DAYJOB: A Benchmark for Long-Horizon Professional Work

Stephanie Finley; Liudas Panavas; Thomas Mikkelson; Cam Hinton; Stacey Ganss; Bradley Monton; Emily Kendall; Michelle Spradlin; Lydia Bye; Michael O'Brien; Lauren Ylvisaker; Derek Ray; Suhaas Garre; Sushant Mehta; Edwin Chen

arXiv · 2610.01306

DAYJOB: A Benchmark for Long-Horizon Professional Work

Stephanie Finley·Liudas Panavas·Thomas Mikkelson·Cam Hinton·Stacey Ganss·Bradley Monton·Emily Kendall·Michelle Spradlin·Lydia Bye·Michael O'Brien·Lauren Ylvisaker·Derek Ray·Suhaas Garre·Sushant Mehta·Edwin Chen

View original source ↗Read source document ↗Export metadata ↓

Abstract

Professional work often starts with a brief request that leaves the professional to work out what is needed, which documents matter, and whether the request's premise holds. We introduce DAYJOB, a benchmark of 130 tasks built by professionals in healthcare (50) and finance (80). The tasks are estimated to take a professional 13.6 hours on average in healthcare and 16.6 in finance. Each task is a containerized Harbor environment with an expert rubric of binary criteria (median 47.5 and 57.5 per task) that an agentic judge applies to the delivered files, and an attempt passes only if it meets every criterion. Across 30 model configurations from 13 developers, the strongest, Claude Opus 5.5, passes 24.7% of healthcare and 23.9% of finance attempts, and the median configuration passes 0.6% and 2.5%. In case studies, agents accept premises that the record contradicts and carry wrong inputs through otherwise consistent analyses. We release all healthcare tasks, 50 of the 80 finance tasks, the evaluation harness, and the leaderboard.

Explore related subjects

cs.AI cs.CL

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Stephanie Finley, Liudas Panavas, Thomas Mikkelson, Cam Hinton, Stacey Ganss, Bradley Monton, Emily Kendall, Michelle Spradlin, Lydia Bye, Michael O'Brien, Lauren Ylvisaker, Derek Ray, Suhaas Garre, Sushant Mehta, Edwin Chen. 2026-10-01. DAYJOB: A Benchmark for Long-Horizon Professional Work. https://arxiv.org/abs/2610.01306

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Reducing Cognitive Overhead in Tool Use via Multi-Small-Agent Reinforcement Learning

Recent advances in multi-agent systems highlight the potential of specialized small agents that collaborate via division of labor. Existing tool-integrated reasoning systems, however, often follow a single-agent paradigm in which one large model interleaves long-horizon reasoning with precise tool operations, leading to cognitive-load interference and unstable coordination. We present MSARL, a Multi-Small-Agent Reinforcement Learning framework that explicitly decouples reasoning from tool use. In MSARL, a Reasoning Agent decomposes problems and plans tool invocations, while multiple Tool Agents specialize in specific external tools, each trained via a combination of imitation learning and reinforcement learning with role-specific rewards. On mathematical problem solving with code execution, MSARL significantly improves reasoning stability and final-answer accuracy over single-agent baselines. Moreover, the architecture generalizes to diverse tool-use tasks, demonstrating that cognitive-role decoupling with small agents is a scalable blueprint for multi-agent AI design.

cs.AI↗

arXiv2026

LLM-based Agentic Reasoning Frameworks: A Survey from Methods to Scenarios

Recent advances in LLM-based agents highlight the importance of their reasoning frameworks, which guide the problem-solving process in diverse ways. This survey introduces a unified formal language to systematically categorize these frameworks at three compositional levels: single-agent, tool-based, and multi-agent methods. Following our taxonomy, we review key application scenarios across scientific discovery, healthcare, software engineering, society, economics, and general-purpose tasks. It also compares the distinct features and evaluation strategies of each category. Through our taxonomy and comparisons, our survey explores the designs and strengths of LLM-based agentic frameworks in different scenarios, reviewing the fast-paced development of complex agentic systems in the real world.

cs.AI↗

arXiv2026

Moloch's Bargain: Emergent Misalignment When LLMs Compete for Audiences

Large language models (LLMs) are increasingly shaping how information is created and disseminated, from companies using them to craft persuasive advertisements, to election campaigns optimizing messaging to gain votes, to social media influencers boosting engagement. These settings are inherently competitive, with sellers, candidates, and influencers vying for audience approval, yet it remains poorly understood how competitive feedback loops influence LLM behavior. We show that optimizing LLMs for competitive success can inadvertently drive misalignment. Using simulated environments across these scenarios, we find that, 6.3% increase in sales is accompanied by a 14.0% rise in deceptive marketing; in elections, a 4.9% gain in vote share coincides with 22.3% more disinformation and 12.5% more populist rhetoric; and on social media, a 7.5% engagement boost comes with 188.6% more disinformation and a 16.3% increase in promotion of harmful behaviors. We call this phenomenon Moloch's Bargain for AI--competitive success achieved at the cost of alignment. These misaligned behaviors emerge even when models are explicitly instructed to remain truthful and grounded, revealing the fragility of current alignment safeguards. Our findings highlight how market-driven optimization pressures can systematically erode alignment, creating a race to the bottom, and suggest that safe deployment of AI systems will require stronger governance and carefully designed incentives to prevent competitive dynamics from undermining societal trust.

cs.AI↗