meeting-lab/docs/experiments.md

# Experiments

This file records durable technical experiments and findings for Meeting Lab.
It is not a diary and does not replace commit history.

## Status values

- Proposed: experiment idea exists, but no result is recorded.
- Running: experiment is in progress and no decision has been made.
- Accepted: finding is the current baseline or design conclusion.
- Rejected: hypothesis was tested and should not be repeated as-is.
- Superseded: finding was useful but has been replaced by a newer baseline.

## EXP-0001 - Whisper JSON interpretation

Status: Accepted

Date or period: 2026-07-29

Hypothesis:

Whisper JSON should be chunked from its segment stream, not from the aggregate
top-level text field.

Setup:

`chunk_transcript.py` was updated to parse JSON input and prefer
`segments[*].text` when `segments` exists. A regression test supplies JSON with
both top-level `text` and separate segment texts.

Inputs:

- Minimal synthetic Whisper-style JSON in `tests/test_chunking.py`.
- Real Whisper artifacts under `samples/whisper/`.

Model / configuration:

- No LLM.

Result:

The test verifies that the block stream is `["alpha", "beta", "gamma"]` and
does not include the aggregate `"alpha beta gamma"` text. The repository history
records this as the fix for the earlier failure where the first chunk contained
the complete transcript.

Decision:

When `segments` exists, `segments[*].text` is the authoritative transcript
stream. The top-level `text` field is only a fallback.

Lessons learned:

Whisper JSON is structured input. Treating it like plain text can duplicate the
entire transcript and invalidate downstream chunking.

Evidence:

- `src/meeting_lab/chunking/chunk_transcript.py`
- `tests/test_chunking.py`
- Commit `4656523` - `Fix Whisper JSON chunk extraction`

## EXP-0002 - Technical transcript chunking baseline

Status: Accepted

Date or period: 2026-07-29 to 2026-07-30

Hypothesis:

Sequential technical chunks around the configured target size can preserve the
transcript while keeping extraction calls small enough for local models.

Setup:

The chunker splits block-aligned text with configurable target, minimum,
maximum and overlap settings. Tests verify no duplicate later blocks when
overlap is zero. Generated meeting artifacts contain a nine-chunk manifest.

Inputs:

- Synthetic block list in `tests/test_chunking.py`.
- `samples/whisper/meeting_speech_cleaned.json`.

Model / configuration:

- No LLM for chunking.
- Manifest uses default chunking behavior recorded in
  `samples/whisper/meeting_speech_cleaned_chunks/manifest.json`.

Result:

With overlap set to zero, tests verify that all blocks appear exactly once. The
real sample manifest contains nine chunks, mostly near the configured target
size, with a smaller final chunk.

Decision:

Independent sequential chunks are the current technical baseline. One
normalized chunk per extraction call is the preferred extraction strategy.

Lessons learned:

Chunking solves model-size constraints only. It must not perform topic
detection or semantic merging.

Evidence:

- `src/meeting_lab/chunking/chunk_transcript.py`
- `tests/test_chunking.py`
- `samples/whisper/meeting_speech_cleaned_chunks/manifest.json`
- `AGENTS.md`
- `PROJECT_KNOWLEDGE.md`

## EXP-0003 - Conservative transcript normalization

Status: Accepted

Date or period: 2026-07-30

Hypothesis:

Transcript cleanup should improve readability without changing meeting
semantics.

Setup:

The normalizer removes isolated filler sounds, immediate duplicate words or
short duplicate phrases, and redundant whitespace. It records changed blocks in
a JSON change log and explicitly preserves semantic content categories.

Inputs:

- Chunk text files under `samples/whisper/meeting_speech_cleaned_chunks/`.
- Change logs such as `chunk_01_changes.json`.

Model / configuration:

- No LLM.

Result:

The implementation and generated change logs show a conservative policy:
negations, qualifiers, dates, numbers, responsibilities, technical statements,
deadlines, decisions and commitments are preserved.

Decision:

Normalization remains deterministic and low-risk. When uncertain, leave text
unchanged.

Lessons learned:

Filler removal is useful only if it is tightly scoped. Broad cleanup can remove
semantic cues needed by extraction.

Evidence:

- `src/meeting_lab/normalization/normalize_transcript.py`
- `samples/whisper/meeting_speech_cleaned_chunks/chunk_01_changes.json`
- `docs/pipeline.md`

## EXP-0004 - Full-context topic segmentation

Status: Superseded

Date or period: 2026-07-21 to 2026-07-22

Hypothesis:

A single full-context topic segmentation call can identify topic boundaries in
a normalized transcript chunk.

Setup:

The initial segmentation prototype asked the model for topic changes and then
converted those boundaries into continuous, non-overlapping segments.

Inputs:

- `samples/chunks/chunk_01_normalized.txt`.

