Tootfinder

Hard to describe that one, but give it time to develop... some Latin influences, warm Moog bass, generally loose, understated, slightly melancholic, minimal...
Map.ache - Loosing Is Not An Option (2018)
https://www.youtube.com/watch?v=miRAyXQ1DXE

Da fährt eins nach Biskoupky (aktuelle Bevölkerung 185 nach cs-wikipedia) nur, um von dort aus die Jihlava hinauf zur Mohelenska hadcovš step zu wandern, und dann ist das erste Haus nach dem Ausstieg aus dem Bus gleich das Geburtshaus des Poetisten Vítězslav Nezval. Allerdings: Die Číslo popisné (40 laut OSM) ist darauf nicht angebracht, aber Nezval hat schließlich eines seiner berühmtesten (von Milča Mayerovš getanzten) Gedichte auch abeceda und nicht 1234 genannt.

Elon Musk will singlehandedly be responsible for the wholly unnecessary deaths & misery of millions worldwide. And he & Trump couldn't care less.
You did this, #Trump voter.
And anyone that didn't vote.
http…

Elon Musk’s Reign of Terror Is Only Just Beginning
His “departure” from the government is anything but.

This https://arxiv.org/abs/2505.19028 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

InfoChartQA: A Benchmark for Multimodal Question Answering on Infographic Charts
Understanding infographic charts with design-driven visual elements (e.g., pictograms, icons) requires both visual recognition and reasoning, posing challenges for multimodal large language models (MLLMs). However, existing visual-question answering benchmarks fall short in evaluating these capabilities of MLLMs due to the lack of paired plain charts and visual-element-based questions. To bridge this gap, we introduce InfoChartQA, a benchmark for evaluating MLLMs on infographic chart understand…

This https://arxiv.org/abs/2503.19990 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

LEGO-Puzzles: How Good Are MLLMs at Multi-Step Spatial Reasoning?
Multi-step spatial reasoning entails understanding and reasoning about spatial relationships across multiple sequential steps, which is crucial for tackling complex real-world applications, such as robotic manipulation, autonomous navigation, and automated assembly. To assess how well current Multimodal Large Language Models (MLLMs) have acquired this fundamental capability, we introduce LEGO-Puzzles, a scalable benchmark designed to evaluate both spatial understanding and sequential reasoning …

Federal #safety regulators looking into whether #MelonHusk's killer robo cars belong on city streets, and writer #Bloomberg's Dana Hull filed a public-records request to see footage and learn more about one of the horrific f…

This https://arxiv.org/abs/2505.19901 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Dynamic-I2V: Exploring Image-to-Video Generaion Models via Multimodal LLM
Recent advancements in image-to-video (I2V) generation have shown promising performance in conventional scenarios. However, these methods still encounter significant challenges when dealing with complex scenes that require a deep understanding of nuanced motion and intricate object-action relationships. To address these challenges, we present Dynamic-I2V, an innovative framework that integrates Multimodal Large Language Models (MLLMs) to jointly encode visual and textual conditions for a diffus…

GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning
Sahiti Yerramilli, Nilay Pande, Rynaa Grover, Jayant Sravan Tamarapalli
https://arxiv.org/abs/2506.00785

GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning
This paper introduces GeoChain, a large-scale benchmark for evaluating step-by-step geographic reasoning in multimodal large language models (MLLMs). Leveraging 1.46 million Mapillary street-level images, GeoChain pairs each image with a 21-step chain-of-thought (CoT) question sequence (over 30 million Q&A pairs). These sequences guide models from coarse attributes to fine-grained localization across four reasoning categories - visual, spatial, cultural, and precise geolocation - annotated by d…

This https://arxiv.org/abs/2505.24139 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Modelwith Spatio-Temporal Visual Representation
The latest advancements in multi-modal large language models (MLLMs) have spurred a strong renewed interest in end-to-end motion planning approaches for autonomous driving. Many end-to-end approaches rely on human annotations to learn intermediate perception and prediction tasks, while purely self-supervised approaches--which directly learn from sensor inputs to generate planning trajectories without human annotations often underperform the state of the art. We observe a key gap in the input re…

RiOSWorld: Benchmarking the Risk of Multimodal Compter-Use Agents
Jingyi Yang, Shuai Shao, Dongrui Liu, Jing Shao
https://arxiv.org/abs/2506.00618 https://…

RiOSWorld: Benchmarking the Risk of Multimodal Compter-Use Agents
With the rapid development of multimodal large language models (MLLMs), they are increasingly deployed as autonomous computer-use agents capable of accomplishing complex computer tasks. However, a pressing issue arises: Can the safety risk principles designed and aligned for general MLLMs in dialogue scenarios be effectively transferred to real-world computer-use scenarios? Existing research on evaluating the safety risks of MLLM-based computer-use agents suffers from several limitations: it ei…