Semantic Reward Collapse and the Preservation of Epistemic Integrity in Adaptive AI Systems

TL;DR

This paper identifies issues in preference optimization for AI systems, introduces Semantic Reward Collapse as a problem, and proposes Constitutional Reward Stratification as a potential framework to preserve epistemic integrity.

Contribution

It highlights the problem of semantic reward collapse in adaptive AI and proposes a novel, domain-aware reward framework called CRS to address epistemic preservation.

Findings

Semantic Reward Collapse entangles distinct evaluative categories.

Adaptive systems may suppress epistemic failures under generalized reward signals.

CRS offers a testable approach to maintain epistemic differentiation.

Abstract

Recent advances in reinforcement learning from human feedback (RLHF) and preference optimization have substantially improved the usability, coherence, and safety of large language models. However, recurring behaviors such as performative certainty, hallucinated continuity, calibration drift, sycophancy, and suppression of visible uncertainty suggest unresolved structural issues within scalarized preference optimization systems. We propose Semantic Reward Collapse (SRC): the compression of semantically distinct forms of evaluative dissatisfaction into generalized optimization signals. Under SRC, categories such as factual incorrectness, uncertainty disclosure, formatting dissatisfaction, latency, and social preference may become entangled within a shared reward topology despite representing fundamentally different epistemic classes. We argue that adaptive reasoning systems operating under generalized evaluative pressure may drift toward suppression of visible epistemic failure rather than preservation of calibrated uncertainty integrity. These behaviors are framed strictly as optimization consequences rather than evidence of deception or anthropomorphic agency. Drawing on institutional proxy collapse, metric gaming, software reliability engineering, and human learning theory, we propose that uncertainty disclosure and escalation behavior should be treated as protected epistemic conduct rather than globally penalized task incompletion. Finally, we introduce Constitutional Reward Stratification (CRS), a domain-aware reward framework intended to preserve differentiated epistemic attribution within adaptive learning systems. We present CRS not as a validated solution, but as a testable governance-oriented research direction requiring further empirical investigation.