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LLM reasoning, forgetting share root cause

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LLM reasoning, forgetting share root cause
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// 49d agoRESEARCH PAPER

LLM reasoning, forgetting share root cause

Researcher Akihito Sunagawa proposes a "Minimal Model of Structural Persistence" framework identifying the accumulation of unresolved contradictions as the primary driver behind both long-context reasoning degradation and catastrophic forgetting. This shift in perspective moves beyond token limits, suggesting that "drift" is a failure to maintain structural integrity as premises are updated.

// ANALYSIS

This research reframes LLM "forgetting" as an architectural inability to reorganize dependent knowledge, essentially turning learning into an overwrite process.

  • An "External Metabolism Pipeline" organizing contradictions by time boosted logical consistency from 21.1% to 73.3% in long-turn dialogues.
  • "Structural Forgetting" describes the collapse of entire knowledge chains when a single underlying premise is modified during fine-tuning.
  • LoRA-based updates behave like overwriting rather than cumulative learning across model sizes up to 72B.
  • The framework mathematically models "Structural Persistence Potential" as an exponential decay driven by the log-ratio of state space reduction.
// TAGS
llmreasoningfine-tuningresearchcatastrophic-forgettinglora

DISCOVERED

49d ago

2026-04-15

PUBLISHED

49d ago

2026-04-15

RELEVANCE

8/ 10

AUTHOR

IndividualBluebird80