Document canonicalization and consolidation milestone

- preserve Working Protocol Synthesizer V0 as comparison baseline
- introduce deterministic canonicalization stage
- define semantic consolidator responsibilities
- clarify Canonical Meeting Knowledge generation
- document source-language output policy
- align roadmap, architecture and experiment log

AGENTS.md

@@ -21,7 +21,8 @@ Audio
 -> normalization
 -> chunking
 -> local chunk extraction
--> consolidation
+-> deterministic canonicalization
+-> semantic consolidation
 -> Canonical Meeting Knowledge
 -> Output Views
 ```
@@ -31,8 +32,8 @@ Status:
 - Implemented: Whisper JSON cleanup script, normalization, technical chunking,
   local chunk extraction, interim Markdown protocol builder.
 - Experimental/prototype: topic segmentation and review tooling.
-- Planned: consolidation, Canonical Meeting Knowledge implementation, final
-  Output Views.
+- Planned: Deterministic Canonicalizer, Semantic Consolidator, Canonical
+  Meeting Knowledge implementation, final Output Views.
 
 ## Architectural Principles
 
@@ -43,11 +44,26 @@ Status:
 - Output views must not silently change meaning. They may select, condense or
   render information for an audience, but not invent new semantics.
 - Extraction, consolidation, synthesis and rendering are separate concerns.
+- Deterministic canonicalization and semantic consolidation are separate
+  concerns.
+- The Deterministic Canonicalizer is planned Python code. It validates and
+  normalizes extraction objects, assigns stable source references and IDs,
+  normalizes category names and basic field structure, performs only safe
+  deterministic cleanup, may group exact duplicates, and must preserve all
+  source evidence. It must not perform uncertain semantic merging.
+- The Semantic Consolidator is planned local-LLM work. It merges semantically
+  equivalent statements, groups content by topic, preserves evidence from all
+  contributing chunks, marks contradictions and uncertainty, separates durable
+  information from transient discussion, and produces Canonical Meeting
+  Knowledge. It does not directly write a protocol.
 - Prefer small, testable processing stages over one monolithic LLM prompt.
 - Current extraction strategy is one normalized chunk per LLM call.
 - Do not expand context windows or redesign the extraction strategy without an
   explicit experiment.
 - Deterministic stages should remain deterministic where possible.
+- Rendered protocol output language should normally match the dominant language
+  of the source transcript or consolidated meeting knowledge unless an explicit
+  output language is requested.
 
 ## Prompt Engineering Rules
 
@@ -116,9 +132,9 @@ Current documented Prompt Version 2 decision baseline:
 - `src/meeting_lab/segmentation/`: experimental topic segmentation tooling.
 - `src/meeting_lab/extraction/`: local LLM extraction flow and category
   extractor modules.
-- `src/meeting_lab/consolidation/`: planned consolidation area.
+- `src/meeting_lab/consolidation/`: planned deterministic canonicalization and
+  semantic consolidation area.
 - `src/meeting_lab/protocol/`: interim protocol rendering.
 - `src/meeting_lab/models/`: current lightweight data models.
 - `samples/`: sample inputs and generated/experimental artifacts; do not treat
   sample output as canonical source data.
-