# Workspace-Bench 1.0: Benchmarking AI Agents on Workspace Tasks with Large-Scale File Dependencies

arXiv: https://arxiv.org/abs/2605.03596 · HTML: https://mlsanity.com/paper/2605.03596/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 5 May 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 18 of 18)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [CogAgent: A Visual Language Model for GUI Agents](https://mlsanity.com/paper/2312.08914/) (2023)
- [SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents](https://mlsanity.com/paper/2401.10935/) (2024)
- [TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks](https://mlsanity.com/paper/2412.14161/) (2024)
- [ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use](https://mlsanity.com/paper/2504.07981/) (2025)
- [ShowUI: One Vision-Language-Action Model for GUI Visual Agent](https://mlsanity.com/paper/2411.17465/) (2024)
- [GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks](https://mlsanity.com/paper/2510.04374/) (2025)
- [WorkBench: a Benchmark Dataset for Agents in a Realistic Workplace Setting](https://mlsanity.com/paper/2405.00823/) (2024)
- [CRMArena-Pro: Holistic Assessment of LLM Agents Across Diverse Business Scenarios and Interactions](https://mlsanity.com/paper/2505.18878/) (2025)
- [OfficeQA Pro: An Enterprise Benchmark for End-to-End Grounded Reasoning](https://mlsanity.com/paper/2603.08655/) (2026)
- [ClawsBench: Evaluating Capability and Safety of LLM Productivity Agents in Simulated Workspaces](https://mlsanity.com/paper/2604.05172/) (2026)
- [Odysseys: Benchmarking Web Agents on Realistic Long Horizon Tasks](https://mlsanity.com/paper/2604.24964/) (2026)
- [ClawMark: A Living-World Benchmark for Multi-Turn, Multi-Day, Multimodal Coworker Agents](https://mlsanity.com/paper/2604.23781/) (2026)
- [OneMillion-Bench: How Far are Language Agents from Human Experts?](https://mlsanity.com/paper/2603.07980/) (2026)
- [Multi-Mission Tool Bench: Assessing the Robustness of LLM based Agents through Related and Dynamic Missions](https://mlsanity.com/paper/2504.02623/) (2025)
- [DataCross: A Unified Benchmark and Agent Framework for Cross-Modal Heterogeneous Data Analysis](https://mlsanity.com/paper/2601.21403/) (2026)

