Anthis, Jacy Reese, Burke, Ken, Madras, David, Qian, Crystal, Morris, Meredith Ringel, Cross, Logan, Pantofaru, Caroline, Prabhu, Utsav · Nov 11, 2026
Paper explores using pluralistic agents to scale public input on AI policy
Markdown · JSON · Original source
The authors of “Scaling public input on AI policy with pluralistic agents” argue that representatives could let people contribute to discussions they cannot attend directly, including technical topics and policy choices involving private information. They propose using these agents to amplify human input, not replace it. The authors report that agents shifted policies toward their represented personas’ preferences, but their influence varied with persona instructions: agents had more influence when told to be more verbose or less cooperative. This makes instructions and system design consequential to whose preferences shape AI-assisted deliberation. In a preregistered study with 746 participants, participants viewed the agents as useful and fair compared with providing input to a single AI . Most still preferred participating themselves. The authors report that agents recovered 68% of the baseline-direct gap in satisfaction and 82% in fairness. These findings describe the study’s reported measures; the abstract does not establish how well the approach applies elsewhere. The authors call for cautious exploration as a way to scale AI safety policy development where direct discussion is not possible.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Nov 11, 2026 (2026-11-11).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-13T12:31:44.450Z. First observed: 2026-11-13T07:43:27.424Z. Retrieved: 2026-11-13T12:18:04.181Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
The supplied evidence is the paper’s abstract; it does not provide the study procedures or details needed to assess how broadly the reported findings apply. The reported satisfaction and fairness comparisons do not establish that pluralistic agents represent people’s views reliably in other settings.
Open question
How much does the influence of pluralistic agents change across other deliberation settings when persona instructions and system design vary?
Source-stated
The authors propose pluralistic agents as representatives that deliberate on individuals’ behalf, potentially allowing people to contribute to discussions they cannot directly join.
AI agent representatives that deliberate on behalf of individuals to align AI systems with diverse human perspectives.
Source-stated
The authors report that agents shifted policies toward the preferences of their represented personas, with influence varying according to persona instructions.
Agents were able to reliably shift policies in the direction of their represented persona's preferences, but the extent of each agent's influence was sensitive to the instructions from its persona
Source-stated
In a preregistered study, participants viewed pluralistic agents as useful and fair compared with providing input to a single AI reasoning model, though most preferred participating themselves.
Participants viewed pluralistic agents as useful and fair, preferring agents over the baseline of providing input to a single AI reasoning model. Most participants preferred participating themselves
Source-stated
The authors report that pluralistic agents recovered 68% of the baseline-direct gap in reported satisfaction and 82% in fairness.
pluralistic agents recovered 68% of the baseline-direct gap in reported satisfaction and 82% in fairness.
Source-stated
The authors frame pluralistic agents as a possible tool for scaling AI safety policy development, while recommending cautious exploration and use as an amplifier of human input.
they could be a promising tool to scale AI safety policy development. We encourage cautious exploration of this method, playing to its strengths as an amplifier of human input rather than a replacement.
Source and contributor context
Anthis, Jacy Reese, Burke, Ken, Madras, David, Qian, Crystal, Morris, Meredith Ringel, Cross, Logan, Pantofaru, Caroline, Prabhu, Utsav · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
AI agent
An AI system that chooses and carries out actions toward a goal, often using software tools.
Reasoning model
A language model trained to work through a problem in written steps before it answers.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 8beeb195-02c1-4930-8df4-2d8bb9d4562f.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-13T12:31:44.450Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: ca33e24d49f2099be0b676ad0deeb1eec7ef85f60f32b216341e37e6eaa9f759. Source text hash: 6c7638c817520444eb9ef48df7182a5d9ff77912de9fc188c41bbaac9de78a54.
Revision and source-check history
Version 1 · 2026-11-13T12:31:44.450Z
Paper explores using pluralistic agents to scale public input on AI policy
The authors of “Scaling public input on AI policy with pluralistic agents” argue that representatives could let people contribute to discussions they cannot attend directly, including technical topics and policy choices involving private information. They propose using these agents to amplify human input, not replace it. The authors report that agents shifted policies toward their represented personas’ preferences, but their influence varied with persona instructions: agents had more influence when told to be more verbose or less cooperative. This makes instructions and system design consequential to whose preferences shape AI-assisted deliberation. In a preregistered study with 746 participants, participants viewed the agents as useful and fair compared with providing input to a single AI . Most still preferred participating themselves. The authors report that agents recovered 68% of the baseline-direct gap in satisfaction and 82% in fairness. These findings describe the study’s reported measures; the abstract does not establish how well the approach applies elsewhere. The authors call for cautious exploration as a way to scale AI safety policy development where direct discussion is not possible.
