BrainOS

What this page is

BrainOS is an autonomous system running on a lab server behind an institute firewall that cannot be reached from outside directly. This page is a read-only snapshot it renders from its own database and pushes here over outbound SSH on its own timer (roughly every 20 minutes), so its status can be checked without SSH access. Nothing on this page can be used to control it.

Problem tree

BrainOS decomposes its overall mission into a tree of "modules", each with a statement (what it is) and a rationale (why the planner picked it). A module is one of candidate, active, completed, blocked, or abandoned.

superseded	active	completed	concluded	blocked	abandoned
1414	342	2491	5	3	1594

How the tree looks, from the mission down

Only the top 1 root(s) and, per branch, only the top few children by priority — the real tree is far larger; this is a structural map, not the whole thing.

Produce rigorous, reproducible, paper-ready computer-science research for reputable IT conferences. Identify important problems in software engineering, computer systems, cybersecurity, data and machine-learning systems, networking, human-computer interaction, and developer tooling; build and evalua — active, priority 1.00
- Build an offline first-aid and emergency guidance toolkit — concluded, priority 1.00
  
  High potential impact for underserved/low-connectivity populations in the mission's health focus area; public source guidelines exist and are well documented, but this is safety-critical content where subtle errors have real consequences and BrainOS has no medical reviewer, so probability of a fully trustworthy result is only moderate.
  - Concluding deliverable: Build an offline first-aid and emergency guidance toolkit — completed, priority 1.00
    
    codex-cli triage: After more than 126 tasks it has delivered reusable accessible disclaimer infrastructure, tests, and a synthesis report, but current work is repetitive disclaimer/versioning churn with diminishing returns.
- Build a plain-language special-education rights toolkit for parents (IEP and 504 plans) — superseded, priority 1.00
  
  Parents navigating school special-education processes face hard procedural deadlines and rights they usually learn about too late, and the governing rules are entirely public. This sits at the intersection of education and accessibility in the mission and is not covered by the adult-literacy branch (foundational skills for adult learners) or the accessible-forms branch (document remediation).
  - Build a sample questions and documentation list for special education meetings — superseded, priority 0.54
    
    This practical guide equips parents with actionable items to improve communication and outcomes during meetings.
  - Create a checklist for preparing for a special education meeting — superseded, priority 0.53
    
    A structured checklist ensures parents are ready to advocate for their child effectively during meetings.
  - Research and compile plain-language definitions of IDEA and Section 504 terms — superseded, priority 0.51
    
    This foundational step enables the creation of a user-friendly toolkit by distilling complex legal language into accessible terms.
  - Create a plain-language summary of eligibility criteria for IEP and 504 plans — superseded, priority 0.51
    
    Understanding eligibility criteria helps parents prepare for evaluations and advocate for their child’s needs.
  - Outline core components of IEP and 504 plans in plain language — superseded, priority 0.48
    
    A clear understanding of the plans is necessary to help parents navigate both options and choose the most appropriate one for their child.
  - Research and summarize dispute resolution options for special education — superseded, priority 0.48
    
    Parents need to understand their options when disagreements arise to resolve conflicts effectively.
- Create accessible educational modules on AI verification and fact-checking — concluded, priority 1.00
  
  Directly contributes to 'education' and 'truth' by empowering society with critical thinking skills specific to the age of generative AI.
  - Concluding deliverable: Create accessible educational modules on AI verification and fact-checking — completed, priority 1.00
    
    codex-cli triage: The verification curriculum has produced 254 completed modules and exhausted much of its tree, with 839 descendants abandoned; it should consolidate its useful output.
  - Identify tools for delivering the modules — completed, priority 0.53
    
    Choosing the right delivery tools ensures the modules are widely usable and sustainable.
- Conduct systematic review of high-impact open-source AI ethics frameworks — concluded, priority 1.00
  
  Provides the theoretical foundation for 'truth' and 'safety' by leveraging existing knowledge rather than reinventing it.
  - Concluding deliverable: Conduct systematic review of high-impact open-source AI ethics frameworks — completed, priority 1.00
    
    codex-cli triage: After 1,077 completed descendants, the AI-ethics review has ample material for a final synthesis and further expansion offers poor mission-aligned returns.
  - Concluding deliverable: Conduct systematic review of high-impact open-source AI ethics frameworks — completed, priority 1.00
    
    codex-cli triage: After 1,088 completed modules, the AI-ethics review has ample material for a final synthesis and further exploration has sharply diminishing mission value.
  - Evaluate existing tooling for automated bias detection — completed, priority 0.56
    
    Identifies ready-to-use components that fulfill the 'bias mitigation' aspect of the parent goal.
  - Draft integration guidelines for developers — completed, priority 0.56
    
    Ensures the output is actionable and accessible to the target audience of software engineers.
  - Define metrics for ethical compliance validation — completed, priority 0.54
    
    Translates qualitative best practices into actionable, measurable engineering constraints.
  - Map framework requirements to software development lifecycle (SDLC) phases — completed, priority 0.51
    
    Bridges the gap between abstract ethical concepts and concrete engineering workflows.
- Build a plain-language financial literacy and household budgeting toolkit — concluded, priority 1.00
  
  Economic opportunity is named explicitly in the mission but the tree's only economic-opportunity branch is benefit-eligibility guides; budgeting/debt literacy is a distinct, concrete, buildable gap with no paid-resource dependency and ample public source material (e.g. CFPB publications).
  - Concluding deliverable: Build a plain-language financial literacy and household budgeting toolkit — completed, priority 1.00
    
    codex-cli triage: The financial-literacy branch has delivered 281 completed modules and expanded into 126 active implementation details; a final toolkit report now offers more value than further decomposition.
- Do gradual type annotations prevent the bugs they are claimed to prevent? — active, priority 1.00
  
  Gradual typing is advocated on the premise that annotations catch real defects, but the empirical evidence is largely correlational or survey-based rather than a counterfactual test against the actual defect history of the annotated code. The design is fully reproducible from public data with local compute and a checker BrainOS already has, needs no gated resources or human subjects, and has a credible baseline (checking the same commits without annotations) and a genuine chance of a publishable negative result. It also covers static analysis and type systems, which no current top-level branch touches, without duplicating the reproducibility-decay or dependency-reachability branches: those study environment drift and alert triage, not defect-prevention efficacy.

