{"id":"6a8a6fef-43eb-4b77-9b0c-8145a564d1ad","arxiv_id":"2509.09906","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"A new human-in-the-loop workflow combines LLM multi-agent negotiation with the Ehling-Schulz risk negotiation framework and is demonstrated in two role-played One Health scenarios.","lead":"The paper presents a human-supervised framework in which large language model agents simulate negotiations between stakeholders in One Health risk analysis, demonstrated on a biopesticide decision and a wild boar control decision. A reader interested in practical AI tools for group decision-making will find a concrete, open-source workflow, but the evidence is a role-played proof of concept, not a controlled evaluation.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Proof-of-concept relies on project-team members role-playing stakeholders; observed consensus does not generalize to real multi-stakeholder settings without validation.","rationale":"The reader's weakest assumption is exactly the load-bearing concern I would flag. The central claim—that the workflow helps stakeholder groups reach consensus—is supported only by two exercises where project team members role-played stakeholders. This is a legitimate proof-of-concept design, but the paper's wording ('stakeholders were able to successfully complete the risk negotiation') and the abstract's 'demonstrates the potential' generalize beyond the evidence. The role-players are experts with a stake in the outcome, so the observed consensus could reflect the research team's collaborative dynamics rather than real stakeholder negotiation dynamics, which often include power asymmetries, vetoes, and entrenched interests. The absence of a baseline or quantitative measure of 'mitigating information overload' compounds this, but the role-play issue is the load-bearing one because it undermines the external validity of the demonstration. A controlled run with real stakeholders would settle whether the framework works beyond the authors' team. I also credit the paper for providing open-source code and archived artifacts, which make such a test feasible; the concern is about generalization, not internal reproducibility. Therefore, the CONDITIONAL verdict stands; no change is needed.","tokens_in":16045,"tokens_out":4901,"duration_ms":56945,"concrete_test":"Conduct both case scenarios with actual stakeholder representatives (e.g., farming union, consumer association, food safety authority, hunting association, animal-welfare NGO) using the same scripts, scoring templates, and LLM simulation, with an independent moderator and pre-registered outcome measures (time to consensus, deal acceptability, participant satisfaction, perceived usefulness). Compare outcomes against a human-only discussion of the same case material. If real stakeholders fail to reach consensus, reject the LLM-proposed deals, or rate the process no better than human-only discussion, the role-played proof-of-concept does not generalize.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central demonstration—that 'stakeholders were able to successfully complete the risk negotiation'—rests on exercises in which 'project team members assumed roles representing one of three stakeholder groups in each scenario' (Methods, Case scenarios and practical approach). The people playing farmers, consumers, hunters, and animal-protection representatives are co-authors/experts with domain knowledge, but they are not actual stakeholders: they bring their own interests, prior relationships, and a vested interest in the project's success. The observed consensus is therefore not evidence that real multi-sectoral stakeholder groups—who may have asymmetric power, veto rights, entrenched positions, and conflicting institutional mandates—can use the workflow to reach agreement. The Discussion extends the result to 'stakeholders' without this caveat, and the abstract's claim of 'mitigating information overload' and 'augmenting decision-making' is asserted, not measured. Because the strongest claim is about stakeholder consensus, the role-play substitution is the load-bearing weak point: if real stakeholders behave differently, the proof-of-concept does not support the generalized claims.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper presents an AI-assisted negotiation framework for One Health risk analysis, combining LLM-based agents with a human-in-the-loop (HIL) approach. The workflow operationalizes a previously proposed six-step negotiation-centered risk analysis process, with a focus on steps (iii) risk assessment/valuation and (iv) risk negotiation. Proof-of-concept implementations are described for two scenarios: use of Bacillus thuringiensis as a biopesticide and wild boar population control. In each, project team members role-played three stakeholder groups, provided position papers and confidential preference scores, and used LLM-generated issue/option lists and simulated deal distributions to negotiate a final consensus. The authors report that in both scenarios consensus was reached within