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.