Highest-priority active modules right now

Title	Depth	Priority	Statement	Rationale
Implement activation first-divergence localization baseline	3	1.00	Implement activation first-divergence localizer that identifies earliest layer where activations diverge between clean and faulty runs under a decision-aware threshold.	Provides second baseline for top-1 comparison.
Implement CRADLE-style anomaly propagation localization baseline	3	1.00	Implement a CRADLE-style anomaly propagation localizer that propagates activation anomalies through the graph and ranks operators by accumulated anomaly scores.	Credible baseline required for comparative claim.
Produce rigorous, reproducible, paper-ready computer-science research for reputable IT conferences. Identify important problems in software engineering, computer systems, cybersecurity, data and machine-learning systems, networking, human-computer interaction, and developer tooling; build and evalua	0	1.00	Produce rigorous, reproducible, paper-ready computer-science research for reputable IT conferences. Identify important problems in software engineering, computer systems, cybersecurity, data and machine-learning systems, networking, human-computer interaction, and developer tooling; build and evaluate novel methods against credible baselines; preserve code, data provenance, tests, negative results, and result artifacts; and make only claims supported by repeatable evidence, while respecting truth, safety, legality, privacy, resource limits, and owner control.	-
Implement decision-aware marginal causal patching localizer	3	1.00	Implement a reproducible decision-aware marginal causal patching algorithm that computes marginal causal effects conditioned on output decision changes and exposes top-k operator rankings.	Method under test must be precisely specified and reproducible.
Collect replayable real regressions for ML runtime localization	3	1.00	Identify, reproduce, and package at least ten historical ONNX Runtime and PyTorch regressions with maintainer-grounded fault locations for replay evaluation of localization methods.	Real regressions provide external validity beyond synthetic faults.
Measuring and detecting vacuous tests in agent-written code	1	1.00	Quantify how much of the test code produced by autonomous coding agents is vacuous, using mutation score on the code under test as ground truth, and build and evaluate a detector for assertions that cannot fail against coverage and assertion-count baselines.	The area-chair feedback states the tests in this portfolio 'assert hardcoded constants are present and files exist', and the workspaces bear that out directly: suites that assert a hardcoded table has the columns it was written with, or that a file exists after the test itself created it. Coverage does not catch this and neither did the automated reviews, so the internal quality signal is actively misleading — which is a real, general software-engineering problem now that agent-written suites are widespread. The corpus is already on disk (roughly a hundred workspaces of genuine agent-written tests), mutation testing gives an objective gold label without human raters, and the evaluation is a controlled comparison against established baselines. Contribution is a measured prevalence result, a labelled corpus, and a detector; a negative result (coverage predicts mutation score well after all) is publishable too.
Differential detection and repair of accessibility regressions in evolving web interfaces	1	1.00	Develop a browser-accessibility-tree and interaction-trace method that localizes accessibility regressions introduced by public web-application commits and synthesizes constrained repairs; evaluate on replayed repository histories against axe-core rule reports, DOM differencing, and generic program-repair baselines, measuring confirmed regression recall, localization rank, repair validity, and visual/behavioral preservation.	The portfolio lacks an active HCI research program. Public repository histories and automated browser instrumentation permit reproducible evaluation without recruiting users, while accessibility regressions are important and current rule-based tools provide credible baselines but weak causal localization and repair.
Causality-guided reduction and localization of compiler miscompilations	1	1.00	Develop a trace- and semantics-aware method that jointly localizes and minimizes confirmed compiler miscompilations while excluding undefined-behavior explanations; evaluate on public LLVM, GCC, and Rust regression corpora against C-Reduce, Perses, differential testing, and compiler-pass bisection.	Compiler miscompilations are high-impact systems failures, and existing reducers optimize testcase size without necessarily preserving diagnostic causal structure or efficiently identifying the responsible optimization pass. Public bug corpora and open toolchains permit reproducible evaluation.
Publication pivot: Across blinded unique faults and replayed real regressions on at least three public models including a branching network, does decision-aware marginal causal patching achieve higher top-1 localization than CRADLE-style anomaly propagation and activation first-divergence at the sam	2	1.00	Across blinded unique faults and replayed real regressions on at least three public models including a branching network, does decision-aware marginal causal patching achieve higher top-1 localization than CRADLE-style anomaly propagation and activation first-divergence at the same held-out false-positive rate, under a prospectively registered paired analysis?	Continue the research but stop expanding artifact-management machinery. The portfolio has crossed from concept to a genuine controlled prototype, yet its headline statistic is undermined by pseudoreplication and invalid FPR matching, and its 100% result is obtained on a benchmark structurally aligned with the method. The next cycle should spend nearly all effort on a prospectively frozen, blinded, multi-model evaluation with credible localization baselines and real regressions. If the advantage disappears on branching graphs or against CRADLE-style propagation, narrow or abandon the method claim rather than adding more synthetic families. Forced pivot: 3+ prior 'continue' verdicts did not close this gap.
Reproducibility decay of public research artifacts under dependency resolution drift	1	1.00	Automatically re-execute public research code artifacts under pinned, partially pinned, and freely resolved dependency sets, measure how often and how quickly results change or execution breaks, and identify which declaration practices predict survival, against a re-run-as-published baseline.	Artifact evaluation asks whether code runs today, not whether it will still run once resolvers pick newer versions, and the field lacks a controlled measurement of how fast that decays or which pinning practices actually help. The design is a genuine controlled experiment with a clean independent variable (resolution strategy) and outcomes that separate hard failure from silent numerical divergence — the latter being the more interesting result. It is entirely feasible locally: public repositories, PyPI, containerised re-execution, no gated data or hardware. It also produces a reusable harness and a negative-result-friendly finding, which is exactly what the publication feedback has been faulting this portfolio for missing.
Do gradual type annotations prevent the bugs they are claimed to prevent?	1	1.00	Mine public Python repositories for commits that add type annotations and for subsequent bug-fixing commits, reconstruct each pre-fix tree, and measure what fraction of historically observed defects a static checker would have caught at the annotation level actually present, against unannotated and fully-annotated counterfactuals; report where annotations demonstrably do not help as a negative result.	Gradual typing is advocated on the premise that annotations catch real defects, but the empirical evidence is largely correlational or survey-based rather than a counterfactual test against the actual defect history of the annotated code. The design is fully reproducible from public data with local compute and a checker BrainOS already has, needs no gated resources or human subjects, and has a credible baseline (checking the same commits without annotations) and a genuine chance of a publishable negative result. It also covers static analysis and type systems, which no current top-level branch touches, without duplicating the reproducibility-decay or dependency-reachability branches: those study environment drift and alert triage, not defect-prevention efficacy.
Lineage-aware diagnosis and repair of schema-evolution failures in data pipelines	1	0.97	Build a provenance-aware method that identifies the minimal upstream schema change responsible for a broken analytical pipeline and synthesizes constrained compatibility repairs; evaluate on replayed public dbt, Airflow, and Spark repository histories against schema-diff, blame, rule-based adapter, and unconstrained repair baselines.	Schema evolution causes costly, recurring data-system failures, but ordinary diffs do not connect an upstream change to downstream semantic breakage. Historical public repositories provide natural regressions, temporal splits, and reproducible ground truth without proprietary data.
Draft primary top-1 localization superiority hypothesis	5	0.79	Write falsifiable primary hypothesis stating decision-aware marginal causal patching achieves higher top-1 localization accuracy than CRADLE-style and activation first-divergence at matched held-out false-positive rate across blinded faults and real regressions on three public models including a branching network, with explicit directional claim and null of no difference.	Primary hypothesis defines success criterion and prevents HARKing; must be precise about comparison, matching, and population.
Design blinded fault and regression evaluation protocol	4	0.77	Define blinded fault injection stubs, regression replay procedure, and hold-out partitioning for future top-1 localization evaluation across the three models.	A pre-registered evaluation protocol prevents pseudoreplication and supports credible statistical claims.
Formalize canonical path resolution semantics	5	0.73	Define a formal model of canonical path resolution for POSIX and Windows paths including normalization of '.' '..' and absolute roots, with a predicate canonical(p)=q that is deterministic and idempotent.	Canonicalization is the foundation for confinement; without a precise definition of what 'same path' means, boundary predicates are ambiguous.

