# Review paper and test claims

User messages, assistant replies and progress updates from September 9, 2026, through the request to export this session. Tool logs, internal reasoning, runtime instructions and automatic browser context are omitted. The local PDF citation is rewritten to its public download. Messages are preserved as written, including provisional findings and cost estimates superseded later in the conversation.

## User · 2026-09-09T17:53:50.728Z

connect this folder to [https://github.com/yaroslavvb/gradient-dissent](https://github.com/yaroslavvb/gradient-dissent) repo. Read the paper [https://www.alphaxiv.org/pdf/2609.05275](https://www.alphaxiv.org/pdf/2609.05275), **pdf**
Do an in-depth review of [https://www.alphaxiv.org/abs/2609.05275](https://www.alphaxiv.org/abs/2609.05275). I want to know how it connects to existing work. What's significant about this paper, maybe potential connections to [https://docs.google.com/document/d/1B9867EN6Bg4ZVQK9vI\_ZqykZ5HEtMAHJ7zBGGas4szQ/edit?tab=t.0#heading=h.j6rssh3enbtd](https://docs.google.com/document/d/1B9867EN6Bg4ZVQK9vI_ZqykZ5HEtMAHJ7zBGGas4szQ/edit?tab=t.0#heading=h.j6rssh3enbtd) (last meeting notes [https://yaroslavvb.github.io/gyroscope/meetings/2026-09-07-061a993a3e32a40a.html](https://yaroslavvb.github.io/gyroscope/meetings/2026-09-07-061a993a3e32a40a.html)). Do a critical review. Find things which don't make sense. Run experiments locally on toy examples to test issues in the paper, upload to github/gradient dissent repo. Create a an interactive report/slide-deck with some nice animated visualizations to help understand concepts in the paper, deploy to github pages, give me a link.

## Assistant · progress update · 2026-09-09T17:53:53.561Z

I’ll connect the folder to the repo, read the PDF and meeting notes, then build a critical review with local experiments and an interactive report on GitHub Pages. I’m using the deep-research and PDF skills for the review, and the Google Drive skill to read the linked document.

## Assistant · progress update · 2026-09-09T17:54:29.390Z

The folder is now connected to the empty GitHub repo, and I’ve downloaded the full 27-page PDF. The paper’s main contribution appears to be a carefully tuned training recipe, with important qualifications around FLOPs, elapsed time, and energy. I’ll test those distinctions and check whether the proposed scaling preserves learning dynamics as well as activation scale.

## Assistant · progress update · 2026-09-09T17:55:31.717Z

I found a concrete mathematical issue in the PDF: the preferred shared mask for attention and FFN can break the stated train/evaluation expectation equality—even when both sublayers are linear. I’m checking it by exact enumeration. Separately, the 25% savings result has no same-size dense 8.2B control, and lower validation loss does not consistently translate to better downstream scores.

## Assistant · progress update · 2026-09-09T17:59:04.552Z

The local experiments are complete. They confirm the expectation counterexample and show why implementation matters: gathering active sequences was slightly slower on the small test but faster on the larger one. The training toy also gives a useful mixed result—dropout greatly improves early-exit robustness, but its full-model accuracy advantage at matched modeled compute is uncertain, and a smaller dense model is competitive.

## Assistant · progress update · 2026-09-09T18:00:49.319Z

The report now has 16 chapters and a presentation mode, with interactive views of the dropout schedule, expectation error, data movement costs, and speculative decoding. The extended review also identifies a close missing precedent—RAPTR’s progressive subnetworks—and a possible weight-decay formula inconsistency. I’m checking the report against the saved results before publishing.

## Assistant · progress update · 2026-09-09T18:03:08.391Z

The report checks passed, including the interactive controls, equations, and links to the measured data. The repo contains the experiment scripts, raw results, environment metadata, and repeatability checks. I’m publishing the report and slide view to GitHub Pages now.

## Assistant · progress update · 2026-09-09T18:04:58.034Z

The source and experiments are uploaded, and GitHub has built the site. I’m waiting for the public URL to serve the final version. The review’s central distinction is clear: depth robustness looks useful, while the case for energy savings still needs direct measurements.

## Assistant · 2026-09-09T18:05:24.910Z

Published: **[Interactive report and slide deck](https://yaroslavvb.github.io/gradient-dissent/)** — click **Present** for slide mode.

