Tootfinder

#Error Handling #Patterns for #ApacheKafka Applications

This https://arxiv.org/abs/2503.20197 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Enhancing the Robustness of LLM-Generated Code: Empirical Study and Framework
Ensuring the robustness of code generated by large language models (LLMs) is crucial for real-world reliability. However, existing evaluations predominantly focus on correctness, often neglecting key robustness concerns such as missing input validation and insufficient error handling. In this paper, we present the first empirical study on the robustness of LLM-generated code. We introduce novel robustness metrics and analyze four state-of-the-art code LLMs, revealing that, on average, 43.1% of …

Day 3 1/2
TL;DR:
Added global `ValidationPipe` and unified `AllExceptionsFilter` in NestJS. API errors now follow a consistent format: `{ statusCode, error, message, code, details }`.
Today’s work focused on:
1. Standardizing input validation and error handling in the backend of the insurance sales network management system (NestJS, TypeScript, ESM, Turbo monorepo).
- Enabled global `ValidationPipe` with: `whitelist`, `forbidNonWhitelisted`, `transform`, `enableI…

This https://arxiv.org/abs/2505.10930 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Physics-informed Temporal Alignment for Auto-regressive PDE Foundation Models
Auto-regressive partial differential equation (PDE) foundation models have shown great potential in handling time-dependent data. However, these models suffer from the shortcut problem deeply rooted in auto-regressive prediction, causing error accumulation. The challenge becomes particularly evident for out-of-distribution data, as the pretraining performance may approach random model initialization for downstream tasks with long-term dynamics. To deal with this problem, we propose physics-info…

Saw this "Followed hashtags" collapsible list on mastodon.social (v4.4.0 nightly), wondering how long the list of hashtags will be.
Turns out it's hard-coded to 4 🤷‍♂️
- Initial PR: https://github.com/mastodon/mastodon/p

{purrr} has some lesser known functions that make handling of failing function calls easier: safely, quietly, possibly: #rstats…

Wrap a function to capture errors — safely
Creates a modified version of .f that always succeeds. It returns a list with components result and error. If the function succeeds, result contains the returned value and error is NULL. If an error occurred, error is an error object and result is either NULL or otherwise.

Russian developer Yegor from yegor256.com uses a simple example of two similar approaches to modeling an action, and their implications from an object-oriented design and programming patterns perspective. One of the two approaches provides superior extensibility, data encapsulation, and more flexible error handling.
"remove(42) vs. find(42).remove()"

AutoIndexer: A Reinforcement Learning-Enhanced Index Advisor Towards Scaling Workloads
Taiyi Wang, Eiko Yoneki
https://arxiv.org/abs/2507.23084 https://arx…

AutoIndexer: A Reinforcement Learning-Enhanced Index Advisor Towards Scaling Workloads
Efficiently selecting indexes is fundamental to database performance optimization, particularly for systems handling large-scale analytical workloads. While deep reinforcement learning (DRL) has shown promise in automating index selection through its ability to learn from experience, few works address how these RL-based index advisors can adapt to scaling workloads due to exponentially growing action spaces and heavy trial and error. To address these challenges, we introduce AutoIndexer, a fram…

Decompiling Rust: An Empirical Study of Compiler Optimizations and Reverse Engineering Challenges
Zixu Zhou
https://arxiv.org/abs/2507.18792 https://arxiv.…

Decompiling Rust: An Empirical Study of Compiler Optimizations and Reverse Engineering Challenges
Decompiling Rust binaries is challenging due to the language's rich type system, aggressive compiler optimizations, and widespread use of high-level abstractions. In this work, we conduct a benchmark-driven evaluation of decompilation quality across core Rust features and compiler build modes. Our automated scoring framework shows that generic types, trait methods, and error handling constructs significantly reduce decompilation quality, especially in release builds. Through representative case…

Diverse and Adaptive Behavior Curriculum for Autonomous Driving: A Student-Teacher Framework with Multi-Agent RL
Ahmed Abouelazm, Johannes Ratz, Philip Sch\"orner, J. Marius Z\"ollner
https://arxiv.org/abs/2507.19146

Diverse and Adaptive Behavior Curriculum for Autonomous Driving: A Student-Teacher Framework with Multi-Agent RL
Autonomous driving faces challenges in navigating complex real-world traffic, requiring safe handling of both common and critical scenarios. Reinforcement learning (RL), a prominent method in end-to-end driving, enables agents to learn through trial and error in simulation. However, RL training often relies on rule-based traffic scenarios, limiting generalization. Additionally, current scenario generation methods focus heavily on critical scenarios, neglecting a balance with routine driving beh…

Here's an article on the still-racist Grok "AI".
In the article is a chunk of Python code that was generated by Grok.
What a stinkin' pile of code.
It is really simple code, but it has unacceptably weak error detection and handling that would give a user almost no useful information if something goes awry.
The hardest (and largest part) of most code (at least the code I work with) is validating inputs and handling errors. It is the most boring part …

Elon Musk Updated Grok. Guess What It Said?
After praising Hitler earlier this week, the chatbot is now listing the “good races.”

