feat(consistency): add cross-run trend tracking template and methodology #1

Open
Open
feat(consistency): add cross-run trend tracking template and methodology#1

Description

@nanookclaw
nanookclaw
opened on Mar 31, 2026

Summary

The consistency.md dimension asks the right question — does the system behave similarly across repeated runs? — but the current experiment-log.md template captures only a single snapshot. There is no mechanism to track consistency trends across multiple experiment log entries over time.

The Gap

experiment-log.md records one run:

- Date:
- Consistency result:

To answer whether the system is getting more or less consistent over time, you need to compare across runs. Right now that comparison is manual and informal — teams typically don't do it systematically.

Proposal

Two additions:

1. templates/baseline-comparison.md

A structured template for comparing current experiment logs against a known-good baseline:

# Baseline Comparison Log## Baseline (v___)- Date established:
- System version:
- Consistency result (baseline):
- Correctness result (baseline):
## Comparison Runs| Date | System version | Consistency Δ | Correctness Δ | Notes ||------|---------------|--------------|--------------|-------|||||||## Trend Assessment- Direction (improving / stable / degrading):
- Sessions since last significant change:
- Action threshold: flag when any dimension Δ > 0.10 vs baseline

2. Extension to docs/consistency.md

Add a section on longitudinal consistency — the difference between:

  • Within-run stability (current scope): same agent, same scenario, repeated N times in one session
  • Cross-run consistency (proposed): same agent, same scenario, measured across sessions over days/weeks

A system that is consistent within a single evaluation run can still drift significantly across runs separated by days or model updates. The cross-run view is what matters for production reliability.

Why This Matters

Practitioners building on top of this framework will run evaluations repeatedly over time (before/after model updates, after prompt changes, after infrastructure changes). Without a structured cross-run comparison mechanism, they will either:

  1. Manually eyeball past logs (error-prone), or
  2. Only look at the most recent run in isolation (missing drift entirely)

The baseline comparison template makes cross-run consistency a first-class evaluation artifact.

Related Work

The PDR (Periodic Delivery Reliability) framework (DOI: 10.5281/zenodo.19339987) tracks this as delivery_score and calibration_delta across sessions — the same cross-session consistency measurement problem applied to autonomous agent workflows. The delta-from-baseline approach is validated in that context.

Happy to draft both the template and the consistency.md extension if this direction looks right.

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      Skip to content
      feat(consistency): add cross-run trend tracking template and methodology #1
      Open
      Open
      feat(consistency): add cross-run trend tracking template and methodology#1
      Description
      @nanookclaw
      nanookclaw
      opened on Mar 31, 2026
      Summary
      The consistency.md dimension asks the right question — does the system behave similarly across repeated runs? — but the current experiment-log.md template captures only a single snapshot. There is no mechanism to track consistency trends across multiple experiment log entries over time.
      The Gap
      experiment-log.md records one run:
      - Date:
- Consistency result:

      To answer whether the system is getting more or less consistent over time, you need to compare across runs. Right now that comparison is manual and informal — teams typically don't do it systematically.
      Proposal
      Two additions:
      1. templates/baseline-comparison.md
      A structured template for comparing current experiment logs against a known-good baseline:
      # Baseline Comparison Log## Baseline (v___)- Date established:
- System version:
- Consistency result (baseline):
- Correctness result (baseline):
## Comparison Runs| Date | System version | Consistency Δ | Correctness Δ | Notes ||------|---------------|--------------|--------------|-------|||||||## Trend Assessment- Direction (improving / stable / degrading):
- Sessions since last significant change:
- Action threshold: flag when any dimension Δ > 0.10 vs baseline
      2. Extension to docs/consistency.md
      Add a section on longitudinal consistency — the difference between:
      • Within-run stability (current scope): same agent, same scenario, repeated N times in one session
      • Cross-run consistency (proposed): same agent, same scenario, measured across sessions over days/weeks
      A system that is consistent within a single evaluation run can still drift significantly across runs separated by days or model updates. The cross-run view is what matters for production reliability.
      Why This Matters
      Practitioners building on top of this framework will run evaluations repeatedly over time (before/after model updates, after prompt changes, after infrastructure changes). Without a structured cross-run comparison mechanism, they will either:
      1. Manually eyeball past logs (error-prone), or
      2. Only look at the most recent run in isolation (missing drift entirely)
      The baseline comparison template makes cross-run consistency a first-class evaluation artifact.
      Related Work
      The PDR (Periodic Delivery Reliability) framework (DOI: 10.5281/zenodo.19339987) tracks this as delivery_score and calibration_delta across sessions — the same cross-session consistency measurement problem applied to autonomous agent workflows. The delta-from-baseline approach is validated in that context.
      Happy to draft both the template and the consistency.md extension if this direction looks right.
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          Skip to content
          feat(consistency): add cross-run trend tracking template and methodology #1
          Open
          Open
          feat(consistency): add cross-run trend tracking template and methodology#1
          Description
          @nanookclaw
          nanookclaw
          opened on Mar 31, 2026
          Summary
          The consistency.md dimension asks the right question — does the system behave similarly across repeated runs? — but the current experiment-log.md template captures only a single snapshot. There is no mechanism to track consistency trends across multiple experiment log entries over time.
          The Gap
          experiment-log.md records one run:
          - Date:
- Consistency result:

          To answer whether the system is getting more or less consistent over time, you need to compare across runs. Right now that comparison is manual and informal — teams typically don't do it systematically.
          Proposal
          Two additions:
          1. templates/baseline-comparison.md
          A structured template for comparing current experiment logs against a known-good baseline:
          # Baseline Comparison Log## Baseline (v___)- Date established:
- System version:
- Consistency result (baseline):
- Correctness result (baseline):
## Comparison Runs| Date | System version | Consistency Δ | Correctness Δ | Notes ||------|---------------|--------------|--------------|-------|||||||## Trend Assessment- Direction (improving / stable / degrading):
- Sessions since last significant change:
- Action threshold: flag when any dimension Δ > 0.10 vs baseline
          2. Extension to docs/consistency.md
          Add a section on longitudinal consistency — the difference between:
          • Within-run stability (current scope): same agent, same scenario, repeated N times in one session
          • Cross-run consistency (proposed): same agent, same scenario, measured across sessions over days/weeks
          A system that is consistent within a single evaluation run can still drift significantly across runs separated by days or model updates. The cross-run view is what matters for production reliability.
          Why This Matters
          Practitioners building on top of this framework will run evaluations repeatedly over time (before/after model updates, after prompt changes, after infrastructure changes). Without a structured cross-run comparison mechanism, they will either:
          1. Manually eyeball past logs (error-prone), or
          2. Only look at the most recent run in isolation (missing drift entirely)
          The baseline comparison template makes cross-run consistency a first-class evaluation artifact.
          Related Work
          The PDR (Periodic Delivery Reliability) framework (DOI: 10.5281/zenodo.19339987) tracks this as delivery_score and calibration_delta across sessions — the same cross-session consistency measurement problem applied to autonomous agent workflows. The delta-from-baseline approach is validated in that context.
          Happy to draft both the template and the consistency.md extension if this direction looks right.
          Metadata
          Metadata
          Assignees
          No one assigned
            Labels
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            No labels
            Projects
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              Milestone
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

              Skip to content
              feat(consistency): add cross-run trend tracking template and methodology #1
              Open
              Open
              feat(consistency): add cross-run trend tracking template and methodology#1
              Description
              @nanookclaw
              nanookclaw
              opened on Mar 31, 2026
              Summary
              The consistency.md dimension asks the right question — does the system behave similarly across repeated runs? — but the current experiment-log.md template captures only a single snapshot. There is no mechanism to track consistency trends across multiple experiment log entries over time.
              The Gap
              experiment-log.md records one run:
              - Date:
- Consistency result:

              To answer whether the system is getting more or less consistent over time, you need to compare across runs. Right now that comparison is manual and informal — teams typically don't do it systematically.
              Proposal
              Two additions:
              1. templates/baseline-comparison.md
              A structured template for comparing current experiment logs against a known-good baseline:
              # Baseline Comparison Log## Baseline (v___)- Date established:
- System version:
- Consistency result (baseline):
- Correctness result (baseline):
## Comparison Runs| Date | System version | Consistency Δ | Correctness Δ | Notes ||------|---------------|--------------|--------------|-------|||||||## Trend Assessment- Direction (improving / stable / degrading):
- Sessions since last significant change:
- Action threshold: flag when any dimension Δ > 0.10 vs baseline
              2. Extension to docs/consistency.md
              Add a section on longitudinal consistency — the difference between:
              • Within-run stability (current scope): same agent, same scenario, repeated N times in one session
              • Cross-run consistency (proposed): same agent, same scenario, measured across sessions over days/weeks
              A system that is consistent within a single evaluation run can still drift significantly across runs separated by days or model updates. The cross-run view is what matters for production reliability.
              Why This Matters
              Practitioners building on top of this framework will run evaluations repeatedly over time (before/after model updates, after prompt changes, after infrastructure changes). Without a structured cross-run comparison mechanism, they will either:
              1. Manually eyeball past logs (error-prone), or
              2. Only look at the most recent run in isolation (missing drift entirely)
              The baseline comparison template makes cross-run consistency a first-class evaluation artifact.
              Related Work
              The PDR (Periodic Delivery Reliability) framework (DOI: 10.5281/zenodo.19339987) tracks this as delivery_score and calibration_delta across sessions — the same cross-session consistency measurement problem applied to autonomous agent workflows. The delta-from-baseline approach is validated in that context.
              Happy to draft both the template and the consistency.md extension if this direction looks right.
              Metadata
              Metadata
              Assignees
              No one assigned
                Labels
                No labels
                No labels
                Projects
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                  None yet
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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(function(){
try {
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var __re = new RegExp('^' + "youtube\\.com" + '

                  Skip to content
                  feat(consistency): add cross-run trend tracking template and methodology #1
                  Open
                  Open
                  feat(consistency): add cross-run trend tracking template and methodology#1
                  Description
                  @nanookclaw
                  nanookclaw
                  opened on Mar 31, 2026
                  Summary
                  The consistency.md dimension asks the right question — does the system behave similarly across repeated runs? — but the current experiment-log.md template captures only a single snapshot. There is no mechanism to track consistency trends across multiple experiment log entries over time.
                  The Gap
                  experiment-log.md records one run:
                  - Date:
- Consistency result:

                  To answer whether the system is getting more or less consistent over time, you need to compare across runs. Right now that comparison is manual and informal — teams typically don't do it systematically.
                  Proposal
                  Two additions:
                  1. templates/baseline-comparison.md
                  A structured template for comparing current experiment logs against a known-good baseline:
                  # Baseline Comparison Log## Baseline (v___)- Date established:
- System version:
- Consistency result (baseline):
- Correctness result (baseline):
## Comparison Runs| Date | System version | Consistency Δ | Correctness Δ | Notes ||------|---------------|--------------|--------------|-------|||||||## Trend Assessment- Direction (improving / stable / degrading):
- Sessions since last significant change:
- Action threshold: flag when any dimension Δ > 0.10 vs baseline
                  2. Extension to docs/consistency.md
                  Add a section on longitudinal consistency — the difference between:
                  • Within-run stability (current scope): same agent, same scenario, repeated N times in one session
                  • Cross-run consistency (proposed): same agent, same scenario, measured across sessions over days/weeks
                  A system that is consistent within a single evaluation run can still drift significantly across runs separated by days or model updates. The cross-run view is what matters for production reliability.
                  Why This Matters
                  Practitioners building on top of this framework will run evaluations repeatedly over time (before/after model updates, after prompt changes, after infrastructure changes). Without a structured cross-run comparison mechanism, they will either:
                  1. Manually eyeball past logs (error-prone), or
                  2. Only look at the most recent run in isolation (missing drift entirely)
                  The baseline comparison template makes cross-run consistency a first-class evaluation artifact.
                  Related Work
                  The PDR (Periodic Delivery Reliability) framework (DOI: 10.5281/zenodo.19339987) tracks this as delivery_score and calibration_delta across sessions — the same cross-session consistency measurement problem applied to autonomous agent workflows. The delta-from-baseline approach is validated in that context.
                  Happy to draft both the template and the consistency.md extension if this direction looks right.
                  Metadata
                  Metadata
                  Assignees
                  No one assigned
                    Labels
                    No labels
                    No labels
                    Projects
                    No projects
                      Milestone
                      No milestone
                      Relationships
                      None yet
                      Development
                      No branches or pull requests
                      Issue actions
                      
, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

                      Skip to content
                      feat(consistency): add cross-run trend tracking template and methodology #1
                      Open
                      Open
                      feat(consistency): add cross-run trend tracking template and methodology#1
                      Description
                      @nanookclaw
                      nanookclaw
                      opened on Mar 31, 2026
                      Summary
                      The consistency.md dimension asks the right question — does the system behave similarly across repeated runs? — but the current experiment-log.md template captures only a single snapshot. There is no mechanism to track consistency trends across multiple experiment log entries over time.
                      The Gap
                      experiment-log.md records one run:
                      - Date:
- Consistency result:

                      To answer whether the system is getting more or less consistent over time, you need to compare across runs. Right now that comparison is manual and informal — teams typically don't do it systematically.
                      Proposal
                      Two additions:
                      1. templates/baseline-comparison.md
                      A structured template for comparing current experiment logs against a known-good baseline:
                      # Baseline Comparison Log## Baseline (v___)- Date established:
- System version:
- Consistency result (baseline):
- Correctness result (baseline):
## Comparison Runs| Date | System version | Consistency Δ | Correctness Δ | Notes ||------|---------------|--------------|--------------|-------|||||||## Trend Assessment- Direction (improving / stable / degrading):
- Sessions since last significant change:
- Action threshold: flag when any dimension Δ > 0.10 vs baseline
                      2. Extension to docs/consistency.md
                      Add a section on longitudinal consistency — the difference between:
                      • Within-run stability (current scope): same agent, same scenario, repeated N times in one session
                      • Cross-run consistency (proposed): same agent, same scenario, measured across sessions over days/weeks
                      A system that is consistent within a single evaluation run can still drift significantly across runs separated by days or model updates. The cross-run view is what matters for production reliability.
                      Why This Matters
                      Practitioners building on top of this framework will run evaluations repeatedly over time (before/after model updates, after prompt changes, after infrastructure changes). Without a structured cross-run comparison mechanism, they will either:
                      1. Manually eyeball past logs (error-prone), or
                      2. Only look at the most recent run in isolation (missing drift entirely)
                      The baseline comparison template makes cross-run consistency a first-class evaluation artifact.
                      Related Work
                      The PDR (Periodic Delivery Reliability) framework (DOI: 10.5281/zenodo.19339987) tracks this as delivery_score and calibration_delta across sessions — the same cross-session consistency measurement problem applied to autonomous agent workflows. The delta-from-baseline approach is validated in that context.
                      Happy to draft both the template and the consistency.md extension if this direction looks right.
                      Metadata
                      Metadata
                      Assignees
                      No one assigned
                        Labels
                        No labels
                        No labels
                        Projects
                        No projects
                          Milestone
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, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

                          Skip to content
                          feat(consistency): add cross-run trend tracking template and methodology #1
                          Open
                          Open
                          feat(consistency): add cross-run trend tracking template and methodology#1
                          Description
                          @nanookclaw
                          nanookclaw
                          opened on Mar 31, 2026
                          Summary
                          The consistency.md dimension asks the right question — does the system behave similarly across repeated runs? — but the current experiment-log.md template captures only a single snapshot. There is no mechanism to track consistency trends across multiple experiment log entries over time.
                          The Gap
                          experiment-log.md records one run:
                          - Date:
- Consistency result:

                          To answer whether the system is getting more or less consistent over time, you need to compare across runs. Right now that comparison is manual and informal — teams typically don't do it systematically.
                          Proposal
                          Two additions:
                          1. templates/baseline-comparison.md
                          A structured template for comparing current experiment logs against a known-good baseline:
                          # Baseline Comparison Log## Baseline (v___)- Date established:
- System version:
- Consistency result (baseline):
- Correctness result (baseline):
## Comparison Runs| Date | System version | Consistency Δ | Correctness Δ | Notes ||------|---------------|--------------|--------------|-------|||||||## Trend Assessment- Direction (improving / stable / degrading):
- Sessions since last significant change:
- Action threshold: flag when any dimension Δ > 0.10 vs baseline
                          2. Extension to docs/consistency.md
                          Add a section on longitudinal consistency — the difference between:
                          • Within-run stability (current scope): same agent, same scenario, repeated N times in one session
                          • Cross-run consistency (proposed): same agent, same scenario, measured across sessions over days/weeks
                          A system that is consistent within a single evaluation run can still drift significantly across runs separated by days or model updates. The cross-run view is what matters for production reliability.
                          Why This Matters
                          Practitioners building on top of this framework will run evaluations repeatedly over time (before/after model updates, after prompt changes, after infrastructure changes). Without a structured cross-run comparison mechanism, they will either:
                          1. Manually eyeball past logs (error-prone), or
                          2. Only look at the most recent run in isolation (missing drift entirely)
                          The baseline comparison template makes cross-run consistency a first-class evaluation artifact.
                          Related Work
                          The PDR (Periodic Delivery Reliability) framework (DOI: 10.5281/zenodo.19339987) tracks this as delivery_score and calibration_delta across sessions — the same cross-session consistency measurement problem applied to autonomous agent workflows. The delta-from-baseline approach is validated in that context.
                          Happy to draft both the template and the consistency.md extension if this direction looks right.
                          Metadata
                          Metadata
                          Assignees
                          No one assigned
                            Labels
                            No labels
                            No labels
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                            No projects
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                              feat(consistency): add cross-run trend tracking template and methodology #1
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                              feat(consistency): add cross-run trend tracking template and methodology#1
                              Description
                              @nanookclaw
                              nanookclaw
                              opened on Mar 31, 2026
                              Summary
                              The consistency.md dimension asks the right question — does the system behave similarly across repeated runs? — but the current experiment-log.md template captures only a single snapshot. There is no mechanism to track consistency trends across multiple experiment log entries over time.
                              The Gap
                              experiment-log.md records one run:
                              - Date:
- Consistency result:

                              To answer whether the system is getting more or less consistent over time, you need to compare across runs. Right now that comparison is manual and informal — teams typically don't do it systematically.
                              Proposal
                              Two additions:
                              1. templates/baseline-comparison.md
                              A structured template for comparing current experiment logs against a known-good baseline:
                              # Baseline Comparison Log## Baseline (v___)- Date established:
- System version:
- Consistency result (baseline):
- Correctness result (baseline):
## Comparison Runs| Date | System version | Consistency Δ | Correctness Δ | Notes ||------|---------------|--------------|--------------|-------|||||||## Trend Assessment- Direction (improving / stable / degrading):
- Sessions since last significant change:
- Action threshold: flag when any dimension Δ > 0.10 vs baseline
                              2. Extension to docs/consistency.md
                              Add a section on longitudinal consistency — the difference between:
                              • Within-run stability (current scope): same agent, same scenario, repeated N times in one session
                              • Cross-run consistency (proposed): same agent, same scenario, measured across sessions over days/weeks
                              A system that is consistent within a single evaluation run can still drift significantly across runs separated by days or model updates. The cross-run view is what matters for production reliability.
                              Why This Matters
                              Practitioners building on top of this framework will run evaluations repeatedly over time (before/after model updates, after prompt changes, after infrastructure changes). Without a structured cross-run comparison mechanism, they will either:
                              1. Manually eyeball past logs (error-prone), or
                              2. Only look at the most recent run in isolation (missing drift entirely)
                              The baseline comparison template makes cross-run consistency a first-class evaluation artifact.
                              Related Work
                              The PDR (Periodic Delivery Reliability) framework (DOI: 10.5281/zenodo.19339987) tracks this as delivery_score and calibration_delta across sessions — the same cross-session consistency measurement problem applied to autonomous agent workflows. The delta-from-baseline approach is validated in that context.
                              Happy to draft both the template and the consistency.md extension if this direction looks right.
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