Model / configuration:

- Generated artifact records `qwen3:14b`.

Result:

The generated artifact contains 85 blocks, three topic-change boundaries and
four segments. The run metadata records a substantially longer elapsed time
than the later windowed artifact for the same input.

Decision:

Full-context segmentation was useful as a prototype, but it was superseded by
windowed segmentation and manual review tooling.

Lessons learned:

The prototype established the boundary-to-segment representation, but did not
settle segmentation quality.

Evidence:

- `src/meeting_lab/segmentation/segment_topics.py`
- `samples/chunks/chunk_01_normalized_segments.json`
- Commit `f234efc` - `Add initial topic segmentation prototype`
- Commit `889a4fe` - `Detect topic boundaries as continuous segments`

## EXP-0005 - Windowed topic segmentation and review

Status: Accepted

Date or period: 2026-07-22

Hypothesis:

Windowed topic segmentation can reduce runtime and make boundary evaluation
more inspectable than a single full-context call.

Setup:

`segment_topics_windowed.py` analyzes overlapping windows and reports only
boundaries from the decision range. Python merges boundaries into continuous,
non-overlapping segments. `review_segmentation.py` renders each boundary with
neighboring transcript context for manual classification.

Inputs:

- `samples/chunks/chunk_01_normalized.txt`.
- Full meeting normalized chunks under
  `samples/whisper/meeting_speech_cleaned_chunks/`.

Model / configuration:

- `samples/chunks` artifact: `qwen3:8b`, window size 20, overlap 3.
- Full-meeting chunk artifacts: `qwen3.5:9b`, window size 20, overlap 3.

Result:

The `samples/chunks` windowed artifact produced 11 boundaries and 12 segments
for 85 blocks, with recorded elapsed time lower than the full-context artifact.
Manual review output shows that some boundaries were assessed as subtopics
rather than full topic changes. Full-meeting artifacts show one window per
already-small normalized chunk and two segments per chunk.

Decision:

Windowed segmentation and review tooling are accepted as prototype tooling, not
as a stable production segmentation stage.

Lessons learned:

Windowing improves inspectability and can reduce runtime, but it can also
cluster boundaries and over-segment. Manual review remains necessary.

Evidence:

- `src/meeting_lab/segmentation/segment_topics_windowed.py`
- `src/meeting_lab/segmentation/review_segmentation.py`
- `samples/chunks/chunk_01_normalized_windowed_segments.json`
- `samples/chunks/chunk_01_normalized_windowed_segments_review.md`
- `samples/whisper/meeting_speech_cleaned_chunks/*_windowed_segments.json`
- Commit `1a6d731` - `Add windowed segmentation pipeline and review tooling`

## EXP-0006 - Qwen model comparison

Status: Accepted

Hypothesis:

Larger local Qwen-family models should improve meaningful extraction and
segmentation, but model size alone will not solve prompt or pipeline problems.

Setup:

Project work used smaller models for smoke checks and larger local models for
meaningful extraction or segmentation. Artifacts and project knowledge record
the currently useful model roles.

Inputs:

- Gold Standard scenarios under `tests/gold/`.
- Generated segmentation artifacts under `samples/`.
- Generated extraction artifacts under
  `samples/whisper/meeting_speech_cleaned_chunks/`.

Model / configuration:

- `qwen3:1.7b`: smoke-test model according to project knowledge.
- `qwen3.5:9b`: current meaningful extraction and segmentation model according
  to project knowledge and generated full-meeting segmentation artifacts.
- `qwen3:8b` and `qwen3:14b`: present in earlier segmentation artifacts.

Result:

The repository supports the conclusion that `qwen3.5:9b` is the meaningful
current experiment model and `qwen3:1.7b` is useful for smoke tests. Larger
models and longer contexts may increase runtime substantially, but no hardware
benchmark suite is recorded.

Decision:

Use `qwen3:1.7b` for smoke tests and `qwen3.5:9b` for meaningful current
experiments. Do not assume model size alone fixes prompt or pipeline design.

Lessons learned:

Evaluation must separate model capability from prompt clarity, context
strategy, extraction schema and consolidation.

Evidence:

- `PROJECT_KNOWLEDGE.md`
- `samples/whisper/meeting_speech_cleaned_chunks/*_windowed_segments.json`
- `samples/chunks/chunk_01_normalized_segments.json`
- `samples/chunks/chunk_01_normalized_windowed_segments.json`

## EXP-0007 - Thinking output and Ollama API behavior

Status: Accepted

Date or period: 2026-07-30

Hypothesis:

Thinking-capable Qwen models may return reasoning separately from the final
answer, and extraction parsing should not fail merely because extra text or
multiple JSON objects appear.