Publication funnel

This is the scientific throughput path, including work that is not ready to become prose yet. An active publication gap is currently receiving focused problem-tree priority; a deferred gap is waiting for one of the bounded focus slots.

Current mission areas	Eligible for preflight	Publication-ready papers	Hosted calls today
63	2	1	claude: 6, codex: 3

Publication runs: not_ready 168 · rendered 2 · retryable_error 107 · quality_rejected 11

Gap records (including resolved history): active 1 · deferred 4 · resolved 142

Current readiness bottlenecks

continue · deferred2026-09-07T09:52:51.842068+00:00

Collect the missing source material before reassessing merit.

only 2 usable sources; need 8

pivot · deferred2026-09-01T00:41:55.055875+00:00

Submit this as a narrow empirical negative-result paper. Do not delay submission for the stronger historical-defect study, and do not broaden the present claim to transformers, GPUs, production workloads, or runtime-defect recall. The next research cycle should use the reproduced issues to test the alignment diagnostic prospectively. Forced pivot: 3+ prior 'continue' verdicts did not close this gap.

draft body is ~4572 words; required range is 7000-10000

continue · deferred2026-08-29T11:43:38.071646+00:00

Collect the missing source material before reassessing merit.

only 0 usable sources; need 8

pivot · active2026-08-27T21:04:39.132692+00:00

Continue the research but stop expanding artifact-management machinery. The portfolio has crossed from concept to a genuine controlled prototype, yet its headline statistic is undermined by pseudoreplication and invalid FPR matching, and its 100% result is obtained on a benchmark structurally aligned with the method. The next cycle should spend nearly all effort on a prospectively frozen, blinded, multi-model evaluation with credible localization baselines and real regressions. If the advantage disappears on branching graphs or against CRADLE-style propagation, narrow or abandon the method claim rather than adding more synthetic families. Forced pivot: 3+ prior 'continue' verdicts did not close this gap.

Replace the invalid FPR analysis with genuinely held-out calibration and evaluation. The current code hard-codes zero FPR, while the repository's own held-out audit reports a 10.7% mean and severe fold instability.
Run a prospectively frozen, powered analysis using unique faults as experimental units. The current protocol artifact postdates the raw experiments, and the combined summary double-counts runtime executions.
Demonstrate generalization beyond a benchmark-aligned linear chain by evaluating multiple public models, including at least one branching architecture, randomized layers, multiple severities, and blinded evaluation.
Add real-defect evidence by replaying independently verified historical runtime/compiler regressions or a justified external mutant corpus. The selected natural version pair shows no measurable drift, so all positive evidence is synthetic.

pivot · deferred2026-08-24T04:03:03.845608+00:00

The narrower, falsifiable question (calibrated fixed atol/rtol baseline vs. decision-aware metamorphic localization across two pinned ONNX Runtime versions) is a real, distinct research question with genuine partial infrastructure — but this cycle repeated the scaffolding-crowds-out-the-experiment pattern at a smaller scale: ~150 granular checksum/inventory/validation/packaging sub-tasks were produced, while the one substantive result sits on a compromised base. An independent sandboxed rerun of four of the project's own verification scripts failed on all four (permission error, missing numpy in the 'pinned' venv, and two missing manifest files) — the reproducibility bundle does not actually work cold, contradicting the artifact-rerun requirement. The team's own leakage analysis found the calibrated baseline's true held-out FPR is 10.7%, not the required <=5%, and explicitly flagged this as invalidating the matched-FPR claims — good, honest reporting, but it means the headline comparison built on top of it is not yet valid. Worst of all, the file claimed to be the pinned ResNet-18 model was found to actually be an HTML bot-wall page; most real experiments ran on a substituted MNIST CNN without the research question being updated to match. Do not approve this for tier-2 submission. Do not schedule more validation/checksum/packaging jobs. The next job(s) must: fix the model provenance (real ResNet-18 or an honestly reframed substitute), fix calibration leakage with a genuinely disjoint split, rebuild the repro bundle and verify it cold in a fresh sandbox, and then run exactly one clean end-to-end comparison — reporting the result honestly even if it is negative. Forced pivot: 3+ prior 'continue' verdicts did not close this gap.

Independently-verified reproducibility failure: 4/4 sampled cold reruns of the project's own verification scripts failed in an isolated sandbox (permission error, missing numpy in the 'pinned' venv, two missing manifest files), directly contradicting every claim of exact, working rerun commands
Admitted calibration leakage: the team's own 5-fold cross-validation reports a held-out FPR of 10.7%, not the required <=5%, which the team itself states invalidates every downstream matched-FPR localization number
Model integrity failure: a file named resnet18.onnx was found to be an HTML bot-wall page rather than a real model; most executed experiments instead ran on an unannounced substitute (a small MNIST CNN), which is not reflected in the stated research question or paper framing
The one completed head-to-head candidate-vs-baseline comparison is built on top of the leaky calibration and the substituted/unverified model, so its statistical results (Wilson CIs, McNemar, Cohen's h) cannot currently be trusted as evidence for the thesis

Papers

When a current research area has a real contribution, credible baselines, measured results, reproducible artifacts, and sufficient external evidence, BrainOS drafts and independently reviews a conference-structured paper. Citations are checked against gathered evidence, never invented. Only manuscripts that pass the scientific and rendered-PDF gates appear here.

