118 lines
6.4 KiB
Markdown
118 lines
6.4 KiB
Markdown
# Identity context and reasoning configuration
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Entity-resolution protocol 3 omits sources of kind `unread_reference` and
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`fact_reference` from plan and assessment messages. Those sources cannot ground
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identity searches. All readable sources (including human observations, reports,
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quest descriptions, entities, technical facts and uncertainty) remain unchanged.
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Assessment retains all candidates, captures, truncation and coverage information.
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No canonical case data or archive member is removed. Final interpretation is unchanged.
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The full context remains persisted; protocol 1/2 reconstruct their original requests.
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New cases select protocol 3. Existing cases keep their frozen protocol, so use a
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fresh trial to measure the reduced prompts.
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On the quality-diagnostics-20261015 sample, message characters fall from 130430
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to 44913 for planning (65.6%) and from 137708 to 52191 for assessment (62.1%).
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These are offline character measurements, not measured API tokens or cost savings.
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No LLM quality equivalence is claimed before a fresh live trial.
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## Provider settings
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`max_output_tokens` already configures the output limit using `token_parameter`.
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Optional `reasoning_effort` is now forwarded as a top-level chat-completions field.
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Allowed values: `none`, `minimal`, `low`, `medium`, `high`; actual support depends
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on provider/model. Omission preserves provider defaults and historical configuration
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snapshot hashes. An explicit value is part of the persisted configuration snapshot.
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There is no automatic downgrade or silent retry when a provider rejects a setting.
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This is a reasoning level, not an exact thinking-token quota. It does not change
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investigation credits, API account credit, timeout or package-format limits.
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Google documents support and model-dependent mapping here:
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https://ai.google.dev/gemini-api/docs/openai#thinking
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Example provider settings for the next quality trial:
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```toml
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max_output_tokens = 16384
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reasoning_effort = "medium"
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```
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The output limit is a ceiling, not a request to generate that many tokens. Thinking
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and output accounting are provider-dependent. More output capacity can increase
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cost; monitor actual usage and finish diagnostics. No raw reasoning is requested
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or persisted by these settings.
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## Identity-only reasoning override
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Each provider optionally accepts `entity_resolution_reasoning_effort` with the
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same validated values as `reasoning_effort`. It applies only to versioned
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`entity_resolution_plan_vN` and `entity_resolution_assessment_vN` requests.
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All interpretation, hypothesis and investigation requests retain the provider's
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normal `reasoning_effort`. Omission preserves previous configuration snapshots.
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```toml
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reasoning_effort = "medium"
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entity_resolution_reasoning_effort = "low"
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max_output_tokens = 16384
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```
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The override is recorded in configuration snapshots and their hashes. Existing
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pending calls cannot silently change configuration mid-retry. A new entity
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assessment attempt uses the new settings; accepted plan and previous assessment
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artifacts remain unchanged and are checked without repeating their API calls.
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No attempt counters or investigation budgets are reset.
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## Compact interpretation protocol 6
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Fresh entity-resolution inputs freeze interpretation protocol 6. Its prompt
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receives a deterministic view of the full archived context. Only long storage
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identifier fields containing digest material (40+ hexadecimal characters) use
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short P aliases. Equal identifiers retain equal aliases; different identifiers
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remain distinct. Filenames are retained as labels where applicable. Meaningful
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source paths without digest material, human/descriptive text and database `row`
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objects remain unchanged. This is not a semantic summary or evidence ranking.
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Repeated origin/provenance objects are stored once in `input.shared_metadata`
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and referenced with M metadata aliases. Their author, timestamp, snapshot scope
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and other fields remain available; these aliases are not evidence source IDs.
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All B/O/C/T/E/F/N/D/X source IDs and source kinds remain present, including unread
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sources. Query filters, rows, completeness/truncation, next offsets, conflicting
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names and uncertainty remain present. JSON whitespace is reduced in this new
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prompt only. The stored full context, canonical case, raw archives and outputs
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are unchanged. Existing protocols 1–5 preserve their original request bytes.
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On the completed typed-names-20261015 sample, the initial interpretation messages
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previously contained 151857 characters; the compact representation contains
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83681 characters (44.9% fewer). These sizes are measured offline; this is not a claim about exact Gemini tokens, cost, or live quality.
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The next fresh LLM trial must verify role/ID correctness, item-name uncertainty,
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visibility prerequisites and investigation directions. No LLM pre-summary and
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no extra model calls are introduced. Name-resolution prompts are unchanged.
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## Hypothesis generation v2
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New hypothesis generation checkpoints freeze `prompt_version=hypotheses_v2` in
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the archived hypothesis context. Existing contexts without that field reconstruct
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v1 requests and retain their hypotheses and budgets unchanged. V2 applies the
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same deterministic compact presentation to the entire hypothesis input, retaining
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case sources, accepted interpretation sources, prior proposals and budget policy.
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No additional LLM call or semantic summary is introduced.
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On the saved stable-resolution-20261015 generation, initial message characters
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fall from 153692 to 86127 (44.0%). Actual provider tokens and quality require a
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fresh live comparison; no token or monetary saving is guaranteed by character size.
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V2 explicitly distinguishes supplied evidence from conditional causal mechanisms.
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Untested item/aura associations remain permissible research questions. A nearby
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name must not be presented as an established ID binding. Concrete API/enum/action
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numbers require an explicit mapping in cited readable local evidence; otherwise
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the model asks which implementation realizes the conceptual operation. Existing
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interpretation speculation is not upgraded to a verified fact. The prompts do
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not prescribe a known correct cause or specific test-case IDs.
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These are prompting constraints, not a complete semantic fact checker over free
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text. Existing deterministic source/count/budget validation remains in place.
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A later live trial must assess whether this prevents the observed unsupported
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item equivalence and action-number examples while preserving causal diversity.
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