OfficeTown is a generative simulation framework that models professional organizations, where tasks and context evolve from ongoing work. Starting from a natural‑language description of an organization’s purpose, the system initializes members who coordinate, delegate assignments to assistant agents, and review deliverables, while artifacts and decisions persist to influence future tasks. The authors built OfficeTown Bench with 406 tasks and 4,836 evaluation criteria across 23 simulated organizations in eight domains, and show that fine‑tuning and on‑policy distillation on this data improves Qwen3.5‑9B on external professional‑work benchmarks, with a principal–assistant setup outperforming single‑ and multi‑agent baselines.
arXiv:2609.39325v1 Announce Type: new Abstract: The ability of Large Language Model (LLM) agents to complete daily and professional work is receiving increasing attention. Training such agents requir...
WorkFactory is a scalable framework that transforms professional intents into executable tasks by building an information support graph and using probe executions to detect and repair support failures. The authors generated 112,686 tasks across 612 occupations and fine‑tuned Qwen models (4B, 27B, 35B-A3B) to create the WorkPilot family, which outperforms base models on four professional‑work benchmarks. The study shows that planning and repair reduce task environment defects and improve downstream performance, with diminishing returns observed as model size increases.
arXiv:2606. 17819v1 Announce Type: cross Abstract: Agent skills -- structured, reusable knowledge artifacts that augment LLM agent capabilities -- have been rapidly adopted in industry, yet their cross-domain impact and use across commercial and open-source models remain under-studied, and no reusable methodology exists for evaluating an individual skill.
arXiv:2605. 18401v2 Announce Type: replace-cross Abstract: Long-horizon LLM agents generate traces that could become reusable experience, but raw trajectories are noisy, local, and hard to govern.
The paper proposes a method for assessing AI suitability for workplace tasks by profiling both AI agents and tasks along shared cognitive capability dimensions. It uses benchmark performance to infer agent capabilities and expert input to weight task requirements, allowing independent updates as models and roles evolve. The authors validate the approach on synthetic agents, six AI systems, and 410 employees across six domains, showing that AI systems vary more by cognitive dimension than by model family, while workplace tasks share a common cognitive core.
The paper introduces TerminalHorizon, an automated engine that synthesizes long‑horizon trajectories for terminal agents by reconstructing environments from real‑world professional work and progressively expanding tasks to require further exploration and improvement. Using this engine, the authors create 1.5K task environments and fine‑tune Qwen3.5‑27B, producing TerminalHorizon‑27B, which shows significant performance gains across 12 benchmarks in coding, agentic production tasks, and reasoning. Case studies demonstrate the model’s ability to experiment beyond initial solutions, refine methods based on feedback, and preserve effective improvements in final deliverables.
arXiv:2607. 02975v1 Announce Type: new Abstract: Effective agency in social environments depends on when an agent seeks knowledge, when it acts, and whether its actions are justified by acquired information.
arXiv:2610.35616v1 Announce Type: new Abstract: Skills provide agents with reusable experience to guide future decisions without modifying the underlying model. However, coding trajectories are often...
arXiv:2606. 09833v1 Announce Type: cross Abstract: AI agents are reshaping the workspace, leading to drastic change of how humans work.
Kolab is a new dataset comprising over 115 hours of screen recordings from 50 professionals across 40 occupations who collaborate with AI agents on realistic, long‑horizon tasks. The dataset includes participants’ judgments of collaboration quality and output, revealing varied coordination patterns and the need for agents to anticipate how experts will use or modify their contributions. Analysis shows current state‑of‑the‑art models overestimate collaboration success and struggle to differentiate high‑ from low‑quality interactions, indicating a gap in understanding long‑horizon human‑agent teamwork.
arXiv:2609.05677v1 Announce Type: cross Abstract: Lifelong LLM agents increasingly rely on external skill artifacts as one element for preserving and reusing capabilities over time. These skills (usu...
arXiv:2609.23806v1 Announce Type: new Abstract: Many knowledge-work benchmarks are constructed around individual tasks, with the context needed for each task selected together with or after the task...