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The Landscape of Agentic Reinforcement Learning for LLMs: A Survey, Zhang et al.,
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Benchmarks
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Model Context Protocol (MCP), Anthropic,
+CoMAS: Co-Evolving Multi-Agent Systems via Interaction Rewards, Xue et al.,
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+Achilles Heel of Distributed Multi-Agent Systems, Zhang et al.,
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Agent-Pro: Learning to Evolve Coder Agents via Proposal-based Programming, Zhang et al.,
+GenoTEX: An LLM Agent Benchmark for Automated Gene Expression Data Analysis, Liu et al.,
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Solving Olympiad Geometry without Human Demonstrations, Trinh et al.,
+GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis, Liu et al.,
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+aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists, Zhang et al.,
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AI for Science Integration and Future Directions