NovelForge DocsReference

Corpus Policy · Govern evidence without turning source access into story or style authority

NovelForge uses corpus material to study mechanisms, test preference/craft hypotheses, build eval evidence and identify counterexamples. Corpus is an evidence domain. It is never Project Canon, character knowledge, a hid

NovelForge uses corpus material to study mechanisms, test preference/craft hypotheses, build eval evidence and identify counterexamples. Corpus is an evidence domain. It is never Project Canon, character knowledge, a hidden imitation prompt, or automatic Framework guidance.

Core invariant ✦ Access, rights, storage, analysis, learning and promotion are separate gates. Passing one gate never implies that the next gate has passed.

01 · What Corpus may support

Corpus evidence may support:

project-specific craft analysis;
user-taste hypothesis testing;
cross-work General Craft research;
capability/regression eval design;
mechanism benchmarks;
counterexample/profile-boundary discovery;
external evidence for a bounded research question.

Corpus evidence may not by itself:

create or modify Project Canon;
prove that a character knows something;
settle relationship/resource/information state;
override explicit user/project authority;
activate durable user taste;
promote Framework behavior;
create a reusable named-author imitation profile.

02 · Rights class and storage intent are different fields

Every source candidate that may enter durable Corpus handling needs a declared rights_class and storage_intent.

Reference rights classes:

`redistributable`

Full-text storage may be permitted when there is a documented basis such as public-domain status, a compatible open license, explicit permission, or user-owned/user-authored material with permission to store.

A non-empty rights basis and provenance are required.

`analysis_only`

The material may be accessed/analyzed under the declared basis, but NovelForge must not store the full text as Corpus data.

Allowed storage may include:

source metadata;
derived observations/metrics;
mechanism analysis;
summaries;
a short excerpt only when genuinely required for the declared analysis/eval purpose.

`unknown`

Rights have not been established well enough for content storage. Only metadata-level storage is permitted until the evidence changes.

A private repository does not turn unknown rights into redistributable rights.

03 · Deterministic Rights Gate is not legal analysis

rights_gate.py validates whether declared metadata and requested storage intent are internally consistent with NovelForge policy.

It enforces, for example:

unknown + anything beyond metadata_only → reject
analysis_only + full_text                → reject
short_excerpt without excerpt_purpose   → reject
redistributable without rights_basis     → reject

The validator does not infer copyright status from a URL, title, creator name or repository visibility. legal_analysis_performed = false is deliberate.

Rights/source status must be established from real evidence through the authorized research process.

04 · Provenance is mandatory evidence

A durable Corpus record should be able to answer:

corpus_id:
source_title:
creator:
source_url_or_ref:
source_type:
language:
publication_date:
rights_class:
rights_basis:
storage_intent:
accessed_at:
content_fingerprint:
analysis_scope:
research_question:
source_tool_or_capability:

Not every field must apply to every source class, but substantive claims must remain traceable to a real source/ref and the capability that retrieved it.

If provenance cannot be established, lower confidence, keep only safe metadata, or block the step. Never fabricate a quotation or source access event to complete a pipeline.

05 · Discovery is not ingestion

A discovery result says a candidate source was found. It does not mean NovelForge may copy or persist the source content.

discovery
→ verify source identity + provenance
→ establish declared rights basis
→ choose storage intent
→ deterministic rights gate
→ bounded ingestion / observation

The Corpus Scout and discovery runtime may prepare and normalize candidate evidence, but they cannot manufacture authorization that the host/source did not provide.

06 · Analysis is question-bounded

Do not analyze an entire work merely because it is available.

Start from a concrete research question, such as:

how does a fast scene create pressure without pseudo-speed fragmentation?
how is exposition made causal through an active task or conflict?
how does a supporting character maintain an independent agenda inside a protagonist-centered scene?
how does a chapter create forward pull without narrator advertising?

Select the minimum sufficient range/evidence required to answer the question.

Bounded analysis reduces context waste, imitation pressure, accidental source leakage and confirmation bias.

07 · Counterexamples are required for generalization

Corpus research should actively seek:

evidence supporting the candidate mechanism;
successful examples that violate the superficial pattern;
examples where the mechanism fails;
genre/platform/profile exceptions;
alternative explanations for the same observed effect.

A search that only retrieves examples agreeing with the current hypothesis is not strong General Craft evidence.

One work may create an observation. It does not create a universal rule.

08 · Named-author imitation boundary

NovelForge may analyze broad, transferable craft mechanisms such as:

scene causality and pressure sequencing;
information timing;
paragraph function;
dialogue embodiment;
character-agenda independence;
setup/payoff management;
broad genre/platform conventions.

It must not turn modern/living authors into reusable imitation fingerprints.

Do not create Framework behavior whose goal is:

“write exactly like Author X”;
reproduce signature phrases/cadence from copyrighted work;
preserve extensive copyrighted passages as style prompts;
optimize writer context for source imitation rather than mechanism understanding.

User-owned evidence and public-domain material remain subject to their actual rights/provenance and the same authority boundaries.

09 · Writer isolation

Raw Writer context should normally receive task-relevant mechanism/profile guidance, not bulk Corpus text.

Preferred path:

source evidence
→ rights-safe bounded observation
→ per-work mechanism analysis
→ counterexample / cross-work synthesis
→ benchmark / eval calibration
→ minimal relevant guidance
→ Writer

Regression bad examples and hidden eval answers stay outside Writer pre-draft context. Corpus/learning memory defaults to post-generation use unless a higher-level contract explicitly makes a particular bounded item writer-safe.

10 · Learning and promotion remain separate gates

Corpus observations can support project, user_taste or general_craft learning scopes, but the Corpus layer cannot activate them.

General Craft normally requires:

multiple independent cross-work refs;
counterexample/profile-boundary evidence;
capability + regression evals;
provenance;
version/rollback evidence;
green Framework CI;
authorized promotion after prerequisites pass.

See the Self-Improvement Protocol.

11 · Correction and removal

If rights, provenance or analysis evidence later proves invalid:

mark source/item invalid
→ remove storage that is no longer permitted
→ identify dependent analyses / benchmarks / evals
→ invalidate or rebuild derived evidence
→ narrow / contest / deprecate dependent learning hypotheses
→ roll back affected promoted behavior when required
→ preserve correction provenance

Derived evidence must remain traceable enough for this dependency repair to be possible.

12 · Autonomous behavior boundary

Corpus automation may:

detect an evidence gap;
prepare a discovery request;
normalize real returned source metadata;
run deterministic rights/storage checks;
package bounded evidence for semantic analysis;
record missing capability or blocked rights state.

It may not:

pretend Web/GitHub/MCP retrieval occurred without an eligible authorized capability;
infer legal rights from weak metadata;
fabricate quotations;
promote Corpus observations into Canon, user taste or Framework behavior.

Corpus Intelligence — end-to-end evidence pipeline.
Corpus Ingest Protocol — bounded ingestion mechanics.
rights_gate.py — deterministic declared-rights/storage validator.
discovery_runtime.py — typed discovery runtime.
Corpus Benchmarks — cross-work mechanism evidence.
Adaptive Learning — learning scopes and hypotheses.

A useful Corpus system remembers enough to support evidence and rollback, but never so much that source possession becomes a shortcut around rights, authority, or craft reasoning.