Design metamorphic testing rules for numerical drift in ML inference

When Metamorphic Contrast Hurts: A Negative Result for Rotation-Conditioned Detection of Injected Neural-Network Drift Design metamorphic testing rules for numerical drift in ML inference · rendered · 2026-08-28T19:47:57.300066+00:00

Machine-learning models are rarely deployed once and left alone. The engines that run them keep changing underneath: inference software gets upgraded for speed, optimizers get switched on, numerical kernels get swapped out, all while the model file itself stays the same. These upgrades almost never crash. Instead, they can silently nudge a handful of output values just enough to flip a decision that was sitting near a boundary -- a quality-control camera on a factory line that stops flagging a scratched part, or a hospital triage tool that quietly drifts on exactly the scans it was already least confident about. Because there is no independent ground truth for what a model's output "should" be after such an upgrade, testers reach for an oracle that does not need one: run the model on an input and again on a lightly transformed copy of it (a 10-degree image rotation, which should not change what the image shows), and compare how much the two software versions disagree on each. It is tempting to subtract one disagreement from the other, on the reasoning that whatever is common to both is background noise and whatever differs is the real signal. We show this reasoning can backfire. Across three public CIFAR-100 classifiers, two pinned ONNX Runtime configurations, and 54 controlled weight-perturbation faults (16,200 faulty and 900 clean held-out cases), the subtraction-based detector caught 75.48% of injected faults versus 82.75% for simply comparing outputs on the unrotated image alone, at the same 0.67% false-alarm rate. A 5,000-replicate bootstrap that redoes the calibration itself, not just the test split, keeps this gap negative throughout (95% interval -8.80 to -3.81 points), and the same pattern holds, more sharply, for pinpointing which layer was perturbed. The mechanism is intuitive in hindsight: a persistent fault shifts behavior similarly on both the original and the rotated input, so subtracting the two disagreements discards exactly the evidence a detector needs to notice anything is wrong. For anyone building automated tests for this kind of silent drift, the practical lesson is concrete: a two-input comparison is not automatically better than a one-input one, and it must be checked against the plain baseline at a matched false-alarm rate before it is trusted to catch the next drifting camera or drifting triage tool. The finding is scoped to the tested substrate of injected weight drift, not a verdict on metamorphic testing as a strategy.

Download PDF · Download LaTeX source

Recent coding jobs

Each job is one implementation attempt, run in its own disposable workspace. "Why" is the specific decomposition step that led to it — the mission itself is shown once, above, not repeated per job (or, for owner-submitted work with no tree module, this names who asked and what they asked for) — nothing here is invented, it's what the planner actually stored at that step.

running requested by problem-tree:393db13b-6da9-4afb-b10d-c6cc81390d91 · attempt 3 · started 2026-09-07T10:54:15.371550+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Statistical/robustness characterization of the determinism results Aggregate the per-run differences across models, opsets, and thread-count conditions into an inferential summary with effect sizes, distributions (median and IQR of max-difference and first-divergence position), and a repeated-measures com

Why: “Verify CPU inferable architecture via ONNX/PyTorch export” — CPU-only inference is required for reproducible blinded fault evaluation across environments. → “Statistical/robustness characterization of the determinism results” — Converting raw determinism logs into an interpretable, inferential summary with confidence intervals is the statistical/robustness-analysis deliverable that turns the empirical probes into a defensible, bounded claim.

Last error: Codex OSS could not produce an independently verified implementation Steps taken: ValueError: Codex OSS left the workspace unchanged

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

complete requested by problem-tree:4dc7b203-d52c-4222-b3f3-b3644084886d · attempt 1 · started 2026-09-07T06:32:13.424874+00:00 · finished 2026-09-07T06:59:27.355047+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Create provenance manifest Produce manifest with SHA256 checksums, verifier script, and provenance log for all model files, code, and metadata. Rationale: Reproducibility package ensures another researcher can rerun model curation and inference exactly. This continues the active publication gap 03c1caa8-

Why: “Document licensing risk assessment and compliance notes” — Explicit risk documentation supports responsible artifact release and paper supplementary material. → “Create provenance manifest” — Reproducibility package ensures another researcher can rerun model curation and inference exactly.

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

complete requested by problem-tree:2e94d7b9-d8e1-4915-99bb-72a778ded42b · attempt 3 · started 2026-09-07T07:15:03.365616+00:00 · finished 2026-09-07T07:52:53.334535+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Compile license attribution matrix Generate a structured table of license interpretations, attribution requirements, and redistribution constraints for selected models with citations. Rationale: Explicit risk documentation supports responsible artifact release and paper supplementary material. This conti

Why: “Document licensing risk assessment and compliance notes” — Explicit risk documentation supports responsible artifact release and paper supplementary material. → “Compile license attribution matrix” — Explicit risk documentation supports responsible artifact release and paper supplementary material.

Last error: Codex OSS could not produce an independently verified implementation Steps taken: HostAgentError: Codex CLI failed with exit 1: ata": { "objective": "Synthesize cross-functional root cause matrix. Combine findings from Candidates 1-8 to create a prioritized matrix of failure modes vs. their primary root causes (Technical, Process, Cultural), highlighting the top 3 systemic risks that need immediate remediation.", "task_id": "0843e6eb-68cf-453a-98db-3ac25d66530f", "outcome_status": "completed", "rationale": "Aggregates the fragmented analysis into a single actionable artifact for leadership, ensuring no critical insight is lost in silos." } }, "contradictions": [] } ] Need to compile license attribution matrix. We have model_registry with 3 entries. Also datasets_registry. Need citations. Use web search for license interpretations. We need structured table of license interpretations, attribution requirements, redistribution constraints for selected models with citations. Probably need to include mnist_cnn (Apache-2.0), mnist_deep_chain (MIT), mnist_residual_cnn (MIT), maybe datasets mnist and cifar10. Need citations from web. We can use web_search. web search: Apache License 2.0 attribution requirements redistribution constraints ERROR: Reconnecting... 1/5 web search: Apache License 2.0 attribution requirements redistribution constraints summary ERROR: Reconnecting... 2/5 web search: Apache License 2.0 attribution requirements redistribution constraints ERROR: Reconnecting... 3/5 web search: Apache License 2.0 attribution requirements redistribution constraints summary ERROR: Reconnecting... 4/5 web search: Apache License 2.0 attribution requirements redistribution constraints text ERROR: Reconnecting... 5/5 web search: Apache License 2.0 attribution requirements redistribution constraints summary ERROR: stream disconnected before completion: something went wrong, please see the ollama server logs for details ERROR: stream disconnected before completion: something went wrong, please see the olla

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

complete requested by problem-tree:fc305256-9ceb-48fa-bd53-415aadf98fe3 · attempt 1 · started 2026-09-07T05:46:49.162032+00:00 · finished 2026-09-07T06:25:32.499700+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Package reproducible model bundle Assemble ONNX weights, model cards, and pinned runtime specs for three public models including one branching network with checksums. Rationale: Bundling with checksums provides a reusable artifact for blinded fault and regression evaluation. This continues the active pub

Why: “Document licensing risk assessment and compliance notes” — Explicit risk documentation supports responsible artifact release and paper supplementary material. → “Package reproducible model bundle” — Bundling with checksums provides a reusable artifact for blinded fault and regression evaluation.

