Running the Gauntlet: Re-evaluating the Capabilities of Agents Beyond Familiar Environments

• URL: http://arxiv.org/abs/2606.14397v1
• Date: Fri, 12 Jun 2026 12:32:24 GMT
• Title: Running the Gauntlet: Re-evaluating the Capabilities of Agents Beyond Familiar Environments
• Abstract summary: GauntletBench is a web-based benchmark for evaluating agent generalisation in challenging scenarios.<n>It focuses on three underexplored capabilities (temporal perception, graphical understanding, and 3D reasoning) across five less-covered professional applications.<n>Our empirical results reveal that frontier agentic systems remain far from achieving human-level performance.
• Score: 67.2234316079859
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As agentic systems continue to evolve and are widely deployed in real-world scenarios, there is a growing demand to faithfully evaluate their capabilities. However, current benchmarks are typically built on popular applications with relatively simple tasks and focus on a narrow set of capabilities while overlooking broader dimensions, resulting in saturated performance on modern agents and failing to probe their limitations. To this end, we introduce GauntletBench, a web-based benchmark for evaluating agent generalisation in challenging scenarios, focusing on three underexplored capabilities (temporal perception, graphical understanding, and 3D reasoning), across five less-covered professional applications (Video Editor, Workflow Builder, 3D Modeller, Flight Analyser, and Circuit Designer), each with 20 vision-intensive tasks (100 in total). Our benchmark provides a modular pipeline that comprises an environment compatible with both open- and closed-source agent frameworks, a controlled web-based application, a well-structured task suite, and an automated evaluation engine with diverse metrics. Contrary to widespread expectations, our empirical results reveal that frontier agentic systems remain far from achieving human-level performance. Even the state-of-the-art agent achieves only a 19.1% success rate on our GauntletBench, highlighting the limitations in these overlooked capabilities and generalisation. By comparison, non-expert human annotators achieve over 80% success on our challenging yet feasible tasks, revealing the substantial gap between current agent capabilities and those required for complex real-world scenarios.

Related papers

• StartupBench: Benchmarking General-Purpose Agents on Market-Validated End-to-End Workflows [63.57092302641323]
  We introduce textbfStartupBench, an E2E agent benchmark grounded in market-validated AI startup products.<n>We study AI products with demonstrated adoption, together with their product and users, to identify real-world tasks for which AI has established practical demand.<n>Our results reveal that many market-validated agents remain beyond the reliable capabilities of current general-purpose agents.
  arXiv  Detail & Related papers  (2026-08-18T14:01:32Z)
• UniClawBench: A Universal Benchmark for Proactive Agents on Real-World Tasks [43.36285223987099]
  We introduce UniClawBench, the first capability-driven benchmark to evaluate proactive agents in dynamic, real-world settings.<n>UniClawBench is built around five executor model capabilities: Skill Usage, Exploration, Long-Context Reasoning, Multimodal Understanding, and Cross-Platform Coordination.<n>Unlike previous benchmarks that rely on static, pre-recorded answers, our benchmark evaluates agents in live Docker containers using fine-grained, step-by-step completion checkpoints.
  arXiv  Detail & Related papers  (2026-07-09T17:59:32Z)
• WebRetriever: A Large-Scale Comprehensive Benchmark for Efficient Web Agent Evaluation [26.889929086766003]
  Web agents increasingly demonstrate capabilities in automated task execution.<n>Existing benchmarks suffer from insufficient scale and limited domain diversity.<n>We introduce WebRetriever, a large-scale benchmark encompassing 800 websites and 1,550 tasks.
  arXiv  Detail & Related papers  (2026-07-07T10:27:31Z)
• EgoBench: An Interactive Egocentric Multimodal Benchmark for Tool-Using Agents [17.727481701114556]
  We introduce EgoBench, the first interactive multimodal benchmark for tool-using agents.<n>We implement a three-stage synergistic pipeline through which each task is designed to enforce the joint application of visual perception and tool-augmented multi-hop reasoning.<n>We additionally develop a multi-agent simulated user within EgoBench to evaluate agents' interaction capabilities, which generates high-fidelity, task-aligned responses.
  arXiv  Detail & Related papers  (2026-05-27T01:28:15Z)
• SimuWoB: Simulating Real-World Mobile Apps for Fast and Faithful GUI Agent Benchmarking [23.006082521142137]
  SimuWoB is a fully synthetic benchmark for mobile GUI agents with 120 challenging tasks spanning diverse types and difficulty levels.<n>We build a robust virtual environment generation framework that synthesizes high-fidelity tasks and environments.<n>We conduct comprehensive experiments on several state-of-the-art mobile GUI agents.
  arXiv  Detail & Related papers  (2026-05-24T16:33:14Z)
• BeyondSWE: Can Current Code Agent Survive Beyond Single-Repo Bug Fixing? [61.247730037229815]
  We introduce BeyondSWE, a comprehensive benchmark that broadens existing evaluations along two axes - resolution scope and knowledge scope.<n>To investigate the role of external knowledge, we develop SearchSWE, a framework that integrates deep search with coding abilities.<n>This work offers both a realistic, challenging evaluation benchmark and a flexible framework to advance research toward more capable code agents.
  arXiv  Detail & Related papers  (2026-03-03T17:52:01Z)
• UltraHorizon: Benchmarking Agent Capabilities in Ultra Long-Horizon Scenarios [63.67884284105684]
  We introduce textbfUltraHorizon, a novel benchmark that measures the foundational capabilities essential for complex real-world challenges.<n>Agents are designed in long-horizon discovery tasks where they must iteratively uncover hidden rules.<n>Our experiments reveal that LLM-agents consistently underperform in these settings, whereas human participants achieve higher scores.
  arXiv  Detail & Related papers  (2025-09-26T02:04:00Z)
• Agent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agentic Tasks [94.19506319646376]
  We introduce Agent-X, a benchmark for evaluating vision-centric agents in real-world, multimodal settings.<n>Agent-X features 828 agentic tasks with authentic visual contexts, including images, multi-image comparisons, videos, and instructional text.<n>Our results reveal that even the best-performing models, including GPT, Gemini, and Qwen families, struggle to solve multi-step vision tasks.
  arXiv  Detail & Related papers  (2025-05-30T17:59:53Z)
• WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks? [83.19032025950986]
  We study the use of large language model-based agents for interacting with software via web browsers. WorkArena is a benchmark of 33 tasks based on the widely-used ServiceNow platform. BrowserGym is an environment for the design and evaluation of such agents.
  arXiv  Detail & Related papers  (2024-03-12T14:58:45Z)

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.