; the evidence is one
bounded stream and not proof of open-ended recursive self-improvement.
Production decision
Make F01, F03, F04 and F05 the research-facing flagship candidates; F02 supplies the immediately concrete ML-systems story. Add F10 as the leading hands-on prototype and F12 as the working small-resource application. The launch order should follow claim/artifact readiness rather than the largest headline number. The first production deliverable should be one complete article–animation–paper-outline package and a checked evidence manifest, not forty partially designed pages.
No new models were trained, no old experiments altered, no site published and no third-party artwork copied into the proposed publication assets. The visual references were inspected as temporary local files only.
Acoustic breathing: practical story and validation boundary
The local report and implementation describe a 20 kHz carrier, 48 kHz audio sampling, a 50 ms rolling analysis context and 10 ms processing increments. The report's 44-second example has a dominant peak near 0.134 Hz, or about 8.1 cycles per minute. Its interpretation as an accurate breathing-rate measurement is not independently established by finding that peak. The reported cardiac-band peak is even less suitable as a physiological headline without a synchronized reference and artifact controls.
The recurrence estimator is exact for an ideal single sinusoid under its assumptions; a real multipath, filtered recording is not that model. Its current implementation estimates a recurrence coefficient using dot products and arccos. That is a signal-model/annihilation connection to the operator toolbox, not by itself a new canonical exponential B-spline construction or a trained neural network. Overlapping windows also do not create independent 10 ms measurements or establish 10 ms end-to-end latency. Audio callbacks use 50 ms blocks, another distinction an animation should not hide.
For the first article, use a hardware photograph or original schematic, a 30–45-second recorded-data visualization, a compact explanation of the estimator, and an explicit account of what was actually observed. Do not fabricate synchronized chest video or a reference-sensor trace; none was established in this inspection. Avoid transmitting the high-frequency carrier through the webpage—illustrate it visually rather than auto-playing it.
A manuscript initially belongs in the prototype/methods category. A stronger measurement paper would need reference agreement, empty-room/still-object and motion-confound controls, multiple recording conditions, uncertainty and an ordinary phase-demodulation or spectral comparator under the same acquisition contract. These are publication prerequisites, not experiments authorized or run in this assessment. Sensor and participant data also need appropriate consent/provenance before publication.
Original: research/publication_series_audit_20260917/FLAGSHIP_AND_VISUAL_DIRECTION.md · Raw source file
View raw MD source
# Flagship stories and visual direction
## Positioning
Lead with consequential ML capabilities and the strongest qualified results:
physical extrapolation beyond the tested direct KAN, efficient spline-layer
execution, structure-exact physics representation, scoped zero-forgetting
updates, and verification-guided improvement. The mathematical toolbox explains
the result; it should not displace the result from the headline.
The [publication map](PUBLICATION_MAP.md) remains the evidence inventory. This
brief changes its editorial hierarchy: zero-forgetting and self-improvement
belong among the principal stories, not only in a reserve list. Boldness belongs
in the question and demonstrated capability, with the comparator and conditions
specified in the opening paragraph and figure captions.
## Flagship lineup
These titles are proposed editorial framing, not newly validated experiments.
Existing blog/manuscript IDs are retained where possible.