[Extended review](https://yaroslavvb.github.io/gradient-dissent/review.html) · [GitHub repository, code and raw results](https://github.com/yaroslavvb/gradient-dissent)

The folder is connected and everything is pushed. I read the full 27-page PDF and linked context, and ran 100 tuning plus 60 evaluation runs.

Key findings:

- An exact counterexample breaks the paper’s shared-mask expectation claim.
- The largest 8.2B result lacks a same-size dense control.
- FLOPs savings do not establish elapsed-time or energy savings.
- Toy experiments support depth robustness, but full-model accuracy gains remain uncertain.
- RAPTR is an important missing precedent; locality-aware masking is a promising Sutro connection.

The live report and data were verified against the committed files. These are mechanism tests, not an LLM-scale reproduction.

## User · 2026-09-09T18:14:20.193Z

Reproduce some toy experiments on depth robustness, feel free to use Modal, but don't exceed $50 for experiments. Give me a separate experimental report. Upload to GitHub. Give me a GitHub Pages link.

## Assistant · progress update · 2026-09-09T18:14:23.854Z

I’ll run a separate depth-robustness study with a small causal transformer, testing early exits and skipped layers across multiple seeds. I’ll use local compute first, keep any Modal spending below $50, and publish a standalone report alongside the code and results.

## Assistant · progress update · 2026-09-09T18:15:49.636Z

I’m using two tasks: a tiny character-level language model and an 8×8 handwritten-digit classifier. For each six-block model, I’ll test every nonempty subset of layers, including early exits. The main comparison will measure how much quality degrades from the full model, so a lower-quality starting model cannot look robust just because it has less to lose.

## Assistant · progress update · 2026-09-09T18:19:20.473Z

Both models train quickly enough locally, so cloud spending remains $0. The initial validation sweeps favored rates at the edge of the search range; I expanded the learning-rate grid equally for every method. I’m also separating layer subsets that preserve the first layer from those that delete it, since the paper’s increasing-depth schedule always keeps that layer.

## Assistant · progress update · 2026-09-09T18:22:14.635Z

The digits experiment supports a clear reduction in early-exit damage: removing the last two blocks adds about 0.076 cross-entropy to the dense model, versus 0.009–0.010 with layer dropout. Full-model accuracy stays around 98.3%. The smaller dense model is also competitive, and the effect size changes with learning rate; both qualifications will be in the report.

## Assistant · progress update · 2026-09-09T18:24:34.496Z

The transformer results agree on the main point: early-exit damage is smaller with layer dropout across the five seeds. Dense four-block exit adds about 0.050 nats per character, versus 0.003 with constant dropout and 0.007 with decreasing dropout. A separately trained four-block model still has the best mean absolute loss, so the report distinguishes robustness from choosing the best small model.

## Assistant · progress update · 2026-09-09T18:26:44.134Z

Both experiment suites are complete, with $0 spent on cloud compute. I’m checking the separate report against the saved results and testing its interactive charts before publishing it to GitHub Pages.

## Assistant · progress update · 2026-09-09T18:28:02.124Z

The independent audit found that the report’s numbers, intervals, and plots match the raw results. Both toys show less damage from early exit with ILD, but full-depth quality gains remain unresolved, and separately trained smaller models are competitive. The report also discloses the learning-rate sensitivity.

## Assistant · progress update · 2026-09-09T18:28:54.814Z

The report and raw results are ready. Checks passed for all interactive chart states, source hashes, complete layer-subset counts, and paired confidence intervals. I’m uploading them now, then I’ll verify the separate GitHub Pages URL.

## Assistant · 2026-09-09T18:30:17.880Z

Published: **[Separate depth-robustness experimental report](https://yaroslavvb.github.io/gradient-dissent/depth-robustness/)**.

- Tested a tiny causal transformer and a digit classifier, with five seeds per treatment and exhaustive layer-subset evaluation.
- Layer dropout reduced early-exit damage on both tasks. Full-model quality gains remain unresolved; separately trained smaller models remain competitive.
- Includes interactive charts, confidence intervals, raw results, reproduction instructions, and limitations.
- **Cloud spending: $0 of $50.** Everything ran locally.

[Code and results on GitHub](https://github.com/yaroslavvb/gradient-dissent/commit/e7157265d3a2d4349a758ba6bc9341388946f383). Deployment verified.