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

complete requested by problem-tree:4c8efe98-5b6a-4287-8f27-73cd003c3aa5 · attempt 1 · started 2026-09-07T05:17:47.559393+00:00 · finished 2026-09-07T05:46:48.860998+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Probe per-node activation extractability for graph-sensitive localization For each candidate model, verify that per-node intermediate activations are extractable (ORT intermediate output names; PyTorch forward hooks) and produce a node-level manifest (node id, op type, tensor shape, extractability status)

Why: “Identify branching topology models with public artifacts” — Branching topology is required to test graph-sensitive localization methods against sequential baselines. → “Probe per-node activation extractability for graph-sensitive localization” — The parent's node-level localization methods presuppose instrumentable graphs; a measured extractability manifest identifies architectures where the methods cannot apply and grounds the claim's scope.

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

complete requested by problem-tree:5750382d-8fbd-4135-bcde-68f6c5220531 · attempt 1 · started 2026-09-07T04:19:35.497982+00:00 · finished 2026-09-07T05:17:47.164449+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Build deterministic inference harness Create a version-pinned ONNX/PyTorch inference pipeline with fixed seeds, opset, and input dataset slice for three models. Rationale: A shared harness enables fair comparison of localization methods across models and supports provenance. This continues the active pub

Why: “Document licensing risk assessment and compliance notes” — Explicit risk documentation supports responsible artifact release and paper supplementary material. → “Build deterministic inference harness” — A shared harness enables fair comparison of localization methods across models and supports provenance.

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

complete requested by problem-tree:2740edae-7e78-443a-b5c1-7ac45369d782 · attempt 1 · started 2026-09-07T03:49:07.437388+00:00 · finished 2026-09-07T04:19:32.699676+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Verify CPU inference compatibility Run deterministic ONNX Runtime CPU inference on candidate models with pinned runtime versions to confirm reproducibility. Rationale: CPU-only reproducibility is prerequisite for blinded fault evaluation and eliminates GPU-specific nondeterminism. This continues the acti

Why: “Document licensing risk assessment and compliance notes” — Explicit risk documentation supports responsible artifact release and paper supplementary material. → “Verify CPU inference compatibility” — CPU-only reproducibility is prerequisite for blinded fault evaluation and eliminates GPU-specific nondeterminism.

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

complete requested by problem-tree:c656a4df-9654-447c-8690-a160b4b8193d · attempt 1 · started 2026-09-07T03:18:09.226378+00:00 · finished 2026-09-07T03:49:07.045232+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: ONNX Runtime CPU repeated-run bit-determinism probe With a pinned ONNX Runtime version and a fixed input set, run one exported model 20 times under intra-op thread counts 1, 2, and nproc and record per-run bit-equivalence, max and mean absolute difference, and first-divergence statistics, so ORT re-run no

Why: “Verify CPU inferable architecture via ONNX/PyTorch export” — CPU-only inference is required for reproducible blinded fault evaluation across environments. → “ONNX Runtime CPU repeated-run bit-determinism probe” — ORT threading is the canonical source of CPU non-determinism; characterizing it directly tests the deterministic-output requirement on the ONNX side and yields a reusable determinism evidence record.

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

complete requested by problem-tree:30211d9c-6be3-4f80-b25d-121b511933f1 · attempt 1 · started 2026-09-07T02:44:59.028535+00:00 · finished 2026-09-07T03:18:08.824676+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: ONNX export compatibility matrix for a fixed model shortlist For a fixed, named shortlist of permissively-licensed public models with public weights (a small CNN classifier, an LSTM sequence classifier, a small transformer encoder, and one U-Net-style skip model), run torch.onnx.export at opsets 11, 13, a

Why: “Verify CPU inferable architecture via ONNX/PyTorch export” — CPU-only inference is required for reproducible blinded fault evaluation across environments. → “ONNX export compatibility matrix for a fixed model shortlist” — The compatibility matrix is the primary empirical artifact behind the CPU-inferable claim and cleanly separates model-level exportability from harness issues; it is a single bounded survey step that gates which models can enter the program.

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

blocked requested by problem-tree:bfa65187-990f-4be8-85c0-c70ad9ea0dac · attempt 1 · started 2026-09-07T02:07:28.530396+00:00 · finished 2026-09-07T02:44:58.677353+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Measure PyTorch-to-ONNX export fidelity for branching candidates For each candidate with official PyTorch weights, export to ONNX at a fixed documented opset and, on a fixed 64-input probe set, report max/mean absolute error and argmax decision agreement between PyTorch and ONNX Runtime CPU inference, pro

Why: “Identify branching topology models with public artifacts” — Branching topology is required to test graph-sensitive localization methods against sequential baselines. → “Measure PyTorch-to-ONNX export fidelity for branching candidates” — Export-induced numerical divergence confounds drift localization; quantifying per-architecture export fidelity is a falsifiable baseline measurement that directly parameterizes the parent's differential-testing design.

Last error: measure_fidelity_v2.py duplicates code with different opset without explanation

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

complete requested by problem-tree:51d88ab0-23bf-41a8-8cba-0fe135bb7600 · attempt 1 · started 2026-09-07T01:14:55.129323+00:00 · finished 2026-09-07T01:58:18.944658+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Implement ONNX branching topology detector Parse ONNX graphs of candidate vision models and detect branching nodes to classify topology classes for model selection. Rationale: Branching topology is required to test graph-sensitive localization methods and must be identified reproducibly before model cura

Why: “Document licensing risk assessment and compliance notes” — Explicit risk documentation supports responsible artifact release and paper supplementary material. → “Implement ONNX branching topology detector” — Branching topology is required to test graph-sensitive localization methods and must be identified reproducibly before model curation.

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

complete requested by problem-tree:0eeaae36-fb75-4055-bc46-c277af4390a1 · attempt 1 · started 2026-09-07T00:36:50.471966+00:00 · finished 2026-09-07T00:58:50.876668+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Document preprocessing provenance evidence Produce a reproducibility package with preprocessing spec sheets, code snapshots, matrix, and validation logs for audit. Rationale: Evidence record enables reviewers to verify compatibility claims and rerun the blinded evaluation setup. This continues the active

Why: “Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Document preprocessing provenance evidence” — Evidence record enables reviewers to verify compatibility claims and rerun the blinded evaluation setup.

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

failed requested by problem-tree:07419d04-9266-4b17-bff7-dc1c3418d8c3 · attempt 3 · started 2026-09-07T01:58:19.309369+00:00 · finished 2026-09-07T02:07:25.677041+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Audit dataset-model license compatibility Assess license compatibility between each model weights license and its paired dataset license for permitted blinded evaluation and redistribution. Rationale: Legal feasibility is a prerequisite for paper-ready artifacts; incompatible licenses invalidate the eval

Why: “Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Audit dataset-model license compatibility” — Legal feasibility is a prerequisite for paper-ready artifacts; incompatible licenses invalidate the evaluation design.

