token healing

**Token healing** is the **inference technique that repairs token-boundary inconsistencies between prompt endings and generated continuations** - it reduces artifacts caused by subword tokenization boundaries. **What Is Token healing?** - **Definition**: Adjustment step that revisits boundary tokens to ensure smooth continuation tokenization. - **Problem Source**: Prompt truncation or partial-token endings can misalign next-token probabilities. - **Healing Behavior**: Decoder may re-evaluate recent token boundary choices before continuing. - **Applicability**: Especially useful for code, markup, and languages with complex subword splits. **Why Token healing Matters** - **Fluency Improvement**: Reduces awkward seams at prompt-to-generation transitions. - **Syntax Stability**: Helps prevent malformed tokens in structured outputs. - **Quality Consistency**: Lowers edge-case regressions in prefix-cached and resumed decoding. - **Developer Trust**: Improves predictability when prompts end near token boundaries. - **Serving Robustness**: Mitigates artifacts in streaming and continuation-heavy workloads. **How It Is Used in Practice** - **Boundary Detection**: Identify risky prompt endings where token splits are ambiguous. - **Selective Recompute**: Re-score only local boundary region to limit latency overhead. - **A/B Validation**: Measure artifact reduction and ensure no regression in throughput. Token healing is **a targeted fix for tokenizer-boundary generation artifacts** - token healing improves continuity with modest runtime complexity.