arxiv: 2604.10866 · v2 · submitted 2026-04-13 · 💻 cs.CL

Recognition: unknown

OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation

Xiaomeng Hu , Yinger Zhang , Fei Huang , Jianhong Tu , Yang Su , Lianghao Deng , Yuxuan Liu , Yantao Liu

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Dayiheng Liu Tsung-Yi Ho

Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:39 UTC · model grok-4.3

classification 💻 cs.CL

keywords AI agentsbenchmarkprofessional taskslanguage environment simulationfault injectionoccupational domainsmodel evaluationtask completion

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The pith

No single AI model dominates all professional industries when tested on 100 real-world tasks via simulated environments.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces OccuBench to evaluate AI agents across 100 professional task scenarios in 10 industries and 65 domains by using language models to simulate otherwise inaccessible environments and tool responses. It establishes that each model has a distinct profile of strengths and weaknesses by industry, that agents struggle more with implicit data degradation than with explicit errors or mixed faults, and that performance rises with larger models and greater reasoning effort. This approach matters because agents are expected to handle specialized work in fields like medicine and safety monitoring where public testbeds do not exist. The results also separate the ability to complete tasks from the ability to generate reliable simulations for evaluation.

Core claim

OccuBench covers 100 real-world professional task scenarios across 10 industry categories and 65 specialized domains, enabled by Language Environment Simulators that simulate domain-specific environments through LLM-driven tool response generation. Evaluation of 15 frontier models across 8 families shows that no single model dominates all industries, implicit faults are harder than explicit or mixed faults because they lack overt error signals, larger models and higher reasoning effort improve performance, and strong agents are not necessarily strong environment simulators.

What carries the argument

Language Environment Simulators (LESs) that generate domain-specific tool responses and environment states via LLMs to enable controlled agent evaluation in professional domains without public real-world access.

If this is right

Models will need to be chosen for specific industries rather than relying on overall performance averages.
Agents must develop the ability to detect and respond to data degradation without receiving explicit error signals.
Further increases in model scale and reasoning effort will likely produce additional gains on complex occupational tasks.
Benchmark validity requires separate verification that the simulators themselves match real tool behaviors.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Benchmarks of this form could guide targeted development of agents for particular occupational fields.
The gap between task performance and simulator quality points to the value of creating dedicated simulation models.
Extending fault injection to more subtle or domain-specific degradation patterns could expose additional capability limits.

Load-bearing premise

That LLM-driven simulations of domain-specific tool responses and professional environments are accurate and unbiased enough to support reliable agent evaluation.

What would settle it

Direct comparison of the same agents' success rates and relative rankings when interacting with the simulated benchmark environments versus the actual deployed professional software systems.

Figures

Figures reproduced from arXiv: 2604.10866 by Dayiheng Liu, Fei Huang, Jianhong Tu, Lianghao Deng, Tsung-Yi Ho, Xiaomeng Hu, Yang Su, Yantao Liu, Yinger Zhang, Yuxuan Liu.

**Figure 2.** Figure 2: Radar chart showing model performance profiles across 10 industry categories [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Figure 3: Completion rates under clean (E0) and fault-injected (E1–E3) environments. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

**Figure 4.** Figure 4: Fault parameter ablation under E3 mixed faults. (a) Varying fault count with fixed [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗

**Figure 5.** Figure 5: Large vs. small model variants within each family (E0). Gaps range from 0.3% to [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗

**Figure 6.** Figure 6: Claude generational progress from v4 to v4.6 (E0). Opus shows consistent im [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗

**Figure 7.** Figure 7: Effect of reasoning effort on agent performance (E0). [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗

**Figure 8.** Figure 8: Pairwise ranking agreement across simulators. Each cell shows the fraction of the [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗

**Figure 9.** Figure 9: Cross-simulator case 1: Emergency Department Triage. GPT-5.2 fabricates envi [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗

**Figure 10.** Figure 10: Cross-simulator case 2: Escalation Workflow. GPT-5.2 drops a critical entity from [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗

**Figure 11.** Figure 11: Cross-simulator case 3: Order Return. GPT-5.2 fabricates a business rule rejection [PITH_FULL_IMAGE:figures/full_fig_p014_11.png] view at source ↗

**Figure 12.** Figure 12: Average completion rate across 14 models per industry category (E0). Green: [PITH_FULL_IMAGE:figures/full_fig_p014_12.png] view at source ↗

**Figure 13.** Figure 13: Case study: Last-Mile Delivery Routing. Top: task specification and tool schema. [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗

**Figure 14.** Figure 14: Case study 2: Fish Farm Water Quality Control. Both agents achieve the target [PITH_FULL_IMAGE:figures/full_fig_p017_14.png] view at source ↗

**Figure 15.** Figure 15: Case study 3: Building Inspection Compliance. Both agents perform similar tool [PITH_FULL_IMAGE:figures/full_fig_p018_15.png] view at source ↗

**Figure 16.** Figure 16: Case study 4 (E1): Public Transit Schedule Recovery. Opus persists through 4 [PITH_FULL_IMAGE:figures/full_fig_p019_16.png] view at source ↗

**Figure 17.** Figure 17: Case study 5 (E2): Property Valuation Assessment. Opus detects truncated [PITH_FULL_IMAGE:figures/full_fig_p020_17.png] view at source ↗

read the original abstract

AI agents are expected to perform professional work across hundreds of occupational domains (from emergency department triage to nuclear reactor safety monitoring to customs import processing), yet existing benchmarks can only evaluate agents in the few domains where public environments exist. We introduce OccuBench, a benchmark covering 100 real-world professional task scenarios across 10 industry categories and 65 specialized domains, enabled by Language Environment Simulators (LESs) that simulate domain-specific environments through LLM-driven tool response generation. Our multi-agent synthesis pipeline automatically produces evaluation instances with guaranteed solvability, calibrated difficulty, and document-grounded diversity. OccuBench evaluates agents along two complementary dimensions: task completion across professional domains and environmental robustness under controlled fault injection (explicit errors, implicit data degradation, and mixed faults). We evaluate 15 frontier models across 8 model families and find that: (1) no single model dominates all industries, as each has a distinct occupational capability profile; (2) implicit faults (truncated data, missing fields) are harder than both explicit errors (timeouts, 500s) and mixed faults, because they lack overt error signals and require the agent to independently detect data degradation; (3) larger models, newer generations, and higher reasoning effort consistently improve performance. GPT-5.2 improves by 27.5 points from minimal to maximum reasoning effort; and (4) strong agents are not necessarily strong environment simulators. Simulator quality is critical for LES-based evaluation reliability. OccuBench provides the first systematic cross-industry evaluation of AI agents on professional occupational tasks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces OccuBench, a benchmark covering 100 professional task scenarios across 10 industry categories and 65 specialized domains. It relies on Language Environment Simulators (LESs) that use LLMs to generate domain-specific tool responses and environments. A multi-agent synthesis pipeline automatically creates evaluation instances with guaranteed solvability, calibrated difficulty, and controlled fault injection (explicit errors, implicit data degradation, mixed). The evaluation of 15 frontier models across 8 families yields four main findings: no single model dominates all industries; implicit faults are harder than explicit or mixed faults; performance scales with model size, generation, and reasoning effort; and strong agents are not necessarily strong simulators.

Significance. If the LES simulations are faithful proxies for real occupational environments, OccuBench would fill an important gap by enabling systematic cross-industry evaluation of agents on tasks where public environments do not exist. The automatic pipeline with guaranteed solvability and explicit fault injection is a clear methodological strength that supports reproducible instance generation. The empirical observation that implicit faults lack overt signals and thus require independent detection is a useful distinction for future robustness work.

major comments (2)

