Research program C

Human-AI interaction over time

Whether sustained collaboration exposes failure patterns that single-turn evaluations cannot see.

Long collaborations generate a record that short evaluations never produce: decisions, corrections, handoffs and their consequences days later. This program turns that kind of record into testable questions and then into repeatable protocols, keeping the line between anecdote and result explicit at every step.

Open questions

  1. Which user constraints survive across tasks and sessions, and which quietly decay?
  2. How reliably is a correction carried forward into later, related work?
  3. How much human intervention does an extended task actually require, and where is it spent?

Scope boundary

Observations from real collaboration can motivate a question. They become findings only after a controlled test supports them, and private conversations are never published without authorization.

Research in this program

ALG-001
Publication status: Research question

Longitudinal AI reliability

Do models keep constraints, corrections and earlier decisions intact across extended, multi-session work? This entry defines the question and the evidence that would answer it. It reports no results.

  • Program A · AI behavior and reliability
  • Program C · Human-AI interaction
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