From AutoRecSys to AutoRecLab: A Call to Build, Evaluate, and Govern Autonomous Recommender-Systems Research Labs

• URL: http://arxiv.org/abs/2510.18104v1
• Date: Mon, 20 Oct 2025 20:58:50 GMT
• Title: From AutoRecSys to AutoRecLab: A Call to Build, Evaluate, and Govern Autonomous Recommender-Systems Research Labs
• Abstract summary: We argue for a shift from narrow AutoRecSys tools to an Autonomous Recommender-Systems Research Lab (AutoRecLab)<n>AutoRecLab integrates end-to-end automation: problem ideation, literature analysis, experimental design and execution, result interpretation, manuscript drafting, and logging.<n>We conclude with a call to organise a community retreat to coordinate next steps and co-author guidance for the responsible integration of automated research systems.
• Score: 5.34658805289521
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender-systems research has accelerated model and evaluation advances, yet largely neglects automating the research process itself. We argue for a shift from narrow AutoRecSys tools -- focused on algorithm selection and hyper-parameter tuning -- to an Autonomous Recommender-Systems Research Lab (AutoRecLab) that integrates end-to-end automation: problem ideation, literature analysis, experimental design and execution, result interpretation, manuscript drafting, and provenance logging. Drawing on recent progress in automated science (e.g., multi-agent AI Scientist and AI Co-Scientist systems), we outline an agenda for the RecSys community: (1) build open AutoRecLab prototypes that combine LLM-driven ideation and reporting with automated experimentation; (2) establish benchmarks and competitions that evaluate agents on producing reproducible RecSys findings with minimal human input; (3) create review venues for transparently AI-generated submissions; (4) define standards for attribution and reproducibility via detailed research logs and metadata; and (5) foster interdisciplinary dialogue on ethics, governance, privacy, and fairness in autonomous research. Advancing this agenda can increase research throughput, surface non-obvious insights, and position RecSys to contribute to emerging Artificial Research Intelligence. We conclude with a call to organise a community retreat to coordinate next steps and co-author guidance for the responsible integration of automated research systems.

