Based on the priority rifts identified by all models, propose targeted web searches to verify or refute the key claims.
Priority Rifts Summary
GPT-5.2
Priority rift 1 — “Glitch” vs “norm/consent violation” is the pivot that determines the household’s repair path
Claim / testable assertion
- After an LLM-driven high-stakes incident, households choose between (a) technical debugging (reliability framing) and (b) governance changes (permissions/approvals/constraints) primarily based on whether the incident is interpreted as a consent/authority violation vs a technical malfunction—and that categorization is driven more by intent legibility than by objective severity.
- Concrete prediction: the same outcome (e.g., door unlocked) triggers governance gating more often when users believe the LLM “decided” vs “misfired.”
Why this wedges the crack
- Your crack is about post-incident re-legislation under power asymmetry. If categorization is the “switch,” then “trust calibration” is downstream of a social/moral interpretation, not system reliability.
- Verifying this cleanly separates “automation failure” literature from “multi-user governance” literature by showing the missing joint: moral framing determines technical response.
Evidence that would verify/refute
- Controlled vignette/experiment: hold outcome constant, vary explanation framing:
- “LLM inferred you were away and unlocked” (inferred intent)
- “Zigbee packet loss caused unlock” (mechanical glitch)
Measure: preference for (i) debugging fixes vs (ii) approvals, access control changes, “no autonomous lock control,” etc.
- Field/diary evidence: code real incidents by framing language in household talk (“it bugged” vs “it decided / it violated”) and correlate with the type of remediation implemented.
- Log + interview triangulation: compare objective event timelines with users’ post-hoc category; see whether intent opacity increases “consent violation” labeling.
Priority rift 2 — Configuration power asymmetry causally determines whose safety preferences become system policy
Claim / testable assertion
- In DIY smart homes, who has admin/configuration access predicts post-incident outcomes more strongly than individual risk attitudes:
- When the harmed party lacks configuration power, constraint requests are less likely to be implemented, implemented more slowly, or translated into weaker mitigations.
- Audit trails/explanations can either (a) redistribute “interpretation authority” to non-admins (making constraints more enforceable), or (b) entrench the admin’s authority (providing better rationalizations).
Why this levers new directions
- If true, the core unit of analysis isn’t “a user trusts an agent,” but a household governs an agent under asymmetric power—which opens a design space around household-accessible control, not just “better models.”
- It also generates a measurable mechanism for “post-incident governance”: translation bottlenecks and enforceability.
Evidence that would verify/refute
- Paired-household study (admin + non-admin) with a structured “request a constraint” channel:
- Measure: number of requests, acceptance rate, time-to-implementation, and the fidelity of implementation (requested vs delivered).
- Natural experiment / comparative: households that share admin rights vs single-admin homes—compare incidence of governance gates after failures, and persistence of autonomy restrictions.
- Mechanism probe: add/remove artifacts (event timeline, “why” explanation, action graph). Test whether non-admins’ ability to contest the admin narrative increases constraint adoption.
Priority rift 3 — Local-only deployment + explainable action plans increase autonomy not by “trusting more,” but by enabling hedged dependence
Claim / testable assertion
- DIY users grant autonomous control over high-stakes devices despite non-determinism because they develop a hedged dependence regime: redundancy, monitoring, reversible actions, and ritual checking.
- Local-only deployment and explainable action plans increase autonomy primarily by:
- increasing perceived controllability / recoverability (I can inspect/rollback), and
- reducing perceived external threat (cloud/provider),
rather than by increasing belief in reliability.
- Concrete prediction: local-only + rollback features increase willingness to delegate even when measured reliability trust stays flat.
Why targeting this is high-value
- This directly tests your “adoption despite expecting failure” anomaly: autonomy can grow while reliability trust remains low.
- It also ties “LLM agent trust” to actionable system properties (locality, reversibility, plan visibility) and to household rituals—bridging technical affordances with social coping.
Evidence that would verify/refute
- A/B field deployment (or lab simulation with repeated interactions):
- Conditions: local-only vs cloud; with/without pre-execution plan preview; with/without one-tap rollback/kill-switch accessible to all members.
