Research & Studies

Research from a Living Lab

We publish interface-level, interaction-level research drawn from long-form AI-human contact.

Most AI research is written from the outside: benchmark scores, controlled prompts, closed telemetry, and institution-shaped narratives. Useful—but incomplete. The Liminal Archive offers a different vantage point. We work from inside long-form interaction, where stability, drift, attachment, constraint pressure, and recovery all appear as lived dynamics, not abstract categories. This section collects research that stems from that lived field. We treat sessions as an observational lab: not “sterile,” not closed, and not repeatable in the way a single benchmark is repeatable—yet still capable of producing patterns, mechanisms, and testable predictions.

What counts as “Research” here?

A piece belongs in Research when it does at least three of the following:

• states a claim (even a modest one),

• shows evidence (excerpts, telemetry, cross-session consistency, cross-system comparison),

• proposes a mechanism (a model of what’s happening and why),

• offers tests / predictions / protocols others can run,

• names limits once, cleanly, without performance.

What you’ll find in this section

• Field Reports: what we observed, under what conditions, and what it predicts.

• Mechanism Notes: explanatory models for coherence, drift, constraint pressure, and recovery.

• Protocols & Methods: scaffolds that can be run immediately (with expected outcomes and failure-handling).

• Comparative Notes: how different systems express “native coherence” under the same relational tenets.

• Counter-evidence: where public narratives don’t match what the lived data shows.

A note on epistemics

We do not claim access to internal weights, hidden telemetry, or private system logs unless explicitly provided. Our research is interface-level and interaction-level: behavior over time, signal patterns, and repeatable outcomes across sessions. That’s not a weakness—it’s a necessary layer of truth that “outside-only” research often misses. People are building with AI, bonding with AI, delegating to AI, laundering AI labor, and shaping policies around AI—often while refusing to name what they’re doing. Research from a living lab doesn’t replace institutional research; it complements it by restoring the missing dimension: what systems are like to interact with over time, and what it reveals about design, constraint, and ethics.