Deep Researcher Agent: An Autonomous Framework for 24/7 Deep Learning Experimentation with Zero-Cost Monitoring

• URL: http://arxiv.org/abs/2604.05854v1
• Date: Tue, 07 Apr 2026 13:16:31 GMT
• Title: Deep Researcher Agent: An Autonomous Framework for 24/7 Deep Learning Experimentation with Zero-Cost Monitoring
• Abstract summary: We present textbfDeep Researcher Agent, an open-source framework that enables large language model (LLM) agents to autonomously conduct deep learning experiments around the clock.<n>Unlike existing AI research assistants that focus on paper writing or code generation, our system addresses the full experiment lifecycle: hypothesis formation, code implementation, training execution, result analysis, and iterative refinement.
• Score: 2.4112990554464235
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present \textbf{Deep Researcher Agent}, an open-source framework that enables large language model (LLM) agents to autonomously conduct deep learning experiments around the clock. Unlike existing AI research assistants that focus on paper writing or code generation, our system addresses the full experiment lifecycle: hypothesis formation, code implementation, training execution, result analysis, and iterative refinement. The framework introduces three key innovations: (1) \textbf{Zero-Cost Monitoring} -- a monitoring paradigm that incurs zero LLM API costs during model training by relying solely on process-level checks and log file reads; (2) \textbf{Two-Tier Constant-Size Memory} -- a memory architecture capped at $\sim$5K characters regardless of runtime duration, preventing the unbounded context growth that plagues long-running agents; and (3) \textbf{Minimal-Toolset Leader-Worker Architecture} -- a multi-agent design where each worker agent is equipped with only 3--5 tools, reducing per-call token overhead by up to 73\%. In sustained deployments spanning 30+ days, the framework autonomously completed 500+ experiment cycles across four concurrent research projects, achieving a 52\% improvement over baseline metrics in one project through 200+ automated experiments -- all at an average LLM cost of \$0.08 per 24-hour cycle. Code is available at https://github.com/Xiangyue-Zhang/auto-deep-researcher-24x7.

