Problem
Research questions and friction points this paper is trying to address.
personalized language systems
commitment failures
long-context memory
feasibility validation
evidence recall
Recall Isn't Enough: Bounding Commitments in Personalized Language Systems
🤖 AI Summary
Personalized language systems are vulnerable during the commitment phase to noisy prompts, loss of rare evidence, or infeasible responses, and reliance solely on recall mechanisms is insufficient to ensure reliability. This work proposes the Contract-Bounded Evidence Activation (CBEA) and Lexicographic Commitment Validation (LCV) framework, which for the first time explicitly models commitment control as a structured verification-and-repair process. The approach enables bounded evidence activation through type coverage, tail witnessing, and consequence debt, integrated with hierarchical validation and an infeasibility-aware routing strategy. Evaluated across 360 test cases and three generative backends, the method achieves zero failures within validator scope, demonstrates usability scores of 0.49–0.60, and reduces median input load by 74–75%.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Commitment Validation
Evidence Activation
Personalized Language Systems
Bounded Memory
Feasibility-aware Generation
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