Tootfinder

Bonsai: Intentional and Personalized Social Media Feeds
Omar El Malki, Marianne Aubin Le Qu\'er\'e, Andr\'es Monroy-Hern\'andez, Manoel Horta Ribeiro
https://arxiv.org/abs/2509.10776

Bonsai: Intentional and Personalized Social Media Feeds
Modern social media feeds use predictive models to maximize engagement, often misaligning how people consume content with how they wish to. We introduce Bonsai, a system that enables people to build personalized and intentional feeds. Bonsai implements a platform-agnostic framework comprising Planning, Sourcing, Curating, and Ranking modules. Altogether, this framework allows users to express their intent in natural language and exert fine-grained control over a procedurally transparent feed cr…

Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning
Yijia Xiao, Edward Sun, Tong Chen, Fang Wu, Di Luo, Wei Wang
https://arxiv.org/abs/2509.11420 https:/…

Trading-R1: Financial Trading with LLM Reasoning via Reinforcement Learning
Developing professional, structured reasoning on par with human financial analysts and traders remains a central challenge in AI for finance, where markets demand interpretability and trust. Traditional time-series models lack explainability, while LLMs face challenges in turning natural-language analysis into disciplined, executable trades. Although reasoning LLMs have advanced in step-by-step planning and verification, their application to risk-sensitive financial decisions is underexplored. …

Performance of GPT-5 in Brain Tumor MRI Reasoning
Mojtaba Safari, Shansong Wang, Mingzhe Hu, Zach Eidex, Qiang Li, Xiaofeng Yang
https://arxiv.org/abs/2508.10865 https://…

Performance of GPT-5 in Brain Tumor MRI Reasoning
Accurate differentiation of brain tumor types on magnetic resonance imaging (MRI) is critical for guiding treatment planning in neuro-oncology. Recent advances in large language models (LLMs) have enabled visual question answering (VQA) approaches that integrate image interpretation with natural language reasoning. In this study, we evaluated GPT-4o, GPT-5-nano, GPT-5-mini, and GPT-5 on a curated brain tumor VQA benchmark derived from 3 Brain Tumor Segmentation (BraTS) datasets - glioblastoma (…

The Bin Packing Problem with Setups: Formulation, Structural Properties and Computational Insights
Roberto Baldacci, Fabio Ciccarelli, Stefano Conglio, Valerio Dose, Fabio Furini
https://arxiv.org/abs/2509.10075

The Bin Packing Problem with Setups: Formulation, Structural Properties and Computational Insights
We introduce and study a novel generalization of the classical Bin Packing Problem (BPP), called the Bin Packing Problem with Setups (BPPS). In this problem, which has many practical applications in production planning and logistics, the items are partitioned into classes and, whenever an item from a given class is packed into a bin, a setup weight and cost are incurred. We present a natural Integer Linear Programming (ILP) formulation for the BPPS and analyze the structural properties of its L…

Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networks
Simen Storesund, Kristian Valset Aars, Robin Dietrich, Nicolai Waniek
https://arxiv.org/abs/2509.10077

Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networks
Efficient planning and sequence selection are central to intelligence, yet current approaches remain largely incompatible with biological computation. Classical graph algorithms like Dijkstra's or A* require global state and biologically implausible operations such as backtracing, while reinforcement learning methods rely on slow gradient-based policy updates that appear inconsistent with rapid behavioral adaptation observed in natural systems. We propose a biologically plausible algorithm fo…

Urban-STA4CLC: Urban Theory-Informed Spatio-Temporal Attention Model for Predicting Post-Disaster Commercial Land Use Change
Ziyi Guo, Yan Wang
https://arxiv.org/abs/2508.08976 …

