# Available but Unclaimed: An Empirical Study of Human-AI Synergy

arXiv: https://arxiv.org/abs/2609.16793 · HTML: https://mlsanity.com/paper/2609.16793/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 83 references that are in the map.
- First posted on arXiv 15 Sep 2026.

## Builds on (most cited first, 20 of 20)

- [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://mlsanity.com/paper/2206.04615/) (2022)
- [Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance](https://mlsanity.com/paper/2006.14779/) (2020)
- [Measuring abstract reasoning in neural networks](https://mlsanity.com/paper/1807.04225/) (2018)
- [Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations](https://mlsanity.com/paper/2411.00640/) (2024)
- [Measuring what Matters: Construct Validity in Large Language Model Benchmarks](https://mlsanity.com/paper/2511.04703/) (2025)
- [Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertainty](https://mlsanity.com/paper/2401.06730/) (2024)
- [When combinations of humans and AI are useful: A systematic review and meta-analysis](https://mlsanity.com/paper/2405.06087/) (2024)
- [Algorithmic Monoculture and Social Welfare](https://mlsanity.com/paper/2101.05853/) (2021)
- [Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations](https://mlsanity.com/paper/2302.02187/) (2023)
- ["I'm Not Sure, But...": Examining the Impact of Large Language Models' Uncertainty Expression on User Reliance and Trust](https://mlsanity.com/paper/2405.00623/) (2024)
- [Does Spatial Cognition Emerge in Frontier Models?](https://mlsanity.com/paper/2410.06468/) (2024)
- [Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence](https://mlsanity.com/paper/2404.00029/) (2024)
- [ChatBench: From Static Benchmarks to Human-AI Evaluation](https://mlsanity.com/paper/2504.07114/) (2025)
- [Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making](https://mlsanity.com/paper/2208.07960/) (2022)
- [Exploring Reasoning Biases in Large Language Models Through Syllogism: Insights from the NeuBAROCO Dataset](https://mlsanity.com/paper/2408.04403/) (2024)
- [Take It, Leave It, or Fix It: Measuring Productivity and Trust in Human-AI Collaboration](https://mlsanity.com/paper/2402.18498/) (2024)
- [Performance and Metacognition Disconnect when Reasoning in Human-AI Interaction](https://mlsanity.com/paper/2409.16708/) (2024)
- [People over trust AI-generated medical responses and view them to be as valid as doctors, despite low accuracy](https://mlsanity.com/paper/2408.15266/) (2024)
- [Algorithmic Monocultures in Hiring](https://mlsanity.com/paper/2605.27371/) (2026)
- [CentaurBench: Benchmarking LLM Capabilities on Augmenting vs. Automating Real-World Work Tasks](https://mlsanity.com/paper/2608.18554/) (2026)

## Abstract (arXiv)

People increasingly reason with large language models (LLMs), yet complementary capabilities do not guarantee outperforming both components. In a between-subjects study, participants (N=535) solved a 40-item battery of matrix reasoning, mental rotation, syllogisms, and letter-string analogies, unaided or with GPT-5.6-Luna, Claude Opus 4.8, Gemini 3.6 Flash, or Kimi K3. Each assisted trial required consultation with the model. Each model answered every item alone 100 times under matched elicitation. The assisted-unaided accuracy difference increased with item-level LLM competence. Deference varied across tasks and increased with competence within tasks. Post-advice confidence distinguished correct from incorrect answers less strongly than unaided confidence. In a reference comparison, about half the increase in LLM accuracy carried through to assisted accuracy. How much of that accuracy gain reached participants differed across the models. These findings motivate evaluating LLMs in interaction with humans and designing support for selective deference that preserves independent reasoning.