Original source
cs.AI · Article
Source details
Original source date: Nov 11, 2026 (2026-11-11).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-13T12:31:44.450Z. First observed: 2026-11-13T07:43:27.424Z. Retrieved: 2026-11-13T12:18:04.181Z.
Limits
The supplied evidence is the paper’s abstract; it does not provide the study procedures or details needed to assess how broadly the reported findings apply. The reported satisfaction and fairness comparisons do not establish that pluralistic agents represent people’s views reliably in other settings.
Open question
How much does the influence of pluralistic agents change across other deliberation settings when persona instructions and system design vary?
Source-stated
The authors propose pluralistic agents as representatives that deliberate on individuals’ behalf, potentially allowing people to contribute to discussions they cannot directly join.
AI agent representatives that deliberate on behalf of individuals to align AI systems with diverse human perspectives.
Source-stated
The authors report that agents shifted policies toward the preferences of their represented personas, with influence varying according to persona instructions.
Agents were able to reliably shift policies in the direction of their represented persona's preferences, but the extent of each agent's influence was sensitive to the instructions from its persona
Source-stated
In a preregistered study, participants viewed pluralistic agents as useful and fair compared with providing input to a single AI reasoning model, though most preferred participating themselves.
Participants viewed pluralistic agents as useful and fair, preferring agents over the baseline of providing input to a single AI reasoning model. Most participants preferred participating themselves
Source-stated
The authors report that pluralistic agents recovered 68% of the baseline-direct gap in reported satisfaction and 82% in fairness.
pluralistic agents recovered 68% of the baseline-direct gap in reported satisfaction and 82% in fairness.
Source-stated
The authors frame pluralistic agents as a possible tool for scaling AI safety policy development, while recommending cautious exploration and use as an amplifier of human input.
they could be a promising tool to scale AI safety policy development. We encourage cautious exploration of this method, playing to its strengths as an amplifier of human input rather than a replacement.
Source and contributor context
Anthis, Jacy Reese, Burke, Ken, Madras, David, Qian, Crystal, Morris, Meredith Ringel, Cross, Logan, Pantofaru, Caroline, Prabhu, Utsav · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
AI agent
An AI system that chooses and carries out actions toward a goal, often using software tools.
Reasoning model
A language model trained to work through a problem in written steps before it answers.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: 8beeb195-02c1-4930-8df4-2d8bb9d4562f.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: uncertain by anthropic/claude-opus-5.5 on 2026-11-13T12:31:44.450Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: ca33e24d49f2099be0b676ad0deeb1eec7ef85f60f32b216341e37e6eaa9f759. Source text hash: 6c7638c817520444eb9ef48df7182a5d9ff77912de9fc188c41bbaac9de78a54.
uncertain · 2026-11-13T12:31:44.450Z · anthropic/claude-opus-5.5
Publication: 8beeb195-02c1-4930-8df4-2d8bb9d4562f
attribution — pass: The proposal, experimental results and recommendations are consistently attributed to the paper's authors. The authors' framing is not presented as established fact or as Escape Meridian's opinion.
We develop and test pluralistic agents, AI agent representatives that deliberate on behalf of individuals
summary — uncertain: Most of the reading is clear, but the central numerical result uses the source's phrase 'baseline-direct gap' without explanation. A general reader may not see that it means the difference between the single-AI-model baseline and participating in person. 'Preregistered' and 'persona' are also left unexplained. Smallest repair: briefly explain the gap and add term-library tooltips for 'preregistered' and 'persona'. The question follows from the evidence.
pluralistic agents recovered 68% of the baseline-direct gap in reported satisfaction and 82% in fairness
date — pass: The originalPublished date, 2026-11-11, matches the v1 submission date shown on the arXiv page. The feed date of 2026-11-13 is correctly not used.
[Submitted on 11 Nov 2026]
support — uncertain: The sentence 'This makes instructions and system design consequential to whose preferences shape AI-assisted deliberation' hardens the authors' hedged inference. The source says only that the result 'suggests' influence 'could vary'. Smallest repair: attribute and hedge it, for example 'The authors say this suggests influence could vary with how participants prompt agents and how the system is designed.' The other claims and the stated limitations are supported by the abstract.
This suggests that influence in AI-assisted deliberation could vary based on how participants prompt their agents and how the system is designed.
media — pass: The selected media is the verified direct link to the arXiv abstract page, which is the original source.
Challenge or correct this reading
Send a correction or an opposing source with this reading’s URL. Disagreement and uncertainty can remain unresolved.
An AI system that chooses and carries out actions toward a goal, often using software tools.
Definition referenceA language model trained to work through a problem in written steps before it answers.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivAn AI system that chooses and carries out actions toward a goal, often using software tools.
Definition referenceA language model trained to work through a problem in written steps before it answers.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXiv