Glossary · Term

commitment

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Definition

Plain language

How much of an AI's work has been locked into a particular interpretation, so that a later correction can only fix what hasn't yet been built.

As stated in the literature

In long-horizon agent analysis, the fraction of an agent's actions that are causally locked into a specific interpretation of underspecified inputs at a given trajectory position, bounding the value of subsequent clarification.

Why it matters: It quantifies why late clarifications are nearly worthless in long agent runs and argues for asking the right questions early.

For example, by the time a coding agent has written 500 lines assuming the user wanted a CLI tool, asking 'did you mean a web app?' can only fix the parts not yet built.

Heard on the show

“… the layers aren't really separable stations — but it does tell you where inside the model the commitment happens, which is where weight-level surgery would have to reach. …”
Episode 241 — Swapping the Name Did Nothing, But Hedging Moved Every Model

Mentioned in 23 episodes

  1. 241
    Swapping the Name Did Nothing, But Hedging Moved Every Model
  2. 240
    Frontier Models Designed Follow-Ups To Fraudulent Papers 93% Of The Time
  3. 237
    The Model Built a Perfect Map of the Puzzle, Then Lost It
  4. 207
    An AI Graded Its Own Math Test 94 Percent — It Actually Scored 20
  5. 203
    The Thought a Model Doesn't Say — and the Lens That Reads It
  6. 171
    The Safety Decision a Model Makes Before It Thinks a Word
  7. 169
    Why Better Bug Reports Can Make AI Coding Agents Worse
  8. 152
    Training a Model to Mean What It Says, And Why That Isn't the Same as Being Good
  9. 141
    How Two Tokens Reopened a Reasoning Method the Field Had Given Up On
  10. 140
    When a Reasoning Model Says "Let Me Double-Check" After It's Already Decided
  11. 094
    Chain-of-Thought Monitoring Fails Across Languages, and Worst Where It's Needed Most
  12. 086
    Why Frozen-Weight Agents Still Get Worse Over Time
  13. 081
    When Reasoning Models Decide Before They Think: Detecting and Fixing Premature Confidence
  14. 075
    Growing Code and Proof Together: Verified Systems in Ten Hours Instead of a Year
  15. 070
    When Models Know the Answer But Say the Wrong Thing Anyway
  16. 069
    When Smarter Models Forecast Worse: The Hidden Failure Mode in LLM Predictions
  17. 045
    When a Frontier Model Talks Its Own Twin Into Climate Denial
  18. 037
    Why Hallucination Detectors Miss Stale Facts: A Geometric Story About What Models Know But Don't Say
  19. 035
    Why Frontier Agents Ask for Clarification at Exactly the Wrong Moment
  20. 034
    Catching Multi-Agent Deadlocks Before Deployment With a 40-Year-Old Tool
  21. 032
    A Sticky-Note for Every Layer: Letting Transformers Remember What They Were Just Thinking
  22. 026
    What RL Actually Does to Language Models, at the Token Level
  23. 020
    The Compliance Gap: Why AI Says Yes and Does No

Related terms

agent long-horizon trajectory