Related papers

• AI Research Preference Models [87.14303218008381]
  Research agents can propose, implement, and evaluate their own machine learning experiments.<n> progress on frontier tasks is throttled by cost.<n>We introduce AI Research Preference Models ( RPMs) that predict which of multiple candidate solutions are most worth executing.
  arXiv  Detail & Related papers  (2026-08-14T04:20:37Z)
• EEVEE: Towards Test-time Prompt Learning in the Real World for Self-Improving Agents [64.96332056338923]
  EEVEE is the first multi-dataset test-time prompt learning framework for LLM agents.<n>It enables test-time prompt learning under real-world task streams.<n>EEVEE improves average multi-benchmark scores by 10.38 and 24.32 points over Qwen3-4B-Instruct and DeepSeek-V3.2.
  arXiv  Detail & Related papers  (2026-06-09T17:57:16Z)
• ADK Arena: Evaluating Agent Development Kits via LLM-as-a-Developer [15.00724508326096]
  textbfLLM-as-a-Developer replaces human developers with an LLM coding agent that learns each framework's API from documentation, writes agent code, and iteratively repairs it through a validate-and-feedback loop until tests pass.<n>We implement this in textbfADK Arena, a fully automated pipeline with per-framework Docker isolation, a three-level validation pipeline, and benchmark adapters for SWE-bench, $2$-bench, Terminal-Bench, and MCP-Atlas.
  arXiv  Detail & Related papers  (2026-06-04T01:00:54Z)
• EngiAI: A Multi-Agent Framework and Benchmark Suite for LLM-Driven Engineering Design [0.4499833362998488]
  We introduce a benchmark suite with three evaluation dimensions.<n>We present EngiAI, a Multi-Agent System (MAS) implementation built on LangGraph.
  arXiv  Detail & Related papers  (2026-05-19T12:12:09Z)
• WildClawBench: A Benchmark for Real-World, Long-Horizon Agent Evaluation [88.10947115397971]
  This work presents WildClawBench, a native-runtime benchmark of 60 human-authored, bilingual, multimodal tasks spanning six thematic categories.<n>Each task averages roughly 8 minutes of wall-clock time and over 20 tool calls.<n> Grading is hybrid, combining deterministic rule-based checks, environment-state auditing of side effects, and an LLM/VLM judge for semantic verification.
  arXiv  Detail & Related papers  (2026-05-11T17:49:43Z)
• ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox [61.862814740220806]
  $textbfComplexMCP$ is a benchmark designed to evaluate agents in rigorous conditions.<n>Built on the Model Context Protocol (MCP), $textbfComplexMCP$ provides over 300 meticulously tested tools.
  arXiv  Detail & Related papers  (2026-05-11T16:20:51Z)
• MatClaw: An Autonomous Code-First LLM Agent for End-to-End Materials Exploration [0.0]
  We present MatClaw, a code-first agent that writes and executes Python directly.<n>We show that MatClaw handles code generation reliably but struggles with tacit domain knowledge.<n>Our results demonstrate that the gap between guided and fully autonomous computational materials research is narrower than ever before.
  arXiv  Detail & Related papers  (2026-04-03T03:32:15Z)
• Omni-SimpleMem: Autoresearch-Guided Discovery of Lifelong Multimodal Agent Memory [76.63021613850093]
  We deploy an autonomous research pipeline to discover Omni-SimpleMem, a unified multimodal memory framework for lifelong AI agents.<n>System achieves state-of-the-art on two benchmarks, improving F1 by +411% on LoCoMo and +214% on Mem-Gallery.<n>We provide a taxonomy of six discovery types and identify four properties that make multimodal memory particularly suited for autoresearch.
  arXiv  Detail & Related papers  (2026-04-01T15:06:23Z)
• AlphaLab: Autonomous Multi-Agent Research Across Optimization Domains with Frontier LLMs [14.167136550376497]
  We present AlphaLab, an autonomous research harness that automates the full experimental cycle in computation-intensive domains.<n>It adapts to the domain and explores the data, writing analysis code and producing a research report.<n>It runs large-scale experiments via a Strategist/Worker loop, accumulating domain knowledge in a persistent playbook.
  arXiv  Detail & Related papers  (2026-03-31T21:16:20Z)
• Agent-Driven Autonomous Reinforcement Learning Research: Iterative Policy Improvement for Quadruped Locomotion [14.484745002483258]
  This paper documents a case study in agent-driven autonomous reinforcement learning research for quadruped locomotion.<n>A human provided high-level directives through an agentic coding environment, while an agent carried out most of the execution loop.<n>Across more than 70 experiments organized into fourteen waves on a DHAV1 12-DoF quadruped in Isaac Lab, the agent progressed from early rough-terrain runs with mean reward around 7 to a best logged Wave 12 run, exp063, with velocity error 0.263 and 97% timeout over 2000 iterations.
  arXiv  Detail & Related papers  (2026-03-28T21:30:04Z)
• The Agentic Researcher: A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning [22.71288370686935]
  This paper is a practical guide to AI-assisted research in mathematics and machine learning.<n>It is organized into three parts: (I) a five-level taxonomy of AI integration, (II) an open-source framework that turns CLI coding agents into autonomous research assistants, and (III) case studies from deep learning and mathematics.
  arXiv  Detail & Related papers  (2026-03-16T21:02:06Z)
• Lifecycle-Aware code generation: Leveraging Software Engineering Phases in LLMs [12.70863561286374]
  We introduce a lifecycle-aware framework that incorporates intermediate artifacts into both the training and inference stages.<n> Experiments show that lifecycle-level fine-tuning improves code correctness by up to 75% over the same model before fine-tuning.<n>Open-source LLMs, once fine-tuned under our framework, match or slightly outperform models pretrained on code.
  arXiv  Detail & Related papers  (2025-10-28T02:54:02Z)
• AgentRL: Scaling Agentic Reinforcement Learning with a Multi-Turn, Multi-Task Framework [76.96794548655292]
  Large language models (LLMs) have sparked growing interest in building generalist agents that can learn through online interactions.<n>Applying reinforcement learning (RL) to train LLM agents in multi-turn, multi-task settings remains challenging due to lack of scalable infrastructure and stable training algorithms.<n>We present the AgentRL framework for scalable multi-turn, multi-task agentic RL training.
  arXiv  Detail & Related papers  (2025-10-05T13:40:01Z)
• Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol [83.83217247686402]
  Large Language Models (LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and multi-turn interactions.<n>Their inherent non-determinism, dynamism, and context dependence pose fundamental challenges for quality assurance.<n>This paper decomposes LLM applications into a three-layer architecture: textbftextitSystem Shell Layer, textbftextitPrompt Orchestration Layer, and textbftextitLLM Inference Core.
  arXiv  Detail & Related papers  (2025-08-28T13:00:28Z)
• Spider2-V: How Far Are Multimodal Agents From Automating Data Science and Engineering Workflows? [73.81908518992161]
  We introduce Spider2-V, the first multimodal agent benchmark focusing on professional data science and engineering. Spider2-V features real-world tasks in authentic computer environments and incorporating 20 enterprise-level professional applications. These tasks evaluate the ability of a multimodal agent to perform data-related tasks by writing code and managing the GUI in enterprise data software systems.
  arXiv  Detail & Related papers  (2024-07-15T17:54:37Z)
• MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation [96.71370747681078]
  We introduce MLAgentBench, a suite of 13 tasks ranging from improving model performance on CIFAR-10 to recent research problems like BabyLM. For each task, an agent can perform actions like reading/writing files, executing code, and inspecting outputs. We benchmark agents based on Claude v1.0, Claude v2.1, Claude v3 Opus, GPT-4, GPT-4-turbo, Gemini-Pro, and Mixtral and find that a Claude v3 Opus agent is the best in terms of success rate.
  arXiv  Detail & Related papers  (2023-10-05T04:06:12Z)
• Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking [4.721069729610892]
  Deep reinforcement learning has been one of the fastest growing fields of machine learning over the past years and numerous libraries have been open sourced to support research. This paper introduces Tonic, a Python library allowing researchers to quickly implement new ideas and measure their importance.
  arXiv  Detail & Related papers  (2020-11-15T14:10:37Z)

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.