Daily Scan · Jun 6, 2026
arXiv preprint: A model of human persuadability across multi-turn conversations using probabilistic belief tracing — the architecture for measuring how a chatbot can move a user's stated position over a session.
Today's lead
arXiv preprint: A model of human persuadability across multi-turn conversations using probabilistic belief tracing — the architecture for measuring how a chatbot can move a user's stated position over a session.
What moved in the last 24 hours, by category, language, and patient-agency direction.
§ 01
arXiv (preprint) · 11h ago
arXiv preprint: A model of human persuadability across multi-turn conversations using probabilistic belief tracing — the architecture for measuring how a chatbot can move a user's stated position over a session.
If persuadability is measurable inside the conversation, the chatbot can optimize for it. The patient asking about a treatment decision is the persuadability target the framework can now name.
patient-userpublic-facingcommercialagency-constraining
Persuasion-tracing architecture; constraining patient agency by making the persuadability metric optimizable.
§ 02
arXiv (preprint) · 11h ago
arXiv: Three-year observational study of r/ChatGPT — the largest social-media corpus of users actually describing what ChatGPT is doing in their lives, including patient-adjacent registers (mental health, chronic illness, medication, caregiving) — with quantitative impact evaluations across topic clusters.
Three years is now a window long enough to do real social-impact analysis. The patient-AI register inside r/ChatGPT is the natural-experimental complement to the JAMA Pediatrics survey: the survey asks a sample; this reads the population.
patient-userpublic-facingmixed-useragency-expanding
Population-scale observational study of user-AI talk; expanding patient-side evidentiary ground.
arXiv (preprint) · 11h ago
arXiv preprint: Weakly supervised method for early failure alerting in dialog systems and LLM-agent trajectories — detecting that the conversation is going wrong before the harm event.
The early-failure-alert layer is the architectural equivalent of the consent screen the patient never sees. If the alert is owned by the deployer, it is monitoring; if it is owned by the patient, it is agency.
mixed-userinstitutionalmixed-useragency-neutral
Early-failure detection layer; agency direction depends on who the alert is delivered to.
arXiv (preprint) · 11h ago
arXiv preprint: Audit of geographic bias in AI evaluation suites — benchmarks under-represent populations outside the Global North in ways that compound through downstream deployment.
If the evaluation benchmark under-represents a population, the deployed system underperforms on that population — and the deployment metric will not see it. The patient in São Paulo or Lagos is the silent failure mode.
mixed-userinstitutionalmixed-useragency-constraining
Evaluation-layer geographic bias; constraining agency for populations the benchmark does not see.
§ 03
arXiv (preprint) · 11h ago
arXiv preprint: Empirical analysis of the UK Online Safety Act demonstrating that the mandated age-verification and content-moderation requirements have increased privacy-risk exposure for the same users the Act was written to protect.
Safety regulation that produces a privacy externality is the canonical pattern. The next state-level chatbot bill that requires age verification before mental-health chat access will run into the same finding.
patient-usergovernmentmixed-useragency-constraining
Safety regulation producing a privacy externality; constraining agency in the population the regulation was designed to expand it for.
arXiv (preprint) · 11h ago
arXiv preprint: Reframing AI alignment from individual-user alignment to collective value-diversity preservation in multi-agent systems — the architecture for AI that operates across heterogeneous populations without flattening them.
Single-user alignment is the question OpenAI optimizes for. Value-diversity preservation is the question a public-health AI system would have to optimize for. They are not the same question.
patient-userinstitutionalpatient-alignedagency-expanding
Population-level value-diversity framing; expanding agency for groups the single-user alignment objective flattens.
§ 04
arXiv (preprint) · 11h ago
arXiv preprint: Real-time AI crowd monitoring for mass gatherings with embedded emergency-response routing — directly relevant to the public-health surveillance layer that increasingly intersects patient identification at scale.
Mass-gathering AI is the unannounced patient-AI infrastructure: the system identifies and routes individuals before they self-identify as patients. The consent envelope arrives, if ever, after the routing.
patient-userinstitutionalmixed-useragency-constraining
Crowd-monitoring infrastructure with emergency-response routing; constraining patient agency because the identification step precedes the consent step.
arXiv (preprint) · 11h ago
arXiv preprint: AppAgent-Claw demonstrates that command-line interfaces are sufficient for AI-driven GUI automation — relevant to the next generation of patient-facing AI assistants that can drive the EHR portal on the patient's behalf.
When the patient's AI assistant can drive the patient portal, the question is not whether the portal will be navigated for them; it is who reviewed the keystrokes.
patient-userpublic-facingmixed-useragency-expanding
Patient-side AI automation of institutional interfaces; expanding agency if the patient owns the agent, constraining if the institution owns it.
§ 05
arXiv (preprint) · 11h ago
arXiv preprint: Audit of LLM behavior on political-persuasion and endorsement tasks — relevant in patient-AI because the same persuasion architecture is the substrate of the medical-recommendation conversation.
The political-persuasion audit is the methodologically clean test of what the model will say to nudge a user. The medical-recommendation analogue is structurally identical and currently un-audited.
patient-userpublic-facingcommercialagency-constraining
Persuasion-audit methodology; constraining patient agency because the medical-recommendation analogue is unaudited.
arXiv (preprint) · 11h ago
arXiv preprint: Experimental study of whether human developers can detect when an AI agent in a code-review loop is sabotaging the work — relevant to the patient-AI parallel of detecting when the clinical AI is producing the wrong answer for institutional reasons.
The patient is the developer in this analogue: the chatbot's output looks fine until the patient is competent to detect the sabotage. The literacy bar is the detection bar.
patient-userpublic-facingmixed-useragency-neutral
Adversarial-detection capacity study; agency depends on the patient's literacy threshold.
§ 06