Setup:

The Ollama response reader checks `response`, chat-style `message.content` and
then `thinking`. The JSON parser tries a full parse first, then scans JSON
object candidates and returns the final valid object. A regression test covers
thinking text before final JSON.

Inputs:

- Synthetic parser test in `tests/test_extraction_protocol.py`.

Model / configuration:

- No LLM run in the test.
- Code path is used by Ollama extraction.

Result:

The parser can handle additional text and multiple JSON objects where the final
valid object is the intended answer. Current code still falls back to `thinking`
only if no usable response or message content is present.

Decision:

Keep parser robustness, but do not treat thinking output as the root cause of
all extraction failures.

Lessons learned:

API response shape and model output shape are separate concerns. Preserve raw
responses when diagnosing failures.

Evidence:

- `src/meeting_lab/extraction/extract_chunks.py`
- `tests/test_extraction_protocol.py`
- `PROJECT_KNOWLEDGE.md`

## EXP-0008 - JSON truncation and generation limits

Status: Accepted

Hypothesis:

Some extraction failures are caused by generation limits truncating JSON rather
than by prompt wording or parser behavior.

Setup:

A `qwen3.5:9b` extraction failure was diagnosed as truncated JSON. The
generation limit was increased for the successful path. Exact failing limit is
not recorded in the repository; the current extractor default is verifiably
`--num-predict 8192`.

Inputs:

- Local extraction runs referenced by project knowledge.
- Current extraction CLI.

Model / configuration:

- `qwen3.5:9b`.
- Current extractor default: `num_predict=8192`.

Result:

Increasing the generation limit fixed the technical JSON failure. This was not
primarily a parser or prompt problem.

Decision:

When JSON is truncated, inspect raw output and generation limits before editing
prompts.

Lessons learned:

Invalid JSON can be a runtime-budget symptom. Prompt changes are the wrong
first response if the model simply ran out of output tokens.

Evidence:

- `src/meeting_lab/extraction/extract_chunks.py`
- `PROJECT_KNOWLEDGE.md`
- `AGENTS.md`

## EXP-0009 - Minimal end-to-end pipeline

Status: Accepted

Date or period: 2026-07-30

Hypothesis:

A minimal local pipeline can transform Whisper output into chunk extractions
and an interim protocol, proving the technical path before the final
architecture exists.

Setup:

The repository added cleanup, normalization, chunking, extraction and protocol
builder scripts, with sample generated artifacts.

Inputs:

- `samples/whisper/meeting_speech.json`
- `samples/whisper/meeting_speech_cleaned.json`
- Generated chunks and normalized chunks under
  `samples/whisper/meeting_speech_cleaned_chunks/`.

Model / configuration:

- Local Ollama extraction for chunk JSON.
- Windowed segmentation artifacts use `qwen3.5:9b`.

Result:

The repository contains cleaned input, nine chunks, nine normalized chunks, nine
extraction JSON files, windowed segmentation artifacts and
`meeting_protocol.md`.

Decision:

The minimal pipeline is technically validated. The first protocol builder is an
interim validation tool, not the final architecture.

Lessons learned:

End-to-end execution exposed the next limitation: extraction output needs
consolidation and purpose-specific rendering.

Evidence:

- `scripts/clean_whisper_json.py`
- `src/meeting_lab/normalization/normalize_transcript.py`
- `src/meeting_lab/chunking/chunk_transcript.py`
- `src/meeting_lab/extraction/extract_chunks.py`
- `src/meeting_lab/protocol/build_protocol.py`
- `samples/whisper/meeting_speech_cleaned_chunks/`
- Commit `07b0d80` - `Implement first end-to-end meeting analysis pipeline`

## EXP-0010 - Gold Standard corpus

Status: Accepted

Date or period: 2026-07-30

Hypothesis:

Reproducible prompt engineering requires synthetic transcripts with explicit
expected semantic outputs.

Setup:

The Gold Standard corpus defines scenario directories with `transcript.txt`,
`expected.json` and README files describing ground truth and common model
mistakes. The runner validates schema keys and writes `actual.json` for a
scenario.

Inputs:

- Gold scenarios under `tests/gold/`.

Model / configuration:

- Runner requires an explicit Ollama model for LLM evaluation.
- Existing unit tests for the runner do not invoke Ollama.

Result:

The corpus gives stable semantics for decisions, facts, positions, todos,
questions, technical details and difficult mixed cases. Initial structured
transcripts are Phase 1 and easier than raw Whisper-style transcripts.

Decision:

Use Gold Standard tests as both regression tests and formal meeting-semantics
specification. Raw or unlabelled transcript cases remain later-phase work.

Lessons learned:

Without expected outputs, prompt changes cannot be evaluated reproducibly.

