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  • RISED: Rubrics for Agentic Multi-Environment Selection and Self-Distillation

    Training a single LLM agent jointly across diverse interactive environments has attracted increasing attention as a route to generalist agents. Existing curriculum and data-selection strategies often allocate training at the environment level or prioritize local reward-based signals, without…

    Apple ML Research2 min read
  • Shared Selective Persistent Memory for Agentic LLM Systems

    Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive.

    Apple ML Research3 min read

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