Related papers

• AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery [184.2309450554096]
  AutoResearch is defined as the developmental spectrum of AI-powered scientific workflow automation.<n>We analyze how research systems redistribute control, evidence, execution, validation, and accountability across and organize the field.<n>We propose five evaluation dimensions--novelty, impact, reliability, and provenance--and show that AutoResearch autonomy is domain-conditioned.
  arXiv  Detail & Related papers  (2026-05-22T03:40:30Z)
• 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)
• MLReplicate: Benchmarking Autonomous Research Systems for Machine Learning Reproducibility [2.2969445973129123]
  We introduce MLReplicate, an end-to-end benchmark evaluating autonomous research systems on machine learning.<n>The benchmark was constructed from ICML 2025 papers reformulated into standardized input specifications and evaluated across 6 state-of-the-art research systems.<n>In contrast to automated reviews, human reviewers consistently identified methodological flaws, hallucinated experimental results, and failures across all systems.
  arXiv  Detail & Related papers  (2026-05-15T20:35:32Z)
• 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)
• AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery [49.77172027295716]
  AutoSOTA is an end-to-end automated research system that advances the latest State-Of-The-Art (SOTA) models published in top-tier AI papers.<n>It successfully discovers 105 new SOTA models that surpass the original reported methods, averaging approximately five hours per paper.
  arXiv  Detail & Related papers  (2026-04-07T07:52:01Z)
• AI Co-Scientist for Ranking: Discovering Novel Search Ranking Models alongside LLM-based AI Agents with Cloud Computing Access [60.37646376282544]
  We present an AI Co-Scientist framework that automates the full search ranking research pipeline.<n>This is the first study in the ranking community to utilize an AI Co-Scientist framework for algorithmic research.
  arXiv  Detail & Related papers  (2026-03-23T09:29:08Z)
• Training AI Co-Scientists Using Rubric Rewards [36.857905469178796]
  A crucial feature of AI co-scientists is the ability to generate a research plan given a set of aims and constraints.<n>In this work, we study how to leverage the vast corpus of existing research papers to train language models that generate better research plans.<n>We build a scalable, diverse training corpus by automatically extracting research goals and goal-specific grading rubrics from papers across several domains.
  arXiv  Detail & Related papers  (2025-12-29T18:59:33Z)
• SelfAI: Building a Self-Training AI System with LLM Agents [79.10991818561907]
  SelfAI is a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations.<n>An Experiment Manager orchestrates parallel, fault-tolerant training across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback.<n>Across regression, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials.
  arXiv  Detail & Related papers  (2025-11-29T09:18:39Z)
• Task Allocation for Autonomous Machines using Computational Intelligence and Deep Reinforcement Learning [27.325920343591033]
  We focus on task allocation methods using computational intelligence (CI) and deep reinforcement learning (RL)<n>The recent development of deep RL has greatly contributed to the literature on controlling and coordinating autonomous machines.<n>It is envisaged that this paper will provide researchers and engineers with a comprehensive overview of progress in machine learning research related to autonomous machines.
  arXiv  Detail & Related papers  (2025-08-28T11:48:55Z)
• MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research [70.72318131988102]
  MLR-Bench is a comprehensive benchmark for evaluating AI agents on open-ended machine learning research.<n>MLR-Bench includes three key components: (1) 201 research tasks sourced from NeurIPS, ICLR, and ICML workshops covering diverse ML topics; (2) MLR-Judge, an automated evaluation framework combining LLM-based reviewers with carefully designed review rubrics to assess research quality; and (3) MLR-Agent, a modular agent scaffold capable of completing research tasks through four stages: idea generation, proposal formulation, experimentation, and paper writing.
  arXiv  Detail & Related papers  (2025-05-26T13:18:37Z)
• From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery [67.07598263346591]
  Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery.<n>This survey systematically charts this burgeoning field, placing a central focus on the changing roles and escalating capabilities of LLMs in science.
  arXiv  Detail & Related papers  (2025-05-19T15:41:32Z)
• A Vision for Auto Research with LLM Agents [46.95148319863236]
  This paper introduces Agent-Based Auto Research, a structured multi-agent framework designed to automate, coordinate, and optimize the full lifecycle of scientific research.<n>The system spans all major research phases, including literature review, ideation, methodology, experimentation, paper writing, peer review response, and dissemination.
  arXiv  Detail & Related papers  (2025-04-26T02:06:10Z)
• CycleResearcher: Improving Automated Research via Automated Review [37.03497673861402]
  This paper explores the possibility of using open-source post-trained large language models (LLMs) as autonomous agents capable of performing the full cycle of automated research and review.<n>To train these models, we develop two new datasets, reflecting real-world machine learning research and peer review dynamics.<n>Our results demonstrate that CycleReviewer achieves promising performance with a 26.89% reduction in mean absolute error (MAE) compared to individual human reviewers in predicting paper scores.
  arXiv  Detail & Related papers  (2024-10-28T08:10:21Z)
• ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
  ResearchAgent is an AI-based system for ideation and operationalization of novel work.<n>ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them.<n>We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
  arXiv  Detail & Related papers  (2024-04-11T13:36:29Z)
• System for systematic literature review using multiple AI agents: Concept and an empirical evaluation [3.453564255183234]
  Systematic literature review ( SLR) is foundational to evidence-based research.<n>We introduce a novel multi-AI-agent system designed to fully automate SLRs.<n>We show how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
  arXiv  Detail & Related papers  (2024-03-13T10:27:52Z)
• Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents [61.36681529571202]
  We describe a new concept for reproducible robotics research that integrates development and benchmarking. One of the central components of this setup is the Duckietown Autolab, a standardized setup that is itself relatively low-cost and reproducible. We validate the system by analyzing the repeatability of experiments conducted using the infrastructure and show that there is low variance across different robot hardware and across different remote labs.
  arXiv  Detail & Related papers  (2020-09-09T15:31:29Z)

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.