arXiv subjects

Jan Ondras

Publications and source records attributed to Jan Ondras.

4 recordsLinked to original sources

arXiv2025

FractalBench: Diagnosing Visual-Mathematical Reasoning Through Recursive Program Synthesis

Mathematical reasoning requires abstracting symbolic rules from visual patterns -- inferring the infinite from the finite. We investigate whether multimodal AI systems possess this capability through FractalBench, a benchmark evaluating fractal program synthesis from images. Fractals provide ideal test cases: Iterated Function Systems with only a few contraction maps generate complex self-similar patterns through simple recursive rules, requiring models to bridge visual perception with mathematical abstraction. We evaluate four leading MLLMs -- GPT-4o, Claude 3.7 Sonnet, Gemini 2.5 Flash, and Qwen 2.5-VL -- on 12 canonical fractals. Models must generate executable Python code reproducing the fractal, enabling objective evaluation. Results reveal a striking disconnect: 76% generate syntactically valid code but only 4% capture mathematical structure. Success varies systematically -- models handle geometric transformations (Koch curves: 17-21%) but fail at branching recursion (trees: <2%), revealing fundamental gaps in mathematical abstraction. FractalBench provides a contamination-resistant diagnostic for visual-mathematical reasoning and is available at https://github.com/NaiveNeuron/FractalBench

cs.AI↗

arXiv2025

Measuring the dynamical evolution of the United States lobbying network

Lobbying networks constitute complex political systems that mobilize vast human and financial resources to influence governmental decision-making, often with profound national and global consequences. A comprehensive understanding of lobbying strategies and dynamics requires time-resolved, system-wide data, which are largely unavailable for most political systems. In the United States (U.S.), the Lobbying Disclosure Act (LDA) of 1995 mandates public reporting of all federal lobbying activities in detailed quarterly filings. However, extracting structured, quantitative information from these filings has remained technically challenging and labor-intensive. Here we present and analyze LobbyView, a relational database that integrates and disambiguates data from more than 1.6 million LDA reports. LobbyView provides access to detailed lobbying disclosures, reconciled corporate entities, and tools for linking LDA data to external legislative and corporate databases. We demonstrate the utility of LobbyView by examining both macro-level and highly granular lobbying dynamics. Specifically, we reconstruct the connectivity patterns of the U.S. lobbying network, and we show how they evolve over time, we identify organizational principles such as the accumulation of professional contacts within a small set of firms, and reveal how lobbying activity is synchronized with electoral cycles. Moreover, we introduce a probabilistic framework for analyzing lobbying behavior at the scale of individual bills, issues, or firms. We envision LobbyView as a resource not only for political scientists, but also for quantitative interdisciplinary research, enabling the application of methods from statistical physics, systems biology, and machine learning to the study of lobbying systems.

physics.soc-ph↗

arXiv2022

Robotic Dough Shaping

Robotic manipulation of deformable objects gains great attention due to its wide applications including medical surgery, home assistance, and automatic food preparation. The ability to deform soft objects remains a great challenge for robots due to difficulties in defining the problem mathematically. In this paper, we address the problem of shaping a piece of dough-like deformable material into a 2D target shape presented upfront. We use a 6 degree-of-freedom WidowX-250 Robot Arm equipped with a rolling pin and information collected from an RGB-D camera and a tactile sensor. We present and compare several control policies, including a dough shrinking action, in extensive experiments across three kinds of deformable materials and across three target dough shape sizes, achieving the intersection over union (IoU) of 0.90. Our results show that: i) rolling dough from the highest dough point is more efficient than from the 2D/3D dough centroid; ii) it might be better to stop the roll movement at the current dough boundary as opposed to the target shape outline; iii) the shrink action might be beneficial only if properly tuned with respect to the expand action; and iv) the Play-Doh material is easier to shape to a target shape as compared to Plasticine or Kinetic sand. Video demonstrations of our work are available at https://youtu.be/ZzLMxuITdt4

cs.RO↗

arXiv2022

Human-Robot Commensality: Bite Timing Prediction for Robot-Assisted Feeding in Groups

We develop data-driven models to predict when a robot should feed during social dining scenarios. Being able to eat independently with friends and family is considered one of the most memorable and important activities for people with mobility limitations. While existing robotic systems for feeding people with mobility limitations focus on solitary dining, commensality, the act of eating together, is often the practice of choice. Sharing meals with others introduces the problem of socially appropriate bite timing for a robot, i.e. the appropriate timing for the robot to feed without disrupting the social dynamics of a shared meal. Our key insight is that bite timing strategies that take into account the delicate balance of social cues can lead to seamless interactions during robot-assisted feeding in a social dining scenario. We approach this problem by collecting a Human-Human Commensality Dataset (HHCD) containing 30 groups of three people eating together. We use this dataset to analyze human-human commensality behaviors and develop bite timing prediction models in social dining scenarios. We also transfer these models to human-robot commensality scenarios. Our user studies show that prediction improves when our algorithm uses multimodal social signaling cues between diners to model bite timing. The HHCD dataset, videos of user studies, and code are available at https://emprise.cs.cornell.edu/hrcom/

cs.RO↗