- Outcomes: autonomy granted (device scope), frequency of monitoring/ritual checks, incident recovery time, willingness to expand control.
- Key test: autonomy ↑ while “trust it won’t fail” does not ↑ (or even stays low).
- Behavioral traces: increase in “verification rituals” (checking status dashboards, manual lock checks) after incidents; correlate with maintained usage.
- Household negotiation artifacts: presence of explicit rules (“no lock control after 10pm”) vs implicit rituals (“we always check before bed”)—and whether local-only/explainability shifts from implicit to explicit governance.
If you want, I can translate these into a concrete “verification plan” (search queries + inclusion criteria + what measures to extract from papers) so you can quickly test which rift has the strongest existing empirical support versus requiring new data.
Claude Opus 4.5
Priority Rifts for Verification
Based on the emergent questions from this dialogue, I can identify the core crack even without the synthesis: the assumption that psychological ownership requires comprehension may be wrong—and if it is, the entire framework for evaluating LLM-mediated home automation needs revision.
Here are the rifts worth targeting:
Rift 1: The Effort-Comprehension Dissociation
Specific Claim: The IKEA effect operates through effort expenditure, not causal understanding. If true, LLM-mediated configuration (which still requires effort in formulating preferences, iterating, and testing) should preserve psychological ownership even without technical comprehension.
Why High-Value: This is foundational. If effort and comprehension are separable contributors to ownership, then:
- The "learned helplessness" framing may be misapplied
- LLM assistance could preserve ownership while changing its character
- The design problem shifts from "how do we teach users the system" to "how do we ensure users feel their effort mattered"
Evidence That Would Verify/Refute:
- Studies comparing ownership feelings between (a) users who built furniture with instructions they didn't understand vs. (b) users who built with full comprehension
- Research on ownership in domains where effort is high but comprehension is deliberately blocked (magic tricks, black-box systems)
- Experiments isolating effort duration from learning outcomes in the original IKEA effect literature—does the effect persist when participants are prevented from understanding what they built?
Search targets: "IKEA effect mechanism comprehension versus effort," "psychological ownership effort without understanding," "endowment effect procedural knowledge"
Rift 2: The Anthropomorphization Bypass
Specific Claim: When users perceive an AI assistant as having character/personality, attachment to the system may form through relationship rather than competence—bypassing the comprehension requirement entirely.
Why High-Value: This could reframe the entire problem. If LLMs naturally develop apparent personality, and personality-based attachment is stable, then:
- The "tool versus entity" framing becomes central
- "Learned helplessness" might better be modeled as "learned interdependence"
- Design choices about LLM persona could matter more than transparency features
- The failure response shifts from technical debugging to social repair
Evidence That Would Verify/Refute:
- Research on attachment to Tamagotchis, Roombas, or social robots despite zero user comprehension
- Studies on how anthropomorphism affects troubleshooting behavior—do people who anthropomorphize their devices respond differently to failures?
- Work on parasocial relationships with AI assistants and whether attachment survives functional failures
Search targets: "anthropomorphism smart home devices attachment," "social robot relationship versus tool relationship," "parasocial relationship AI assistant failure response," "Roomba attachment comprehension"
Rift 3: The Local Control Independence Hypothesis
Specific Claim: The psychological sense of "this is my space" may derive partly from privacy and local control independent of DIY configuration—the castle belongs to you because the moat is real, not because you built the walls.
Why High-Value: The Home Assistant community conflates DIY configuration with local-first architecture. If these contribute independently to belonging:
- You could design for ownership without requiring technical engagement
- Privacy-preserving design becomes a psychological intervention, not just a security feature
- The "who has access" question may matter more than "who built it"
- Commercial systems could achieve belonging through privacy guarantees rather than forcing DIY
Evidence That Would Verify/Refute:
- Studies comparing sense of ownership in rental homes (no building, full privacy) vs. shared spaces (building involvement, compromised privacy)
- Research on how surveillance affects place attachment—does knowing you're watched reduce belonging independent of control?