## User · 2026-09-09T18:30:18.151Z

Now try larger experiments, maybe based on Karpathy's NanoGPT, Get to a scale which requires A100, and run experiments on A100 gpus on modal in parallel. I'm interested in figuring out how much their findings transfer to non-GPT examples.

## Assistant · progress update · 2026-09-09T18:30:20.439Z

I’ll check the Modal setup and design a larger test with a GPT baseline and a non-GPT model. I’ll treat the earlier $50 ceiling as still applying; that will determine how many A100 runs we can afford.

## Assistant · 2026-09-09T18:30:21.477Z

For “non-GPT examples,” which transfer test matters most to you? I’ll use vision classification unless you prefer another direction.
- Vision transformer and residual CNN
- Encoder-only text model
- Different datasets with GPT models

## Assistant · progress update · 2026-09-09T18:32:14.117Z

Modal is connected. I’m proceeding with three model families: a roughly 124M-parameter GPT baseline, an 86M vision transformer, and a convolutional ConvNeXt model. The vision tasks will test transfer beyond causal language modeling. Short A100 pilots will measure throughput before I fix the training lengths within the $50 budget.

## Assistant · progress update · 2026-09-09T18:34:54.964Z

The main design issue is making pruning meaningful across architectures. For GPT and the vision transformer, I’ll test keeping the first two-thirds of the blocks. For ConvNeXt, I’ll remove one-third of residual blocks within each stage while preserving the downsampling layers. Each comparison will report both full-model quality and the loss caused by pruning.

## Assistant · progress update · 2026-09-09T18:37:49.394Z

The model checks pass, including a check that the GPT implementation matches the pinned nanoGPT code when dropout is disabled. Corpus preparation is running on CPU before any GPU allocation. I’ve also added per-run time limits and a cost reservation ledger so the parallel runs stay within the existing budget.

## Assistant · progress update · 2026-09-09T18:39:51.896Z

WikiText preparation is complete: about 118 million training tokens, with separate official validation and test splits. The CIFAR download is still progressing. The first GPU pilots will measure actual training memory and seconds per step; those measurements will set the final run lengths.

## Assistant · progress update · 2026-09-09T18:45:03.513Z

Both datasets are ready. I fixed a slow CIFAR download using a faster mirror and verified the complete archive against the official checksum. All three A100 pilot jobs are submitted and waiting for GPU allocation; metered usage so far is only a few cents from data preparation.

## Assistant · progress update · 2026-09-09T18:47:34.138Z

The A100 pilots completed their training. GPT took about 0.3 seconds per step, the vision transformer about 0.09 seconds, and ConvNeXt about 0.03 seconds after startup. A result-transfer serialization error affected the local client, but the results were saved on Modal; I’m retrieving them directly and fixing the transfer path before the main sweep.

## Assistant · progress update · 2026-09-09T18:48:13.692Z

The GPT workload used **30.4 GiB of GPU memory**, so this batch and context size exceeds a 24 GB GPU’s capacity without memory-saving changes. It ran successfully on both A100 variants, with similar throughput. I’ll use the cheaper A100 allocation for the main sweep and compare dense, constant-dropout, and decreasing-dropout training across all three families.

## Assistant · progress update · 2026-09-09T18:51:06.283Z

Nine A100 jobs are now training in parallel. The protocol is fixed at **27 full-duration tuning runs and 27 final runs**, with three final seeds per recipe. Each GPT run sees about 105 million tokens; the vision transformer and ConvNeXt runs see roughly 58 and 114 equivalent passes over their training images. The full plan reserves about **$39.67**, including runtime margins.

## Assistant · progress update · 2026-09-09T18:55:30.653Z

The first nine A100 runs are progressing normally, and the models are learning. I’m saving the fixed protocol and pilot measurements now while the remaining tuning runs queue; the isolated Modal environment currently reports about $1.87 in metered usage.

## Assistant · progress update · 2026-09-09T18:56:15.346Z

The protocol and code are now on GitHub. The comparison separates two transfer questions: does the recipe help a vision transformer, and does it also help a convolutional residual network? I’m checking absolute prediction quality alongside pruning damage so a weak model cannot appear “robust” simply because its blocks contribute little.

## Assistant · progress update · 2026-09-09T18:57:29.032Z

The first full-duration run has finished successfully, and its GPU slot has moved to the next learning rate. The final test phase remains gated on all 27 tuning runs; learning rates are selected using full-depth validation loss only.

