Codex bootstrap for #1064 by stranske · Pull Request #1066 · stranske/Portable-Alpha-Extension-Model

stranske · 2026-01-09T13:43:56Z

Automated Status Summary

Scope

PR #1062 addressed issue #1060 but verification identified concerns (verdict: Unknown). This follow-up addresses the remaining gaps with improved task structure to ensure deterministic behavior and RNG isolation.

Context for Agent

Related Issues/PRs

#1062
#1060

Tasks

Modify simulation_code.py to replace module-level RNG usage with a local np.random.Generator instance. Update functions to accept a generator parameter if necessary.
Refactor regime_switching.py to eliminate module-level RNG usage by using a per-run np.random.Generator instance. Pass the generator as a parameter or instantiate one within the simulation run.
Update simulation_initialization.py to instantiate a new np.random.Generator at the start of each simulation run and ensure it is passed to all downstream functions.
Review and update existing tests in test_simulation.py to explicitly validate RNG isolation by checking that simulations with the same seed produce identical results and those with different seeds produce different results.

Acceptance criteria

All functions in simulation_code.py that previously used module-level RNG now accept an np.random.Generator instance as a parameter or create one locally.
All functions in regime_switching.py that previously used module-level RNG now accept an np.random.Generator instance as a parameter or create one locally.
A new np.random.Generator is instantiated at the start of each simulation run in simulation_initialization.py and passed to all downstream functions.
Tests in test_simulation.py validate that simulations with the same seed produce identical results and those with different seeds produce different results.

Full Issue Text

## Why
PR #1062 addressed issue #1060 but verification identified concerns (verdict: **Unknown**). This follow-up addresses the remaining gaps with improved task structure to ensure deterministic behavior and RNG isolation.

## Tasks
- [ ] Modify `simulation_code.py` to replace module-level RNG usage with a local `np.random.Generator` instance. Update functions to accept a generator parameter if necessary.
- [ ] Refactor `regime_switching.py` to eliminate module-level RNG usage by using a per-run `np.random.Generator` instance. Pass the generator as a parameter or instantiate one within the simulation run.
- [ ] Update `simulation_initialization.py` to instantiate a new `np.random.Generator` at the start of each simulation run and ensure it is passed to all downstream functions.
- [ ] Review and update existing tests in `test_simulation.py` to explicitly validate RNG isolation by checking that simulations with the same seed produce identical results and those with different seeds produce different results.

## Acceptance Criteria
- [ ] All functions in `simulation_code.py` that previously used module-level RNG now accept an `np.random.Generator` instance as a parameter or create one locally.
- [ ] All functions in `regime_switching.py` that previously used module-level RNG now accept an `np.random.Generator` instance as a parameter or create one locally.
- [ ] A new `np.random.Generator` is instantiated at the start of each simulation run in `simulation_initialization.py` and passed to all downstream functions.
- [ ] Tests in `test_simulation.py` validate that simulations with the same seed produce identical results and those with different seeds produce different results.

## Implementation Notes
Ensure that modifications are applied directly to `simulation_code.py` and `regime_switching.py` to eliminate module-level RNG usage. Verify through code review and targeted tests that these changes occur.

<details>
<summary>Background (previous attempt context)</summary>

The previous PR changes only updated pa_core and test files while leaving the core simulation modules (`simulation_code.py` and `regime_switching.py`) untouched. Because the diff did not incorporate the required file-specific RNG refactoring, the tests may pass due to previous behavior rather than enforcing the new RNG isolation design.

</details>

—
PR created automatically to engage Codex.

Source: Issue #1064

github-actions · 2026-01-09T13:44:01Z

Issue #1064: [Follow-up] Modify 'simulation_code.py' so that any module-lev (PR #1062)

Automated Status Summary

Scope

PR #1062 addressed issue #1060 but verification identified concerns (verdict: Unknown). This follow-up addresses the remaining gaps with improved task structure to ensure deterministic behavior and RNG isolation.

Tasks

Modify simulation_code.py to replace module-level RNG usage with a local np.random.Generator instance. Update functions to accept a generator parameter if necessary.
Refactor regime_switching.py to eliminate module-level RNG usage by using a per-run np.random.Generator instance. Pass the generator as a parameter or instantiate one within the simulation run.
Update simulation_initialization.py to instantiate a new np.random.Generator at the start of each simulation run and ensure it is passed to all downstream functions.
Review and update existing tests in test_simulation.py to explicitly validate RNG isolation by checking that simulations with the same seed produce identical results and those with different seeds produce different results.