Evidence:

- `tests/gold/`
- `scripts/run_gold_test.py`
- `tests/test_gold_runner.py`
- Commit `f7ad9ba` - `Establish prompt engineering baseline with Gold Standard tests`

## EXP-0011 - Gold-test quality and unique ground truth

Status: Accepted

Date or period: 2026-07-30

Hypothesis:

If a prompt produces unexpected behavior, the gold test itself may be ambiguous
and should be reviewed before the prompt is changed.

Setup:

Decision-focused scenarios were clarified during baseline creation. The current
methodology requires checking unique ground truth before changing prompts.

Inputs:

- `decision_simple`
- `decision_deferred`
- `decision_none`
- Gold methodology document.

Model / configuration:

- Prompt Version 2 baseline work.

Result:

`decision_simple` required clarification around the explicit agreement and
nearby non-decision wording. The earlier negative/deferral ambiguity is now
represented by distinct `decision_none` and `decision_deferred` scenarios in
the repository. Punctuation is not reliable evidence for Whisper transcripts;
agreement language and wording must carry the semantics.

Decision:

Ambiguous gold tests must be reviewed before prompt changes. Do not treat
punctuation as reliable evidence in real Whisper-style transcripts.

Lessons learned:

Bad gold tests create false prompt failures and can encourage overfitting.

Evidence:

- `tests/gold/PROMPT_ENGINEERING_METHODOLOGY.md`
- `tests/gold/decision_simple/README.md`
- `tests/gold/decision_deferred/README.md`
- `tests/gold/decision_none/README.md`
- Commit `f7ad9ba`

## EXP-0012 - Decision taxonomy

Status: Accepted

Date or period: 2026-07-30

Hypothesis:

Decision extraction needs a formal taxonomy that distinguishes substantive
decisions from process decisions and non-decisions.

Setup:

The decision definition document and decision prompt define included and
excluded categories. Gold tests cover explicit decisions, true no-decision
cases and deferrals.

Inputs:

- `tests/gold/DECISION_DEFINITION.md`
- `prompts/decisions.md`
- Decision gold scenarios.

Model / configuration:

- Prompt Version 2 baseline.

Result:

Accepted decision categories include substantive decisions, organizational
decisions, process decisions, approvals, rejections, deferrals, explicit
decisions not to decide yet and explicit agreement to gather more information
before deciding. Opinions, preferences and proposals without agreement are not
decisions.

Decision:

"No decision was reached" and "the decision was deferred" are distinct semantic
outcomes.

Lessons learned:

Deferral can be a valid process decision even when the substantive topic remains
unresolved.

Evidence:

- `tests/gold/DECISION_DEFINITION.md`
- `tests/gold/decision_deferred/`
- `tests/gold/decision_none/`
- `prompts/decisions.md`

## EXP-0013 - Prompt engineering methodology

Status: Accepted

Date or period: 2026-07-30

Hypothesis:

Prompt iteration needs strict experimental controls to prevent regression,
overfitting and arbitrary prompt churn.

Setup:

The methodology was documented alongside the Gold Standard corpus and later
summarized for agents.

Inputs:

- Gold scenarios.
- Prompt files.

Model / configuration:

- Applies to all prompt experiments.

Result:

The accepted method is one prompt change per iteration, one target test at a
time, immediate validation, no regressions, no `expected.json` edits merely to
force a pass, no test-specific prompt hacks, stopping after two consecutive
non-improving iterations, and verifying unique ground truth before prompt
changes.

Decision:

Prompt changes are controlled experiments. See `AGENTS.md` for agent operating
rules.

Lessons learned:

Most prompt changes are not isolated unless the experiment explicitly constrains
the target and regression set.

Evidence:

- `tests/gold/PROMPT_ENGINEERING_METHODOLOGY.md`
- `AGENTS.md`
- Commit `f7ad9ba`

## EXP-0014 - Decision Prompt Version 2

Status: Accepted

Date or period: 2026-07-30

Hypothesis:

Adding explicit process-decision language to the decision prompt can preserve
true decision detection while recognizing deferrals.

Setup:

Prompt Version 2 added explicit support for deferrals and process decisions.
The baseline was validated on three decision scenarios.

Inputs:

- `decision_simple`
- `decision_deferred`
- `decision_none`

Model / configuration:

- Prompt Version 2.
- Model used for validation is not recorded in the committed methodology.

Result:

The committed methodology records all three baseline scenarios as passing. The
current generated `actual.json` files also show the expected decision count for
these decision scenarios, although some non-decision categories remain less
complete.

Decision:

Prompt Version 2 is the current decision baseline. Explicit deferrals are
recognized as process decisions.

Lessons learned:

Decision-count success does not imply all categories are solved. Category-level
evaluation must continue.

