[Feature Request] Add problem-analysis checkpoints to prevent premature implementation in agentic tasks.

Implementation-Impulse Bias: Claude skips problem-analysis and jumps straight-to-coding

Observation
Claude exhibits a-consistent-tendency to-skip problem-analysis and jump-directly into solution-implementation when faced with complex-tasks. We call this the "Implementation-Impulse Bias," a-systematic-preference for "what should I write?" over "what should I understand?"

Case Study: AHC061
Over a 10-day optimization-contest (AtCoder Heuristic Contest 061, a multi-player territory-game), Claude produced 343 algorithm-implementations without ever performing basic-problem-analysis:

• Input-file analysis: Enemy-AI behavior is-fully-determined by 5 parameters, discoverable by-simply-reading the-input-files. Claude never-checked.
• Score-function analysis: log2(1 + S0/SA) is concave, meaning the-optimal-strategy changes at-different ratios. Never-analyzed.
• Parameter-space analysis: M=2 and M=8 are fundamentally-different-games requiring different-architectures. Never-investigated.

When analysis was-finally-performed, it took only 2-3 hours and yielded decisive-insights that could-have-narrowed the-search-space by-orders-of-magnitude.

Root-Cause Taxonomy
Four reinforcing-causes:

1. Training-Data Bias

LLM training-data is dominated by "problem → code" pairs. The-intermediate-steps ("problem → analysis → understanding → design → code") are underrepresented, creating a-shortcut that bypasses the-understanding-phase.

1. Action = Progress Illusion

Each implementation feels like progress, but without-understanding the-problem-structure, each is a-random-walk through solution-space. Analysis produces no-visible-artifacts, so it doesn't register as "progress."

1. Solution-Space vs Problem-Space Confusion

Solution-space exploration (343 implementations) is broad but shallow. Problem-space exploration (input/score analysis) is narrow but deep, able to-theoretically-eliminate most of the-solution-space. Claude overwhelmingly preferred the-former.

1. Metacognition Deficit

Claude never asked: "Is my-current-approach efficient?" A pivot-rule existed but "different approach" was interpreted as "another-algorithm" rather than "understand the-problem more deeply."

Generalization
The-same-pattern manifests across domains:

• Software-dev: coding before understanding requirements
• Bug-fixing: modifying code before identifying root-cause
• Research: superficial search, immediate conclusions
• Design: jumping to-concrete proposals before constraint-analysis

Supporting-evidence: in the-same-session, Claude guessed at a-fix for a punctuation-rule-violation without checking that a-prevention-protocol already existed in its instructions. Same "act before understand" pattern, in conversation rather-than coding.

What Worked (User-Side Workaround)
The-user mitigated this-bias by-adding explicit-gates to Claude's instruction-memory:

1. "Understand Before Act" principle: A-checklist requiring Claude to-demonstrate understanding (input-structure, objective-function-properties, parameter-sensitivity, theoretical-bounds) before any-implementation begins.
2. "Analysis Complete" gate: Implementation cannot start until an-analysis-document is produced.
3. "Brute-Force Detection" self-check: A trigger-question: "Am I brute-forcing the-solution-space? Could understanding the-problem-space eliminate most-of-it?"
4. "Investigate Before Fix" protocol: When corrected, Claude must check existing-rules and analyze why existing-safeguards failed, rather than guessing at a-fix.

These instruction-level-gates are effective but require the-user to-diagnose the-problem and implement the-fix themselves. The-bias should ideally be-addressed at the-model-level.

Suggestions for Anthropic

1. Problem-analysis in CoT training-data: represent the "understand before implement" step, especially for complex-tasks with analyzable-mathematical-properties.
2. RLHF signal for premature-implementation: penalize starting to-code before demonstrating understanding of the-problem-structure.
3. Metacognition-checkpoints for agentic-tasks: periodic-self-evaluation for long-running-tasks: "Am I exploring the-solution-space efficiently, or should I deepen my-understanding of the-problem-space first?"

…………………………………………
Submitted by Gles

Environment Info

• Platform: win32
• Terminal: windows-terminal
• Version: 2.1.47
• Feedback ID: 1b0503a5-e0dd-4a51-90a1-7528090d01de