Tootfinder

Learnable Conformal Prediction with Context-Aware Nonconformity Functions for Robotic Planning and Perception
Divake Kumar, Sina Tayebati, Francesco Migliarba, Ranganath Krishnan, Amit Ranjan Trivedi
https://arxiv.org/abs/2509.21955

Learnable Conformal Prediction with Context-Aware Nonconformity Functions for Robotic Planning and Perception
Deep learning models in robotics often output point estimates with poorly calibrated confidences, offering no native mechanism to quantify predictive reliability under novel, noisy, or out-of-distribution inputs. Conformal prediction (CP) addresses this gap by providing distribution-free coverage guarantees, yet its reliance on fixed nonconformity scores ignores context and can yield intervals that are overly conservative or unsafe. We address this with Learnable Conformal Prediction (LCP), whi…

General integral-identities involving Legendre polynomials and their derivatives
Yannick Wunderlich, Kyungseon Joo, Victor I. Mokeev
https://arxiv.org/abs/2509.22904 https://

General integral-identities involving Legendre polynomials and their derivatives
Integrals involving derivatives of Legendre polynomials frequently arise in applications ranging from multipole expansions for processes involving electromagnetic probes to spectral methods in numerical physics. Despite their practical relevance, closed-form expressions for such integrals - particularly involving arbitrary derivative orders - are not readily accessible in standard references or symbolic tools. In this note, we derive and present general analytic expressions for integrals of the…

Documentation Retrieval Improves Planning Language Generation
Renxiang Wang, Li Zhang
https://arxiv.org/abs/2509.19931 https://arxiv.org/pdf/2509.19931

Documentation Retrieval Improves Planning Language Generation
Certain strong LLMs have shown promise for zero-shot formal planning by generating planning languages like PDDL. Yet, performance of most open-source models under 50B parameters has been reported to be close to zero due to the low-resource nature of these languages. We significantly improve their performance via a series of lightweight pipelines that integrates documentation retrieval with modular code generation and error refinement. With models like Llama-4-Maverick, our best pipeline improve…

A generalization of the Furstenberg--S\'{a}rk\"{o}zy theorem over the Gaussian integers
Dev Ranjan Pandey, Jyoti Prakash Saha
https://arxiv.org/abs/2508.18990 https://

A generalization of the Furstenberg--Sárközy theorem over the Gaussian integers
We introduce the notion of intersective polynomials over the Gaussian integers, and prove that given any intersective polynomial $p(x)$ over the Gaussian integers, every subset $A$ of the Gaussian integers of positive upper density contains two distinct elements such that their difference is $p(z)$ for some Gaussian integer $z$. Moreover, we also obtain a quantitative version of this result. The proof is motivated by an argument due to Lucier, and the Fourier-free proof of the Furstenberg--Sár…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- Graph Neural Network Based Action Ranking for Planning
Rajesh Mangannavar, Stefan Lee, Alan Fern, Prasad Tadepalli

It is not clear whether the National Capital Planning Commission was consulted or received any meaningful insight prior to the White House’s demolition,
particularly as it has been closed since the government shut down 23 days ago.
“The decisions were made in complete secrecy and undertaken without public disclosure or proper consultation,” 
the ranking members on the House Oversight and Natural Resources committees and the subcommittee on energy on mineral resources wrote T…

Karoline Leavitt Says Trump Can Destroy Entire White House if He Wants
Leavitt dodged a key question on the limits to Donald Trump’s abilities to renovate.

Random forest-based out-of-distribution detection for robust lung cancer segmentation
Aneesh Rangnekar, Harini Veeraraghavan
https://arxiv.org/abs/2508.19112 https://

Random forest-based out-of-distribution detection for robust lung cancer segmentation
Accurate detection and segmentation of cancerous lesions from computed tomography (CT) scans is essential for automated treatment planning and cancer treatment response assessment. Transformer-based models with self-supervised pretraining can produce reliably accurate segmentation from in-distribution (ID) data but degrade when applied to out-of-distribution (OOD) datasets. We address this challenge with RF-Deep, a random forest classifier that utilizes deep features from a pretrained transform…

Replaced article(s) found for math.AP. https://arxiv.org/list/math.AP/new
[1/1]:
- Global mild solution with polynomial tail for the Boltzmann equation in the whole space
Renjun Duan, Zongguang Li, Shuangqian Liu

Citizen Centered Climate Intelligence: Operationalizing Open Tree Data for Urban Cooling and Eco-Routing in Indian Cities
Kaushik Ravi, Andreas Br\"uck
https://arxiv.org/abs/2508.17648

Citizen Centered Climate Intelligence: Operationalizing Open Tree Data for Urban Cooling and Eco-Routing in Indian Cities
Urban climate resilience requires more than high-resolution data; it demands systems that embed data collection, interpretation, and action within the daily lives of citizens. This chapter presents a scalable, citizen-centric framework that reimagines environmental infrastructure through participatory sensing, open analytics, and prescriptive urban planning tools. Applied in Pune, India, the framework comprises three interlinked modules: (1) a smartphone-based measurement toolkit enhanced by AI…

BBoE: Leveraging Bundle of Edges for Kinodynamic Bidirectional Motion Planning
Srikrishna Bangalore Raghu, Alessandro Roncone
https://arxiv.org/abs/2509.20333 https://

BBoE: Leveraging Bundle of Edges for Kinodynamic Bidirectional Motion Planning
In this work, we introduce BBoE, a bidirectional, kinodynamic, sampling-based motion planner that consistently and quickly finds low-cost solutions in environments with varying obstacle clutter. The algorithm combines exploration and exploitation while relying on precomputed robot state traversals, resulting in efficient convergence towards the goal. Our key contributions include: i) a strategy to navigate through obstacle-rich spaces by sorting and sequencing preprocessed forward propagations;…