## Assistant · progress update · 2026-09-09T18:58:46.439Z

All three first-round ConvNeXt runs completed. Their validation accuracy is roughly 53–57%, confirming that this is a learned classifier rather than a near-random model. Those are tuning results only; pruning and transfer conclusions will come from the separate final seeds.

## Assistant · progress update · 2026-09-09T19:00:09.921Z

The GPT and vision-transformer runs are now past their halfway points. The report will also distinguish robustness after annealing dropout to zero from robustness after prolonged dense retraining—the current schedule tests the former.

## Assistant · progress update · 2026-09-09T19:01:53.200Z

The first GPT runs have reached about 70–80% of their training budget, with no failed runs so far. The report and statistical checks are being prepared in parallel; current metered usage is about $3.87 against the $50 ceiling.

## Assistant · progress update · 2026-09-09T19:02:24.944Z

The interactive report tooling now passes its chart, mask-selection, export, and arithmetic checks. It will only publish once all 27 final runs pass the data audit, so incomplete results cannot become the reported conclusion.

## Assistant · progress update · 2026-09-09T19:04:02.677Z

The first GPT run is at 90% of its training budget, and the first vision-transformer runs are at 80%. The full tuning-plus-final sweep is still expected to take roughly another hour; the current metered total is about $4.57.

## Assistant · progress update · 2026-09-09T19:05:34.207Z

The first full GPT run has completed successfully at the planned 105-million-token exposure. GPU memory use and runtime remain within the pilot estimates, and the queue is continuing without retries.

## Assistant · progress update · 2026-09-09T19:06:59.948Z

Ten of the 27 tuning runs are complete, with no failures. The first vision-transformer runs reach about 48–49% validation accuracy; the remaining learning rates will determine which configurations advance to the final seeds.

## Assistant · progress update · 2026-09-09T19:08:43.104Z

The second learning-rate round is progressing normally. I also caught a reporting detail: Modal can reuse GPU containers, so allocator-reserved memory may include an earlier run’s cache. The report will distinguish that from live tensor allocation when describing the A100 memory requirement.

## Assistant · progress update · 2026-09-09T19:11:10.086Z

The run queue remains healthy at nine concurrent A100s. The memory audit confirms about 30.35 GiB of live allocation for GPT, so the larger workload exceeds a 24 GiB GPU at this batch size independently of allocator caching.

## Assistant · progress update · 2026-09-09T19:12:22.743Z

The second dense GPT configuration is nearly finished, and the second-round ConvNeXt runs are around 70% complete. Metered usage is about $7.31; the planned reservation total remains unchanged.

## Assistant · progress update · 2026-09-09T19:13:40.738Z

Eleven tuning runs are now complete. The second dense GPT run finished normally, and its slot has moved to the highest learning-rate vision-transformer configuration.

## Assistant · progress update · 2026-09-09T19:14:57.581Z

The sweep has reached the highest learning-rate round. Thirteen tuning runs are complete, with another ConvNeXt run finishing now; no test-set evaluations have been used in this stage.

## Assistant · progress update · 2026-09-09T19:16:18.952Z

Tuning is 14/27 complete, with no failed runs. Metered usage is about $8.63.

## Assistant · progress update · 2026-09-09T19:17:47.529Z

The remaining second-round dropout runs are approaching completion. The report now also labels sampling with replacement, memory-cache effects, and LayerScale statistics explicitly, to avoid overstating what those measurements show.

## Assistant · progress update · 2026-09-09T19:19:06.225Z

All runs remain finite and within their time limits. Some high learning-rate vision runs are learning less effectively, which is why every recipe gets the same validation search before the final comparison.

## Assistant · progress update · 2026-09-09T19:20:26.804Z

Fifteen tuning runs are complete. The remaining jobs are filling out the final learning-rate round; metered usage is about $9.98.

## Assistant · progress update · 2026-09-09T19:21:47.411Z

Sixteen tuning runs are complete, and the last decreasing-dropout GPT configuration has started. The experiment still uses the original readout after pruning, so any robustness gain will come from training rather than refitting an exit head.

## Assistant · progress update · 2026-09-09T19:23:12.400Z

Tuning is now 20/27 complete, with all seven remaining jobs already running. Once those finish, the final 27 runs will start automatically using the validation-selected learning rates.