Acceptance Criteria

All functions in simulation_code.py that previously used module-level RNG now accept an np.random.Generator instance as a parameter or create one locally.
All functions in regime_switching.py that previously used module-level RNG now accept an np.random.Generator instance as a parameter or create one locally.
A new np.random.Generator is instantiated at the start of each simulation run in simulation_initialization.py and passed to all downstream functions.
Tests in test_simulation.py validate that simulations with the same seed produce identical results and those with different seeds produce different results.

Full Issue Text

## Why
PR #1062 addressed issue #1060 but verification identified concerns (verdict: **Unknown**). This follow-up addresses the remaining gaps with improved task structure to ensure deterministic behavior and RNG isolation.

## Tasks
- [ ] Modify `simulation_code.py` to replace module-level RNG usage with a local `np.random.Generator` instance. Update functions to accept a generator parameter if necessary.
- [ ] Refactor `regime_switching.py` to eliminate module-level RNG usage by using a per-run `np.random.Generator` instance. Pass the generator as a parameter or instantiate one within the simulation run.
- [ ] Update `simulation_initialization.py` to instantiate a new `np.random.Generator` at the start of each simulation run and ensure it is passed to all downstream functions.
- [ ] Review and update existing tests in `test_simulation.py` to explicitly validate RNG isolation by checking that simulations with the same seed produce identical results and those with different seeds produce different results.

## Acceptance Criteria
- [ ] All functions in `simulation_code.py` that previously used module-level RNG now accept an `np.random.Generator` instance as a parameter or create one locally.
- [ ] All functions in `regime_switching.py` that previously used module-level RNG now accept an `np.random.Generator` instance as a parameter or create one locally.
- [ ] A new `np.random.Generator` is instantiated at the start of each simulation run in `simulation_initialization.py` and passed to all downstream functions.
- [ ] Tests in `test_simulation.py` validate that simulations with the same seed produce identical results and those with different seeds produce different results.

## Implementation Notes
Ensure that modifications are applied directly to `simulation_code.py` and `regime_switching.py` to eliminate module-level RNG usage. Verify through code review and targeted tests that these changes occur.

<details>
<summary>Background (previous attempt context)</summary>

The previous PR changes only updated pa_core and test files while leaving the core simulation modules (`simulation_code.py` and `regime_switching.py`) untouched. Because the diff did not incorporate the required file-specific RNG refactoring, the tests may pass due to previous behavior rather than enforcing the new RNG isolation design.

</details>

github-actions · 2026-01-09T13:44:02Z

PR created. Comment @codex start to request the plan. Tell Codex to reuse the scope, acceptance criteria, and task list from the source issue and publish them here with - [ ] checklists so keepalive keeps watching. After Codex replies, follow the instructions posted on the source issue to begin execution.

github-actions · 2026-01-09T13:45:05Z

Copilot

Pull request overview

This PR creates a bootstrap marker file to engage the Codex agent system for issue #1064, which aims to refactor RNG usage in simulation code to eliminate module-level randomness and ensure deterministic, isolated per-run behavior. This is purely an organizational/tracking mechanism and contains no implementation of the actual RNG refactoring tasks described in the issue.

Key changes:

Added a single-line markdown comment file as a bootstrap marker

github-actions · 2026-01-09T13:46:12Z

🤖 Keepalive Loop Status

PR #1066 | Agent: Codex | Iteration 3/5

Current State

Metric	Value
Iteration progress	[######----] 3/5
Action	stop (tasks-complete)
Gate	success
Tasks	8/8 complete
Keepalive	✅ enabled
Autofix	❌ disabled

🔍 Failure Classification

stranske · 2026-01-09T14:21:25Z

@codex start

Reuse the scope, acceptance criteria, and task list from the source issue and publish them here with - [ ] checklists so keepalive keeps watching.

chatgpt-codex-connector · 2026-01-09T14:24:56Z

Below are the requested checklists from the source issue.

Scope

PR Codex bootstrap for #1060 #1062 addressed issue [Follow-up] Audit and refactor the simulation code to locate a (PR #1058) #1060 but verification identified concerns (verdict: Unknown). This follow-up addresses the remaining gaps with improved task structure to ensure deterministic behavior and RNG isolation.

