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

2026-03-17 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

SoCRATES is a new benchmark designed to reliably evaluate proactive LLM mediators across diverse conflict domains and socio-cognitive variations. Developed by researchers at KAIST and Chungnam National University, this system addresses limitations of prior testbeds by constructing scenarios from real conflicts via an agentic pipeline across eight distinct domains. It systematically probes five socio-cognitive adaptation axes: strategic posture, party composition, history length, emotional reactivity, and cultural identity. A key innovation is its topic-localized evaluator, which scores only turns advancing specific topics, achieving a 0.82 Pearson correlation with human experts. Benchmarking eight frontier LLMs revealed that the top-performing mediator closed only about 34.4% of the unmediated consensus gap, with performance significantly influenced by socio-cognitive factors.

Key takeaway

For NLP Engineers developing LLM mediators, you must move beyond single-domain benchmarks. Your evaluation strategy should incorporate diverse socio-cognitive conditions and multi-turn, topic-localized scoring to accurately assess model performance. Current frontier LLMs struggle with social adaptation, closing only a third of the consensus gap in realistic settings. Focus your development on improving an LLM's ability to adapt intervention timing and content to specific strategic, emotional, and cultural demands.

Key insights

Effective LLM mediation requires multi-dimensional evaluation across diverse socio-cognitive contexts to reveal adaptation gaps.

Principles

• Mediation quality hinges on social adaptation, not uniform capability.
• Evaluating LLMs requires realistic, multi-domain scenarios.
• Topic-localized scoring improves evaluation reliability.

Method

SoCRATES uses a three-stage pipeline: agentic scenario curation from real conflicts, socio-cognitive probing across five axes, and topic-localized evaluation with three metrics (consensus gain, timeliness, effectiveness).

In practice

• Use multi-domain testbeds for LLM mediator evaluation.
• Isolate socio-cognitive axes to diagnose mediator failures.
• Implement topic-localized scoring for trajectory evaluation.

Topics

• LLM Mediation
• Automated Evaluation
• Socio-cognitive Adaptation
• Conflict Resolution
• Benchmark Development
• Multi-domain Scenarios

Best for: Research Scientist, AI Scientist, NLP Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.