Hugging Face Trending Papers
Work Agents: Systems, Capabilities, and Optimization
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arXiv Machine Learning
Oct 28
Work Agents: Systems, Capabilities, and Optimization
The paper introduces a work-centric framework for understanding and optimizing work agents, which combine foundation models with runtime harnesses to perform tasks in stateful workspaces. It defines two key dimensions—work persistence and work openness—to characterize working conditions and classifies digital work into five forms based on task scope and organization. The authors identify five core capability families necessary for reliable execution, explore optimization strategies for models and harnesses, and evaluate existing metrics from the perspective of verifiable work outcomes.
arXiv AI
Oct 9
Scaling Verifiable Environments for Long-horizon Work Agents
The paper introduces WorkForge, a scalable synthesis framework that builds verifiable work‑agent environments from real‑world resources. By extracting factual anchors from expert workflows and associated files, WorkForge generates task instructions, solution plans, and verifiers that remain traceable to observable workspace evidence. The authors constructed 16.7K environments across 40 professional domains, demonstrating significant performance gains for large language models such as Qwen3.5-35B-A3B-Base and Qwen3.5-27B.
arXiv AI
Oct 16
Harness-Bench: Evaluating Agent Harnesses across Models and Realistic Workflows
Harness-Bench is a diagnostic benchmark that evaluates how different harness configurations affect LLM agent performance in realistic workflows. It tests 106 sandboxed offline tasks across multiple model backends, recording artifacts, execution traces, and usage statistics to analyze completion, process quality, efficiency, and failure behavior. The study finds significant variation in outcomes across model-harness pairings, indicating that agent capability should be reported at the configuration level rather than solely by the base model.
arXiv AI
Aug 6
OneDayAgent: Towards a Long-Horizon Harness for Autonomous Agents
arXiv:2608. 05013v1 Announce Type: cross Abstract: LLM agents are increasingly applied to open-ended everyday requests that span work, study, and life.
arXiv AI
Aug 25
Designing Benchmarks for Knowledge Work
The paper proposes a new way to describe benchmarks for AI systems that perform knowledge work, outlining four explicit fields: represented activity, tested setting, required work product, and evaluated result. It builds an inventory of 18 work activities from O*NET to enable activity-level reporting across occupations, and evaluates these activities for semantic coherence, algorithm sensitivity, ontology legibility, and human interpretability. The authors apply their framework to three existing benchmarks—GDPval, OfficeQA Pro, and APEX-SWE—to show how different aspects of work can be captured within the same representation.
arXiv AI
Oct 9
AgentEvolver: System-Wide Self-Evolution Through Task Execution
AgentEvolver is a system that enables agents to develop reusable capabilities while executing tasks, without altering the underlying foundation model. It uses eight entity families to expose operations, methods, agents, control flow, interfaces, and state for revision, coordinated by a shared Runtime and persistent planning. In evaluations on SWE-bench Pro Public, it achieved an 82.08% resolution rate, demonstrating retained capabilities across subsequent website, game, and research tasks, though some objectives remained incomplete.