What Makes a Social Agent's Interactions Effective? Learning from Verifiable Outcomes in Paired Worlds
Effective social interaction requires more than fluent responses: agents must reach plans that fit the participants' needs and circumstances. Existing evaluations of social agents mainly rely on LLM judges that rate the conversation, without checking the resulting plan. We introduce HICO-Bench, a benchmark of 180 everyday coordination scenarios, such as resource allocation, scheduling, and authorization, in which two agents with private information converse and then independently record the plan they agreed on. A program checks Feasibility (the two records match and satisfy both participants' private conditions), and an LLM judge checks Integrity (the conversation supports the recorded pl...
| Authors | Caixin Kang, Ke Wang, Yuchuan Wu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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