Recall Isn't Enough: Bounding Commitments in Personalized Language Systems

TL;DR

The paper introduces a novel method combining Contract-Bounded Evidence Activation and Lexicographic Commitment Validation to improve commitment reliability in personalized language systems, significantly reducing failures and input payload.

Contribution

It presents CBEA+LCV, a new approach for structured commitment validation that outperforms traditional recall-based methods in personalized language systems.

Findings

CBEA+LCV achieves zero failures in 360 tests at 0.49-0.60 availability.

Baseline methods reach zero failures only at 0.003-0.092 availability.

CBEA+LCV recalls 0.012 of facts versus 0.53 by raw methods.

Abstract

Long-context and memory systems usually treat personalization as a recall problem. In practice, many failures occur later, when a system commits: it turns noisy hints into hard constraints, drops rare witnesses, forgets downstream obligations, or answers despite infeasibility. We introduce Contract-Bounded Evidence Activation (CBEA) with Lexicographic Commitment Validation (LCV). CBEA activates a bounded evidence set using typed coverage, tail witnesses, and consequence debt; LCV validates structured commitments before prose and routes infeasible states to repair, abstention, or recontract. Across 360 fixtures and three generation backends, CBEA+LCV reaches zero failures within validator scope at 0.49-0.60 availability over attempted runs. Raw and long-context baselines with the same LCV gate reach zero only at 0.003-0.092. A shadow oracle diagnostic marks the limit: CBEA+LCV recalls 0.012 of uncompiled visible facts, while raw recalls 0.53. The result is a bounded operating point: explicit commitment control and 74-75% lower median input payload, not universal memory dominance.