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SkillProx is introduced, a proximal-gradient-inspired forward--backward framework that couples closed-loop diagnostic evolution with utility-aware proximal refinement and demonstrates the complementary effects of closed-loop diagnosis and proximal refinement.
This work formalizes this meta-capability as Delegation Intelligence in deep search and decomposes it into complementary dimensions-Search Decision-Making and Information Synthesis and Verification and develops a controllable synthesis pipeline built on document-grounded reverse engineering.
Experiments show that HiRA significantly outperforms state-of-the-art RAG and agent-based systems, highlighting the effectiveness of decoupled planning and execution for multi-step information seeking tasks.
This work introduces SearchOS, a system-level multi-agent framework that turns fragile, implicit search progress into explicit, persistent, and shared state, and introduces a Search Tool Middleware Harness that intercepts model and tool interactions to record grounded evidence and react to stalls or budget exhaustion.
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