Tootfinder

Argentina pushes pipeline exports through protected Patagonian waters https://news.mongabay.com/2025/08/argentina-pushes-pipeline-exports-through-protected-patagonian-waters/

Argentina pushes pipeline exports through protected Patagonian waters
San Matías Gulf, on the northern coast of Argentina’s Patagonia region, is one of the most biodiverse areas in the Argentine Sea. The Valdés Peninsula — a UNESCO World Heritage Site since 1999 and a biosphere reserve since 2014 — forms its southern border. It’s surrounded by Islote Lobos National Park, the Puerto Lobos and […]

Mayer--Vietoris sequences for complexes of tori
Nguyen Manh Linh
https://arxiv.org/abs/2509.16056 https://arxiv.org/pdf/2509.16056

Mayer--Vietoris sequences for complexes of tori
In the patching setting, given a factorization inverse system of fields over which patching for finite-dimensional vector spaces holds, together with a crossed module over the inverse limit field, the corresponding six-term Mayer--Vietoris sequence is constructed, generalizing the classical result of Harbater--Hartmann--Krashen for linear algebraic groups. When the crossed module is a two-term complex of tori, the above sequence is extended into a nine-term exact sequence, notably without any a…

Weekend #Plankton Factoid 🦠🦐
Last week, I mentioned a ciliate that can cause red tide #blooms. Mesodinium rubrum is an example of a unicellular organism that can both injest food and photosynthesize. Its main prey are cryptomonad

President Pathogen
bot by @…
No laws were broken in the making of this bot: please don't torture me.
#satire #potus45

Self-Supervised Inductive Logic Programming
Stassa Patsantzis
https://arxiv.org/abs/2507.16405 https://arxiv.org/pdf/2507.16405

Self-Supervised Inductive Logic Programming
Inductive Logic Programming (ILP) approaches like Meta \-/ Interpretive Learning (MIL) can learn, from few examples, recursive logic programs with invented predicates that generalise well to unseen instances. This ability relies on a background theory and negative examples, both carefully selected with expert knowledge of a learning problem and its solutions. But what if such a problem-specific background theory or negative examples are not available? We formalise this question as a new setting…

Scaling Group Inference for Diverse and High-Quality Generation
Gaurav Parmar, Or Patashnik, Daniil Ostashev, Kuan-Chieh Wang, Kfir Aberman, Srinivasa Narasimhan, Jun-Yan Zhu
https://arxiv.org/abs/2508.15773

Scaling Group Inference for Diverse and High-Quality Generation
Generative models typically sample outputs independently, and recent inference-time guidance and scaling algorithms focus on improving the quality of individual samples. However, in real-world applications, users are often presented with a set of multiple images (e.g., 4-8) for each prompt, where independent sampling tends to lead to redundant results, limiting user choices and hindering idea exploration. In this work, we introduce a scalable group inference method that improves both the divers…

Do children receive too many vaccines in too short of a period of time? Something I read on birdsite years ago put this in perspective.
Every day, our immune systems are exposed to antigens from bacteria, virus, fungi, & parasites, including many that can't infect humans. Also, non-pathogens such as plant pollens and the foods we eat.
In that context, a few vaccines are not a big increase.

From model-based learning to model-free behaviour with Meta-Interpretive Learning
Stassa Patsantzis
https://arxiv.org/abs/2507.16434 https://

From model-based learning to model-free behaviour with Meta-Interpretive Learning
A "model" is a theory that describes the state of an environment and the effects of an agent's decisions on the environment. A model-based agent can use its model to predict the effects of its future actions and so plan ahead, but must know the state of the environment. A model-free agent cannot plan, but can act without a model and without completely observing the environment. An autonomous agent capable of acting independently in novel environments must combine both sets of capabilities. We s…

GRAFT: GRaPH and Table Reasoning for Textual Alignment -- A Benchmark for Structured Instruction Following and Visual Reasoning
Abhigya Verma, Sriram Puttagunta, Seganrasan Subramanian, Sravan Ramachandran
https://arxiv.org/abs/2508.15690

GRAFT: GRaPH and Table Reasoning for Textual Alignment -- A Benchmark for Structured Instruction Following and Visual Reasoning
GRAFT is a structured multimodal benchmark for evaluating models on instruction-following, visual reasoning, and visual-textual alignment tasks. It features programmatically generated charts and synthetically rendered tables, created with Python visualization libraries to ensure control over data semantics, structure, and clarity. Each GRAFT instance pairs a chart or table image with a systematically generated, multi-step analytical question based solely on visual content. Answers are provided …

GraSP: A Unified Graph-Based Framework for Scalable Generation, Quality Tagging, and Management of Synthetic Data for SFT and DPO
Bidyapati Pradhan, Surajit Dasgupta, Amit Kumar Saha, Omkar Anustoop, Sriram Puttagunta, Vipul Mittal, Gopal Sarda
https://arxiv.org/abs/2508.15432

GraSP: A Unified Graph-Based Framework for Scalable Generation, Quality Tagging, and Management of Synthetic Data for SFT and DPO
The advancement of large language models (LLMs) is critically dependent on the availability of high-quality datasets for Supervised Fine-Tuning (SFT), alignment tasks like Direct Preference Optimization (DPO), etc. In this work, we present a comprehensive synthetic data generation framework that facilitates scalable, configurable, and high-fidelity generation of synthetic data tailored for these training paradigms. Our approach employs a modular and configuration-based pipeline capable of model…