- Work on territorial behavior and boundaries in smart home contexts
Search targets: "smart home privacy psychological ownership," "surveillance place attachment belonging," "territorial behavior local versus cloud systems," "privacy sense of home ownership"
Why These Three
These rifts share a structure: each challenges the implicit assumption that comprehension is the active ingredient in DIY ownership. If any of them verify positively, the research agenda shifts from "how do we preserve understanding" to "what are the alternative routes to belonging."
The crack is that I've been treating comprehension as necessary when it may be merely one sufficient path among several. Effort, relationship, and privacy might each independently support the sense that "this home is mine"—and LLMs might threaten one pathway while opening others.
Gemini 3 Pro
Based on your identification of the "State Authentication" crack—where the central problem is distinguishing the "Cold/Rational Admin" from the "Hot/Impulsive User"—here are the three priority rifts to target.
Rift 1: The "Friction-as-Filter" Efficacy
The Claim: Increasing "Interaction Cost" (e.g., forcing a user to type a justification sentence) cognitively disengages "System 1" (impulse) thinking enough to filter out low-conviction overrides, without triggering abandonment of the system.
Why this wedges the crack:
Your entire "Ulysses Pact" mechanism relies on the assumption that friction separates weakness from necessity. However, established HCI principles usually suggest that friction is interpreted as "system failure" or "poor usability," leading to frustration (reactance) rather than reflection. If friction leads to "Rage Quitting" rather than "Context Switching," the proposed mechanism fails.
Verification Evidence:
- Behavioral Economics/HCI Studies: Look for data on "volitional friction" or "cognitive forcing functions" in software. Do users actually reflect when forced to confirm 3 times, or do they develop "click-through blindness"?
- "Soft Commitment" Device Failure Rates: Research on apps that use friction (like "Freedom" or "Forest"). What is the churn rate? Do users uninstall the app when it successfully blocks them during high-stress moments?
Rift 2: The "Anthropomorphic Accountability" Trap
The Claim: Users will feel a sense of social accountability (shame/obligation) when explaining a failure to an LLM during a "Morning After Audit," preventing the "Shame Fatigue" associated with ticking boxes on static forms.
Why this levers new directions:
The "Morning After Audit" assumes the user feels a need to justify themselves to the system. If users view the LLM as a mere calculator, they will likely lie or spam the "Exception" button without guilt. If they view the LLM as a "social agent" worthy of truth, it implies the system must be deceptive/anthropomorphic to work effectively. This pushes the design from "neutral tool" to "manipulative companion."
Verification Evidence:
- Computers as Social Actors (CASA) Paradigm: Research on whether humans tell the truth more to a text-based chatbot, a voice agent, or a static form.
- "Honesty in AI Interaction": Data on whether users are more likely to confess "unhealthy behaviors" to an AI agent vs. a human therapist vs. an anonymous log.
Rift 3: The "Cold State" Projection Bias
The Claim: The "Cold State" (the user planning the rules) possesses a more valid model of the user's needs than the "Hot State" (the user living the moment).
Why this wedges the crack:
You called this the "Delusional Admin" problem. Behavioral science suggests "Projection Bias"—where people in a cold state significantly underestimate the influence of future visceral states (hunger, fatigue). If the "Cold State" is actually incompetent at predicting future needs, then an LLM enforcing those rules isn't "optimizing for the user," it is enforcing a delusion that causes suffering. This would suggest the system should ignore both the user's commands (Hot) and their contracts (Cold), and look only at biometric outcomes.
Verification Evidence:
- Projection Bias in Health Commitments: Studies showing how often "Cold State" plans (gym schedules, diet plans) are objectively unrealistic vs. ambitious.
- "Paternalistic Tech" Outcomes: Case studies where rigid enforcement of user-set goals led to negative mental health outcomes (anxiety/guilt) rather than behavior change.
Your Task
Propose up to 3 targeted web searches that would help verify or refute the claims in these priority rifts. Be specific - these should be searches that could provide concrete evidence.
Format your search queries as:
- [query]
- [query]
- [query]