Evidence:

- `tests/gold/PROMPT_ENGINEERING_METHODOLOGY.md`
- `tests/gold/decision_simple/actual.json`
- `tests/gold/decision_deferred/actual.json`
- `tests/gold/decision_none/actual.json`
- `prompts/decisions.md`

## EXP-0015 - Difficult synthetic meeting

Status: Accepted

Date or period: 2026-07-30

Hypothesis:

A deliberately adversarial synthetic meeting can expose extraction failures
that simple category tests miss.

Setup:

`evil_meeting` includes interruptions, corrections, absent referenced people,
near-decisions, changed positions and one expected explicit decision.

Inputs:

- `tests/gold/evil_meeting/`.

Model / configuration:

- Existing generated `actual.json`; exact model is not stored in the artifact.

Result:

The generated result found the expected FR-7 exclusion decision. It also
classified "do not migrate until the mapping table is checked" as an additional
decision. The current expected file treats that statement as a position, but
contextual review suggests it may be a valid process instruction or decision.

Decision:

Do not classify this as a simple model failure without reviewing the gold
standard. The scenario exposes a semantic gap in the expected output.

Lessons learned:

Difficult synthetic cases are valuable because they reveal ambiguity in the
specification as well as model mistakes.

Evidence:

- `tests/gold/evil_meeting/README.md`
- `tests/gold/evil_meeting/expected.json`
- `tests/gold/evil_meeting/actual.json`
- See EXP-0011 and EXP-0012.

## EXP-0016 - Context-size extraction comparisons

Status: Rejected

Hypothesis:

Increasing extraction context from one chunk to neighboring chunk groups should
make extraction more complete and therefore should become the baseline.

Setup:

Comparisons were made across one chunk and grouped contexts: 1+2, 2 only, 2+3
and 1+2+3.

Inputs:

- Normalized meeting chunks.

Model / configuration:

- Current project knowledge identifies `qwen3.5:9b` as the meaningful model for
  extraction experiments.

Result:

More context sometimes improved completeness, but it also shifted category
classification, added duplicates and reduced stability. No numeric winner is
recorded in the repository.

Decision:

Do not adopt larger extraction windows as the baseline. Independent chunk
extraction remains current strategy. Recover global context through
consolidation rather than continuously enlarging extraction windows.

Lessons learned:

Context size is not a monotonic quality knob. It changes the task the model is
performing.

Evidence:

- `PROJECT_KNOWLEDGE.md`
- `AGENTS.md`
- `ROADMAP.md`
- See EXP-0002 and EXP-0018.

## EXP-0017 - Independent full-meeting chunk extraction

Status: Accepted

Hypothesis:

Extracting every normalized chunk independently can produce enough structured
material for a useful protocol draft.

Setup:

Nine normalized chunks were extracted into separate JSON files and then
rendered by the interim protocol builder.

Inputs:

- `samples/whisper/meeting_speech_cleaned_chunks/chunk_01_normalized.txt`
  through `chunk_09_normalized.txt`.

Model / configuration:

- Existing extraction artifacts do not record model metadata.

Result:

The nine extraction files contain facts, decisions, todos, questions and
technical details. `meeting_protocol.md` aggregates them into a readable draft.
Duplicates, category shifts and synthesis became the dominant limitations.

Decision:

Independent chunk extraction is useful enough to keep as the baseline, but it
requires a consolidation stage.

Lessons learned:

Per-chunk extraction gives recall-oriented raw material. It does not by itself
produce a polished or canonical meeting representation.

Evidence:

- `samples/whisper/meeting_speech_cleaned_chunks/chunk_*_extraction.json`
- `samples/whisper/meeting_speech_cleaned_chunks/meeting_protocol.md`
- `src/meeting_lab/protocol/build_protocol.py`
- See EXP-0018.

## EXP-0018 - Human protocol comparison and output-view split

Status: Accepted

Date or period: 2026-07-30

Hypothesis:

One generated protocol cannot satisfy every use case; protocol output should be
separated by purpose and audience.

Setup:

The interim machine protocol was compared against the desired human protocol
shape and then the architecture was revised toward parallel output views.

Inputs:

- Interim `meeting_protocol.md`.
- Architecture and output-view documentation.

Model / configuration:

- Not applicable; this is a design evaluation.

Result:

The human protocol target is denser and organized by purpose and topic rather
than extraction categories. The machine extraction retains more context and is
useful for recall, but it is not the right direct source for a concise
distribution artifact or durable knowledge entry.

Decision:

Separate final outputs into Working Protocol / Arbeitsprotokoll, Distribution
Protocol / Verteilerprotokoll and Knowledge Objects /
Wissensdatenbankeintrag.

Lessons learned:

Rendering is a separate concern from extraction and consolidation. Output views
must be parallel renderings of shared semantics, not transformations of one
another.