Urban-STA4CLC: Urban Theory-Informed Spatio-Temporal Attention Model for Predicting Post-Disaster Commercial Land Use Change
Natural disasters such as hurricanes and wildfires increasingly introduce unusual disturbance on economic activities, which are especially likely to reshape commercial land use pattern given their sensitive to customer visitation. However, current modeling approaches are limited in capturing such complex interplay between human activities and commercial land use change under and following disturbances. Such interactions have been more effectively captured in current resilient urban planning the…

And when I'm talking about understanding the drives to violence, I did write about something similar recently.
https://write.as/hexmhell/algorithmic-violence
The drives behind this and the shooting last week are pretty radically different, but there's some overlap. People like Kirk are part a huge political machine slowly crushing people all over the world. There's a hopeless rage that would naturally drive even the most calm person to the edge of violence. You can't look at the world honestly and be OK. We want to do something. We want to react. But everything we do is silenced or must rmain silent. So it's easy to understand why someone might choose violence. Very different situation, but everyone is subject to the same national and international influences.
I don't promote violence, not because I disagree with it but because I think it's expensive. It takes time to plan, especially for those trying to get away. Guns are not cheap, nor are bullets, nor is the range time you need to get somewhat good under pressure. It's not cheap for the person doing it, and it's not cheap for the community that has to clean up. The community will face police repression (which, if we're honest, was gonna come anyway). The community will have to post bail, will lose a person for a while, will need to support the family, will go to hearings, will write reports, will do interviews.
Sun Tzu said that deploying one soldier to the front takes 7 in the field. Logistics are a huge invisible cost. Some of that time and energy could be reused. It's never bad to be armed and able to defend if needed. But a lot of that energy and time would be better spent planning a community pantry, a tool library, organizing a union, etc. We are living in a disaster, and we need to invest in thriving through the next crumble.
Kirk is replacable. They're almost all replacable, because they don't really care about human life. We do, so none of us are. It's not really a worth while trade, IMHO.

DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation
Haoxiang Shi, Xiang Deng, Zaijing Li, Gongwei Chen, Yaowei Wang, Liqiang Nie
https://arxiv.org/abs/2508.09444

DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation
Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural language instructions through free-form 3D spaces. Existing VLN-CE approaches typically use a two-stage waypoint planning framework, where a high-level waypoint predictor generates the navigable waypoints, and then a navigation planner suggests the intermediate goals in the high-level action space. However, this two-stage decomposition framework suffers from: (1) global sub-optimization due to the p…

A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modeling
Meher V. R. Malladi, Tiziano Guadagnino, Luca Lobefaro, Cyrill Stachniss
https://arxiv.org/abs/2509.06593

A Robust Approach for LiDAR-Inertial Odometry Without Sensor-Specific Modeling
Accurate odometry is a critical component in a robotic navigation stack, and subsequent modules such as planning and control often rely on an estimate of the robot's motion. Sensor-based odometry approaches should be robust across sensor types and deployable in different target domains, from solid-state LiDARs mounted on cars in urban-driving scenarios to spinning LiDARs on handheld packages used in unstructured natural environments. In this paper, we propose a robust LiDAR-inertial odometry sy…

MedReasoner: Reinforcement Learning Drives Reasoning Grounding from Clinical Thought to Pixel-Level Precision
Zhonghao Yan, Muxi Diao, Yuxuan Yang, Jiayuan Xu, Kaizhou Zhang, Ruoyan Jing, Lele Yang, Yanxi Liu, Kongming Liang, Zhanyu Ma
https://arxiv.org/abs/2508.08177

MedReasoner: Reinforcement Learning Drives Reasoning Grounding from Clinical Thought to Pixel-Level Precision
Accurately grounding regions of interest (ROIs) is critical for diagnosis and treatment planning in medical imaging. While multimodal large language models (MLLMs) combine visual perception with natural language, current medical-grounding pipelines still rely on supervised fine-tuning with explicit spatial hints, making them ill-equipped to handle the implicit queries common in clinical practice. This work makes three core contributions. We first define Unified Medical Reasoning Grounding (UMRG…