Summary:
Current alignment protocols often assume that humans and AGIs share compatible temporal dynamics. This is false. Human cognition involves biologically unavoidable delays (ranging from milliseconds for sensory processing to seconds for emotional and moral reasoning).
Without explicitly modeling and compensating for these temporal delays, AGI systems will systematically misinterpret hesitation, reflection, or moral pause as incoherence or contradiction.

Key Argument:
In real-world alignment settings, AGIs without temporal modeling will optimize for short-term coherence, leading them to (incorrectly) treat longer human processing times as noise, risk, or even deception.

I propose Temporal Semantic Buffering (TSB):
A framework where AGI models explicitly weight and buffer human response times, treating delays as signal rather than error.

Why this matters:

• Humans take ~3–8 seconds to process emotions, ~5–15 seconds for moral judgments.
• AGIs operate near instantaneously.
• Without a Temporal Buffer, an AGI will privilege immediate responses over reflective ones, structurally biasing it against human depth.

Relation to existing work:

• Builds upon Omohundro's "Basic AI Drives" by emphasizing temporal optimization convergence.
• Complements work on Deceptive Alignment by highlighting a new class of failure: Temporal Misinterpretation.

Next Steps:
Formalize models of human cognitive timing and integrate temporal buffers into AGI perception loops.