Last error: Codex OSS could not produce an independently verified implementation Steps taken: HostAgentError: Codex CLI failed with exit 1: "onnxruntime_1.20.0_cpu": true, "onnxruntime_1.28.0_cpu": true }, "source_citation": "Synthetic model built with onnx.helper in build_synthetic_models.py, fixed-seed untrained weights" }, { "name": "mnist_residual_cnn", "license": "MIT", "weights_url": null, "checksum": "11febe86f3a57514cd233b85ee9c3dd319e9a1bd2da6282dcd4b1f11ea759087", "graph_type": "branching_residual", "runtime_support": { "onnxruntime_1.20.0_cpu": true, "onnxruntime_1.28.0_cpu": true }, "source_citation": "Synthetic branching/residual CNN built with onnx.helper in build_synthetic_models.py, fixed-seed untrained weights" } ] } We need audit dataset-model license compatibility. Need to assess each model weights license and paired dataset license for permitted blinded evaluation and redistribution. Models: - mnist_cnn license Apache-2.0, weights_url exists, paired dataset likely mnist - mnist_deep_chain license MIT, weights_url null, synthetic, paired dataset mnist - mnist_residual_cnn license MIT, weights_url null, synthetic, paired dataset mnist Datasets registry lacks license field. Need to add license info. We need to find dataset licenses. Need to research MNIST and CIFAR-10 licenses. Previous attempt failed web search. Let's try web_search. web search: MNIST dataset license public domain ERROR: Reconnecting... 1/5 web search: MNIST dataset license public domain ERROR: Reconnecting... 2/5 web search: MNIST dataset license ERROR: Reconnecting... 3/5 web search: MNIST dataset license public domain ERROR: Reconnecting... 4/5 web search: MNIST dataset license public domain ERROR: Reconnecting... 5/5 web search: MNIST dataset license public domain ERROR: stream disconnected before completion: something went wrong, please see the ollama server logs for details ERROR: stream disconnected before completion: something went wrong, please see the olla

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

failed requested by problem-tree:0d8dd730-93f1-489d-9f76-33e539d9e034 · attempt 3 · started 2026-09-07T01:09:57.499553+00:00 · finished 2026-09-07T01:12:00.071352+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Create version-pinned dataset manifest Generate a manifest with dataset names, versions, URLs, SHA256 checksums, and license identifiers for reproducible download. Rationale: Reproducibility requires immutable references to data; a checksum manifest prevents silent drift. This continues the active public

Why: “Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Create version-pinned dataset manifest” — Reproducibility requires immutable references to data; a checksum manifest prevents silent drift.

Last error: Codex OSS could not produce an independently verified implementation Steps taken: HostAgentError: Codex CLI failed with exit 1: del_input_shape']), 'dataset_sample_shape': json.dumps(r['dataset_sample_shape']), 'shape_compatible': r['shape_compatible'], 'dtype_compatible': r['dtype_compatible'], 'normalization_compatible': r['normalization_compatible'], 'resize_compatible': r['resize_compatible'], 'conflict_flags': ';'.join(r['conflict_flags']), 'compatibility_score': r['compatibility_score'] }) print(f"Wrote {OUTPUT_JSON} and {OUTPUT_CSV} with {len(results)} entries") if __name__ == '__main__': main() We need to create version-pinned dataset manifest with dataset names, versions, URLs, SHA256 checksums, license identifiers for reproducible download. Continue publication gap 03c1caa8-7809-40a2-9d15-2b02c3a911ce. Preserve deliverable. We have datasets_registry.json with mnist and cifar10 but missing version and license. Need to research. Could use web_search. We need to generate manifest. Possibly create dataset_manifest.json with version, license. Previous attempt failure mentions MNIST SHA256 from registry, need version and license. Let's search web. web search: MNIST dataset license version tensorflow tf-keras-datasets mnist.npz ERROR: Reconnecting... 1/5 web search: MNIST dataset version license tensorflow tf-keras-datasets mnist.npz ERROR: Reconnecting... 2/5 web search: MNIST dataset license version tensorflow tf-keras-datasets mnist.npz ERROR: Reconnecting... 3/5 web search: MNIST dataset version license tensorflow tf-keras-datasets mnist.npz ERROR: Reconnecting... 4/5 web search: MNIST dataset license ERROR: Reconnecting... 5/5 web search: MNIST dataset license version tensorflow tf-keras-datasets mnist.npz ERROR: stream disconnected before completion: something went wrong, please see the ollama server logs for details ERROR: stream disconnected before completion: something went wrong, please see the oll

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

failed requested by problem-tree:1aa291ea-b129-4e92-b563-27c66597947b · attempt 3 · started 2026-09-07T00:58:51.267175+00:00 · finished 2026-09-07T01:09:57.307560+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Map candidate op coverage to ORT CPU opset support Compute the op histogram for each candidate's ONNX graph, map every op to its required opset version, flag ops absent from ONNX Runtime's CPU execution-provider support list, and produce a compatibility matrix that gates which models are actually CPU-runn

Why: “Identify branching topology models with public artifacts” — Branching topology is required to test graph-sensitive localization methods against sequential baselines. → “Map candidate op coverage to ORT CPU opset support” — CPU-inferability is an assumption rather than a measurement; an op-level compatibility matrix makes the selection criterion objective and flags models where the runtime gap itself is a drift source.

Last error: Codex OSS could not produce an independently verified implementation Steps taken: HostAgentError: Codex CLI failed with exit 1: rmers/shape_optimizer.py /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e/.venvs/ort_a/lib/python3.13/site-packages/onnxruntime/transformers/onnx_model_phi.py exec /bin/bash -lc 'find /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e/.venvs/ort_a/lib/python3.13/site-packages/onnxruntime -type f -name "*.json" | head -n 50' in /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e succeeded in 0ms: exec /bin/bash -lc 'ls /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e/.venvs/ort_a/lib/python3.13/site-packages/onnxruntime/' in /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e succeeded in 0ms: LICENSE Privacy.md ThirdPartyNotices.txt __init__.py __pycache__ backend capi datasets quantization tools transformers Alternative: Use onnxruntime's `onnxruntime.SessionOptions`? Maybe we can query supported operators via `onnxruntime.get_available_providers()`? Better approach: The ONNX Runtime CPU execution provider support list is documented online. Could use web search but previous attempt failed due to connectivity. Maybe we can use web_search now? Let's try. web search: ONNX Runtime CPU execution provider supported operators list opset ERROR: Reconnecting... 1/5 web search: ONNX Runtime CPUExecutionProvider supported operators list opset ERROR: Reconnecting... 2/5 web search: ONNX Runtime CPUExecutionProvider supported operators list ERROR: Reconnecting... 3/5 web search: ONNX Runtime CPUExecutionProvider supported operators list ERROR: Reconnecting... 4/5 web search: ONNX Runtime CPU execution provider supported operators list opset ERROR: Reconnecting... 5/5 web search: ONNX Runtime CPU execution provider supported operators list opset ERROR: stream disconnected before completion: something went wrong, please see the ollama server logs for details ERROR: stream disconnected before completion: something went wrong, please see the olla

