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.
Opening excerpt (first ~120 words) tap to expand
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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