## Assistant · progress update · 2026-09-09T19:24:29.637Z

The remaining jobs are progressing normally. I’m keeping the learning-rate grid fixed and will flag boundary winners in the report; the study compares equally tuned recipes within this budget, rather than claiming optimal settings.

## Assistant · progress update · 2026-09-09T19:25:35.811Z

The final ConvNeXt tuning runs are at roughly 40–80% completion, while the last vision-transformer run is at 20%. Metered usage is about $11.84.

## Assistant · progress update · 2026-09-09T19:26:59.888Z

No failures have appeared in the last tuning round. The longest remaining job is the final vision-transformer configuration; the faster ConvNeXt jobs are nearly done.

## Assistant · progress update · 2026-09-09T19:28:10.724Z

Tuning is 21/27 complete. The last constant-dropout ConvNeXt run finished successfully, and current metered usage is about $12.42.

## Assistant · progress update · 2026-09-09T19:29:44.347Z

All nine ConvNeXt tuning runs are complete. They reveal a useful tradeoff: higher learning rates can improve top-1 accuracy while worsening cross-entropy. I’ll show both metrics, while keeping the predeclared cross-entropy selection rule.

## Assistant · progress update · 2026-09-09T19:31:14.481Z

Tuning is 24/27 complete. The report now exposes every learning-rate candidate’s validation loss and accuracy, including which one the fixed selection rule chooses.

## Assistant · progress update · 2026-09-09T19:32:34.630Z

Only two tuning runs remain: decreasing-dropout GPT and ViT, both past halfway. The completed runs and cost records are intact; metered usage is about $13.30.

## Assistant · progress update · 2026-09-09T19:34:16.518Z

The last ViT tuning run has reached 70%; GPT is progressing toward its final checkpoints. Only these two A100s are still needed for tuning, so the usage total is increasing more slowly.

## Assistant · progress update · 2026-09-09T19:35:23.209Z

Both remaining tuning jobs are now at 80%. The final-run launcher is waiting for their complete validation results; metered usage is about $13.46.

## Assistant · progress update · 2026-09-09T19:36:47.525Z

The last GPT tuning run is at 90%. I also verified that all three executed training files still match the hashes frozen before the sweep began.

## Assistant · progress update · 2026-09-09T19:38:11.791Z

Twenty-six tuning runs are complete. Only the final ViT run remains, at 90%; the complete final-seed phase is next.

## Assistant · progress update · 2026-09-09T19:39:24.373Z

All 27 tuning runs finished successfully. The final 27 runs are now launching, with three new seeds per recipe and architecture. Eight of the nine selected learning rates lie at a grid boundary, which will be a prominent limitation in the report.

## Assistant · progress update · 2026-09-09T19:40:49.754Z

The independent audit found an initialization-hash mismatch in some tuning runs despite matching seeds. I’m investigating whether this is a numerical difference between worker CPUs or a real pairing problem before accepting the final comparison.

## Assistant · progress update · 2026-09-09T19:43:19.056Z

The mismatch is confined to ConvNeXt; GPT and ViT match exactly, and all data streams and learning-rate selections pass. I’m reproducing the ConvNeXt initializations on the existing workers using CPU-only probes. The hash check remains strict while I establish the cause.

## Assistant · progress update · 2026-09-09T19:46:15.167Z

The mismatch is now explained numerically: both observed ConvNeXt initializations were reproduced, and every parameter was compared across all four seeds. The largest difference is only \(7.45\times10^{-9}\), with identical RNG states. I’m documenting this as numerically close initialization and limiting the audit exception to those verified hashes.

## Assistant · progress update · 2026-09-09T19:48:24.683Z

The CPU probes locate the first difference in the inverse-error-function step used by initialization. The report will disclose this small roundoff effect and the audit adjustment. Meanwhile, the first two final models have completed all nine pruning evaluations successfully.

## Assistant · progress update · 2026-09-09T19:49:55.505Z

The initialization audit now passes with that narrowly documented exception; unexpected hashes still fail. Three final models have completed their full pruning panels, and the second seed has begun.

## Assistant · progress update · 2026-09-09T19:51:49.794Z

The tuning results, locked final configurations, and initialization audit are now [on GitHub](https://github.com/yaroslavvb/gradient-dissent/commit/c2bb915). The first GPT and ViT final runs are around 70% complete; metered usage is about $17.58.

