Codex bootstrap for #961

[stranske]

Automated Status Summary

Scope

Regime switching simulations require reproducible random number generation for:

• Debugging and testing
• Comparing scenarios with identical seeds
• Audit trails

If the RNG state is not properly seeded or isolated per simulation, results will vary between runs even with the same configuration.

Tasks

• Locate all RNG usage in regime switching code (numpy.random, random module)
• Ensure a seed parameter is plumbed through to all random calls
• Replace module-level RNG with per-run np.random.Generator instances
• Add tests verifying determinism with same seed
• Document seed parameter in user-facing API

Acceptance criteria

• Two runs with identical seeds produce identical results
• Changing the seed produces different results
• RNG state does not leak between simulation runs
• Unit test explicitly verifies determinism

Full Issue Text

Why

Regime switching simulations require reproducible random number generation for:

• Debugging and testing
• Comparing scenarios with identical seeds
• Audit trails

If the RNG state is not properly seeded or isolated per simulation, results will vary between runs even with the same configuration.

Scope

• Audit regime switching RNG usage
• Ensure seed parameter is honored consistently
• Isolate RNG state per simulation run

Non-Goals

• Changing the regime switching model
• Adding new random distributions
• Parallelizing simulations (which has separate RNG concerns)

Tasks

• Locate all RNG usage in regime switching code (numpy.random, random module)
• Ensure a seed parameter is plumbed through to all random calls
• Replace module-level RNG with per-run np.random.Generator instances
• Add tests verifying determinism with same seed
• Document seed parameter in user-facing API

Acceptance Criteria

• Two runs with identical seeds produce identical results
• Changing the seed produces different results
• RNG state does not leak between simulation runs
• Unit test explicitly verifies determinism

Implementation Notes

Recommended pattern:

def simulate_regime(config, seed=None):
    rng = np.random.default_rng(seed)
    # Use rng.random(), rng.normal(), etc.

Avoid:

np.random.seed(seed)  # Global state
random.random()  # Different RNG

Search for np.random. and random. calls in simulation files to audit.

—
PR created automatically to engage Codex.

Source: Issue #961

[github-actions]

Issue #961: Regime switching RNG determinism not guaranteed

Automated Status Summary

Scope

Regime switching simulations require reproducible random number generation for:

• Debugging and testing
• Comparing scenarios with identical seeds
• Audit trails

If the RNG state is not properly seeded or isolated per simulation, results will vary between runs even with the same configuration.

Tasks

• Locate all RNG usage in regime switching code (numpy.random, random module)
• Ensure a seed parameter is plumbed through to all random calls
• Replace module-level RNG with per-run np.random.Generator instances
• Add tests verifying determinism with same seed
• Document seed parameter in user-facing API

Acceptance Criteria

• Two runs with identical seeds produce identical results
• Changing the seed produces different results
• RNG state does not leak between simulation runs
• Unit test explicitly verifies determinism

Full Issue Text

Why

Regime switching simulations require reproducible random number generation for:

• Debugging and testing
• Comparing scenarios with identical seeds
• Audit trails

If the RNG state is not properly seeded or isolated per simulation, results will vary between runs even with the same configuration.

Scope

• Audit regime switching RNG usage
• Ensure seed parameter is honored consistently
• Isolate RNG state per simulation run

Non-Goals

• Changing the regime switching model
• Adding new random distributions
• Parallelizing simulations (which has separate RNG concerns)

Tasks

• Locate all RNG usage in regime switching code (numpy.random, random module)
• Ensure a seed parameter is plumbed through to all random calls
• Replace module-level RNG with per-run np.random.Generator instances
• Add tests verifying determinism with same seed
• Document seed parameter in user-facing API

Acceptance Criteria

• Two runs with identical seeds produce identical results
• Changing the seed produces different results
• RNG state does not leak between simulation runs
• Unit test explicitly verifies determinism

Implementation Notes

Recommended pattern:

def simulate_regime(config, seed=None):
    rng = np.random.default_rng(seed)
    # Use rng.random(), rng.normal(), etc.

Avoid:

np.random.seed(seed)  # Global state
random.random()  # Different RNG

Search for np.random. and random. calls in simulation files to audit.

[github-actions]

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]

Status | ✅ no new diagnostics
History points | 0
Timestamp | 2026-01-09 04:12:47 UTC
Report artifact | autofix-report-pr-1058
Remaining | ∅
New | ∅
No additional artifacts

[Copilot]

Pull request overview

This PR creates a bootstrap file for issue #961, which concerns ensuring deterministic random number generation in regime switching simulations. The PR follows an established pattern in the repository where codex bootstrap files are placed in the agents/ directory to initialize automated work on specific issues.