Evidence:

- `samples/whisper/meeting_speech_cleaned_chunks/meeting_protocol.md`
- `docs/output-views.md`
- `docs/architecture.md`
- `docs/pipeline.md`
- Commit `5c03ed7` - `Refine canonical meeting knowledge architecture`

## EXP-0019 - Consolidation and Canonical Meeting Knowledge

Status: Accepted

Date or period: 2026-07-30

Hypothesis:

Extraction, consolidation and output rendering are separate problems and should
not be collapsed into one LLM prompt or one protocol file.

Setup:

The architecture was refined after the minimal pipeline and protocol draft
showed duplicate, synthesis and audience-specific rendering limitations.

Inputs:

- Extraction JSON artifacts.
- Interim protocol draft.
- Architecture and data-model documentation.

Model / configuration:

- Not applicable; this is an architectural conclusion.

Result:

The accepted design is a planned Canonical Meeting Knowledge layer as the
semantic source of truth, with Working Protocol, Distribution Protocol and
Knowledge Objects as parallel output views. The next consolidation architecture
is split into a Deterministic Canonicalizer and a Semantic Consolidator. The
canonicalizer prepares validated evidence-bearing objects without uncertain
semantic merging. The consolidator then merges semantically equivalent
statements, preserves evidence, reconciles category shifts where supported and
marks contradictions or uncertainty.

Decision:

Deterministic canonicalization is implemented as Canonicalizer V1. The first
semantic consolidation milestone is implemented as Semantic Consolidator V0 for
facts-only duplicate detection. Broader semantic consolidation, Canonical
Meeting Knowledge and final output views remain planned.

Lessons learned:

Global meeting understanding should be recovered by consolidation over
evidence-bearing extractions, not by silently changing output views or expanding
LLM context indefinitely.

Evidence:

- `docs/architecture.md`
- `docs/pipeline.md`
- `docs/data-models.md`
- `docs/output-views.md`
- `PROJECT_KNOWLEDGE.md`
- `ROADMAP.md`
- Commit `5c03ed7`

## EXP-0020 - Working Protocol Synthesizer V0

Status: Accepted

Date or period: 2026-07-31

Hypothesis:

The current local synthesis model may be able to generate a useful detailed
Working Protocol directly from the existing independent chunk extraction JSON
files, before canonicalization or semantic consolidation exists.

Setup:

One synthesis prompt was constructed from exactly nine chunk extraction JSON
files. The model was instructed to use only those extraction files, merge
duplicates, group related information into topics, preserve useful discussion
context and write a neutral technical Working Protocol.

Inputs:

- `chunk_01_extraction.json` through `chunk_09_extraction.json`.
- No original transcript, normalized chunks or Whisper output were used as
  synthesis input.

Model / configuration:

- Model: `qwen3.5:9b`
- Prompt characters: 24,979
- Actual prompt eval tokens: 5,929
- Output tokens: 1,486
- Runtime: 294.204 seconds

Result:

The generated Working Protocol was readable, well structured and
topic-oriented. It was still based directly on raw chunk extractions, without a
separate deterministic canonicalization stage or semantic consolidation stage.
The output language was English even though the source meeting material was
German.

Decision:

Preserve this output as the Working Protocol Synthesizer V0 benchmark baseline
for later canonicalizer, consolidator and renderer comparisons. This selected
generated artifact is intentionally versioned even though generated runtime
artifacts are normally ignored.

Lessons learned:

Direct synthesis from chunk extractions can create a useful recall-oriented
draft, but it does not replace Canonical Meeting Knowledge. The language
mismatch also establishes a default renderer rule: protocol output should
normally match the dominant source language unless an explicit output language
is requested.

Evidence:

- `samples/benchmarks/working_protocol_synthesizer_v0/README.md`
- `samples/benchmarks/working_protocol_synthesizer_v0/working_protocol.md`
- `PROJECT_KNOWLEDGE.md`
- `docs/output-views.md`
- See EXP-0017 and EXP-0019.

## EXP-0021 - Canonicalizer V1

Status: Accepted

Date or period: 2026-07-31

Hypothesis:

Independent chunk extraction JSON can be converted into a stable deterministic
intermediate format before any semantic LLM consolidation is attempted.

Setup:

Canonicalizer V1 discovers `chunk_*_extraction.json` files in stable chunk
order, validates required categories, normalizes category names and basic field
structure, parses existing legacy string formats where safe, trims redundant
whitespace, assigns deterministic IDs, preserves original values and source
references, and merges only exact duplicates when all semantic fields are
identical.

Inputs:

- Synthetic unit-test fixtures.
- Existing nine extraction JSON files under
  `samples/whisper/meeting_speech_cleaned_chunks/`.

Model / configuration:

- No LLM.
- CLI module: `meeting_lab.consolidation.canonicalize`.

