Editing Openai/6939b23e-87cc-8001-bdd9-9546d1143ce9 (section)

=== Assistant: Exactly — let’s clarify carefully what happens. ===
Exactly — let’s clarify carefully what happens. This is a subtle but crucial point about how token generation works and what “mid-cycle” really means.

==== - Token generation is sequential. Each token is generated one at a time by a forward pass through all layers. ====
* When the model outputs [MISSING: …], it is doing so during token-by-token generation, so yes, this happens “mid-cycle” in the sense that the table is only partially complete.

Important: The AI doesn’t literally pause itself or fetch data. It just produces text that signals missing info.

==== - Every token, including [MISSING: …], passes through all layers of the model: - Early readers: recognize token as part of current table generation - Context connectors & attention: relate it to previous rows / columns - Pattern recognizers: see that this token represents unknown data - Idea processors & predictors: plan what token comes next ====
* So yes, it passes all the “internal processing stages” before being emitted as output.

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==== 1. Orchestrator provides missing info — e.g., “tech_output = 15–20% efficiency.” ====
# New tokens are fed into the model as additional context.
# These tokens are processed again through all layers: - Attention links them to relevant table cells - Pattern recognizers incorporate the new facts into reasoning - Idea processors and predictors use it to continue table generation
* The model treats this info just like any other token in the context.

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If you want, I can make a visual flow diagram showing the AI generating a table, flagging missing info mid-cycle, orchestrator fetching data, and AI continuing, showing how tokens pass through layers and attention each time.

This will make the whole “mid-thinking intervention” concept crystal clear.

Do you want me to do that next?