## Built on by (most cited first, 29 of 29)

- [The Last AI Built by Humans: Toward Genuine Recursive Self-Improvement](https://mlsanity.com/paper/2609.11873/) (2026)
- [From Chatbot to Digital Colleague: The Paradigm Shift Toward Persistent Autonomous AI](https://mlsanity.com/paper/2606.14502/) (2026)
- [Tencent WorkBuddy Bench: A Multi-Domain Coding-Agent Benchmark with Contamination-Resistant Task Construction](https://mlsanity.com/paper/2607.20911/) (2026)
- [OneDayAgent: Towards a Long-Horizon Harness for Autonomous Agents](https://mlsanity.com/paper/2608.05013/) (2026)
- [VibeLifeBench: Can Your Life Agent Be Proactive and Persistent in a Living World?](https://mlsanity.com/paper/2608.10875/) (2026)
- [ORAgentBench: Can LLM Agents Solve Challenging Operations Research Tasks End to End?](https://mlsanity.com/paper/2606.19787/) (2026)
- [StartupBench: Benchmarking General-Purpose Agents on Market-Validated End-to-End Workflows](https://mlsanity.com/paper/2608.17800/) (2026)
- [DuMateBench: Evaluating Autonomous Agents in Complex Real-World Workflows](https://mlsanity.com/paper/2608.26546/) (2026)
- [Agents in the Large: Perception-Centered Architecture for Persistent Agents](https://mlsanity.com/paper/2608.30478/) (2026)
- [AI Snitches Get Glitches: Towards Evading Agentic Surveillance](https://mlsanity.com/paper/2606.25836/) (2026)
- [StagedWorkspace: A Versioned Workspace for Knowledge-Work Agents](https://mlsanity.com/paper/2608.18050/) (2026)
- [Benchmarking General Mobile Assistants in Challenging Real-World Scenarios](https://mlsanity.com/paper/2608.27477/) (2026)
- [Atria Dawn: The Dawn of Agentic Superintelligence](https://mlsanity.com/paper/2609.15818/) (2026)
- [WorkWorlds: An Infrastructure for Evaluating AI Agents on Workplace Tasks](https://mlsanity.com/paper/2609.23806/) (2026)
- [Breaking the Environment Wall: Evolving LLM Agent Environments for Recursive Self-Improvement](https://mlsanity.com/paper/2609.29773/) (2026)
- [HM-ROUTER: Joint Model and Harness Routing for Agentic Systems](https://mlsanity.com/paper/2609.32213/) (2026)
- ["You're Right, Let Me Fix It": How LLM Agents Damage Correct Work When Falsely Accused](https://mlsanity.com/paper/2609.32616/) (2026)
- [LLMs as Adaptive Meta-Solvers: Strategy-Diverse RL for Industrial-Scale Optimization](https://mlsanity.com/paper/2609.34427/) (2026)
- [GraphForge: Training Working Agents with Graph-Anchored Workspace Synthesis](https://mlsanity.com/paper/2609.38923/) (2026)
- [VeriHarness: Scaling Agentic Verification for Long-Horizon Tasks](https://mlsanity.com/paper/2610.00972/) (2026)
- [Scaling Verifiable Environments for Long-horizon Work Agents](https://mlsanity.com/paper/2610.04906/) (2026)
- [RunningTab: Direct Workspace Interaction with Environment-Side Tabs](https://mlsanity.com/paper/2610.10444/) (2026)
- [EvoCowork: A Benchmark for Real-World Office Tasks](https://mlsanity.com/paper/2610.14828/) (2026)
- [WorkBorne: Discovering Agentic Tasks from Real-World Artifact Transitions](https://mlsanity.com/paper/2610.14915/) (2026)
- [Recursive Environment Scaling for General Long-Horizon Agents](https://mlsanity.com/paper/2610.16397/) (2026)
- [Cowork-Gym: Synthesizing Interactive Work Environments at Scale](https://mlsanity.com/paper/2610.17670/) (2026)
- [Agent-Reflex: Teaching Language Agents to Act Reflectively](https://mlsanity.com/paper/2610.17953/) (2026)
- [WorkRover: Scaling Long-Horizon Cowork Agent Training Via Recursive Environment Evolution](https://mlsanity.com/paper/2610.18067/) (2026)
- [AssetForge: Asset-Augmented Task Synthesis for Business Automation Agents](https://mlsanity.com/paper/2610.18330/) (2026)

## Abstract (arXiv)

Workspace learning requires AI agents to identify, reason over, exploit, and update explicit and implicit dependencies among heterogeneous files in a worker's workspace, enabling them to complete both routine and advanced tasks effectively. Despite its importance, existing relevant benchmarks largely evaluate agents on pre-specified or synthesized files with limited real-world dependencies, leaving workspace-level evaluation underexplored. To this end, we introduce Workspace-Bench, a benchmark for evaluating AI agents on Workspace Learning involving Large-Scale File Dependencies. We construct realistic workspaces with 5 worker profiles, 74 file types, 20,476 files (up to 20GB) and curate 388 tasks, each with its own file dependency graph, evaluated across 7,399 total rubrics that require cross-file retrieval, contextual reasoning, and adaptive decision-making. We further provide Workspace-Bench-Lite, a 100-task subset that preserves the benchmark distribution while reducing evaluation costs by about 70%. We evaluate 4 popular agent harnesses and 7 foundation models. Experimental results show that current agents remain far from reliable workspace learning, where the best reaches only about 60%, substantially below the human result of 80.7%, and the average performance across agents is only 43.3%.