Tasks

Modify simulation_code.py to replace module-level RNG usage with a local np.random.Generator instance. Update functions to accept a generator parameter if necessary.
Refactor regime_switching.py to eliminate module-level RNG usage by using a per-run np.random.Generator instance. Pass the generator as a parameter or instantiate one within the simulation run.
Update simulation_initialization.py to instantiate a new np.random.Generator at the start of each simulation run and ensure it is passed to all downstream functions.
Review and update existing tests in test_simulation.py to explicitly validate RNG isolation by checking that simulations with the same seed produce identical results and those with different seeds produce different results.

Acceptance Criteria

All functions in simulation_code.py that previously used module-level RNG now accept an np.random.Generator instance as a parameter or create one locally.
All functions in regime_switching.py that previously used module-level RNG now accept an np.random.Generator instance as a parameter or create one locally.
A new np.random.Generator is instantiated at the start of each simulation run in simulation_initialization.py and passed to all downstream functions.
Tests in test_simulation.py validate that simulations with the same seed produce identical results and those with different seeds produce different results.

View task →

stranske · 2026-01-09T15:48:40Z

@codex

Please work on the first unchecked task from the task list and commit your changes.

chatgpt-codex-connector · 2026-01-09T15:52:30Z

Summary

Ensured draw_joint_returns always initializes a per-call RNG when none is supplied, avoiding per-regime generator creation and ensuring local RNG usage per run. pa_core/sim/paths.pyL493-L547

Testing

Not run (not requested).

View task →

github-actions · 2026-01-09T18:00:18Z

✅ Codex Completion Checkpoint

Iteration: 1
Commit: 92024fe
Recorded: 2026-01-09T18:14:39.310Z

No new completions recorded this round.

About this comment

This comment is automatically generated to track task completions.
The Automated Status Summary reads these checkboxes to update PR progress.
Do not edit this comment manually.

github-actions · 2026-01-09T21:38:31Z

Provider Comparison Report

Provider Summary

Provider	Model	Verdict	Confidence	Summary
github-models	gpt-4o	PASS	95%	The code changes in PR #1066 fulfill the documented acceptance criteria. The modifications ensure that all functions in `simulation_code.py` and `regime_switching.py` now use a local `np.random.Gen...
openai	gpt-5.2	CONCERNS	64%	Code changes and tests suggest progress toward per-run RNG isolation (passing/creating `np.random.Generator` rather than relying on module-level randomness) and add determinism checks (same seed =>...

📋 Full Provider Details (click to expand)

github-models

Model: gpt-4o
Verdict: PASS
Confidence: 95%
Scores:
- Correctness: 9.0/10
- Completeness: 10.0/10
- Quality: 9.0/10
- Testing: 9.0/10
- Risks: 8.0/10
Summary: The code changes in PR Codex bootstrap for #1064 #1066 fulfill the documented acceptance criteria. The modifications ensure that all functions in simulation_code.py and regime_switching.py now use a local np.random.Generator instance, either passed as a parameter or instantiated locally. The simulation_initialization.py file correctly initializes a new generator at the start of each simulation run and passes it downstream. Tests in test_simulation.py adequately validate RNG isolation by confirming that simulations with the same seed produce identical results and those with different seeds produce different results. The code is well-structured, readable, and adheres to good practices. No significant risks or issues were identified, though further testing in production environments is recommended to ensure no unforeseen edge cases arise.

