Tootfinder

In Sabzevar for the second consecutive night, angry residents held demonstrations.
The protest began at Farmandari Square and Abouzar Street, and protesters blocked Bahonar Street.
Women were at the forefront of the rally, chanting slogans such as:
“Don’t be afraid, don’t be afraid, we’re all in this together,”
“Water, electricity, and life are our basic rights,”
and “We don’t want incompetent officials.”

Iran - Sabzevar: Second Night of Protests Against Water and Power Outages - NCRI
Maryam Rajavi: Like so many of Iran’s resources, the country’s water supplies have not been spared from the plunder and corruption of the clerical regime.

#OpenTelemetry Sampling update
https://opentelemetry.io/blog/2025/sampling-milestones/

Preguss: It Analyzes, It Specifies, It Verifies
Zhongyi Wang, Tengjie Lin, Mingshuai Chen, Mingqi Yang, Haokun Li, Xiao Yi, Shengchao Qin, Jianwei Yin
https://arxiv.org/abs/2508.14532

Preguss: It Analyzes, It Specifies, It Verifies
Fully automated verification of large-scale software and hardware systems is arguably the holy grail of formal methods. Large language models (LLMs) have recently demonstrated their potential for enhancing the degree of automation in formal verification by, e.g., generating formal specifications as essential to deductive verification, yet exhibit poor scalability due to context-length limitations and, more importantly, the difficulty of inferring complex, interprocedural specifications. This pa…

ai but with mood swings to better understand context smh.
science more interested in how menstruation affects ai than it ever was in how it affects women.

Extracting Structured Requirements from Unstructured Building Technical Specifications for Building Information Modeling
Insaf Nahri, Romain Pinqui\'e, Philippe V\'eron, Nicolas Bus, Mathieu Thorel
https://arxiv.org/abs/2508.13833

Extracting Structured Requirements from Unstructured Building Technical Specifications for Building Information Modeling
This study explores the integration of Building Information Modeling (BIM) with Natural Language Processing (NLP) to automate the extraction of requirements from unstructured French Building Technical Specification (BTS) documents within the construction industry. Employing Named Entity Recognition (NER) and Relation Extraction (RE) techniques, the study leverages the transformer-based model CamemBERT and applies transfer learning with the French language model Fr\_core\_news\_lg, both pre-trai…

Today I’m a very proud and happy open standards maintainer: OpenAPI 3.2 is now available! The release notes are a good place to find out what’s new (it’s a lot!) https://github.com/OAI/OpenAPI-Specification/releases
Huge thanks to our contributor community, you are amazing!!

Increasing the Expressiveness of a Gradual Verifier
Priyam Gupta
https://arxiv.org/abs/2507.13533 https://arxiv.org/pdf/2507.13533

Increasing the Expressiveness of a Gradual Verifier
Static verification provides strong correctness guarantees for code; however, fully specifying programs for static verification is a complex, burdensome process for users. Gradual verification was introduced to make this process easier by supporting the verification of partially specified programs. The only currently working gradual verifier, Gradual C0, successfully verifies heap manipulating programs, but lacks expressiveness in its specification language. This paper describes the design and …

Ambiguity Resolution with Human Feedback for Code Writing Tasks
Aditey Nandan, Viraj Kumar
https://arxiv.org/abs/2508.14114 https://arxiv.org/pdf/2508.1411…

Ambiguity Resolution with Human Feedback for Code Writing Tasks
Specifications for code writing tasks are usually expressed in natural language and may be ambiguous. Programmers must therefore develop the ability to recognize ambiguities in task specifications and resolve them by asking clarifying questions. We present and evaluate a prototype system, based on a novel technique (ARHF: Ambiguity Resolution with Human Feedback), that (1) suggests specific inputs on which a given task specification may be ambiguous, (2) seeks limited human feedback about the c…

Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Need
Bhishma Dedhia, Yuval Kansal, Niraj K. Jha
https://arxiv.org/abs/2507.13966

Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Need
Language models traditionally used for cross-domain generalization have recently demonstrated task-specific reasoning. However, their top-down training approach on general corpora is insufficient for acquiring abstractions needed for deep domain expertise. This may require a bottom-up approach that acquires expertise by learning to compose simple domain concepts into more complex ones. A knowledge graph (KG) provides this compositional structure, where domain primitives are represented as head-…