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Token Healing and Partial Token Alignment in Production LLM Serving: Architecture, Prefix Trie Rollback, and Serving Trade-Offs
Modern large language models operate on discrete subword tokens generated by greedy compression algorithms like Byte-Pair Encoding (BPE), WordPiece, or Unigram. While subword tokenization enables high compression rates and fixed vocabulary sizes, it introduces a structural defect at the interface between raw user text and autoregressive inference: the partial token problem, commonly known as the prompt boundary problem. When a user prompt terminates mid-token or at a punctuation boundary that c
1 minFlow Matching for Generative Modeling: How Continuous Normalizing Flows and Optimal Transport Paths Replace Diffusion SDEs
Flow Matching for Generative Modeling: How Continuous Normalizing Flows and Optimal Transport Paths Replace Diffusion SDEs Generative modeling underwent a structural shift with the introduction of Flow Matching (FM), formulated independently by Lipman et al. (2022), Albergo and Vanden-Eijnden (2022), and Liu et al. (2022). While Denoising Diffusion Probabilistic Models (DDPM) and score-based Stochastic Differential Equations (SDEs) established state-of-the-art sample quality across vision and a
1 minRetrieval-Augmented Fine-Tuning (RAFT) in Production: Architecture, Synthetic Distractor Pipelines, and Evaluation
Standard approaches to enterprise domain adaptation typically force a choice between two paradigms: Supervised Fine-Tuning (SFT) or Retrieval-Augmented Generation (RAG). SFT bakes domain knowledge directly into model weights, functioning like a closed-book exam. When facts change or precise source attribution is required, SFT models often hallucinate or fail to incorporate updated context. Conversely, standard RAG operates like an open-book exam without prior preparation. The base model reads re
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