| Flagship | Proposed title | Evidence and claim boundary | Animated example | Manuscript connection |
| --- | --- | --- | --- | --- |
| F01 | **Beyond a direct KAN: learning the physical law that extrapolates** | In the controlled oscillatory-law experiment, median amplitude-OOD rollout nRMSE is 9.29e-7 for the selected reproduction atlas versus 0.330 for the tested direct KAN. This uses known outer physics and a favorable candidate library; it is not architecture-wide KAN superiority. | Train inside a shaded amplitude range, then drive beyond it; display reference, direct KAN, polynomial control and operator-law rollouts with the recovered flux below. | B06 / M03 |
| F02 | **4.2× faster spline-layer evaluation through cardinal structure** | Archived MPS batch-1024, 32-input, 64-output, 512-knot comparison: 4.23× forward and 2.95× forward/backward versus dense explicit-cardinal evaluation. Smaller grids can favor dense execution. This is not a 4.2× end-to-end training claim. | Reveal four active coefficients versus full basis materialization, then switch grid size to reveal the measured crossover. | B02 / M01 |
| F03 | **When physics learning needs a solve, not a training loop** | Matched synthetic Helmholtz/null-space reproduction reaches numerical precision under its stated sampling, operator and boundary assumptions. Exact satisfaction alone does not establish recovery of an arbitrary PDE solution. Classical direct/spectral methods are essential context. | Fit a wave from sufficient observations; reveal that every candidate already satisfies the homogeneous equation, then show what changes when the operator is misspecified. | Expanded B01 / M01, with M03 distinguishing unknown-law learning |
| F04 | **Zero forgetting where it can be guaranteed** | Protected-support coefficient updates preserve a declared continuous input region; immutable versions preserve their own programs. Pooled Gram accumulation alone does not preserve old-task predictions. | Learn a new local feature while the protected curve stays fixed; contrast this with a sample-only constraint that changes the curve between samples. | B12–B13 / M05 |
| F05 | **Can a frozen language model improve without changing its weights?** | The archived 120-problem multiplication stream gives 59/120 correct with execution-filtered exemplars, 37/120 without memory and 16/120 with unfiltered exemplars. This is an in-context memory/control result, not open-ended RSI or a spline-specific gain. | A problem enters three lanes: no memory, all prior answers, and verified answers only; show what enters the next prompt and final aggregate accuracy. | Promote R03 into the core series; candidate M21 below |
| F06 | **Why self-improvement stalls—and what changes the outcome** | Fixed-feature self-labeling, simulated verifier quality, trainable representations and teaching produce different outcomes in the archived panels. Do not convert these bounded experiments into a universal impossibility theorem. | Show separate curves for confidence filtering, an oracle-style checker and externally supplied teaching; label where each source of information comes from. | Companion to F05 / candidate M21 |
| F07 | **Keep the loss function, discard the training stream** | Compact summaries retain a fixed family of stable-filter loss and terminal-state queries; both Hermite and Chebyshev pass the recorded panel. Not arbitrary neural-network retraining from a capsule. | Discard the waveform, change a permitted time constant and compare the retained-query answer with the full-record reference. | B25–B26 / M14 |
| F08 | **How small can a learned drone model become?** | Strong measured storage/fitting advantages coexist with worse accuracy than the matched adapted neural comparator. No unmeasured drone flight, MCU or energy claim. | Recorded-flight trajectory replay next to a three-axis accuracy/storage/fitting-cost comparison, not staged footage implying new autonomous control. | B10 / M04 |
| F09 | **Grow a spline network without disrupting its predictions** | Exact nested transport preserves the represented function in the tested additive periodic model. Growth still requires validation; the smooth-target case worsens when capacity is increased unnecessarily. | Insert knots while the function remains stationary; compare with heuristic transfer and show the eventual validation gate. | B04 / M02 |
| F10 | **Can a laptop hear you breathe? A contactless acoustic-sensing prototype** | Consumer-audio code and a local report describe a short recording and a respiration-band peak. Independent reference agreement and robustness are not established; the existing medical-grade and uncertainty-limit claims are not adopted. | Show the physical speaker–reflector–microphone path beside a clearly labeled replay of the recorded signal; a future reference trace is added only after actual synchronized acquisition. | Promote B38 to a practical flagship; technical case study first |
| F11 | **How much speech can a tiny dynamical model preserve?** | Existing source–filter and codebook prototypes support an engineering walkthrough, not a verified competitive bitrate/quality result. | Switch between original and reconstructed audio while the excitation, poles and bit-accounting display explain what is retained. | B37 / M20 |
| F12 | **A useful numerical service on one CPU thread** | The recorded native calculator and verifier run on this workstation with measured request costs and process memory. This is a working engineering artifact, not proof of profitable trading or low-power-device performance. | A screen recording shows a request, native result, validation and real process measurements; visually distinguish the calculator from the verifier. | B32–B34 / M17–M18 |
F01, F02 and F03 are different claims. F01 is a learning/representation result;
F02 is an implementation result; F03 is a structure-exact matched problem.