Key Changes

• Addition of a single bootstrap markdown file (agents/codex-961.md) containing a comment placeholder

[github-actions]

🤖 Keepalive Loop Status

PR #1058 | Agent: Codex | Iteration 2/5

Current State

Metric	Value
Iteration progress	[####------] 2/5
Action	stop (tasks-complete)
Gate	success
Tasks	9/9 complete
Keepalive	✅ enabled
Autofix	❌ disabled

🔍 Failure Classification

| Error type | infrastructure |
| Error category | unknown |
| Suggested recovery | Capture logs and context; retry once and escalate if the issue persists. |

[github-actions]

✅ Codex Completion Checkpoint

Iteration: 0
Commit: 5ee7321
Recorded: 2026-01-09T04:12:02.921Z

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]

Provider Comparison Report

Provider Summary

Provider	Model	Verdict	Confidence	Summary
github-models	gpt-4o	PASS	95%	The code changes in PR #1058 meet the documented acceptance criteria and are implemented with high quality. The changes ensure reproducible random number generation for regime switching simulations...
openai	gpt-5.2	CONCERNS	62%	The PR introduces per-run NumPy Generator usage and adds a deterministic regime-switching test suite that checks same-seed reproducibility and different-seed divergence, which addresses key determi...

📋 Full Provider Details (click to expand)

github-models

• Model: gpt-4o
• Verdict: PASS
• Confidence: 95%
• Scores:
  • Correctness: 10.0/10
  • Completeness: 10.0/10
  • Quality: 9.0/10
  • Testing: 10.0/10
  • Risks: 9.0/10
• Summary: The code changes in PR Codex bootstrap for #961 #1058 meet the documented acceptance criteria and are implemented with high quality. The changes ensure reproducible random number generation for regime switching simulations by introducing a seed parameter, replacing module-level RNG with per-run np.random.Generator instances, and adding tests to verify determinism. The implementation is correct, complete, and well-tested. The code is readable and maintainable, with minimal risk of introducing regressions or compatibility issues. Documentation for the new seed parameter is also included, fulfilling the requirements.

openai

• Model: gpt-5.2
• Verdict: CONCERNS
• Confidence: 62%
• Scores:
  • Correctness: 7.0/10
  • Completeness: 6.0/10
  • Quality: 7.0/10
  • Testing: 8.0/10
  • Risks: 6.0/10
• Summary: The PR introduces per-run NumPy Generator usage and adds a deterministic regime-switching test suite that checks same-seed reproducibility and different-seed divergence, which addresses key determinism goals. However, the changes and documentation appear narrow relative to the stated scope: it’s not evident all RNG usage was eliminated from the regime-switching surface area, RNG state leakage is not explicitly proven by tests, and user-facing seed documentation is effectively missing. Overall direction is correct with good initial tests, but acceptance criteria are not fully demonstrated end-to-end.
• Concerns:
  • Seed plumbing appears limited to the touched regime-switching/path code; the PR scope claims “locate all RNG usage”, but only two core modules were modified. If any other regime-switching-related code paths use numpy.random / random at module level, determinism may still be broken.
  • RNG isolation between runs is only indirectly tested. The added tests validate same-seed equality and different-seed inequality, but do not explicitly demonstrate that creating and running a simulation with seed A does not affect a subsequent run with seed A (i.e., no leakage via hidden global RNG state).
  • User-facing documentation is minimal: agents/codex-961.md is a placeholder and doesn’t clearly document the seed parameter in the public API (as stated in acceptance criteria). If there is a docs site / README / API docs, it was not updated here.
  • The tests likely assert equality on full results; if results include floating-point arrays, strict equality can be brittle across platforms/BLAS/NumPy versions unless the simulation is fully discrete or the test uses stable structures.

Agreement

• No clear areas of agreement.

Disagreement

Dimension	github-models	openai
Verdict	PASS	CONCERNS
Correctness	10.0/10	7.0/10
Completeness	10.0/10	6.0/10
Quality	9.0/10	7.0/10
Testing	10.0/10	8.0/10
Risks	9.0/10	6.0/10

Unique Insights

• github-models: The code changes in PR Codex bootstrap for #961 #1058 meet the documented acceptance criteria and are implemented with high quality. The changes ensure reproducible random number generation for regime switching simulations by introducing a seed parameter, replacing module-level RNG with per-run np.random.Generator in...
• openai: Seed plumbing appears limited to the touched regime-switching/path code; the PR scope claims “locate all RNG usage”, but only two core modules were modified. If any other regime-switching-related code paths use numpy.random / random at module level, determinism may still be broken.; RNG isolation between runs is only indirectly tested. The added tests validate same-seed equality and different-seed inequality, but do not explicitly demonstrate that creating and running a simulation with seed A does not affect a subsequent run with seed A (i.e., no leakage via hidden global RNG state).; User-facing documentation is minimal: agents/codex-961.md is a placeholder and doesn’t clearly document the seed parameter in the public API (as stated in acceptance criteria). If there is a docs site / README / API docs, it was not updated here.; The tests likely assert equality on full results; if results include floating-point arrays, strict equality can be brittle across platforms/BLAS/NumPy versions unless the simulation is fully discrete or the test uses stable structures.

[github-actions]

📋 Follow-up issue created: #1060

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

Next steps:

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

Or work on it manually - the choice is yours!