SoCRATES: Towards Reliable Automated Evaluation of Proactive LLM Mediation across Domains and Socio-cognitive Variations

Hayoon Park; Hwanjun Song; Hyeonseong Park; Jeonghwan Choi; Taewon Yun; Yeeun Choi

arxiv: 2606.05563 · v1 · pith:HXBCQ2JHnew · submitted 2026-06-04 · 💻 cs.AI · cs.CL

SoCRATES: Towards Reliable Automated Evaluation of Proactive LLM Mediation across Domains and Socio-cognitive Variations

Taewon Yun , Hyeonseong Park , Jeonghwan Choi , Hayoon Park , Yeeun Choi , Hwanjun Song This is my paper

Pith reviewed 2026-06-28 02:10 UTC · model grok-4.3

classification 💻 cs.AI cs.CL

keywords SoCRATESLLM mediationautomated evaluationsocio-cognitive variationsproactive mediatorsconsensus gapmulti-domain benchmarktopic-localized scoring

0 comments

The pith

Even the strongest LLM mediators close only about a third of the unmediated consensus gap across diverse realistic testbeds.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces SoCRATES, a benchmark that builds mediation scenarios from real conflicts across eight domains using an agentic pipeline. It tests models on five socio-cognitive axes such as emotional reactivity and cultural identity while scoring only the turns that advance each topic. A topic-localized evaluator aligns with human experts at 0.82, more than doubling prior per-turn methods. When eight frontier models are tested, the best ones still leave most of the gap to consensus unclosed, and results shift sharply depending on the axis. Readers care because this setup reveals where current systems would fail in varied real disputes rather than in narrow expert-written cases.

Core claim

SoCRATES constructs scenarios from real conflicts through an agentic pipeline across eight domains, probes five socio-cognitive adaptation axes (strategic posture, party composition, history length, emotional reactivity, and cultural identity), and scores each topic only on the turns that advance it via a topic-localized evaluator that reaches 0.82 alignment with human experts. Benchmarking eight frontier LLMs shows that even the strongest mediator closes only about a third of the unmediated consensus gap under diverse and realistic testbeds, with performance varying sharply by socio-cognitive axis.

What carries the argument

The SoCRATES benchmark, which generates multi-domain scenarios from real conflicts via an agentic pipeline, applies probes across five socio-cognitive axes, and uses topic-localized evaluation to isolate mediation quality.

Load-bearing premise

The agentic pipeline produces scenarios that faithfully represent real-world conflicts and the topic-localized evaluator with 0.82 human alignment accurately captures mediation quality without introducing new biases.

What would settle it

Running human participants through the same generated scenarios with the tested LLMs as mediators and measuring the actual fraction of consensus gap closed compared to the benchmark scores.

Figures

Figures reproduced from arXiv: 2606.05563 by Hayoon Park, Hwanjun Song, Hyeonseong Park, Jeonghwan Choi, Taewon Yun, Yeeun Choi.

**Figure 1.** Figure 1: Overview of SoCRATES: agentic scenario curation grounds scenarios in a real conflict, socio-cognitive probing expands scenarios along five axes to expose where mediators fails, and topiclocalized evaluation scores each trajectory with three metrics to quantify the mediator’s contribution. through three stages, curating real-grounded scenarios, probing them along socio-cognitive axes, and evaluating trajec… view at source ↗

**Figure 2.** Figure 2: Mediator adaptation across general condition and five socio-cognitive axes, measured by [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Figure 3: Consensus gain shift from the general (unperturbed) condition along three axes: (a) strategic [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Intervention Effectiveness over conversation progress, where turns are mapped to a 0–100% [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: Trend comparison of consensus score trajecto [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗

**Figure 6.** Figure 6: Mediator adaptation of three mediators under [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗

**Figure 7.** Figure 7: Example of annotation template for pairwise simulation fidelity evaluation. [PITH_FULL_IMAGE:figures/full_fig_p027_7.png] view at source ↗

**Figure 8.** Figure 8: Example of annotation template for consensus score evaluation. [PITH_FULL_IMAGE:figures/full_fig_p028_8.png] view at source ↗

read the original abstract

Evaluating LLM mediators remains challenging, as mediation unfolds as a real-time trajectory shaped by disputants' shifting emotions, intentions, and context. Existing testbeds rely on a few expert-authored domains, vary mainly strategic posture, and score every turn against every topic, introducing off-topic noise. We introduce SoCRATES, a benchmark for evaluating proactive LLM mediators in realistic, multi-domain testbeds. It constructs scenarios from real conflicts through an agentic pipeline across eight domains, probes five socio-cognitive adaptation axes (strategic posture, party composition, history length, emotional reactivity, and cultural identity), and scores each topic only on the turns that advance it via a topic-localized evaluator. The evaluator reaches 0.82 alignment with human experts, more than doubling a per-turn baseline. Benchmarking eight frontier LLMs, we find that even the strongest mediator closes only about a third of the unmediated consensus gap under diverse and realistic testbeds, with performance varying sharply by socio-cognitive axis, highlighting that progress lies in social adaptation to diverse conditions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SoCRATES builds a benchmark from real conflicts with socio-cognitive tests and localized scoring, but the one-third gap closure result rests on unshown validation of scenario fidelity and evaluator accuracy.

read the letter

The main takeaway is that this paper introduces SoCRATES as a benchmark that pulls scenarios from real conflicts across eight domains, tests LLMs on five socio-cognitive axes including emotional reactivity and cultural identity, and uses topic-localized scoring instead of blanket per-turn evaluation. The evaluator hits 0.82 human alignment, and the key finding is that even the strongest model closes only about a third of the unmediated consensus gap, with sharp differences across axes.

The work does a reasonable job identifying shortcomings in prior testbeds, such as reliance on a few expert scenarios and off-topic scoring noise. The agentic pipeline for scenario construction and the shift to localized evaluation are concrete steps that address those issues. Reporting the alignment number and the performance variation gives readers something specific to consider.

The soft spots sit in the missing validation. The abstract states the pipeline produces faithful scenarios and the evaluator avoids noise, but supplies no checks that the generated cases preserve real conflict statistics on the five axes or that localization does not introduce topic-specific biases. The one-third gap result and the axis-wise differences depend directly on those assumptions holding. The stress-test concern about pipeline fidelity and potential new biases in the evaluator is fair based on the abstract alone.

This is for researchers building or evaluating LLM systems for negotiation, moderation, or cross-cultural tasks. A reader focused on practical benchmarks would get value from the construction approach and the socio-cognitive probing. It deserves a serious referee because the evaluation problem is real and the benchmark design is structured, though the paper will need to show the validation steps and any statistical controls to be convincing.

I would send it to peer review with requests for the missing methods details.

Referee Report

2 major / 0 minor

Summary. The paper introduces SoCRATES, a benchmark for evaluating proactive LLM mediators across realistic multi-domain scenarios. It builds scenarios from real conflicts via an agentic pipeline spanning eight domains, probes five socio-cognitive axes (strategic posture, party composition, history length, emotional reactivity, cultural identity), and introduces a topic-localized evaluator that scores only relevant turns and reaches 0.82 alignment with human experts (more than double a per-turn baseline). Benchmarking eight frontier LLMs shows that even the strongest mediator closes only about one-third of the unmediated consensus gap, with sharp variation by socio-cognitive axis.

Significance. If the benchmark construction and evaluator hold, the work supplies a more realistic, multi-axis testbed than prior expert-authored single-domain setups and supplies falsifiable predictions about LLM mediator limitations under socio-cognitive variation. The agentic pipeline from real conflicts and the topic-localized scoring are concrete strengths that could support reproducible follow-on studies.

major comments (2)

[Abstract] Abstract: the claim that the topic-localized evaluator reaches 0.82 human alignment (more than doubling the per-turn baseline) supplies no details on the number of experts, annotation protocol, inter-rater reliability, or statistical controls. This measurement is load-bearing for the reliability of the gap-closure metric and the headline result.
[Abstract] Abstract / benchmark construction: no quantitative evidence is supplied that the agentic pipeline preserves the statistical structure of real disputes on the five probed socio-cognitive axes. Without such validation the claim that the testbeds are 'diverse and realistic' remains unanchored and directly affects the interpretation of the one-third gap-closure finding.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and will revise the manuscript to strengthen the presentation of the evaluator validation and benchmark construction.

read point-by-point responses

Referee: [Abstract] Abstract: the claim that the topic-localized evaluator reaches 0.82 human alignment (more than doubling the per-turn baseline) supplies no details on the number of experts, annotation protocol, inter-rater reliability, or statistical controls. This measurement is load-bearing for the reliability of the gap-closure metric and the headline result.

Authors: We agree that the abstract would benefit from additional details on this key measurement. In the revised version we will expand the abstract to briefly report the number of experts, a summary of the annotation protocol, the inter-rater reliability statistic, and the statistical controls employed. These elements are already documented in the main text; we will ensure they are also visible at the abstract level given the centrality of the 0.82 alignment figure to the gap-closure results. revision: yes
Referee: [Abstract] Abstract / benchmark construction: no quantitative evidence is supplied that the agentic pipeline preserves the statistical structure of real disputes on the five probed socio-cognitive axes. Without such validation the claim that the testbeds are 'diverse and realistic' remains unanchored and directly affects the interpretation of the one-third gap-closure finding.

Authors: We acknowledge that the manuscript does not currently supply quantitative comparisons (e.g., distributional statistics on the five socio-cognitive axes) between the generated scenarios and the original real-world disputes. The agentic pipeline is designed to instantiate the axes from real conflict sources, but explicit validation of statistical fidelity is absent. In revision we will add such quantitative evidence where feasible or, if data limitations prevent it, qualify the 'diverse and realistic' phrasing and discuss implications for interpreting the one-third gap-closure result. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark construction with empirical evaluation only

full rationale

The paper constructs a benchmark (SoCRATES) via an agentic pipeline from real conflicts, defines five socio-cognitive axes, and reports an empirical evaluator alignment of 0.82 with humans plus LLM performance metrics (e.g., closing ~1/3 of consensus gap). No equations, fitted parameters, or predictions are described that reduce to inputs by construction. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The work is self-contained as an empirical comparison; the 0.82 figure is a reported correlation, not a self-referential derivation. This matches the default non-circular case for benchmark papers.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract supplies no information on free parameters, axioms, or invented entities used in the benchmark construction or evaluation.

pith-pipeline@v0.9.1-grok · 5735 in / 1108 out tokens · 36989 ms · 2026-06-28T02:10:12.253471+00:00 · methodology

discussion (0)

Reference graph

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emits a single utterance, which is inserted before the next party speaks. H Additional Analysis H.1 Intervention Analysis Type Mediator IF (%) FI (%) Prop. Gemini-3.1-FL 22.6 32.3 GPT-5.4-mini 22.6 31.0 Open Source DeepSeek-V3.2 16.1 42.8 Qwen3-235B 20.8 39.5 Nemotron-3-120B 14.6 45.6 Solar-Pro-3 32.3 26.9 Gemma-4-26B 16.4 37.3 Qwen3-30B 31.1 25.3 Table 7...

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2019