{"ID":5676765,"CreatedAt":"2026-07-03T03:29:23.032456456Z","UpdatedAt":"2026-07-07T01:06:03.009715918Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.02467","arxiv_id":"2607.02467","title":"Human Capital, Not Model Benchmarks, Predicts Hybrid Intelligence in Forecasting","abstract":"Whether pairing people with AI helps or hurts is usually reported as a single average effect. Using a real-money prediction market (Polymarket) as an objective, externally resolved benchmark, this pilot shows that the value of human-AI collaboration depends on a specific, measurable form of human capital. Analyzed at the level of the individual forecaster, hybrid performance is trimodal: most people either deferred to the model (matching it) or used it to rubber-stamp a prior guess (performing worse than the model alone), while a minority engaged in genuine complementary reasoning and reached accuracy matching or even exceeding (i.e., lower error than) the market itself. Collaborative traits (perspective-taking, intellectual humility, and curiosity) rather than raw cognitive ability or model benchmarks, distinguished who reached that mode. The results are preliminary but statistically robust, and motivate a pre-registered replication now in preparation.","short_abstract":"Whether pairing people with AI helps or hurts is usually reported as a single average effect. Using a real-money prediction market (Polymarket) as an objective, externally resolved benchmark, this pilot shows that the value of human-AI collaboration depends on a specific, measurable form of human capital. Analyzed at t...","url_abs":"https://arxiv.org/abs/2607.02467","url_pdf":"https://arxiv.org/pdf/2607.02467v1","authors":"[\"Vivienne Ming\"]","published":"2026-07-02T17:34:37Z","proceeding":"cs.CY","tasks":"[\"cs.CY\",\"cs.AI\"]","methods":"[]","has_code":false}