Result:

Canonicalizer V1 produces `schema_version`, `source_files`, `stats` and
`items`. It is deterministic preparation for the future Semantic Consolidator
and is not Canonical Meeting Knowledge.

Decision:

Canonicalizer V1 is the current implemented deterministic canonicalization
stage. Semantic Consolidator V0 now uses this representation for facts-only
semantic duplicate detection; broader semantic consolidation and Canonical
Meeting Knowledge remain planned.

Lessons learned:

Exact duplicate handling, source-reference preservation and legacy string
parsing can be tested without model calls. Any uncertain semantic merge remains
out of scope for this stage.

Evidence:

- `src/meeting_lab/consolidation/canonicalize.py`
- `tests/test_canonicalize.py`
- `docs/data-models.md`
- `docs/pipeline.md`

## EXP-0022 - Semantic Consolidator V0 facts-only merge

Status: Accepted

Date or period: 2026-07-31

Hypothesis:

The Canonicalizer V1 output contains enough stable structure for a local LLM to
identify semantically equivalent fact items without losing source coverage or
changing non-fact categories.

Setup:

Semantic Consolidator V0 consumed the existing Canonicalizer V1 benchmark JSON,
selected only items with `category: "fact"`, and sent one bounded consolidation
request to local Ollama. The merge rules required semantic equivalence, not
topic similarity, and validation required every source fact ID to appear
exactly once.

Inputs:

- `samples/benchmarks/canonicalizer_v1/canonicalized_extractions.json`
- 33 fact items.

Model / configuration:

- `qwen3.5:9B`
- Ollama endpoint: `http://127.0.0.1:11434/api/generate`
- Thinking disabled.
- One LLM call.
- `num_ctx=32768`
- `num_predict=4096`

Result:

- Runtime: 390.119 seconds on the current machine.
- Merged fact groups: 1.
- Source facts involved in merges: 2.
- Singleton fact groups: 31.
- Validation: passed.
- No source fact was lost or duplicated.
- Non-fact categories remained unchanged.

Accepted merge:

- `fact_0025` + `fact_0031`
- Canonical statement: "Der Leiter F&E führt die Projektliste auf dem
  zweiwöchentlichen Schnittstellen-Stand-Up."

Decision:

Semantic Consolidator V0 is complete for its current narrow scope:
conservative facts-only semantic duplicate detection with source evidence
preserved. The selected `report.md` and `consolidated_extractions.json`
benchmark artifacts should be versioned for later comparison. The raw model
response remains a local diagnostic artifact and is not versioned.

Lessons learned:

Semantic duplicate consolidation is technically viable and conservative enough
for continued evaluation, but broader semantic synthesis remains a separate
future stage. The measured runtime is useful for this machine and run, but
should not be generalized into a universal benchmark.

Evidence:

- `src/meeting_lab/consolidation/consolidate_facts.py`
- `prompts/consolidate_facts.md`
- `tests/test_consolidate_facts.py`
- `samples/benchmarks/semantic_consolidator_v0/report.md`
- `samples/benchmarks/semantic_consolidator_v0/consolidated_extractions.json`
- Local diagnostic only: `samples/benchmarks/semantic_consolidator_v0/raw_model_response.txt`

## EXP-0023 - Responsibility attribution integrity

Status: Running

Date or period: 2026-07-31

Hypothesis:

Protocol generation is operationally unsafe if responsibility, ownership or
departmental role attribution is inferred from discussion context rather than
explicit meeting evidence.

Setup:

The real-life Working Protocol Renderer V2 benchmark was inspected against the
consolidated input. A false assignment connected a Marketing participant to
Business Development criteria work even though the participant's contribution
was critical or reluctant and did not establish acceptance of that task.

Inputs:

- `samples/benchmarks/working_protocol_renderer_v2/working_protocol.md`
- `samples/benchmarks/semantic_consolidator_v0/consolidated_extractions.json`
- `samples/benchmarks/canonicalizer_v1/canonicalized_extractions.json`

Model / configuration:

- Not rerun for this finding.
- Finding is based on existing benchmark artifacts.

Result:

The false responsibility attribution is visible in the structured input before
rendering, so the issue is not merely stylistic renderer wording. The root
cause may originate earlier in extraction and then be preserved by
canonicalization and consolidation. Renderer guardrails are still required so
output views do not strengthen ambiguous ownership.

Decision:

Responsibility attribution is now treated as a critical project-wide
invariant. A person, team or department may be recorded as responsible only
when the evidence explicitly assigns, accepts or confirms that responsibility.
Discussion, expertise, objection, suggestion, thematic proximity, speaker
adjacency, organizational assumptions and likely job roles do not establish
ownership.