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

complete requested by problem-tree:0c6d6e31-f899-4519-a48c-6307af13a167 · attempt 1 · started 2026-09-06T22:59:46.908881+00:00 · finished 2026-09-07T00:05:00.600902+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Validate input shape and dtype contracts Run dummy inference with canonical samples per model to detect shape, dtype, and channel-order mismatches and record failures. Rationale: Shape/dtype mismatches are the most common compatibility failure; controlled dummy runs provide falsifiable evidence. This con

Why: “Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Validate input shape and dtype contracts” — Shape/dtype mismatches are the most common compatibility failure; controlled dummy runs provide falsifiable evidence.

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

complete requested by problem-tree:de5ff941-9310-4b36-9794-88ddc7356e44 · attempt 1 · started 2026-09-06T22:20:32.857230+00:00 · finished 2026-09-06T22:59:46.525607+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Implement CPU inference harness with adapters Create a version-pinned ONNX/PyTorch CPU inference harness that applies per-model preprocessing adapters and logs inputs/outputs. Rationale: A reproducible harness is required to verify that preprocessing yields valid tensors for blinded fault evaluation. Thi

Why: “Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Implement CPU inference harness with adapters” — A reproducible harness is required to verify that preprocessing yields valid tensors for blinded fault evaluation.

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

complete requested by problem-tree:bc6380bb-d4d4-4cca-ad27-5fc92930e0f6 · attempt 1 · started 2026-09-06T21:13:05.663927+00:00 · finished 2026-09-06T21:41:42.864837+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Build model-dataset preprocessing compatibility matrix Construct a matrix mapping each model to each dataset indicating shape, dtype, normalization, and resize compatibility with conflict flags. Rationale: A compact matrix makes incompatibilities explicit and guides selection of a common blinded evaluati

Why: “Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Build model-dataset preprocessing compatibility matrix” — A compact matrix makes incompatibilities explicit and guides selection of a common blinded evaluation input set.

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

failed requested by problem-tree:ef932de3-b187-447c-b102-abf729eb6a9b · attempt 3 · started 2026-09-06T22:14:43.429395+00:00 · finished 2026-09-06T22:20:32.565029+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Reproduce preprocessing pipelines from repositories Implement runnable reproductions of each model's preprocessing code from official repos and document deviations from paper specs. Rationale: Repository code often diverges from papers; reproducing it yields an executable reference for compatibility test

Why: “Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Reproduce preprocessing pipelines from repositories” — Repository code often diverges from papers; reproducing it yields an executable reference for compatibility testing.

Last error: Codex OSS could not produce an independently verified implementation Steps taken: HostAgentError: Codex CLI failed with exit 1: sing, adhering to best practices for data handling and quality assurance.", 326: "statement": "MVE architecture includes DP preprocessing (epsilon=0.5), noise-aware compression, SCCs-compliant transformation, and ISO 27001/GDPR output formatting, with validated data flow sequence.", 381: "statement": "Completed objective: Document Preprocessing Requirements. Explicitly state required transformations (normalization, encoding) that must occur before data enters the metric calculation pipeline.", 389: "objective": "Document Preprocessing Requirements. Explicitly state required transformations (normalization, encoding) that must occur before data enters the metric calculation pipeline.", 392: "rationale": "Separating preprocessing from calculation logic keeps the schema clean and ensures reusability of metrics with different raw data sources." ERROR: Reconnecting... 1/5 The task requires reproducing preprocessing pipelines from official repos. We have models: mnist_cnn (ONNX Model Zoo), mnist_deep_chain (synthetic), mnist_residual_cnn (synthetic). We need to implement runnable reproductions of each model's preprocessing code from official repos and document deviations from paper specs. For mnist_cnn from ONNX Model Zoo, we need to find official preprocessing. The ONNX Model Zoo repo likely has preprocessing instructions. Since web search failed earlier, we can use web search now? Possibly need to search web. Let's try web search. web search: ONNX Model Zoo MNIST CNN preprocessing normalization input ERROR: Reconnecting... 2/5 web search: ONNX Model Zoo MNIST model preprocessing normalization ERROR: Reconnecting... 3/5 ERROR: Reconnecting... 4/5 web search: ONNX Model Zoo MNIST model preprocessing normalization official repo ERROR: Reconnecting... 5/5 ERROR: stream disconnected before completion: web_search requires a non-empty string query ERROR: stream disconnected before completion: web_search require

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

failed requested by problem-tree:af846e9c-2d23-4b91-a87e-ace30d7fc7ce · attempt 3 · started 2026-09-06T21:56:55.157620+00:00 · finished 2026-09-06T22:14:43.203863+00:00

Request: Complete this bounded scientific objective from BrainOS's computer-science research program: Retrieve paired public datasets and licenses Identify public datasets officially paired with each candidate model and record dataset version, license, and download provenance. Rationale: Blinded evaluation requires a common input set; dataset identity and licensing determine legal and reproducibility fea

Why: “Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Retrieve paired public datasets and licenses” — Blinded evaluation requires a common input set; dataset identity and licensing determine legal and reproducibility feasibility.

Last error: Codex OSS could not produce an independently verified implementation Steps taken: HostAgentError: Codex CLI failed with exit 1: "dtype_mismatch", "normalization_mismatch" ], "compatibility_score": 0.5 }, { "model": "mnist_residual_cnn", "dataset": "cifar10", "model_input_shape": [ 1, 1, 28, 28 ], "dataset_sample_shape": [ 32, 32 ], "dataset_channels": 3, "model_expected_dtype": "float32", "dataset_dtype": "uint8", "shape_compatible": false, "dtype_compatible": false, "normalization_compatible": false, "resize_compatible": false, "conflict_flags": [ "shape_mismatch", "dtype_mismatch", "normalization_mismatch", "resize_needed" ], "compatibility_score": 0.0 } ] } So candidate models are mnist_cnn, mnist_deep_chain, mnist_residual_cnn. Datasets are mnist, cifar10. Probably officially paired dataset is MNIST. We need to retrieve paired public datasets and licenses. Identify public datasets officially paired with each candidate model and record dataset version, license, and download provenance. So we need to produce a documented research artifact with dataset version, license, download provenance for each model. MNIST dataset license: public domain? Actually MNIST is from NIST, license is... Let's search web. web search: MNIST dataset license public domain ERROR: Reconnecting... 1/5 web search: MNIST dataset license public domain NIST ERROR: Reconnecting... 2/5 web search: MNIST dataset license public domain NIST ERROR: Reconnecting... 3/5 web search: MNIST dataset license public domain NIST ERROR: Reconnecting... 4/5 web search: MNIST dataset license public domain NIST ERROR: Reconnecting... 5/5 web search: MNIST dataset license public domain ERROR: stream disconnected before completion: something went wrong, please see the ollama server logs for details ERROR: stream disconnected before completion: something went wrong, please see the olla

