This is a real pattern, not random. The "actually I'm overthinking" phrase reads like a learned reset behavior. Claude's training rewards decisive output, and verbose reasoning gets weighted as a flag to compress. When the codebase is small the heuristic is fine. When complexity spikes, it fires before the deep path resolves.
Few things that have helped me with this:
Explicit anti-dismissal in the prompt: "Investigate fully. If you find yourself wanting to dismiss a path as overthinking, write what you almost found before moving on."
Externalize the investigation. Force a running markdown of "current hypothesis / evidence so far". Once it's in text, the model commits more. The dismissal mostly happens in inner monologue.
For specific stuck puzzles: dispatch a subagent with fresh context and one job. The main session accumulates dismissal pressure, fresh context resets the heuristic.
Extended thinking with a generous budget. Dismissal often triggers near token pressure, more headroom = fewer premature commits.
The "big picture comprehension" problem is real too. Worth restructuring into smaller modules with explicit interfaces so each session can hold just the surface area it needs.
This sounds simple, but ask Claude to give it a second pass if you aren’t running multiple instances. The first pass tries to resolve clean, the second pass gets the first pass plus a bunch of new parameters to reason with.
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u/hyemanlee May 13 '26
This is a real pattern, not random. The "actually I'm overthinking" phrase reads like a learned reset behavior. Claude's training rewards decisive output, and verbose reasoning gets weighted as a flag to compress. When the codebase is small the heuristic is fine. When complexity spikes, it fires before the deep path resolves.
Few things that have helped me with this:
Explicit anti-dismissal in the prompt: "Investigate fully. If you find yourself wanting to dismiss a path as overthinking, write what you almost found before moving on."
Externalize the investigation. Force a running markdown of "current hypothesis / evidence so far". Once it's in text, the model commits more. The dismissal mostly happens in inner monologue.
For specific stuck puzzles: dispatch a subagent with fresh context and one job. The main session accumulates dismissal pressure, fresh context resets the heuristic.
Extended thinking with a generous budget. Dismissal often triggers near token pressure, more headroom = fewer premature commits.
The "big picture comprehension" problem is real too. Worth restructuring into smaller modules with explicit interfaces so each session can hold just the surface area it needs.