Lessons learned:

This class of error affects operational correctness, not only style. The
pipeline needs traceable attribution evidence and future schema support for
responsibility status such as explicit, accepted, proposed or unclear.

Evidence:

- `AGENTS.md`
- `PROJECT_KNOWLEDGE.md`
- `docs/data-models.md`
- `docs/output-views.md`
- `prompts/working_protocol.md`
- `tests/gold/responsibility_attribution_negative/`

## EXP-0024 - Working Protocol V2 contract visibility

Status: Running

Date or period: 2026-08-09

Target:

BUG-011 renderer-only regression using an already validated Semantic
Consolidator artifact.

Hypothesis:

The renderer failures are caused by provenance-heavy 199-350 KB JSON inputs
placing the leading prompt contract outside the model's effective evaluated
context. The preserved failures all report `prompt_eval_count=16386`, while
their outputs either echo trailing JSON or produce an unconstrained generic
category summary instead of the requested Working Protocol.

Iteration 1 change:

- Project every consolidated item to rendering-relevant semantic fields while
  retaining every item and its category/text/responsibility/deadline/status
  information.
- Generate the exact structural contract from renderer validator constants and
  append it after the compact INPUT JSON.
- Replace the independently handwritten prompt skeleton with a reference to
  that authoritative appended contract.
- Enforce the existing prompt rule that emitted sections must not be empty.

This is one renderer-contract prompt iteration. It does not change extraction,
canonicalization, semantic consolidation or responsibility semantics.

Validation before LLM run:

- 15 focused renderer tests pass.
- Tests cover contract generation, compact input projection, valid and invalid
  headings, missing topic sections, empty sections, wrapper cleanup, malformed
  Markdown and final-file write gating.

Decision:

Iteration 1 passed structural validation and wrote `working_protocol.md`, but
the quality sanity check found that the model omitted most of the ten supplied
decisions, two open questions and several action items. The structurally valid
result therefore was not accepted as BUG-011 verification.

Iteration 2 change:

- Add input-derived hidden coverage markers for every decision, action item and
  open question.
- Require every priority item exactly once in its matching section.
- Validate missing, duplicate, unknown and wrong-section markers
  deterministically.
- Keep facts and technical details condensable as background.

This is the second single prompt iteration. It responds to the concrete
omission failure observed in Iteration 1 without changing upstream semantics or
inventing renderer content.

Iteration 2 pre-run validation:

- 18 focused renderer tests pass, including exact required-item coverage and
  wrong-section rejection.

Decision:

Iteration 2 initially exhausted the fixed 4,096-token renderer output budget
after emitting all decisions and most action items. Adaptive renderer budgeting
resolved to 8,192 tokens for this input. The final run stopped normally after
3,709 evaluated output tokens.

The final renderer-only regression passed strict validation and wrote
`working_protocol.md`. Exact coverage was 10/10 decisions, 36/36 renderable
action items and 23/23 open questions, each once in its matching section. One
structurally empty action item whose task, responsible, deadline and evidence
were all null was recorded and excluded rather than fabricated. Optional
background markers were accepted only when they referred to real projected
input items.

Accept the compact renderer input, validator-derived trailing contract,
priority-item coverage markers, empty-section validation and adaptive renderer
output sizing as the BUG-011 baseline. This establishes structural reliability
and priority-item coverage, not complete protocol prose quality.

Evidence:

- `prompts/working_protocol.md`
- `src/meeting_lab/protocol/render_working_protocol.py`
- `tests/test_render_working_protocol.py`
- `samples/benchmarks/progeo_qwen35_9b_20260805_133942/working_protocol/`
- `samples/benchmarks/progeo_qwen35_35b_a3b_20260805_133942/working_protocol/`

## EXP-0025 — BUG-015 classification precision

Date: 2026-08-09

Target: Progeo-derived Decision, Action Item and Open Question precision cases.

Model/configuration: `qwen3.5:9B`, temperature 0, `num_ctx=32768`.

Tests were created before prompt changes. A Decision-only evidence threshold
kept the explicit Dr. Schlummer rejection and omitted an option and preference.
Adding Action and Open Question definitions improved several negatives but was
not stable: the model alternately promoted an unaccepted Textor suggestion or
moved rejected candidates into Open Questions. Moving the standalone category
prompts after the transcript made the partial Decision schema dominate and
misclassified true Action Items as Decisions in two consecutive runs.

The final iteration replaced the competing standalone category prompts with a
single unified classification contract after the transcript. It preserved the
assigned Nina task and ownerless established CET work, and prevented
cross-category leakage in the focused case, but still emitted the unaccepted
Textor suggestion as an Action Item. Further prompt iterations were stopped in
accordance with the Gold Standard methodology.

Result: partially improved, not accepted as a complete BUG-015 fix. BUG-015
remains Open; no phrase-specific deterministic filter was introduced.