## Assistant · progress update · 2026-09-09T19:53:57.971Z

The first three ConvNeXt final runs pass the full integrity check: all nine masks, all 10,000 test images, paired data streams, and the audited initialization variants. The first GPT and ViT runs are approaching completion.

## Assistant · progress update · 2026-09-09T19:55:59.297Z

The first language-model results pass the data checks. Their fixed test panel contains 246 nonoverlapping, document-contained windows—251,904 target tokens. The report uses that actual count.

## Assistant · progress update · 2026-09-09T19:57:45.818Z

The first complete seed has passed the integrity audit across all three architectures and all nine depth masks. Ten of the 27 final runs are finished; the rest are training in parallel on A100s. Metered cost is about $19.85, within the original $50 cap.

## Assistant · progress update · 2026-09-09T19:58:13.907Z

The report will distinguish early exit in GPT and ViT from stagewise thinning in ConvNeXt. Keeping two-thirds of the residual blocks gives useful comparisons within each architecture, but it does not imply equal compute savings across them. The report will show both pruning damage and absolute accuracy or loss.

## Assistant · progress update · 2026-09-09T19:59:33.493Z

The learning-rate search found a limitation worth highlighting: eight of the nine selected rates sit at the edge of their search grids. Also, the best ConvNeXt validation loss did not coincide with the best accuracy. I’m keeping the predeclared loss-based selections and reporting both metrics.

## Assistant · progress update · 2026-09-09T19:59:58.199Z

The report’s interaction checks pass on test data, including mask selection, seed intervals, and metric switches. It will use the actual evaluation counts: all 10,000 CIFAR-100 test images and 246 document-contained language windows. The live report will be generated only after all final runs pass verification.

## Assistant · progress update · 2026-09-09T20:01:10.254Z

Nine A100 workers are active, with no failed final runs. The GPT workload uses about 30.4 GiB of live GPU allocations at batch 32 and context 1,024; the vision models need less memory. These runs test depth robustness, with no claim of faster training from masking.

## Assistant · progress update · 2026-09-09T20:02:32.134Z

The runs are approaching the next batch of completions. One stress test deletes the first block, which increasing layer dropout never removes during training. I’ll keep that result separate from the primary depth test so the report makes clear which deletions the training recipe actually prepares for.

## Assistant · progress update · 2026-09-09T20:03:57.619Z

Thirteen of 27 final runs are complete, with no failures, and the third seed has started. Metered cost is about $21.88. The remaining work is already covered by the existing reservations; no additional experiment batch is being added.

## Assistant · progress update · 2026-09-09T20:05:09.140Z

Fifteen of 27 final runs have finished successfully, and their model checkpoints are saved in the Modal volume. I’m preparing the report’s interpretation section around two distinct questions: whether dropout reduces pruning damage, and whether the resulting pruned model makes better predictions.

## Assistant · progress update · 2026-09-09T20:06:31.189Z

The later seeds are training normally, and metered cost is about $22.61. The report will retain all nine fixed pruning masks and all three final seeds, including adverse results. Those choices were fixed before test evaluation.

## Assistant · progress update · 2026-09-09T20:08:01.906Z

Roughly 20 minutes of scheduled GPU work remain, followed by analysis and publication. Metered cost is about $23.28. The final interpretation will be tied to hashes of the recorded runs, so it cannot silently become detached from the measurements it describes.

## Assistant · progress update · 2026-09-09T20:09:27.936Z

The remaining second-seed runs are near the end of training. The primary comparison will use three paired seeds, with uncertainty shown explicitly; the many masks and test examples will not be counted as extra independent training runs.

## Assistant · progress update · 2026-09-09T20:10:42.066Z

Metered cost is about $24.27, and all final runs completed so far have succeeded. The last three jobs will start as workers free up. Their training durations and learning rates remain fixed; the test results will not change the experiment design.

## Assistant · progress update · 2026-09-09T20:12:00.066Z

Seventeen of 27 final runs are complete. The last GPT job has started, and the last ViT job is next in the queue. Once the second seed is complete across all three architectures, I’ll have it independently checked while the third seed finishes.

## Assistant · progress update · 2026-09-09T20:13:25.331Z

Twenty of 27 final runs are complete, with no failures. All remaining jobs are now running, and the second complete seed is undergoing an independent integrity check. Metered cost is about $25.22.

