Precision Tracked Transformer via Kalman Filtering, Kriging and Process Noise
The paper introduces the Bayesian Filtering Transformer (BFT), which enhances the traditional Transformer model by incorporating uncertainty handling. This approach significantly improves performance in areas like sequential recommendation and supervised fine-tuning of language models. The authors demonstrate that a single modification can lead to substantial gains in robustness and accuracy across various applications.
- ▪The BFT replaces uniform confidence in Transformers with precision-weighted attention and adaptive Kalman updates.
- ▪It shows significant performance improvements on cold-start users and rare items in sequential recommendation tasks.
- ▪BFT also enhances robustness in supervised fine-tuning of language models dealing with noisy data.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.18832 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
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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 > Machine Learning arXiv:2605.18832 (cs) [Submitted on 12 May 2026] Title:Precision Tracked Transformer via Kalman Filtering, Kriging and Process Noise Authors:Bo Long, Deepak Agarwal, Jelena Markovic-Voronov, Yi Wang, Liuqing Li View a PDF of the paper titled Precision Tracked Transformer via Kalman Filtering, Kriging and Process Noise, by Bo Long and 4 other authors View PDF HTML (experimental) Abstract:The Transformer is the foundational building block of modern AI, yet offers no principled handling of \emph{uncertainty}, which is prevalent in real applications: cold-start tokens with sparse histories in sequential recommendation, heterogeneous signal quality in language models, and attention sinks induced by unconstrained softmax.
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