Abstract Efforts to verify Zero-Knowledge Proof circuit encodings have highlighted the challenge of proving the correctness of quantifier-free statements that make use of both bitvector and finite field operations. Existing verification workflows are either manual or rely on SMT solvers, which scale poorly on some classes of problems for reasons that include difficulties with conversion operators and challenges reasoning about inequalities. To address these limitations, we present a novel Lean tactic that leverages range lemmas and case analysis to produce verified translations from finite fields to bitvectors. Our approach, combined with bit-blasting, outperforms state-of-the-art SMT solvers, solving 19% more ZKP arithmetization benchmarks.

Abstract This paper presents a new refutation procedure for multimodular systems of integer constraints that commonly arise when verifying cryptographic protocols. These systems, involving polynomial equalities and disequalities modulo different constants, are challenging for existing solvers due to their inability to exploit multimodular structure. To address this issue, our method partitions constraints by modulus and uses lifting and lowering techniques to share information across subsystems, supported by algebraic tools like weighted Gr bner bases. Our experiments show that the proposed method outperforms existing state-of-the-art solvers in verifying cryptographic implementations related to Montgomery arithmetic and zero-knowledge proofs.

Scientists write and maintain software artifacts to construct, validate, and apply scientific theories. Despite the centrality of software in their work, their practices differ significantly from those of professional software engineers. We sought to understand what makes scientists effective at their work and how software engineering practices and tools can be adapted to fit their workflows. We interviewed 25 scientists and support staff to understand their work. Then, we constructed a theory that relates six factors that contribute to their efficacy in creating and maintaining software systems. We present the theory in the form of a cycle of scientific computing efficacy and identify opportunities for improvement based on the six contributing factors.

In this paper, we develop machine learning techniques to identify unknown printers in early modern (c.~1500--1800) English printed books. Specifically, we focus on matching uniquely damaged character type-imprints in anonymously printed books to works with known printers in order to provide evidence of their origins. Until now, this work has been limited to manual investigations by analytical bibliographers. We present a Contrastive Attention-based Metric Learning approach to identify similar damage across character image pairs, which is sensitive to very subtle differences in glyph shapes, yet robust to various confounding sources of noise associated with digitized historical books. To overcome the scarce amount of supervised data, we design a random data synthesis procedure that aims to simulate bends, fractures, and inking variations induced by the early printing process. Our method successfully improves downstream damaged type-imprint matching among printed works from this period, as validated by in-domain human experts. The results of our approach on two important philosophical works from the Early Modern period demonstrate potential to extend the extant historical research about the origins and content of these books.

While large language models (LLMs) can answer many questions correctly, they can also hallucinate and give wrong answers.Wikidata, with its over 12 billion facts, can be used to ground LLMs to improve their factuality.This paper presents WikiWebQuestions, a highquality question answering benchmark for Wikidata.Ported over from WebQuestions for Freebase, it consists of real-world data with SPARQL annotation.This paper presents a few-shot sequence-tosequence semantic parser for Wikidata.We modify SPARQL to use the unique domain and property names instead of their IDs.We train the parser to use either the results from an entity linker or mentions in the query.We fine-tune LLaMA by adding the few-shot training data to that used to fine-tune Alpaca.Our experimental results demonstrate the effectiveness of this methodology, establishing a strong baseline of 76% and 65% answer accuracy in the dev and test sets of WikiWeb-Questions, respectively.By pairing our semantic parser with GPT-3, we combine verifiable results with qualified GPT-3 guesses to provide useful answers to 96% of the questions in dev.We also show that our method outperforms the state-of-the-art for the QALD-7 Wikidata dataset by 3.6% in F1 score. 1 * Equal contribution 1 Code, data, and model are available at https://github.com/stanford-oval/ wikidata-emnlp23