Where do memorization errors arise in chain-of-thought reasoning?

STIM (Source-aware Token-level Identification of Memorization) argues that memorization in long CoT generations must be identified at the token level, not the sequence level. A single faulty token — produced by memorization rather than reasoning — can trigger cascading errors through subsequent steps. Existing metrics report a single score for the entire sequence, missing where and why individual tokens go wrong.

Three distinct memorization sources influence each token:

1. Local memorization — frequent continuations of immediately preceding tokens. The model generates the next token based on statistical co-occurrence with its local context, not reasoning. This is the dominant error source, responsible for up to 67% of wrong tokens.

2. Mid-range memorization — tokens that frequently co-occur with the generation prefix. The model has seen this pattern in pretraining and reproduces it, even when the current reasoning context requires a different continuation.

3. Long-range memorization — frequent co-occurrence with tokens in the input prompt. The prompt triggers a familiar pattern from pretraining that overrides the reasoning chain.

Key distributional findings:

• Complexity increases memorization. As reasoning complexity increases, models rely more on memorization — they fall back on familiar patterns when the reasoning becomes harder.
• Distributional shift increases memorization. Moving toward rare or atypical inputs strengthens memorization signals. The model has less training experience to draw on, so it relies more on pattern-matching from similar-but-not-identical training examples.
• Base vs long-tail reversal. In base settings, memorization often supports correct answers (familiar patterns lead to right conclusions). In long-tail scenarios, the same memorization mechanisms drive errors — defective recall when faced with unfamiliar contexts.

This connects to the broader reasoning trace reliability cluster. Since Which sentences actually steer a reasoning trace?, STIM adds a complementary mechanism: specific tokens at the sub-sentence level carry memorization-driven influence that can derail even well-structured reasoning chains. The failure is more granular than thought-level — it operates at individual tokens.

The practical implication: high memorization scores are strong indicators of reasoning failures (measured via Precision@k and Recall@k). This offers a potential diagnostic tool for identifying where reasoning chains are unreliable, independent of whether the final answer is correct. This diagnostic capability directly addresses the faithfulness problem: since Do language models actually use their reasoning steps?, STIM's memorization scores provide a token-level mechanism for faithfulness failure — memorized tokens are causally unnecessary (the answer was determined by pattern-matching, not reasoning) and causally insufficient (the memorized continuation may diverge from the reasoning the chain appears to perform).

Source: Memory