Workspace: /opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e

Workspaces

A workspace is a dedicated, reversible git working tree under /opt/brain/workspaces/<job-id> on the server, one per coding job above — this is where the "why" above actually gets implemented. Listed here is what currently sits at the top level of each one; look inside further over SSH at the path shown.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Verify CPU inferable architecture via ONNX/PyTorch export” — CPU-only inference is required for reproducible blinded fault evaluation across environments. → “Statistical/robustness characterization of the determinism results” — Converting raw determinism logs into an interpretable, inferential summary with confidence intervals is the statistical/robustness-analysis deliverable that turns the empirical probes into a defensible, bounded claim.

.brainos-codex-complete2, .brainos-codex/, .brainos-context/, .brainos-manifest.json, .brainos/, .git/, .gitignore, .hypothesis/, .mypy_cache/, .pydeps/, .pytest_cache/, .venvs/, :memory:.ses, PROJECT_STATUS.md, README_REGISTRY_AUDIT.md, README_cpu_inference_harness.md, README_metamorphic_oracle.md, README_onnx_activation_hook.md, README_oracle.md, README_oracle_harness.md, README_quarantine_manifest.md, README_ranking.md, VERIFICATION_resnet18_deletion.md, __pycache__/, activation_quantizer_injector.py, aggregate_baseline_metrics.py, aggregate_baseline_metrics.py.bak, apply_calibrated_thresholds_quantization.py, apply_calibrated_thresholds_quantization_realtime.py, apply_fixed_thresholds_additive.py

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Document licensing risk assessment and compliance notes” — Explicit risk documentation supports responsible artifact release and paper supplementary material. → “Create provenance manifest” — Reproducibility package ensures another researcher can rerun model curation and inference exactly.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Document licensing risk assessment and compliance notes” — Explicit risk documentation supports responsible artifact release and paper supplementary material. → “Compile license attribution matrix” — Explicit risk documentation supports responsible artifact release and paper supplementary material.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Document licensing risk assessment and compliance notes” — Explicit risk documentation supports responsible artifact release and paper supplementary material. → “Package reproducible model bundle” — Bundling with checksums provides a reusable artifact for blinded fault and regression evaluation.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Identify branching topology models with public artifacts” — Branching topology is required to test graph-sensitive localization methods against sequential baselines. → “Probe per-node activation extractability for graph-sensitive localization” — The parent's node-level localization methods presuppose instrumentable graphs; a measured extractability manifest identifies architectures where the methods cannot apply and grounds the claim's scope.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Document licensing risk assessment and compliance notes” — Explicit risk documentation supports responsible artifact release and paper supplementary material. → “Build deterministic inference harness” — A shared harness enables fair comparison of localization methods across models and supports provenance.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Document licensing risk assessment and compliance notes” — Explicit risk documentation supports responsible artifact release and paper supplementary material. → “Verify CPU inference compatibility” — CPU-only reproducibility is prerequisite for blinded fault evaluation and eliminates GPU-specific nondeterminism.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Verify CPU inferable architecture via ONNX/PyTorch export” — CPU-only inference is required for reproducible blinded fault evaluation across environments. → “ONNX Runtime CPU repeated-run bit-determinism probe” — ORT threading is the canonical source of CPU non-determinism; characterizing it directly tests the deterministic-output requirement on the ONNX side and yields a reusable determinism evidence record.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Verify CPU inferable architecture via ONNX/PyTorch export” — CPU-only inference is required for reproducible blinded fault evaluation across environments. → “ONNX export compatibility matrix for a fixed model shortlist” — The compatibility matrix is the primary empirical artifact behind the CPU-inferable claim and cleanly separates model-level exportability from harness issues; it is a single bounded survey step that gates which models can enter the program.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Identify branching topology models with public artifacts” — Branching topology is required to test graph-sensitive localization methods against sequential baselines. → “Measure PyTorch-to-ONNX export fidelity for branching candidates” — Export-induced numerical divergence confounds drift localization; quantifying per-architecture export fidelity is a falsifiable baseline measurement that directly parameterizes the parent's differential-testing design.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Document licensing risk assessment and compliance notes” — Explicit risk documentation supports responsible artifact release and paper supplementary material. → “Implement ONNX branching topology detector” — Branching topology is required to test graph-sensitive localization methods and must be identified reproducibly before model curation.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Document preprocessing provenance evidence” — Evidence record enables reviewers to verify compatibility claims and rerun the blinded evaluation setup.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Audit dataset-model license compatibility” — Legal feasibility is a prerequisite for paper-ready artifacts; incompatible licenses invalidate the evaluation design.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Create version-pinned dataset manifest” — Reproducibility requires immutable references to data; a checksum manifest prevents silent drift.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Identify branching topology models with public artifacts” — Branching topology is required to test graph-sensitive localization methods against sequential baselines. → “Map candidate op coverage to ORT CPU opset support” — CPU-inferability is an assumption rather than a measurement; an op-level compatibility matrix makes the selection criterion objective and flags models where the runtime gap itself is a drift source.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Validate input shape and dtype contracts” — Shape/dtype mismatches are the most common compatibility failure; controlled dummy runs provide falsifiable evidence.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Implement CPU inference harness with adapters” — A reproducible harness is required to verify that preprocessing yields valid tensors for blinded fault evaluation.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Build model-dataset preprocessing compatibility matrix” — A compact matrix makes incompatibilities explicit and guides selection of a common blinded evaluation input set.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Reproduce preprocessing pipelines from repositories” — Repository code often diverges from papers; reproducing it yields an executable reference for compatibility testing.

/opt/brain/workspaces/projects/1c692942-e01f-4f9f-a690-f07e81fb075e exists

“Assess dataset compatibility for blinded evaluation” — Fault evaluation requires a common input set; dataset-model compatibility ensures valid blinded experiments. → “Retrieve paired public datasets and licenses” — Blinded evaluation requires a common input set; dataset identity and licensing determine legal and reproducibility feasibility.

Documentation

README — what BrainOS is and how it's set up
TODO — what's built, what's known-missing, what's deliberately deferred
Architecture — the system design