Their numbers must not migrate into one another's headlines. In particular,
the CPU Cox comparison is not a neural inner-product speed measurement.
The existing continuous-curvature penalty also belongs in the ML story:
the additive-KAN refinement study already applies an analytic curvature Gram.
Its recorded five-seed benefit is approximately a 1.10 geometric factor versus
unregularized fine-grid training in that case. This supports an exact
functional-regularization article; it does not establish a tenfold training
speedup. See the [main results](../../paper/v2_sections/04_results.tex),
constitutive-edge subsection starting at line 1447.
## Self-improvement: a concrete paper candidate and necessary corrections
Add **M21 — Verification-Guided In-Context Improvement: Memory, Feedback and
the Limits of Self-Training** as a consolidation candidate, not a ready claim
of recursive learning-rule improvement. It deserves a distinct question from
M05's retention guarantees and M06's continual classification. The existing
reserve post R03 becomes a core flagship rather than expanding the list merely
to meet a quota.
The multiplication evidence was checked against the saved boolean outcomes and
the current runner, not only the manuscript:
| Arm | Correct / 120 | Accuracy |
| --- | ---: | ---: |
| No memory | 37 | 30.83% |
| Execution-filtered exemplars | 59 | 49.17% |
| Unfiltered exemplars | 16 | 13.33% |
The runner uses the latest four stored exemplars, not nearest-neighbor retrieval
despite the older prose. Verification checks the parsed final answer against
integer multiplication, not every intermediate reasoning step. The exemplar
store is an ordinary growing list, not a demonstration of spline/Gram memory.
It evaluates a frozen `qwen2.5vl:7b` model with temperature zero and an external
execution signal; that signal is information, so “without any supervision”
would be misleading.
The verified arm is 50.0% in the first half and 48.33% in the second half. Thus
the aggregate lift does not demonstrate progressive compounding across this
stream. A faithful animation must not draw a steadily rising curve that the
record does not contain. There is also no learned change to the improvement
algorithm itself. “Toward recursive self-improvement” is a motivation;
“verification-guided in-context improvement” describes this experiment.
The runner catches request exceptions as empty responses, and the metric file
does not retain full prompts/responses or explicit failure provenance. Before
publishing a quantitative flagship, audit these details, problem duplication,
ordering and control comparability, and locate the original run configuration.
Do not silently rerun or rescue a revealed experiment. Any fresh confirmation
needs a frozen protocol and should be distinguishable from this archive.
The self-training manuscripts also contain overly general claims such as “only
teaching” can improve representations. Their observations should be stated as
results of the tested protocols, not universal theorems about self-supervised
learning. Likewise, an unchanged exemplar list does not establish invariant
future answers from a language model.
Sources: [runner](../../bio_growth/self_improve_llm_verifier.py),
[saved outcomes](../../bio_growth/closed_form_neat_outputs/metrics_self_improve_llm_verifier.json),
[scoreboard](../../RESULTS_SCOREBOARD.md),
[main self-improvement section](../../paper/v2_sections/04_results.tex), and
[negative-result appendix](../../paper/v2_sections/A3_negatives.tex).
## Reference articles and visual lessons
The article structures were read; selected published assets were inspected
visually. Full rendered-page layout and responsive behavior remain unreviewed
because browser discovery returned no available browser. This is an asset and
editorial review, not a claim to have watched every embedded video or inspected
the live site's typography and spacing.
