Abstract: The commonsense reasoning capabilities of vision-language models (VLMs), especially in abductive reasoning and defeasible reasoning, remain poorly understood. Most benchmarks focus on ...

Advanced reasoning models are at the frontier of machine intelligence, especially in domains like math problem-solving and symbolic reasoning. These models are designed to perform multi-step ...

Inspired by the success of DeepSeek-R1, we explore the potential of rule-based reinforcement learning (RL) in large reasoning models. To analyze reasoning dynamics, we use synthetic logic puzzles as ...

This is our PyTorch Implementation of "Inferring Past Human Actions in Homes with Abductive Reasoning" using the Action Genome dataset. First, create a new conda ...

Large language models (LLMs) have made significant strides in their post-training phase, like DeepSeek-R1, Kimi-K1.5, and OpenAI-o1, showing impressive reasoning capabilities. While DeepSeek-R1 ...

ABSTRACT: Design thinking has gained prominence as an innovative, human-centered methodology for addressing complex problems across disciplines. Despite its widespread application in fields such as ...

Royalty-free licenses let you pay once to use copyrighted images and video clips in personal and commercial projects on an ongoing basis without requiring additional payments each time you use that ...

The second-year undergraduate Organic Chemistry course sequence is often cited as one of the most, if not the most, challenging for students in the US. Thus, a persistent question remains: What is it ...

This is the official implementation of the paper RCA: Region Conditioned Adaptation for Visual Abductive Reasoning. We achieved the top rank on the official Sherlock Abductive Reasoning Leaderboard ...

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Abductive logic programming (ALP) is a framework that combines logic programming with abductive reasoning to address problems where clear answers are not available. It enables systems to propose ...