openai

Model: gpt-5.2
Verdict: CONCERNS
Confidence: 64%
Scores:
- Correctness: 7.0/10
- Completeness: 6.0/10
- Quality: 8.0/10
- Testing: 7.0/10
- Risks: 7.0/10
Summary: Code changes and tests suggest progress toward per-run RNG isolation (passing/creating np.random.Generator rather than relying on module-level randomness) and add determinism checks (same seed => identical results; different seed => different results). However, the documented acceptance criteria reference different filenames than those changed, and the diff summary alone doesn’t conclusively demonstrate that all module-level RNG usage has been removed across the intended modules or that tests cover isolation from global RNG state. Recommend confirming no remaining np.random.* global usage in the simulation/regime/path code paths and strengthening tests to assert independence from global RNG.
Concerns:
- Acceptance criteria and task list reference files (simulation_code.py, regime_switching.py, simulation_initialization.py, test_simulation.py) that are not the ones actually modified in this PR (changes are in pa_core/facade.py, pa_core/sim/paths.py, pa_core/sim/regimes.py, and tests/test_simulations.py). This mismatch makes it unclear whether all prior module-level RNG usage was eliminated across the intended surfaces.
- The PR appears to move toward RNG isolation by passing/using a per-run generator, but without seeing explicit removal of all module-level RNG uses (e.g., lingering np.random.* calls or cached RNG instances) across the relevant simulation stack, it’s hard to conclude the acceptance criteria are fully met.
- Tests were updated to check same-seed determinism vs different-seed divergence, but it’s not evident they also validate isolation from global RNG state (e.g., that prior calls to np.random.seed() or np.random.random() do not affect simulation results). If global RNG is still touched anywhere, the current tests may not catch it.
- If Generator instances are optionally created internally when none is supplied (common pattern), ensure a seed is always sourced deterministically from the simulation initialization path when determinism is required; otherwise, two runs with the same provided seed might still diverge if any internal generator is created without that seed.

Agreement

Quality: scores within 1 point (avg 8.5/10, range 8.0-9.0)
Risks: scores within 1 point (avg 7.5/10, range 7.0-8.0)

Disagreement

Dimension	github-models	openai
Verdict	PASS	CONCERNS
Correctness	9.0/10	7.0/10
Completeness	10.0/10	6.0/10
Testing	9.0/10	7.0/10

Unique Insights

github-models: The code changes in PR Codex bootstrap for #1064 #1066 fulfill the documented acceptance criteria. The modifications ensure that all functions in simulation_code.py and regime_switching.py now use a local np.random.Generator instance, either passed as a parameter or instantiated locally. The `simulation_initializati...
openai: Acceptance criteria and task list reference files (simulation_code.py, regime_switching.py, simulation_initialization.py, test_simulation.py) that are not the ones actually modified in this PR (changes are in pa_core/facade.py, pa_core/sim/paths.py, pa_core/sim/regimes.py, and tests/test_simulations.py). This mismatch makes it unclear whether all prior module-level RNG usage was eliminated across the intended surfaces.; The PR appears to move toward RNG isolation by passing/using a per-run generator, but without seeing explicit removal of all module-level RNG uses (e.g., lingering np.random.* calls or cached RNG instances) across the relevant simulation stack, it’s hard to conclude the acceptance criteria are fully met.; Tests were updated to check same-seed determinism vs different-seed divergence, but it’s not evident they also validate isolation from global RNG state (e.g., that prior calls to np.random.seed() or np.random.random() do not affect simulation results). If global RNG is still touched anywhere, the current tests may not catch it.; If Generator instances are optionally created internally when none is supplied (common pattern), ensure a seed is always sourced deterministically from the simulation initialization path when determinism is required; otherwise, two runs with the same provided seed might still diverge if any internal generator is created without that seed.

github-actions · 2026-01-09T23:07:13Z

📋 Follow-up issue created: #1073

Verification concerns have been analyzed and structured into a follow-up issue.

Next steps:

Review the generated issue
Add agents:apply-suggestions label to format for agent work
Add agent:codex label to assign to an agent

Or work on it manually - the choice is yours!

chore(codex): bootstrap PR for issue #1064

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Copilot AI review requested due to automatic review settings January 9, 2026 13:43

stranske assigned stranske-automation-bot Jan 9, 2026

github-actions bot added the agent:codex Assign to Codex agent label Jan 9, 2026

github-actions bot mentioned this pull request Jan 9, 2026

[Follow-up] Modify 'simulation_code.py' so that any module-lev (PR #1062) #1064

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fix: auto-start coding agent for issue-triggered PRs stranske/Workflows#695

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docs: Clarify CLI vs UI agent distinction in keepalive system stranske/Workflows#700

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fix: add always() to run-codex job to handle skipped dependency stranske/Workflows#703

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chore(codex-normal): apply updates (PR #1066)

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refactor: isolate per-run rng setup

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stranske merged commit 2f65828 into main Jan 9, 2026
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Codex bootstrap for #1073 #1074

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Issue #1064: [Follow-up] Modify 'simulation_code.py' so that any module-lev (PR #1062)

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