Recall Isn't Enough: Bounding Commitments in Personalized Language Systems
The paper discusses the limitations of current personalized language systems, particularly in how they handle commitments. It introduces a new method called Contract-Bounded Evidence Activation (CBEA) combined with Lexicographic Commitment Validation (LCV) to improve system reliability. The results show that this approach significantly reduces failures compared to traditional methods.
- ▪Current personalized language systems often fail when making commitments, leading to issues such as dropping rare witnesses and forgetting obligations.
- ▪The proposed CBEA+LCV method achieves zero failures within validator scope with an availability of 0.49-0.60 over attempted runs.
- ▪In contrast, traditional methods reach zero failures only at a much lower availability of 0.003-0.092.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.16712 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Artificial Intelligence arXiv:2605.16712 (cs) [Submitted on 15 May 2026] Title:Recall Isn't Enough: Bounding Commitments in Personalized Language Systems Authors:Rui Tang, Yichi Zhang, Xi Chen, Chen Dong, Youwei Yang, Yumeng Shen View a PDF of the paper titled Recall Isn't Enough: Bounding Commitments in Personalized Language Systems, by Rui Tang and 5 other authors View PDF HTML (experimental) 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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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