Tootfinder

On Reconfigurable Bisimulation, with an Application to the Distributed Synthesis Problem
Yehia Abd Alrahman, Nir Piterman
https://arxiv.org/abs/2505.21672 …

On Reconfigurable Bisimulation, with an Application to the Distributed Synthesis Problem
We consider the problem of distributing a centralised transition system to a set of asynchronous agents recognising the same language. Existing solutions are either manual or involve a huge explosion in the number of states from the centralised system. The difficulty arises from the need to keep a rigid communication scheme, specifying a fixed mapping from events to those who can participate in them. Thus, individual agents need to memorise seen events and their order to dynamically compare the…

from my link log —
Thoughts on hashing in Rust.
https://purplesyringa.moe/blog/thoughts-on-rust-hashing/
saved 2024-12-13

Thoughts on Rust hashing
In languages like Python, Java, or C++, values are hashed by calling a “hash me” method on them, implemented by the type author. This fixed-hash size is then immediately used by the hash table or what have you. This design suffers from some obvious problems, like: How do you hash an integer? If you use a no-op hasher (booo), DoS attacks on hash tables are inevitable. If you hash it thoroughly, consumers that only cache hashes to optimize equality checks lose out of performance.

CodeMorph: Mitigating Data Leakage in Large Language Model Assessment
Hongzhou Rao, Yanjie Zhao, Wenjie Zhu, Ling Xiao, Meizhen Wang, Haoyu Wang
https://arxiv.org/abs/2506.17627

CodeMorph: Mitigating Data Leakage in Large Language Model Assessment
Concerns about benchmark leakage in large language models for code (Code LLMs) have raised issues of data contamination and inflated evaluation metrics. The diversity and inaccessibility of many training datasets make it difficult to prevent data leakage entirely, even with time lag strategies. Consequently, generating new datasets through code perturbation has become essential. However, existing methods often fail to produce complex and diverse variations, struggle with complex cross-file depe…

A look at the Chile-led Latam-GPT project, which involves 30 Latin American and Caribbean institutions collaborating to release an open-source LLM in September (Cristišn Vera-Cruz/Rest of World)
https://restofworld.org/2025/chatgpt-latin-america-alternative-latamgpt…

Fed up with ChatGPT, Latin America is building its own
Dozens of organizations in the region have partnered to develop a large language model that better understands Latin America’s cultural and linguistic nuances.

LLMs are Bayesian, in Expectation, not in Realization
Leon Chlon, Sarah Rashidi, Zein Khamis, MarcAntonio M. Awada
https://arxiv.org/abs/2507.11768 https:/…

LLMs are Bayesian, in Expectation, not in Realization
Large language models demonstrate remarkable in-context learning capabilities, adapting to new tasks without parameter updates. While this phenomenon has been successfully modeled as implicit Bayesian inference, recent empirical findings reveal a fundamental contradiction: transformers systematically violate the martingale property, a cornerstone requirement of Bayesian updating on exchangeable data. This violation challenges the theoretical foundations underlying uncertainty quantification in …

InfoFlood: Jailbreaking Large Language Models with Information Overload
Advait Yadav, Haibo Jin, Man Luo, Jun Zhuang, Haohan Wang
https://arxiv.org/abs/2506.12274

InfoFlood: Jailbreaking Large Language Models with Information Overload
Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains. However, their potential to generate harmful responses has raised significant societal and regulatory concerns, especially when manipulated by adversarial techniques known as "jailbreak" attacks. Existing jailbreak methods typically involve appending carefully crafted prefixes or suffixes to malicious prompts in order to bypass the built-in safety mechanisms of these models. In this work, we identif…

AI, AGI, and learning efficiency
My 4-month-old kid is not DDoSing Wikipedia right now, nor will they ever do so before learning to speak, read, or write. Their entire "training corpus" will not top even 100 million "tokens" before they can speak & understand language, and do so with real intentionally.
Just to emphasize that point: 100 words-per-minute times 60 minutes-per-hour times 12 hours-per-day times 365 days-per-year times 4 years is a mere 105,120,000 words. That's a ludicrously *high* estimate of words-per-minute and hours-per-day, and 4 years old (the age of my other kid) is well after basic speech capabilities are developed in many children, etc. More likely the available "training data" is at least 1 or 2 orders of magnitude less than this.
The point here is that large language models, trained as they are on multiple *billions* of tokens, are not developing their behavioral capabilities in a way that's remotely similar to humans, even if you believe those capabilities are similar (they are by certain very biased ways of measurement; they very much aren't by others). This idea that humans must be naturally good at acquiring language is an old one (see e.g. #AI #LLM #AGI

Rust vs. C for Python Libraries: Evaluating Rust-Compatible Bindings Toolchains
Isabella Basso do Amaral (University of S\~ao Paulo), Renato Cordeiro Ferreira (University of S\~ao Paulo, Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University), Alfredo Goldman (University of S\~ao Paulo)
https://

Rust vs. C for Python Libraries: Evaluating Rust-Compatible Bindings Toolchains
The Python programming language is best known for its syntax and scientific libraries, but it is also notorious for its slow interpreter. Optimizing critical sections in Python entails special knowledge of the binary interactions between programming languages, and can be cumbersome to interface manually, with implementers often resorting to convoluted third-party libraries. This comparative study evaluates the performance and ease of use of the PyO3 Python bindings toolchain for Rust against ct…

This https://arxiv.org/abs/2410.18042 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

Charon: An Analysis Framework for Rust
With the explosion in popularity of the Rust programming language, a wealth of tools have recently been developed to analyze, verify, and test Rust programs. Alas, the Rust ecosystem remains relatively young, meaning that every one of these tools has had to re-implement difficult, time-consuming machinery to interface with the Rust compiler and its cargo build system, to hook into the Rust compiler's internal representation, and to expose an abstract syntax tree (AST) that is suitable for analy…

This https://arxiv.org/abs/2506.02791 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Rethinking the effects of data contamination in Code Intelligence
In recent years, code intelligence has gained increasing importance in the field of automated software engineering. Meanwhile, the widespread adoption of Pretrained Language Models (PLMs) and Large Language Models (LLMs) has raised concerns regarding data contamination and its potential impact on model performance evaluation. This paper presents a systematic empirical study to investigate the fine-grained data contamination on code intelligence tasks. Our study involves diverse representative P…