High-compute collaboration call

SPECTRA-RSI needs independent scaling evidence. The manuscript explicitly treats its numerical gains as hypotheses to test, with dense ground-truth deltas, causal-attribution metrics, optional-stopping stress tests, drift tests, and 20–100-cycle long-horizon studies.

Highest-value contributions

Run larger n_slices, n_experts, rank, bootstrap, and multi-seed sweeps and upload the resulting JSON files.
Port the hot numerical paths to PyTorch/JAX/CuPy and benchmark CPU vs single-GPU vs multi-GPU without changing estimands.
Add real open-weight LoRA/MoE-LoRA adapters implementing the paired_scores and probe_delta interface.
Test two model families and two scales, including single-expert gains/regressions, canceling mixtures, interacting experts, non-compressible changes, and out-of-trust-region updates.
Run optional-stopping, dictionary-drift, Fisher/second-order dictionary regularization, and 20–100 update-cycle experiments.

Hardware sought

H100/H200, B100/B200, GB200/GB300, MI300X, 2/4/8/16+ GPU servers, and multi-node clusters. Other accelerators are welcome when the environment is fully reported.

Submission format (no Git required)

Download this Hugging Face repository, run the benchmark, then upload result JSON files back to a Hugging Face discussion/community contribution or share them with the maintainer. Include hardware, software versions, command line, wall time, and any code patch as a ZIP if you changed the backend. Do not submit only a screenshot.

Current reference point and open scaling question

The validated reference point is 1 × NVIDIA GB10 (DGX Spark) at 1,024 capability slices. Sparse single-gain and canceling-mixture cases recover their true expert support exactly in the validated runs, while the genuine off_dictionary case triggers immediate dense fallback.

The most important current stress-test result is deliberately unresolved: for broad_noncompressible at candidate seed 100, the gate returned accept while only 6 of 8 true experts were recovered. Contributors should not tune around or discard this case. We specifically want to learn whether increased sensing budgets, bootstrap repetitions, expert count/rank, alternative structured regularization, Fisher/second-order information, or real-model experiments eliminate this failure mode while preserving evaluation efficiency.

High-compute contributors are especially encouraged to report both successful and failed runs. Negative results, false accepts, unstable support recovery, poor scaling, memory limits, and dense-fallback behavior are scientifically useful.

Reproducibility metadata

The benchmark records accelerator metadata through PyTorch when available and falls back to nvidia-smi on NVIDIA systems. Please also report GPU/accelerator model and count, CPU, RAM, driver/runtime, operating system, and whether the run used bare metal, a container, or a scheduler job.

Use results/validated_results.txt and results/validated_leaderboard.csv as the canonical DGX Spark reference set. Other bundled JSON files may be exploratory or development runs unless listed in the validated manifest.