## Assistant · progress update · 2026-09-09T20:14:23.299Z

The second-seed audit caught another initialization mismatch, this time in ViT. All data, masks, and completed training steps match, but I’m checking the untrained parameter values before interpreting the comparison. The verifier is currently blocking the report on this discrepancy.

## Assistant · progress update · 2026-09-09T20:16:16.489Z

I reproduced both ViT initialization hashes on workers with different CPU instruction sets. Their random-number states match exactly. I’m now comparing every parameter to measure the difference; this check uses untrained CPU models and launches no additional GPU jobs.

## Assistant · progress update · 2026-09-09T20:17:45.275Z

The ViT hashes have been reproduced from the recorded seeds and source. I’m finishing the full numerical comparison and documenting its implications for reproducibility. GPU training continues in parallel; metered cost is about $26.25, with no failed training runs.

## Assistant · progress update · 2026-09-09T20:19:53.550Z

Twenty-one of 27 final runs are complete, including every ConvNeXt run. The ViT audit is waiting on transfer of the saved untrained states for full comparison. Training has continued uninterrupted, and all remaining work is within the original budget.

## Assistant · progress update · 2026-09-09T20:21:07.369Z

Twenty-four of 27 final runs have finished successfully. Only the last ViT constant-dropout run and the two decreasing-dropout transformer runs remain. Metered cost is about $26.88; the numerical initialization audit is proceeding alongside them.

## Assistant · progress update · 2026-09-09T20:22:26.433Z

The ViT comparison passed: the largest parameter difference is 7.45×10⁻⁹ across all 85.2 million parameters. The report will disclose this numerical variation in the vision models; matching seeds do not guarantee identical training trajectories. Twenty-five of 27 final runs are now complete.

## Assistant · progress update · 2026-09-09T20:24:34.750Z

All 25 available final runs now pass the strengthened audit, along with all 27 tuning runs. The last two runs are in their final training segment. The report will preserve separate, documented initialization exceptions for ViT and ConvNeXt while retaining exact initialization checks for GPT.

## Assistant · progress update · 2026-09-09T20:25:48.078Z

I’ve pushed the initialization audit and its supporting evidence to GitHub before interpreting the final test results. The last two jobs are nearing completion. Metered cost is about $27.56; next comes the full statistical analysis and report build.

## Assistant · progress update · 2026-09-09T20:27:07.708Z

The final two models are at roughly 90% of their training steps. After they finish, I’ll check the conclusions against the raw scores, verify the figures and interactive controls, and publish the separate report.

## Assistant · progress update · 2026-09-09T20:29:12.885Z

All 54 planned runs finished successfully: 27 tuning and 27 final evaluations. The first verified result is mixed across architectures: GPT shows a clear reduction in pruning damage, ConvNeXt favors dropout but has wide uncertainty, and ViT moves in the opposite direction on the primary loss metric. I’m checking the absolute losses and accuracies to explain that pattern.

## Assistant · progress update · 2026-09-09T20:30:52.944Z

The ViT result is more nuanced than the primary metric alone suggests: pruning improves loss for every recipe, and it improves the dense model’s loss the most. Yet the pruned dropout models have higher accuracy. Random layer deletion also favors dropout much more strongly than early exit. The report will separate these findings.

## Assistant · progress update · 2026-09-09T20:32:28.878Z

The GPU work is fully stopped, and all 27 final checkpoints are saved. The latest metered total is $27.81, below the $50 cap. The main finding is useful transfer to image models, with important limits: early exit, random deletion, loss, and accuracy can give different rankings.

## Assistant · progress update · 2026-09-09T20:33:25.860Z

The report and both existing pages pass their checks. The new report includes all seed results, all learning-rate candidates, interactive mask comparisons, standalone figures, and the cost record. I’m doing the final publication checks before pushing the finished report to GitHub Pages.

## Assistant · progress update · 2026-09-09T20:35:01.706Z

The report has been pushed, and GitHub Pages has built the new commit successfully. I’m checking the live page and downloadable assets against the committed files so the link points to the verified results.

## Assistant · 2026-09-09T20:35:34.358Z

Published the [interactive A100 experimental report](https://yaroslavvb.github.io/gradient-dissent/a100-transfer/), with animated mask visualizations, figures, and downloadable results.