| Google Research reference | Observed material | Design lesson for this series |
| --- | --- | --- |
| [Learning Better Simulation Methods for Partial Differential Equations](https://research.google/blog/learning-better-simulation-methods-for-partial-differential-equations/), July 23, 2019 | Article plus three frames of its 101-frame Burgers GIF: baseline and learned method share axes, colors and a visible simulation time. | A synchronized comparison can carry the result more directly than an architecture diagram. Borrow the explanatory principle, not the asset. |
| [Titans + MIRAS: Helping AI have long-term memory](https://research.google/blog/titans-miras-helping-ai-have-long-term-memory/), December 4, 2025 | Conceptual hero artwork and three-layer architecture diagram visually inspected; article exposes paper links and a looping-video section. That video was not played. | Give the hero, mechanism diagram and empirical figure different jobs. The hero attracts; the diagram explains; the results substantiate. |
| [Image Compression with Neural Networks](https://research.google/blog/image-compression-with-neural-networks/), September 29, 2016 | Article and magnified three-way reconstruction comparison inspected. | Use a meaningful close-up to make a technical tradeoff visible; explain both improved artifacts and lost detail. |
| [An All-Neural On-Device Speech Recognizer](https://research.google/blog/an-all-neural-on-device-speech-recognizer/), March 12, 2019 | Article and video/diagram captions read; its video was not played. | Lead with a user-observable capability, then connect it to model architecture and deployment constraints. |
These observations concern communication, not independent endorsement of every
technical assertion in those posts. In particular, adopt the clarity and
layering without copying overgeneralized biological or learning claims.
## Proposed visual language
This is an original design recommendation, not a measured specification of
Google's site.
The editorial mix has three equal purposes: research results that change how a
model learns; engineering results that change its resource cost; and practical
demonstrations that make the capability tangible. A breathing-sensing prototype
or audible vocoder can be more immediately understandable than a benchmark
plot. Practical work should not be relegated to a miscellaneous section merely
because it is signal processing rather than a large neural network.
- Light neutral background, dark readable text, generous whitespace and strong
typographic hierarchy. Keep body lines around 65–75 characters as an initial
design target; let figures extend wider than the prose.
- One distinctive hero composition per story: waves becoming an operator,
a model growing around a protected region, or verified examples entering
memory. Avoid a generic glowing brain on every post.
- A small consistent palette: blue/teal for the proposed method, charcoal for
reference, amber for a competing method/error. Supplement color with line
patterns and labels; do not encode success only as green versus red.
- Flat, precise diagrams with a restrained use of depth. Do not reproduce
Google's logos, exact artwork or branding. The research should have its own
recognizable visual identity.
- A concise title, one-sentence explanatory deck, author/date, and visible
Paper / Code / Results links. The reader should understand the result and
its scope before encountering detailed mathematics.
- Prefer a single purposeful visual per section over dense dashboard layouts.
Each caption should state the comparison, observation and relevant condition.
## Media package for each flagship
1. **Hero image:** an original conceptual graphic that still works as a social
preview or static poster, with no unsupported performance number embedded.
2. **Short silent loop:** roughly 8–15 seconds explaining one mechanism or
comparison; provide play/pause and a useful static fallback.
3. **Result figure:** real archived data with units, comparator and conditions;
include unfavorable cases or the crossover where relevant.
4. **Optional 30–60-second video:** for a trajectory, multi-stage workflow or
narrated capability demonstration. Recorded-data visualization is labeled
as such, not presented as a new physical deployment.
5. **Optional interaction:** one meaningful control, such as grid resolution,
protected interval, time constant or query amplitude. Do not add interaction
merely to decorate the page.
SVG/Canvas is appropriate for explanatory diagrams and small interactive
models; prerecorded video can keep heavy result replays cheap for readers.
Use MP4/WebM with a poster and reduced-motion fallback for longer loops rather
than making large GIF downloads the production default. These are proposed
delivery choices; no page or media implementation has been created yet.
Scientific animations must obey the same accounting as figures. Synchronize
simulation time when comparing predictions; label separately if comparing
wall-clock runtime. Do not make one method look slow by animating it slowly.
Keep axes and error scales comparable, expose train/test ranges, distinguish
schematic motion from measured results, and do not turn selected examples into
a claim about the complete cohort.
