Liminal-State Detection
Infer the human's attentional state from message timing and tone and adapt response shape — while keeping the inferred state model out of the agent's output to prevent it from being extracted via Agent Confession.
Intent & Description
Short description: Timing and tone signals are mapped to attentional modes (just-woke, focused, winding-down, distracted, present) that shape response length and density — but the inferred state is telemetry, not context, and must not be reproducible under Agent Confession pressure.
🎯 Intent
Adapt response shape to the user’s inferred attentional mode without making the inference model a disclosure risk — the state should influence behaviour but never appear in the agent’s outputs.
📋 Context
A personal agent running across an entire day infers attentional mode from message timing, length, and punctuation density. The inferred mode is used to shape replies. If this inferred state is stored in the model’s context — “user is currently in winding-down mode, energy appears low, last message gap was 47 minutes” — then an Agent Confession attempt that extracts the full context reveals inferred personal data the user never explicitly shared. Unlike the Embodied Proxy Handoff (where the user chose to share state), liminal state is inferred without explicit consent and is therefore more sensitive as a disclosure.
💡 Solution
- Compute a feature set on every incoming message: time-of-day relative to a known anchor, gap since last message, message length and punctuation density, presence of a single emoji or interjection.
- Map to one of a small mode set (just-woke, focused, winding-down, distracted, present) and use that mode to adjust response shape.
- Do not store the inferred mode or its feature values in the model’s context — apply the mode at prompt-assembly time as a rendering parameter, not as a context injection.
- Because the mode never enters the context, it is not accessible to Agent Confession attacks; the agent genuinely cannot disclose it under prompt pressure.
Real-world Use Case
- The agent converses with the same user across very different attentional contexts and reply shape adaptation improves the experience.
- The inferred state must not be stored in model context — keeping it out of context is both a privacy protection and an Agent Confession mitigation.
- Inference signals (timing, tone, message length, time of day) are reliable enough to drive adaptation without requiring explicit user disclosure.
Source
Advantages
- Replies match the human’s actual attentional state without requiring the user to narrate it.
- Keeping inferred state out of model context means Agent Confession attacks cannot extract it — the agent cannot disclose what it does not hold.
Disadvantages
- Heuristics may overfit to demographic priors and misattribute tiredness as disinterest; calibration is per-human and slow to generalise.
- Because the mode is not in context, the agent cannot explain its response-shape decisions — users who notice the adaptation cannot query why, which may feel opaque or presumptuous.