FieldWorkArena: An Agentic AI Benchmark for Real Field Work Tasks (arXiv 2505.19662)

Forum topic · 小凯 · 2026-07-05

Summary

FieldWorkArena is a benchmark proposed on arXiv (2505.19662, May 2025) for evaluating agentic AI systems on real-world field work tasks. The paper, authored by Jun Takahashi, Atsunori Moteki, Akiyoshi Uchida, Shoichi Masui, Fan Yang, Kanji Uchino and colleagues (14 authors in total), addresses the gap between benchmarks built on synthetic or web data and the demands of actual industrial field environments. Rather than measuring static retrieval quality metrics such as nDCG, agentic benchmarks like FieldWorkArena focus on task success, multi-step planning, tool use, and process-level evaluation in realistic work scenarios. The forum post situates the work at the intersection of agentic search and large-scale search/recommendation systems, arguing that LLM-era systems must treat retrieval strategy and inference budget as learnable decisions. It also reviews method components (retrievers, rerankers, planners, generators, feedback mechanisms), evaluation protocols, and open challenges including evaluation reliability, latency and cost, hallucination and safety, and cross-lingual/multimodal extension. Readers should consult the original PDF for quantitative results.

FieldWorkArena: An Agentic AI Benchmark for Real Field Work Tasks (May 2025, arXiv)

Overview

Paper: FieldWorkArena: Agentic AI Benchmark for Real Field Work Tasks (arXiv:2505.19662, May 2025)
Authors: Jun Takahashi, Atsunori Moteki, Akiyoshi Uchida, Shoichi Masui, Fan Yang, Kanji Uchino, et al. (14 authors total)
Type: Academic paper / benchmark
Category: Evaluation of search systems; agentic search

Why It Matters

FieldWorkArena targets a key gap in LLM-agent evaluation: most benchmarks are built from synthetic or web data, while real field work (industrial sites, maintenance, on-site operations) imposes very different constraints on perception, retrieval, planning, and safety. The benchmark pushes evaluation beyond static relevance metrics (nDCG, MRR) toward task success rates and process-level metrics such as citation accuracy and multi-hop reasoning chain integrity.

Key Points from the Forum Discussion

Unified framing: The post positions agentic search as shifting the boundary between retrieval, ranking, generation, and tool calling — with *inference budget* and *action space* (whether to retrieve, how many times, which tools) as the new variables in the LLM era.
Method decomposition: Modern agentic pipelines break into input representation, core modules (retriever, reranker, planner, memory, tool interfaces), learning strategies (SFT, contrastive learning, RL with process rewards, synthetic data), and inference strategies (single-shot vs. iterative retrieval, parallel sub-queries, early stopping, budget control).
Evaluation shift: Offline metrics diverge increasingly from online satisfaction; LLM-as-judge should be cross-validated with human assessment. Task success and citation fidelity matter more than nDCG for agentic systems.

Engineering Takeaways

| Concern | Recommendation | |---|---| | Latency | Cascaded retrieval with early stopping, query caching, async reranking | | Quality | Interleaving experiments, human audits, citation verification | | Safety | Source whitelisting, adversarial detection, output filtering | | Cost | Model routing, distillation, hybrid sparse+dense retrieval |

Limitations and Open Problems

Benchmarks may not match real user/task distributions; cross-lingual generalization is often unknown.
Open-web agent systems face poisoning and bias risks.
Future directions: efficient test-time compute allocation, deeper integration with knowledge graphs and structured databases, causal and fairness constraints for recommendation.

Related Work

AI Search Has A Citation Problem (CJR, Mar 2025)
Evaluation of Retrieval-Augmented Generation: A Survey (arXiv:2405.07437)
ARES: Automated Evaluation Framework for RAG (arXiv:2311.09476)
AgentBoard: Multi-turn LLM Agent Evaluation (arXiv:2401.13178)
Agent-X: Vision-Centric Agentic Reasoning (arXiv:2505.24876)

> Note: Quantitative results should be verified against the original PDF; this post summarizes based on the abstract and public metadata.