Recall Isn't Enough: Bounding Commitments in Personalized Language Systems
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 15 references that are in the map. First posted on arXiv 15 May 2026.
Builds on
11 of 15 references.
References · 11
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
- 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
- 2024A Survey on LLM-as-a-Judgecited by 1,384
- 2024Justice or Prejudice? Quantifying Biases in LLM-as-a-Judgecited by 354
- 2025Context Length Alone Hurts LLM Performance Despite Perfect Retrievalcited by 118
- 2024Self-Exploring Language Models: Active Preference Elicitation for Online Alignmentcited by 63
- 2025Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalizationcited by 44
- 2025PersonaX: A Recommendation Agent Oriented User Modeling Framework for Long Behavior Sequencecited by 33
- 2026TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agentscited by 26
- 2026HorizonBench: Long-Horizon Personalization with Evolving Preferencescited by 14
- 2026Contract2Plan: Verified Contract-Grounded Retrieval-Augmented Optimization for BOM-Aware Procurement and Multi-Echelon Inventory Planningcited by 1
Built on by
Not cited yet.