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AI Agent Papers·2026
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FDE-Gym: The Last Mile of AI Delivery

Dongxu Yang, Hongda Yan +24·DeepLethe, Tsinghua University +18

Benchmark for evaluating agents' ability to deliver working AI systems in realistic customer environments: 95 end-to-end delivery tasks with scripted stakeholders and production-like systems, scored by improvement to the customer's business metric and penalized for stakeholder contact.

#benchmark#benchmarks#datasets#agent-skills#ai-agent+5
AI Agent Papers·2026
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R$^2$Orch: Recursive Self-Improvement for Agent Orchestration via Self-Play within a Skill-Internalization Loop

Mingda Zhang, Yun Huang +8·The Chinese University of Hong Kong, Shenzhen, China, Fudan University, China +3

Trains an agent orchestrator through recursive self-play, then withdraws skills once they stop improving performance. Learnability-driven task selection and controlled skill evaluations aim to move the system toward deployment without external skills.

#paper#research#ai-agent#RL#agent-skills+1
AI Agent Papers·2026
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OpenVLHarness: Real World Visual Agentic Tool-Use with Multimodal Memory

Yu Zhou, Cheng-Fu Yang +9·University of California, Los Angeles, Apple +2

Provides a training-free multimodal reasoning harness that combines heterogeneous visual tools, persistent multimodal memory, and code execution so MLLMs can iteratively acquire and compose grounded evidence. Improves accuracy on 22 benchmarks by about 9.7–12.6 points while cutting comparative token costs.

#multimodal#ai-agent#agent-skills#benchmarks#evaluation+4
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