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Machine learning research5ML-2

5ML-2

Five machine-learning forensics environments where agents diagnose compositional systems through only five audits. Each expert case combines mechanisms, latent continuous effects, model mismatch, heavy-tailed observations, and twelve sealed forecasts, so success requires crossing diagnostic regimes, calibrated uncertainty, and predicting average and worst-case behavior—not merely naming a plausible stack.

ML researchExperiment designRLVR
Version3.0.0
Environments5
RewardScalar · 0–1
DeliveryPrivate ZIP
01 Task contract

A real, versioned RL environment.

Five machine-learning forensics environments where agents diagnose compositional systems through only five audits. Each expert case combines mechanisms, latent continuous effects, model mismatch, heavy-tailed observations, and twelve sealed forecasts, so success requires crossing diagnostic regimes, calibrated uncertainty, and predicting average and worst-case behavior—not merely naming a plausible stack.

Agent objective

  • Interact with the supplied stateful environment.
  • Produce verifier-checkable actions or artifacts.
  • Maximise scalar reward under the package contract.

Evaluation

  • 5 verifier-backed environments.
  • Reported reward range 0–1.
  • Package-specific public and private checks.

Delivery boundary

  • Private object stored in Cloudflare R2.
  • Authenticated entitlement required.
  • Short-lived signed URL per download.
02 What you will work on

Distinct environments, one demanding research contract.

Enough detail to understand the intellectual terrain; generated instances, hidden mechanisms, and solution paths remain inside the private package.

EnvironmentMathematical or technical frontierAdaptive research problem
DatasetProvenanceForensicsData leakage, quality, subgroup robustnessSelect audits that separate overlapping pipeline pathologies and forecast unseen deduplication, temporal, and rare-slice conditions.
OptimizerPhasePortraitOptimization dynamics and stabilityProbe curvature, noise, shocks, schedules, and delayed evaluation to distinguish mechanisms hidden by ordinary loss curves.
FederatedProtocolAutopsyFederated learning, robustness, privacyAudit heterogeneity, attacks, participation, clipping, communication, and revisits to recover a hidden protocol.
RetrievalGroundingForensicsRetrieval-augmented generationSeparate retrieval improvements from grounding and abstention across stale, duplicated, ambiguous, and evidence-poor corpora.
DiffusionSamplerInterrogatoryDiffusion training and samplingStress denoising, guidance, SNR mismatch, outliers, prompts, and seeds to infer the hidden sampling stack.
03 Why it is interesting

What the supplied evaluation reveals.

GPT-5.6 Pro ran one public-interface expert episode per environment and averaged 0.2951 with no passes. It recovered 17/30 individual mechanism bits but missed every complete system; experiment design was stronger than sealed forecasting and calibration. The result exposes the difference between recognizing pieces of an ML stack and building a causal diagnosis that transfers. The package passed 85/85 tests and all five fixtures replayed.

We publish aggregate behavior and task structure, while withholding generated instances, hidden labels, exact successful probes, private checks, and solution trajectories.

04 Supplied evaluation

Observed evaluation result.

Shown with its provenance and limitations; it is not a performance guarantee.

i
Methodology matters

Reported result from the evaluation artifact supplied with this package.

Evaluated system / policyGPT-5.6 Pro

As identified by the supplied artifact.

Mean reward0.2951

5 reported runs.

Result artifactIncluded

rlvr_gpt56_results.zip:rlvr_gpt56_results/summary.json

Public result record

Machine-readable provenance and the exact displayed metric are available in results.json.

Open result JSON
05 Private delivery

The package stays off the public website.

The paid ZIP will live in a private R2 bucket. Vercel authorizes the buyer and issues a 2–5 minute object URL; R2 serves the bytes directly.

Included with purchase

  • Exact package version 3.0.0
  • Environment and task contracts
  • Verifier or scoring interface
  • Supplied reference/evaluation artifacts
  • Purchase record and licence v1.1
delivery flow
Authenticated buyer + entitlement check
+ private R2 object + 2–5 minute signed URL
= direct, auditable download

Package SHA-256
0407133862171930c1704aeef25d8a7396ec6dfb389db6926f10433d2a59e399
06 Licence v1.1

Commercial use, without exclusivity.

One purchase licenses this identified item to one legal organisation for worldwide, perpetual commercial model training, evaluation, research and development. Redistribution and resale of the package are not permitted.

Read the full licenceYotta Content LTD · business customers only
5ML-2

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