Enhancing the Clique Local Decoder to Correct Length-2 Space Errors in the Surface Code
Zikang Jia, Shravan Veerapaneni, Gokul Subramanian Ravi
https://arxiv.org/abs/2507.11481

Enhancing the Clique Local Decoder to Correct Length-2 Space Errors in the Surface Code
The growing demand for fault-tolerant quantum computing drives the need for efficient, scalable Quantum Error Correction (QEC) strategies. Conventional decoders designed for worst-case error scenarios incur significant overhead, prompting the development of local decoders, that leverage the sparse and often trivial nature of many quantum errors, to support the conventional decoders. The previously proposed Clique decoder addresses this by handling isolated, length-1 space and time errors within…

MemoCoder: Automated Function Synthesis using LLM-Supported Agents
Yiping Jia, Zhen Ming Jiang, Shayan Noei, Ying Zou
https://arxiv.org/abs/2507.18812 https://

MemoCoder: Automated Function Synthesis using LLM-Supported Agents
With the widespread adoption of Large Language Models (LLMs) such as GitHub Copilot and ChatGPT, developers increasingly rely on AI-assisted tools to support code generation. While LLMs can generate syntactically correct solutions for well-structured programming tasks, they often struggle with challenges that require iterative debugging, error handling, or adaptation to diverse problem structures. Existing approaches such as fine-tuning or self-repair strategies either require costly retraining…

HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration
Ganesh Parab, Zishan Ahmad, Dagnachew Birru
https://arxiv.org/abs/2506.18916 https:…

HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration
Text-to-SQL generation bridges the gap between natural language and databases, enabling users to query data without requiring SQL expertise. While large language models (LLMs) have significantly advanced the field, challenges remain in handling complex queries that involve multi-table joins, nested conditions, and intricate operations. Existing methods often rely on multi-step pipelines that incur high computational costs, increase latency, and are prone to error propagation. To address these l…

This https://arxiv.org/abs/2504.07732 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

Efficient Formal Verification of Quantum Error Correcting Programs
Quantum error correction (QEC) is fundamental for suppressing noise in quantum hardware and enabling fault-tolerant quantum computation. In this paper, we propose an efficient verification framework for QEC programs. We define an assertion logic and a program logic specifically crafted for QEC programs and establish a sound proof system. We then develop an efficient method for handling verification conditions (VCs) of QEC programs: for Pauli errors, the VCs are reduced to classical assertions t…

#StructuredConcurrency: Hierarchical Cancellation & Error Handling
https://www.youtube.com/watch?v=XOSR0Asq4h0

ReDi: Rectified Discrete Flow
Jaehoon Yoo, Wonjung Kim, Seunghoon Hong
https://arxiv.org/abs/2507.15897 https://arxiv.org/pdf/2507.15…

ReDi: Rectified Discrete Flow
Discrete Flow-based Models (DFMs) are powerful generative models for high-quality discrete data but typically suffer from slow sampling speeds due to their reliance on iterative decoding processes. This reliance on a multi-step process originates from the factorization approximation of DFMs, which is necessary for handling high-dimensional data. In this paper, we rigorously characterize the approximation error from factorization using Conditional Total Correlation (TC), which depends on the cou…

This https://arxiv.org/abs/2505.23868 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Noise-Robustness Through Noise: Asymmetric LoRA Adaption with Poisoning Expert
Current parameter-efficient fine-tuning methods for adapting pre-trained language models to downstream tasks are susceptible to interference from noisy data. Conventional noise-handling approaches either rely on laborious data pre-processing or employ model architecture modifications prone to error accumulation. In contrast to existing noise-process paradigms, we propose a noise-robust adaptation method via asymmetric LoRA poisoning experts (LoPE), a novel framework that enhances model robustne…

Day 7
✅ 24 test suites, 153 tests passing.
Solid coverage across service and controller layers in my modular monorepo. Strict typing (TypeScript), full DTO validation, and realistic mocks across complex relations (TypeORM).
Next: fine-tuning error handling & exploring e2e strategies.
https://write.tyolabs.com/?p=25