[LES construction and evaluation pipeline] The central claims—no model dominates industries, implicit faults are hardest, scaling with size/reasoning, and agents ≠ simulators—all rest on the assumption that LLM-driven LES outputs are sufficiently accurate and unbiased proxies for real domain tools. The manuscript reports no human-expert or real-system ground-truth comparisons for simulator fidelity across the 65 domains (see LES construction and evaluation sections). Without such validation, systematic biases in tool responses (e.g., missing domain constraints in nuclear monitoring or customs) could distort both task-completion scores and the implicit-vs-explicit fault ordering.
[Multi-agent synthesis pipeline] The multi-agent synthesis pipeline is described as producing 'calibrated difficulty,' yet the free parameters used for difficulty calibration and the procedure for setting them are not fully specified. This leaves open the possibility that calibration choices interact with the fault-injection mechanism and affect the reported relative difficulty of fault types.

minor comments (2)

[Abstract] The abstract reports a 27.5-point improvement for GPT-5.2 from minimal to maximum reasoning effort; clarify whether this is an absolute percentage-point gain on the primary metric and provide the exact metric name.
[Results tables] Tables reporting per-industry or per-fault-type scores should include error bars or statistical tests to support claims of consistent scaling or ordering.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We are grateful to the referee for their insightful comments, which have helped us identify areas to improve the clarity and transparency of our work on OccuBench. Below, we provide point-by-point responses to the major comments.

read point-by-point responses

Referee: [LES construction and evaluation pipeline] The central claims—no model dominates industries, implicit faults are hardest, scaling with size/reasoning, and agents ≠ simulators—all rest on the assumption that LLM-driven LES outputs are sufficiently accurate and unbiased proxies for real domain tools. The manuscript reports no human-expert or real-system ground-truth comparisons for simulator fidelity across the 65 domains (see LES construction and evaluation sections). Without such validation, systematic biases in tool responses (e.g., missing domain constraints in nuclear monitoring or customs) could distort both task-completion scores and the implicit-vs-explicit fault ordering.

Authors: We acknowledge that the manuscript does not include human-expert or real-system ground-truth comparisons for LES fidelity across the 65 domains. Conducting such validations at this scale is practically challenging due to limited access to proprietary occupational systems and the specialized expertise required. Our LES construction instead relies on document-grounded prompting and multi-agent synthesis with solvability guarantees to reduce the risk of arbitrary biases. We will revise the manuscript to add an explicit 'Limitations' subsection discussing potential simulator biases and our mitigation strategies, allowing readers to interpret the results with appropriate caution. revision: yes
Referee: [Multi-agent synthesis pipeline] The multi-agent synthesis pipeline is described as producing 'calibrated difficulty,' yet the free parameters used for difficulty calibration and the procedure for setting them are not fully specified. This leaves open the possibility that calibration choices interact with the fault-injection mechanism and affect the reported relative difficulty of fault types.

Authors: The difficulty calibration procedure, including the free parameters (such as task complexity thresholds and target success rates from pilot evaluations), is described in Appendix B. We will expand this description into the main text of the revised manuscript, explicitly stating the parameter values used and confirming that calibration was performed on a held-out pilot set independently of the final fault-type evaluations to avoid any interaction effects. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical benchmark construction with direct measurements

full rationale

The paper constructs OccuBench via a multi-agent synthesis pipeline and LLM-driven Language Environment Simulators, then reports direct empirical results from evaluating 15 models on the resulting instances. No equations, first-principles derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the provided text. All four main findings (no model dominates, implicit faults hardest, scaling benefits, agents vs simulators) are presented as outcomes of running the benchmark rather than reductions to its own inputs by construction. The evaluation is therefore self-contained against external model runs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central approach rests on the assumption that LLM-generated responses can faithfully stand in for real professional environments and tools; the synthesis pipeline introduces parameters for difficulty and diversity that are calibrated but not independently validated in the abstract.

free parameters (1)

difficulty calibration parameters
Used in the multi-agent pipeline to set task difficulty levels

axioms (1)

domain assumption LLM-driven tool response generation produces realistic domain-specific environments
Invoked as the basis for Language Environment Simulators throughout the benchmark construction

pith-pipeline@v0.9.0 · 5614 in / 1212 out tokens · 62031 ms · 2026-05-10T16:39:06.068063+00:00 · methodology

discussion (0)

Reference graph

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