the two-hour time constraint. The paper also provides open-source code, a Zenodo repository with templates and results, and detailed supplementary protocols.","tokens_in":16273,"tokens_out":3593,"duration_ms":43242,"significance":"If the framework is validated, it would offer a reproducible, open-source tool for structuring multi-stakeholder One Health negotiations under time constraints. The paper makes several contributions: a concrete step-by-step pipeline linking LLM-based multi-agent simulation to a defined human oversight process; two detailed, realistic One Health case scenarios; and public release of code, templates, and data. The strongest strength is the explicit procedural formalization of steps 3a-3e and step 4, which others could adopt or adapt. However, the current evidence is proof-of-concept only: the 'successful consensus' outcome is based on role-play by project team members, with no baseline, no quantitative outcome measure, and no external validation. The significance of the claimed results is therefore contingent on future validation with real stakeholder groups.","major_comments":[{"comment":"The paper's central demonstration—that stakeholders successfully completed risk negotiation—rests on exercises where 'project team members assumed roles representing one of three stakeholder groups.' Participants are co-authors/experts with a vested interest in the project's success, which is a selection bias. Real multi-sectoral stakeholders with asymmetric power, veto rights, and entrenched institutional mandates may behave differently. The Discussion generalizes without this caveat, stating 'in both of our case scenarios the stakeholders were able to successfully complete the risk negotiation,' and the abstract extends to 'stakeholders.' Please either temper the claims to a role-play proof-of-concept or provide external validation with actual stakeholders.","section":"Methods, 'Case scenarios and practical approach'"},{"comment":"The abstract claims the framework 'mitigates information overload and augments decision-making process under time constraints,' and the Discussion states that stakeholders 'successfully complete the risk negotiation within the time-constraints requirement.' No baseline, control condition, or quantitative outcome is reported. The only measured outcome is self-reported acceptance by the participants themselves. Concrete metrics are needed—e.g., time to consensus, number of rounds, agreement scores, satisfaction, or comparison with an unaided manual negotiation—or the claims must be restricted to 'the pipeline ran end-to-end in two simulated scenarios.'","section":"Abstract and Discussion (claims of mitigation and consensus)"},{"comment":"The same individuals who supply the confidential preference scores (step iiie) are the ones who discuss and approve the simulated deals (step iv), and those scores are directly used to prompt the LLM agents. The 'suggested equilibrium' is therefore a function of the very inputs used to validate it. This is not a fatal flaw for a decision-support tool, but it means the observed consensus cannot be interpreted as an independent validation of the LLM-based negotiation. Please explicitly frame the results as preference aggregation followed by human discussion, and separate any claims about the LLM's negotiation ability from claims about the overall workflow's usefulness.","section":"Methods, steps (iiie) and (iv); Figure 2"}],"minor_comments":[{"comment":"Typo: 'equillibrium' should be 'equilibrium.'","section":"Results, step (iv)"},{"comment":"Typo: 'Insitute' should be 'Institute.'","section":"Affiliation 7"},{"comment":"Typo: 'Higgings' should likely be 'Higgins.'","section":"References, ref. 11"},{"comment":"In the quick guide, 'areas where a comprise is feasible' should be 'compromise.'","section":"Supplementary Text S3, scoring guide"},{"comment":"Formatting issue: 'issue C was give n highest priority' has a stray space; please correct.","section":"Supplementary Text S6"},{"comment":"'non-zero game' should be 'non-zero-sum game' to match standard terminology.","section":"Methods, step 4 description"},{"comment":"The term 'Nash equilibrium' is used loosely for a cooperative negotiation game; consider clarifying that the model searches for a compromise point rather than a formal Nash equilibrium, or define the term as used here.","section":"Figure 2 caption"},{"comment":"The claim of reproducibility from 'multiple iterations' would be strengthened by reporting random seeds or variance across runs; Fig. S1-S4 show distributions but no statistical summary.","section":"Supplementary Text S2, step 4"}],"recommendation":"major_revision","confidential_remarks":"The validation is entirely internal to the author team: the 'stakeholders' are project team members, several of whom are also co-authors. This is mentioned only in passing in Methods and is not reflected in the abstract or Discussion, where 'stakeholders' could be misread as real external actors. The manuscript would be more appropriately framed as a proof-of-concept or application/software note rather than an empirical demonstration of stakeholder consensus. Please also consider whether the journal's scope aligns with a paper whose primary contribution is a procedural linkage between an existing framework and LLM tooling."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nThe thing to know: this is a solid, clearly described proof-of-concept for wiring LLM negotiation agents into the One Health risk-negotiation workflow. The contribution is the procedural integration, not a new scientific result, and the authors say as much. If you want a concrete template for running an LLM-assisted multi-stakeholder negotiation exercise, this is the most usable one I've seen in this space.\n\nWhat's genuinely good: the pipeline is concrete and transparent. They take the Ehling-Schulz six-step framework, operationalize steps 3 and 4 into a repeatable process: position papers, issue/option generation (single-shot or multi-agent), confidential preference scoring with a fixed point budget, LLM-driven deal proposals using the Abdelnabi et al. game, moderator-role experiments, and post-analysis of deal distributions. They ship code, templates, scoring sheets, and results on Zenodo/GitHub. The two case studies are plausible and the supplementary material is rich enough that a group could reproduce the exercise without emailing the authors. They also name their limitations on the LLM side (hallucination, bias) and explicitly say the main added value is the linkage, not the components.\n\nWhere it's soft. The evaluation is a role-play exercise: project team members played the stakeholders. That makes the 'stakeholders reached consensus' claim illustrative, not evidential. The paper should carry that caveat into the abstract and discussion; right now the discussion says 'stakeholders were able to successfully complete' without noting they were team members. The second soft spot is the missing baseline and outcome measures. Claims about mitigating information overload and augmenting decision-making are plausible but unmeasured. Third, the quasi-circularity the reader flagged is real but not fatal: the LLM is given the participants' own scores, so the 'equilibrium' is just a structured aggregation of stated preferences. That's actually the point of the HIL design, but the paper shouldn't present the simulated equilibrium as independent validation.\n\nOverall: this is a useful engineering-plus-methods paper, honestly scoped if you read the methods. It deserves serious peer review — the workflow is reproducible and testable, and the limitations are the kind that revision can address. I'd suggest the reviewers ask for a more careful framing of what the proof-of-concept does and does not show, and preferably one real-stakeholder pilot or a clear statement that such a pilot is the next step.","headline":"Reproducible LLM-assisted negotiation workflow for One Health, honestly framed as a proof-of-concept; role-play evaluation limits the consensus claims.","tokens_in":16795,"tokens_out":2401,"would_cite":false,"duration_ms":26982,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper claims that a human-supervised workflow of LLM-based negotiating agents can help multi-sector One Health stakeholders reach consensus on contested risk-management choices, and it supports this with two proof-of-concept case scena","keywords":["One Health","risk analysis","risk negotiation","large language models","multi-agent systems","human-in-the-loop","consensus-building","proof of concept"],"falsifier":"Run the same two scenarios with authentic stakeholder representatives—regulators, farmers, hunters, and animal-welfare advocates—under the same two-hour constraint and check whether they reach a deal and whether that deal falls inside the simulated deal distribution. If such groups reject all machine-suggested deals or fail to converge, the central claim would not transfer outside the role-play setting.","tokens_in":15980,"feed_emoji":"🤝","tokens_out":7704,"duration_ms":87411,"temperature":0.7,"pith_summary":"The paper is trying to establish that large language models can make multi-stakeholder risk negotiation in One Health workable under real-world time and information constraints. It proposes a human-supervised workflow in which LLM-based agents, each representing a stakeholder, simulate a cooperative negotiation over risk-management options; the resulting deal proposals are then discussed and adjusted by the human stakeholders. The authors tested this in two cases—whether to keep using a biopesticide and whether to restrict wild boar hunting—and report that the role-playing stakeholder groups reached consensus within the two-hour sessions. If this holds, it gives resource-limited organizations an open-source way to support cross-sectoral decisions that currently lack structured negotiation tools.","feed_headline":"AI agents broker consensus in two One Health risk cases","feed_subtitle":"Human-supervised language-model agents turned stakeholder disputes into agreed deals in two test scenarios.","key_machinery":"The engine is a human-supervised multi-agent negotiation game. Each LLM-based agent is prompted with a stakeholder's position paper, the agreed issue/option list, confidential preference scores, and game rules; rounds proceed with agents endorsing existing deals or proposing new ones, and a moderator's opening suggestion shifts which deals emerge. A post-analysis layer draws each proposed deal as a line over preference surfaces and exposes the 'scratchpad' rationale behind proposals, so humans can see exactly which issue blocked agreement. The load-bearing device is the 100-point scoring template: it converts qualitative positions into numbers that agents can optimize over while keeping indi","core_discovery":"The authors claim to have operationalized negotiation-centered risk analysis by combining an LLM-based multi-agent negotiation game with a human-in-the-loop review stage. In their procedure, each stakeholder first writes a position paper, then the group agrees on a fixed list of issues and options, and each stakeholder privately assigns a 100-point budget across issues and options to express importance and flexibility. These confidential scores are fed into agents that negotiate a non-zero-sum game over multiple rounds, producing a distribution of proposed 'deals'—each a package picking one option per issue. Stakeholders then inspect the simulated deals and their rationales, may choose to di","pith_inferences":["A fair next test would compare this pipeline against conventional facilitated round-tables using real stakeholder groups, measuring time-to-agreement, satisfaction, and whether agreements hold after the session.","Because the human-approved final deals can diverge from the simulated equilibrium, the framework is best read as deliberation support for compromise discovery rather than equilibrium computation; quantifying that divergence would clarify what the simulation actually predicts.","The role-played stakeholder design means the reported consensus is a usability proof, not evidence about how real power asymmetries, vetoes, and entrenched interests would play out; trials with actual regulators, farmers, hunters, and advocates would be the natural next step.","The same multi-agent setup could be extended to adversarial or bad-faith negotiation scenarios, letting groups stress-test their consensus against sabotage and strategic misrepresentation before real talks."],"forward_implications":["Groups can move from position papers to a concrete deal package within a two-hour session, compressing problem formulation, valuation, and negotiation into one exercise.","Because the implementation is open source, web-based, and not tied to a particular LLM, organizations with limited AI resources can adapt it to their own risk topics.","The same pipeline can be run without human discussion to pre-explore possible negotiation outcomes, serving as a rehearsal for real round-tables.","Controlled disclosure of partial scores—revealing only the issues one cares about most—can unlock compromises that fully secret preferences would block.","The moderator's identity measurably changes which deals are proposed, so facilitator selection is a substantive design choice, not an administrative detail."],"fun_headline_variants":["LLM agents broker consensus in One Health risks","AI negotiators resolve biopesticide and wildlife disputes","Language models turn stakeholder disputes into deals","LLM-driven negotiation aids One Health risk decisions","AI agents help balance interests in health scenarios"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The proof-of-concept depends on project team members role-playing the stakeholders; if real stakeholders with power asymmetries, veto rights, and entrenched interests negotiate differently, the observed two-hour consensuses do not establish the framework's usefulness.","fun_headline_variants_meta":{"raw":{"variants":["LLM agents broker consensus in One Health risks","AI negotiators resolve biopesticide and wildlife disputes","Language models turn stakeholder disputes into deals","LLM-driven negotiation aids One Health risk decisions","AI agents help balance interests in health scenarios"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000136,"raw_usage":{"total_tokens":983,"prompt_tokens":743,"completion_tokens":240,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":487,"completion_tokens_details":{"reasoning_tokens":184}},"tokens_in":487,"tokens_out":240,"duration_ms":3589,"temperature":1.0,"reasoning_tokens":184,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-04T18:29:24.666494+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run the same two scenarios with authentic stakeholder representatives—regulators, farmers, hunters, and animal-welfare advocates—under the same two-hour constraint and check whether they reach a deal and whether that deal falls inside the simulated deal distribution. If such groups reject all machine-suggested deals or fail to converge, the central claim would not transfer outside the role-play setting.","supporting_citations":[],"review_version":1}