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

• URL: http://arxiv.org/abs/2609.16793v1
• Date: Tue, 15 Sep 2026 08:01:37 GMT
• Title: Available but Unclaimed: An Empirical Study of Human-AI Synergy
• Abstract summary: People increasingly reason with large language models (LLMs)<n>In a study, participants 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.<n>The assisted-unaided accuracy difference increased with item-level LLM competence.
• Score: 27.380163033481015
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Beyond the AI Tutor: Social Learning with LLM Agents [9.933416370629923]
  We present two experiments spanning distinct learning contexts.<n>We investigate whether the move from one-on-one AI tutoring toward richer agent configurations can unlock the collaborative and observational benefits long documented in human social learning research.
  arXiv  Detail & Related papers  (2026-04-03T03:17:30Z)
• Verifying Large Language Models' Reasoning Paths via Correlation Matrix Rank [71.09032766271493]
  Large language models (LLMs) are prone to errors and hallucinations.<n>How to check their outputs effectively and efficiently has become a critical problem in their applications.
  arXiv  Detail & Related papers  (2025-10-28T11:01:10Z)
• Multi-Agent Evolve: LLM Self-Improve through Co-evolution [53.00458074754831]
  Reinforcement Learning (RL) has demonstrated significant potential in enhancing the reasoning capabilities of large language models (LLMs)<n>Recent Self-Play RL methods, inspired by the success of the paradigm in games and Go, aim to enhance LLM reasoning capabilities without human-annotated data.<n>We propose Multi-Agent Evolve (MAE), a framework that enables LLMs to self-evolve in solving diverse tasks, including mathematics, reasoning, and general knowledge Q&A.
  arXiv  Detail & Related papers  (2025-10-27T17:58:02Z)
• Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems [55.6590601898194]
  Large Language Models (LLMs) have demonstrated exceptional capabilities, yet selecting the most reliable response from multiple LLMs remains a challenge.<n>Existing approaches often depend on costly external verifiers, human evaluators, or self-consistency techniques that require multiple samples from a single model.<n>We propose a principled, novel and computationally efficient method to select the best response from multiple different LLMs using a calibrated log-likelihood score.
  arXiv  Detail & Related papers  (2025-09-30T01:25:19Z)
• Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers [8.936182649298766]
  Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding, reasoning, and problem-solving.<n>This paper examines the reasoning capabilities of contemporary LLMs, analyzing their strengths, limitations, and potential for improvement.
  arXiv  Detail & Related papers  (2025-05-02T16:16:17Z)
• CLR-Bench: Evaluating Large Language Models in College-level Reasoning [17.081788240112417]
  Large language models (LLMs) have demonstrated their remarkable performance across various language understanding tasks. We present CLR-Bench to comprehensively evaluate the LLMs in complex college-level reasoning.
  arXiv  Detail & Related papers  (2024-10-23T04:55:08Z)
• GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation [108.2008975785364]
  Graph Inspired Veracity Extrapolation (GIVE) is a novel reasoning method that merges parametric and non-parametric memories to improve accurate reasoning with minimal external input.<n>GIVE guides the LLM agent to select the most pertinent expert data (observe), engage in query-specific divergent thinking (reflect), and then synthesize this information to produce the final output (speak)
  arXiv  Detail & Related papers  (2024-10-11T03:05:06Z)
• How Easy is It to Fool Your Multimodal LLMs? An Empirical Analysis on Deceptive Prompts [54.07541591018305]
  We present MAD-Bench, a benchmark that contains 1000 test samples divided into 5 categories, such as non-existent objects, count of objects, and spatial relationship. We provide a comprehensive analysis of popular MLLMs, ranging from GPT-4v, Reka, Gemini-Pro, to open-sourced models, such as LLaVA-NeXT and MiniCPM-Llama3. While GPT-4o achieves 82.82% accuracy on MAD-Bench, the accuracy of any other model in our experiments ranges from 9% to 50%.
  arXiv  Detail & Related papers  (2024-02-20T18:31:27Z)
• Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs [60.61002524947733]
  Previous confidence elicitation methods rely on white-box access to internal model information or model fine-tuning. This leads to a growing need to explore the untapped area of black-box approaches for uncertainty estimation. We define a systematic framework with three components: prompting strategies for eliciting verbalized confidence, sampling methods for generating multiple responses, and aggregation techniques for computing consistency.
  arXiv  Detail & Related papers  (2023-06-22T17:31:44Z)
• Boosting Theory-of-Mind Performance in Large Language Models via Prompting [2.538209532048867]
  This study measures the ToM performance of GPT-4 and three GPT-3.5 variants. We investigated the effectiveness of in-context learning in improving ToM comprehension.
  arXiv  Detail & Related papers  (2023-04-22T22:50:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.