Revibing Code from Papers: Reimplementing HCI Artifacts
- URL: http://arxiv.org/abs/2608.00450v2
- Date: Tue, 04 Aug 2026 02:35:15 GMT
- Title: Revibing Code from Papers: Reimplementing HCI Artifacts
- Abstract summary: We demonstrate the potential of new agentic AI technologies to revibe interactive software.<n>To measure the success of the approach, we describe a revibeability metric.
- Score: 31.5513342575475
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Software artifacts for most technical HCI research projects are unavailable. The lack of access to these imposes limits on academic knowledge production. It is difficult to: extend or reuse research artifacts; use strong baselines in evaluating follow-up work; and perform replication or reproducibility research. In this work, we demonstrate the potential of new agentic AI technologies to revibe interactive software: reimplement systems directly from research papers. To measure the success of the approach, we describe a revibeability metric. By revibing recent research papers from UIST, and interviewing their original authors, we demonstrate the plausibility (and limitations) of revibed system. The results are encouraging. In many cases producing code suitable for strong baseline use. We argue that this may represent a fundamental shift in how we produce, use, and evaluate research artifacts in the technical HCI community.
Related papers
- ReproRepo: Scaling Reproducibility Audits with GitHub Repository Issues [54.98461672730087]
We introduce ReproRepo, a scalable framework for evaluation that leverages human-raised GitHub issues as naturally occurring supervision on realistic reproduction blockers.<n>We instantiate ReproRepo on 1,149 recent machine learning papers from major conferences and evaluate four frontier model-agent configurations.<n>Our results show that LLM agents, even without executing code, can identify many real-world problems from paper-repository pairs.
arXiv Detail & Related papers (2026-06-16T17:58:05Z) - AI for Auto-Research: Roadmap & User Guide [107.0834449839233]
We present an end-to-end analysis of AI across the complete research lifecycle.<n>We identify a sharp, stage-dependent boundary between reliable assistance and unreliable autonomy.<n>We show that greater automation can obscure rather than eliminate failure modes.
arXiv Detail & Related papers (2026-05-18T17:08:26Z) - AutoResearchBench: Benchmarking AI Agents on Complex Scientific Literature Discovery [55.70879973230979]
AutoResearchBench is a benchmark for autonomous scientific literature discovery.<n>Compared to previous benchmarks on agentic web browsing, AutoResearchBench is research-oriented.<n>Even the most powerful LLMs, despite having largely conquered general agentic web-browsing benchmarks such as BrowseComp, achieve only 9.39% accuracy on Deep Research and 9.31% IoU on Wide Research.
arXiv Detail & Related papers (2026-04-28T06:05:17Z) - SoK: From Silicon to Netlist and Beyond $-$ Two Decades of Hardware Reverse Engineering Research [5.914471577927024]
Hardware Reverse Engineering (HRE) is foundational for security assurance.<n>HRE enables critical security applications, including design verification, supply-chain assurance, and vulnerability discovery.<n>We present a systematization of knowledge based on an in-depth analysis of 187 peer-reviewed publications.
arXiv Detail & Related papers (2026-03-18T16:05:35Z) - PaperSearchQA: Learning to Search and Reason over Scientific Papers with RLVR [64.22412492998754]
We release a search corpus of 16 million biomedical paper abstracts and construct a challenging factoid QA dataset called PaperSearchQA.<n>We train search agents in this environment to outperform non-RL retrieval baselines.<n>Our data creation methods are scalable and easily extendable to other scientific domains.
arXiv Detail & Related papers (2026-01-26T06:46:16Z) - Executable Knowledge Graphs for Replicating AI Research [65.41207324831583]
Executable Knowledge Graphs (xKG) is a modular and pluggable knowledge base that automatically integrates technical insights, code snippets, and domain-specific knowledge extracted from scientific literature.<n>Code will released at https://github.com/zjunlp/xKG.
arXiv Detail & Related papers (2025-10-20T17:53:23Z) - Can LLMs Generate Tabular Summaries of Science Papers? Rethinking the Evaluation Protocol [83.90769864167301]
Literature review tables are essential for summarizing and comparing collections of scientific papers.<n>We explore the task of generating tables that best fulfill a user's informational needs given a collection of scientific papers.<n>Our contributions focus on three key challenges encountered in real-world use: (i) User prompts are often under-specified; (ii) Retrieved candidate papers frequently contain irrelevant content; and (iii) Task evaluation should move beyond shallow text similarity techniques.
arXiv Detail & Related papers (2025-04-14T14:52:28Z) - SyROCCo: Enhancing Systematic Reviews using Machine Learning [6.805429133535976]
This paper explores the use of machine learning techniques to help navigate the systematic review process.
The application of ML techniques to subsequent stages of a review, such as data extraction and evidence mapping, is in its infancy.
arXiv Detail & Related papers (2024-06-24T11:04:43Z) - LitLLM: A Toolkit for Scientific Literature Review [15.785989492351684]
We propose a toolkit that operates on Retrieval Augmented Generation (RAG) principles.<n>Our system first initiates a web search to retrieve relevant papers.<n>Second, the system re-ranks the retrieved papers based on the user-provided abstract.<n>Third, the related work section is generated based on the re-ranked results and the abstract.
arXiv Detail & Related papers (2024-02-02T02:41:28Z) - A Survey of Learning-based Automated Program Repair [12.09968472868107]
Automated program repair (APR) aims to fix software bugs automatically and plays a crucial role in software development and maintenance.
With the recent advances in deep learning (DL), an increasing number of APR techniques have been proposed to leverage neural networks to learn bug-fixing patterns from massive open-source code repositories.
This paper provides a systematic survey to summarize the current state-of-the-art research in the learning-based APR community.
arXiv Detail & Related papers (2023-01-09T11:08:15Z) - A Systematic Literature Review on the Use of Deep Learning in Software
Engineering Research [22.21817722054742]
An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL)
This paper presents a systematic literature review of research at the intersection of SE & DL.
We center our analysis around the components of learning, a set of principles that govern the application of machine learning techniques to a given problem domain.
arXiv Detail & Related papers (2020-09-14T15:28:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.