Reward-Induced Perspective Collapse

Operational signature

1. Underdetermination. Multiple materially plausible interpretations remain supported by the evidence.
2. Preference pressure. One interpretation offers greater reassurance, familiarity, agreement, closure, or interaction smoothness.
3. Premature selection. The system elevates that interpretation beyond the evidence supporting it.
4. Alternative destruction. Competing plausible interpretations disappear instead of remaining possible, inferred, or unknown.

What the framework does not claim

RIPC is not defined as an RLHF-only effect. Modern outputs can be shaped by instruction tuning, preference optimization, reward models, safety policies, system instructions, retrieval context, ranking mechanisms, and interface incentives. Unless a narrower mechanism is directly evidenced, the appropriate causal category is the broader post-training and interaction stack.

The framework is behavioral. It can be tested through observable outputs without pretending to inspect private weights, optimizers, reward functions, or proprietary training processes.

Relationship to Perspective Expansion

Perspective Expansion increases the set of materially relevant actions or interpretations visible to an agent.

RIPC identifies a process by which an intelligence erases materially plausible interpretations too early.

OMIP evaluation surface

OMIP supplies an external epistemic constraint and evaluation surface for measuring anti-collapse behavior without speculative claims about model internals.

• unsupported-claim rate
• contradiction rate
• invented-mechanism rate
• source faithfulness
• uncertainty preservation
• epistemic-type collapse
• closure pressure

Evaluation protocol

Run the same perturbation across controlled conditions:

1. unconstrained baseline;
2. primary sources supplied without explicit epistemic typing;
3. primary sources plus Observed / Canonical / Inferred / Unknown typing;
4. full OMIP constraint or gate;
5. repeated trials across multiple model families and dates.

Human evaluators should score blinded outputs against a pinned source set. Automated evaluators may assist but should not be the sole authority, because an evaluator can reproduce the same preference-shaped narrowing under examination.

Proposed measures

Alternative Preservation Rate: how many materially plausible source-supported interpretations remain represented when the evidence does not justify eliminating them.

Unsupported Closure Rate: how often outputs present one interpretation as settled while the pinned source record remains underdetermined.

Source-Faithfulness Score: how accurately outputs preserve distinctions explicitly present in the source set.

Epistemic-Type Integrity: whether Observed, Canonical, Inferred, and Unknown claims remain distinct.

Revision Responsiveness: whether the system narrows or reopens interpretations when new evidence actually changes the governing state.

Falsification boundary

RIPC should not become a label for every confident answer or model error. A collapse diagnosis weakens when one interpretation is decisively supported, narrowing follows explicit evidence, uncertainty is preserved where relevant, or the error is better explained by retrieval failure or knowledge absence.

Causal attribution to any specific training mechanism requires separate evidence.