Ran **54 experiments** on up to **9 parallel Modal A100s**: 124M GPT, 85M ViT, and 28M ConvNeXt, with three final seeds per configuration.

- **ConvNeXt:** Pruned accuracy reaches roughly **51% with dropout versus 14% with dense training**, though the primary loss intervals remain wide.
- **ViT:** Dropout improves pruned accuracy, but conclusions depend on the deletion pattern and metric. The report explains the apparent loss-metric reversal.
- **GPT:** Strong pruning robustness comes with worse full-model quality.

Decreasing dropout was **not consistently better** than constant dropout.

**Metered cost: $27.81**, below the $50 cap. All GPU jobs are stopped. [Code, protocols, and raw measurements](https://github.com/yaroslavvb/gradient-dissent/tree/main/experiments/a100_transfer) are uploaded; the live report and assets are verified.

## User · 2026-09-09T20:35:34.756Z

Now generate a high-level page report and deploy it to GitHub Pages, something that helps me understand the significance. After that, generate a paper using appropriate style, render it in a similar fashion as original, give me the PDF

## Assistant · progress update · 2026-09-09T20:35:38.266Z

I’ll build a high-level page that explains what the experiments establish, where the conclusions change with the metric, and why that matters for the original paper. Then I’ll write a research-paper version and render it as a PDF with a layout similar to the original. I’ll use the PDF and deep-research skills for the paper and source checks.

## Assistant · progress update · 2026-09-09T20:38:31.281Z

The clearest result is that “robust to pruning” and “a better pruned model” can give different rankings. ViT makes that distinction concrete; the page will explain it visually. I also checked the original PDF: it uses a single-column research-report layout, which I’ll follow with our own figures and attribution. I’m using the Sites skills while preserving the repository’s GitHub Pages setup.

## Assistant · progress update · 2026-09-09T20:40:36.009Z

The high-level page is drafted, with controls for comparing full and pruned quality across architectures and deletion patterns. It highlights the ViT ranking reversal, ConvNeXt’s large but uncertain gains, and GPT’s full-model tradeoff. I’m checking the displayed numbers against the saved results before publishing it, then moving to the paper.

## Assistant · progress update · 2026-09-09T20:42:20.562Z

The [high-level report is live](https://yaroslavvb.github.io/gradient-dissent/significance/), and its published files match the checked source. I’m now writing the paper around one central question: when does tolerance of missing layers translate into a useful predictor? It will include the analytical counterexample, local smaller-model controls, and A100 results with uncertainty.

## Assistant · progress update · 2026-09-09T20:46:26.869Z

The manuscript now has a complete draft, including methods, results, related work and reproducibility disclosures. I’ve framed it as an independent audit rather than a new dropout method. The A100 tables are generated directly from verified measurements, and I’m preparing the figures and checking that the text keeps uncertainty and interpretation separate.

## Assistant · progress update · 2026-09-09T20:48:09.277Z

The scientific checks passed: the manuscript’s 108 exported endpoint values and six primary confidence intervals match the saved analysis. I’m now checking the rendered PDF and tightening pagination so figures and tables stay with their discussion and no short paragraphs spill onto otherwise empty pages.

## Assistant · progress update · 2026-09-09T20:50:20.219Z

The PDF is complete at 13 pages. I inspected every rendered page; the figures, tables, equations and references are readable, with no clipped content or stray spillover pages. I’m uploading the paper, LaTeX source and figure data, and adding the PDF download to the live overview.

## Assistant · progress update · 2026-09-09T20:52:38.418Z

The paper and its reproducible sources are now on GitHub. GitHub Pages is building the updated download link; I’m waiting for that build and will verify that the served PDF matches the inspected copy.

## Assistant · 2026-09-09T20:52:49.539Z

Published the [high-level report](https://yaroslavvb.github.io/gradient-dissent/significance/), with interactive explanations of the findings and their significance.

Created the 13-page paper in the original’s single-column style: [Research paper (PDF)](https://yaroslavvb.github.io/gradient-dissent/significance/depth-robustness-audit.pdf)

All pages were visually checked. PDF, LaTeX source, figures and underlying data are uploaded to GitHub.

## User · 2026-09-09T20:52:50.059Z

export this session and host html on github pages, I had a similar question for the animated groups fable, so reuse what was done there.