Reasoning: Research Area — Prolific Citations Library

Discover 6 peer-reviewed studies in Reasoning (2024–2026). Explore research findings powered by Prolific's diverse participant panel.

This page lists 6 peer-reviewed papers in the research area of Reasoning in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (6 of 6)

Bayesian teaching enables probabilistic reasoning in large language models

Authors: L Qiu, F Sha, K Allen, Y Kim, T Linzen, S van Steenkiste

Year: 2026

Published in: Nature …, 2026 - nature.com

Institution: Meta, Google DeepMind, Massachusetts Institute of Technology, Google Research, Google

Research Area: Probabilistic reasoning, Bayesian cognition, Neural language models, Reasoning, AI Evaluations

Discipline: Machine learning, Artificial intelligence

This paper sits at the intersection of machine learning and computational cognitive science, showing that large language models can acquire generalized probabilistic reasoning by being trained to imitate Bayesian belief updating rather than relying on prompting or heuristics.

Citations: 8
To Mask or to Mirror: Human-AI Alignment in Collective Reasoning

Authors: C Qian, AT Parisi, C Bouleau, V Tsai

Year: 2025

Published in: Proceedings of the ..., 2025 - aclanthology.org

Institution: Google, Google DeepMind

Research Area: Human-AI Alignment, Collective Reasoning, Social Biases, LLM Simulation of Human Behavior, AI Bias

Discipline: Natural Language Processing, Artificial Intelligence, Computational Social Science

This study examines human-AI alignment in collective reasoning using an empirical framework, demonstrating how LLMs either mirror or mask human biases depending on context, cues, and model-specific inductive biases.

Methods: The study uses the Lost at Sea social psychology task in a large-scale online experiment, simulating LLM groups conditioned on human decision-making data across varying conditions of visible or pseudonymous demographics.

Key Findings: Alignment of LLM behavior with human social reasoning, focusing on collective decision-making and biases in group interactions.

Citations: 1

Sample Size: 748
All-in-one: Understanding and Generation in Multimodal Reasoning with the MAIA Benchmark

Authors: D Testa, G Bonetta, R Bernardi, A Bondielli

Year: 2025

Published in: arXiv preprint arXiv ..., 2025 - arxiv.org

Institution: Università di Roma La Sapienza

Research Area: Multimodal Reasoning, AI Benchmarking

Discipline: Artificial Intelligence

MAIA is a benchmark designed to evaluate the reasoning abilities of Vision Language Models (VLMs) on video-based tasks, with a focus on Italian culture and language, revealing their fragility in consistency and visually grounded language comprehension and generation.

Methods: MAIA comprises a set of video-related questions tested with two tasks: visual statement verification and open-ended visual question answering, categorized into twelve reasoning types to disentangle language-vision relations.

Key Findings: The ability of Vision Language Models (VLMs) to perform consistent, visually grounded natural language understanding and generation across fine-grained reasoning categories.

DOI: https://doi.org/10.48550/arXiv.2502.16989
Can Large Language Models Understand Symbolic Graphics Programs?

Authors: Z Qiu, W Liu, H Feng, Z Liu, T Xiao

Year: 2024

Published in: ArXiv

Institution: Massachusetts Institute of Technology, Max Planck Institute, University of Cambridge

Research Area: Computational cognition, LLM evaluation, Program synthesis, Multimodal reasoning

Discipline: Artificial Intelligence

Introduces SGP-Bench, a benchmark testing whether LLMs can answer semantic and spatial questions about images purely from graphics programs (SVG/CAD), effectively probing “visual imagination without vision.” The authors show current LLMs struggle - sometimes near chance - even when images are trivial for humans, but demonstrate that Symbolic Instruction Tuning (SIT) can meaningfully improve thi...
The Scene Language: Representing Scenes with Programs, Words, and Embeddings

Authors: Yunzhi Zhang, Zizhang Li, Matt Zhou, Shangzhe Wu, Jiajun Wu

Year: 2024

Published in: ArXiv

Institution: Stanford University, University of California Berkeley

Research Area: Artificial Intelligence, Computer Vision, Multimodal Reasoning

Discipline: Artificial Intelligence
Using Counterfactual Tasks to Evaluate the Generality of Analogical Reasoning in Large Language Models

Authors: Martha Lewis, Melanie Mitchell

Year: 2024

Published in: ArXiv

Institution: Santa Fe Institute, University of Bristol

Research Area: LLM Analogical Reasoning, Counterfactual Evaluation, Generality of AI Reasoning

Discipline: Artificial Intelligence