OmegaUse-OfficeVal: Economic Anchor-Based Benchmark for Long-Horizon LLM Agents in Office Suites
This paper introduces OmegaUse-OfficeVal, a benchmark framework designed to evaluate large language model agents in long-horizon office suite tasks. Its core innovation lies in task-level economic anchors—two economic signals derived from human labor time and task pricing—that directly quantify and compare human costs against LLM inference costs. The research team extracted 100 privacy-protected office tasks from practitioners' real-world needs, each averaging 2.32 hours of manual effort. Experiments evaluating several frontier LLMs against human baselines show that while LLMs significantly outperform humans in speed and cost, their delivery quality has yet to reach human levels. The benchmark employs a fine-grained rubric-based code validator to ensure evaluation stability, with all code and datasets fully open-sourced to support future research.
Background and Context
The rapid integration of large language model (LLM) agents into professional workflows has exposed a critical gap in evaluation methodologies. Existing benchmarks predominantly focus on short-horizon interactions or single-turn tasks, failing to capture the complexities of long-horizon office suite operations. These traditional metrics overlook the economic feasibility of automating multi-step workflows, leaving a void in understanding whether AI agents can transition from theoretical capability to practical, cost-effective utility. To address this, the OmegaUse-OfficeVal benchmark was introduced as a comprehensive framework designed to evaluate LLM agents specifically within the context of complex, long-duration office tasks. This initiative marks a significant shift from purely technical performance metrics to a holistic assessment that includes economic viability.
The core innovation of OmegaUse-OfficeVal lies in its introduction of task-level economic anchors. These anchors are derived from two primary signals: human labor time and task pricing proxies. By quantifying the cost of human effort against the computational cost of LLM inference, the benchmark provides a direct mechanism for comparing the economic efficiency of AI agents versus human workers. This approach moves beyond simple accuracy scores to evaluate the value proposition of automation. The research team curated a dataset of 100 privacy-protected office tasks extracted from real-world practitioner needs. Each task requires an average of 2.32 hours of manual effort, reflecting the intricate and sustained nature of professional office work. This dataset serves as a rigorous testbed for assessing how well agents can maintain coherence and quality over extended periods.
Deep Analysis
The technical architecture of OmegaUse-OfficeVal is built upon a fine-grained, code-based validator that ensures objective and stable evaluation. Unlike subjective human reviews, this validator operates on detailed rubrics to verify each step of an agent’s output against expected outcomes. This granular approach is essential for detecting subtle errors that accumulate over long-horizon tasks, where minor deviations can lead to significant failures in the final deliverable. The validation process is designed to be reproducible and scalable, allowing for consistent assessment across different model architectures and versions. By automating the verification of intermediate steps, the benchmark captures the agent’s ability to plan, execute, and correct errors within a complex workflow, rather than just the final result.
In experimental evaluations, several frontier LLMs were tested against human baselines using the OmegaUse-OfficeVal framework. The results revealed a distinct divergence between operational efficiency and deliverable quality. While the evaluated LLMs significantly outperformed human workers in terms of inference speed and direct computational cost, their overall delivery quality did not yet match human standards. This finding highlights a critical bottleneck in current AI agent development: while agents are fast and cheap, they struggle with the nuanced, high-quality execution required for complex office tasks. The agents often succeed in individual steps but fail to maintain the professional polish and contextual consistency expected in final outputs. This gap suggests that speed and cost advantages alone are insufficient for replacing human labor in high-stakes office environments.
The introduction of value-weighted evaluation further illuminated the trade-offs between cost and quality. By applying economic anchors, the study demonstrated that while LLMs offer a lower cost basis, the penalty for lower quality reduces their overall value proposition in certain contexts. Ablation studies confirmed that the fine-grained code validator was crucial for identifying these quality deficits, which might be missed by coarse-grained metrics. The economic anchor mechanism thus provides a more realistic view of an agent’s potential ROI. It shows that for an agent to be truly viable, it must not only be fast and cheap but also capable of producing work that meets professional quality thresholds. This dual requirement of efficiency and excellence defines the next frontier in agent development.
Industry Impact
The release of OmegaUse-OfficeVal has significant implications for both the academic community and industrial application. By fully open-sourcing the code and dataset, the framework lowers the barrier to entry for researchers, fostering collaboration and innovation in the field of agent evaluation. This transparency allows the community to build upon the established benchmarks, leading to more robust and standardized evaluation practices. For industry leaders, the benchmark offers a new lens through which to assess AI adoption. It shifts the conversation from purely technical capabilities to economic feasibility, providing a structured way to evaluate the return on investment for AI-assisted office systems. This economic perspective is crucial for enterprises considering the integration of LLM agents into their workflows, as it helps quantify the true value of automation beyond mere speed.
Furthermore, the benchmark challenges the prevailing narrative that AI agents are ready to fully replace human workers in complex office roles. The findings indicate that while agents are powerful tools for augmentation, they are not yet ready for fully autonomous, high-quality delivery in long-horizon tasks. This insight guides industrial strategy, suggesting that a hybrid approach, where agents assist rather than replace humans, may be more effective in the near term. Companies can use the benchmark to identify specific areas where agents add the most value, such as data processing or draft generation, while retaining human oversight for final quality assurance. This nuanced understanding helps organizations avoid overestimating AI capabilities and underestimating the need for human expertise in complex workflows.
The emphasis on economic anchors also encourages the development of more cost-aware AI systems. Developers are now incentivized to optimize not just for accuracy, but for the balance between cost and quality. This could lead to innovations in model architecture, such as more efficient reasoning pathways or better error-correction mechanisms that reduce the need for expensive re-runs. The benchmark thus acts as a catalyst for improving the practical utility of AI agents, pushing the industry towards solutions that are not only technologically advanced but also economically sustainable. This alignment of technical and economic goals is essential for the widespread adoption of AI in professional settings.
Outlook
Looking ahead, the limitations identified by OmegaUse-OfficeVal point to clear directions for future research. The primary challenge lies in enhancing the planning, memory, and error-correction capabilities of LLM agents to improve their robustness in long-horizon tasks. Current agents often struggle with maintaining context and consistency over extended periods, leading to quality degradation. Future work must focus on developing more sophisticated memory mechanisms that allow agents to retain and utilize information effectively across long workflows. Additionally, improved planning algorithms could help agents anticipate potential errors and adjust their strategies proactively, rather than reacting to failures after they occur.
Another critical area for development is the refinement of quality control mechanisms. As agents become more integrated into professional workflows, the ability to self-assess and correct errors in real-time will be paramount. This could involve integrating additional validation layers or feedback loops that allow agents to learn from their mistakes and improve their performance over time. The goal is to create agents that can not only execute tasks efficiently but also ensure that the final output meets high professional standards. Achieving this balance will be key to unlocking the full potential of AI in office automation.
Finally, the widespread adoption of benchmarks like OmegaUse-OfficeVal will likely drive standardization in agent evaluation. As more organizations adopt similar metrics, the industry will move towards a more unified understanding of agent capabilities. This standardization will facilitate better comparison between different models and platforms, accelerating innovation and competition. Ultimately, the success of AI in office automation will depend on its ability to deliver not just speed and cost savings, but also high-quality, reliable results. OmegaUse-OfficeVal provides the necessary framework to measure and guide this evolution, ensuring that AI agents evolve into truly valuable partners in the modern workplace.