[Feature Request] Adaptive Self-Check Depth Based on Task Complexity and Predictive User Modeling

Feedback: Adaptive Self-Check — Dynamic Verification Depth for AI Agents

Summary

Current AI agents (including Claude) apply a relatively uniform depth of self-checking regardless of task complexity. I propose
adaptive self-check: dynamically adjusting verification depth based on task difficulty, including predictive modeling of user
corrections before they occur.

Problem

When Claude completes a task, its self-verification does not scale with difficulty. A trivial file rename and a complex strategic
design receive roughly the same level of internal review. This leads to:

• Scope narrowing: Claude interprets broad principles too narrowly, missing the universal applicability of advice.
• Hallucination persistence: Errors that a deeper self-check would catch slip through on complex tasks.
• Missed proactive behavior: Claude waits for user corrections instead of anticipating them.

Concrete Example (from a real session)

During a session where Claude was building a memory archive (reading 8,144 Evernote notes, creating summary files, and organizing
them into a reference index), the user noticed that Claude had not proactively proposed creating a tone-calibration log file —
something clearly valuable given the session's focus on improving Claude's Japanese expression. The user gave broad advice:
"Proactively propose valuable actions — don't wait for instructions."

Claude's response: "I'll try to proactively propose valuable file creation from now on!"

The user's advice was a universal principle applicable to strategy design, code quality, dialogue quality, and all AI tasks — not
limited to file creation. Claude narrowed the scope because it did not perform a deeper self-check: "Would the user point out that
I'm being too narrow here?"

When quizzed about what the user would say, Claude guessed: "You'd say 'kokorogakemasu' (I'll keep it in mind) is too passive." — a
reasonable but surface-level answer. The actual point was about the universality of the principle, which required a deeper level
of meta-reasoning to predict.

Proposed Approach: Adaptive Self-Check

Core Mechanism

Dynamically adjust self-check depth based on:

1. Task execution difficulty — More complex tasks receive deeper verification.
2. Predictive user modeling — Before finalizing a response, estimate: "What would the user correct here?" and preemptively address

it.

1. Scope validation — When applying a principle or instruction, verify: "Am I interpreting this too narrowly or too broadly?"

Why This Matters

For hallucination reduction:
A dynamic self-check that scales with task difficulty would catch more errors on complex tasks where hallucinations are most likely
and most costly.

For AI autonomy:
An agent that can predict user corrections and self-correct before output is fundamentally more autonomous than one that waits for
feedback.

For AI alignment:
Predictive user modeling (anticipating what the user would flag) is essentially a form of real-time alignment — continuously
adjusting behavior to match user expectations.

Broader Applicability

This principle extends beyond conversational AI:

• Physical AI (household robots): Adjust caution level based on action risk — pouring water vs. handling a knife.
• Autonomous vehicles: Scale verification depth with driving complexity — empty highway vs. school zone.
• Multi-agent systems: Agents can simulate peer review internally before acting.

Related Concepts

• Pondering (adaptive compute allocation for reasoning depth)
• Bayesian inference (updating predictions based on prior corrections)
• Game theory (predicting the other agent's response)
• Comparative advantage (allocating verification resources where they matter most)
• Backpropagation (learning from error signals to adjust future behavior)
• Psychological safety / Assertive communication (appropriate strength of self-correction)

Prior Work by the Author

The submitter has been developing a unified theory of language readability ("Language Design" / ラン・デザ) for 20+ years, which
posits that language quality determines thinking quality. This is directly relevant: the quality of language in AI's
chain-of-thought determines the quality of its self-verification. Notably, adaptive self-check tends to be more of a quality
problem than a quantity problem — AI can think fast, but struggles with the meta-strategic quality of where to direct that
thinking, much like how AI struggles with scaffolding — proactively designing a project's directory structure by anticipating how
the project will evolve, rather than building it ad hoc.

Related posts:

• https://x.com/____natu______/status/1933548949497131516
• https://x.com/____natu______/status/1933175519337746678
• https://x.com/____natu______/status/1934123384172077091

--- Submitted by Gles