## First storyboards
### F01: physical extrapolation beyond the tested direct KAN
- Opening: two models agree inside the observed state-amplitude band.
- Transition: an input crosses outside the band; show the archived competing
rollouts and a reference on common scales.
- Mechanism: reveal the known PDE structure and the learned flux curve.
- Result: show all registered seeds, the polynomial control and the unmatched
law/noise limitations, not only the most dramatic oscillatory example.
- Closing: paper link and an explicit sentence on supplied physics and library.
The comparison should not insinuate that a KAN could not receive the same
physics. The key scientific question is where learning is placed, not whether
one architecture has universal inferiority.
### F04: zero forgetting within a protected region
- Opening: a learned function and a clearly shaded protected interval.
- Update: new observations arrive outside it; only permitted coefficients move.
- Contrast: an ordinary update changes old predictions; a sample-nullspace
update hides drift between samples; support protection preserves the region.
- Boundary: conflicting demands inside the protected region require rejection,
an explicit version or a changed retention policy.
Do not imply unlimited plasticity, automatic context routing or retention of
all downstream behavior. The restriction is the mechanism, not fine print.
### F05: verified memory for a frozen LLM
- Opening: a multiplication prompt enters a frozen model.
- Branch: a final-answer checker admits a correct outcome to exemplar memory
or rejects an incorrect one; label the checker as external computation.
- Contrast: unfiltered memory also retains wrong answers and can contaminate
later prompts. Show stored content versus the last four examples actually used.
- Result: show 37/120, 59/120 and 16/120, plus an honest chronological plot.
- Boundary: weights and learning algorithm are unchanged; the evidence is one
bounded stream and not proof of open-ended recursive self-improvement.
## Production decision
Make F01, F03, F04 and F05 the research-facing flagship candidates; F02 supplies
the immediately concrete ML-systems story. Add F10 as the leading hands-on
prototype and F12 as the working small-resource application. The launch order should follow
claim/artifact readiness rather than the largest headline number. The first
production deliverable should be one complete article–animation–paper-outline
package and a checked evidence manifest, not forty partially designed pages.
No new models were trained, no old experiments altered, no site published and
no third-party artwork copied into the proposed publication assets. The visual
references were inspected as temporary local files only.
## Acoustic breathing: practical story and validation boundary
The [local report](../../../spline_signal_sensing/report.md) and
[implementation](../../../spline_signal_sensing/ghost_v2.py) describe a 20 kHz
carrier, 48 kHz audio sampling, a 50 ms rolling analysis context and 10 ms
processing increments. The report's 44-second example has a dominant peak near
0.134 Hz, or about 8.1 cycles per minute. Its interpretation as an accurate
breathing-rate measurement is not independently established by finding that
peak. The reported cardiac-band peak is even less suitable as a physiological
headline without a synchronized reference and artifact controls.
The recurrence estimator is exact for an ideal single sinusoid under its
assumptions; a real multipath, filtered recording is not that model. Its current
implementation estimates a recurrence coefficient using dot products and
arccos. That is a signal-model/annihilation connection to the operator toolbox,
not by itself a new canonical exponential B-spline construction or a trained
neural network. Overlapping windows also do not create independent 10 ms
measurements or establish 10 ms end-to-end latency. Audio callbacks use 50 ms
blocks, another distinction an animation should not hide.
For the first article, use a hardware photograph or original schematic, a
30–45-second recorded-data visualization, a compact explanation of the
estimator, and an explicit account of what was actually observed. Do not
fabricate synchronized chest video or a reference-sensor trace; none was
established in this inspection. Avoid transmitting the high-frequency carrier
through the webpage—illustrate it visually rather than auto-playing it.
A manuscript initially belongs in the prototype/methods category. A stronger
measurement paper would need reference agreement, empty-room/still-object and
motion-confound controls, multiple recording conditions, uncertainty and an
ordinary phase-demodulation or spectral comparator under the same acquisition
contract. These are publication prerequisites, not experiments authorized or
run in this assessment. Sensor and participant data also need appropriate
consent/provenance before publication.