FCGraft: New Framework Speeds Up and Improves Code Generation for Robot Control Systems
Researchers have introduced FCGraft, a system that improves how large language models generate executable code policies for embodied AI agents by reusing validated code fragments and their cached computations. Current approaches suffer from slow generation due to repeated processing of long prompts and unreliable outputs that misuse APIs or lack safety checks. The work, accepted at ICML 2026, reports an 18.31% higher task success rate and 2.3x faster policy synthesis compared to existing prompt-level caching methods.
FCGraft, short for Functional Cache Grafting, addresses two core problems in code-policy generation for embodied agents: computational inefficiency from repetitive prefill operations over long prompts, and fragility in fully generative decoding that can produce API mismatches and unsafe control logic. The framework maintains a library of pre-validated, function-level code skeletons alongside their associated Transformer key-value (KV) caches. When a new task arrives, FCGraft retrieves relevant functions and grafts their cached representations together through two operations: stitching, which composes cached segments into a composite policy, and patching, which adapts only the necessary code regions to meet task-specific requirements. This selective reuse eliminates redundant computation while preserving the reliability of previously validated control structures. Compared to RAGCache, a prompt-level caching baseline, FCGraft achieves an 18.31% improvement in task success rate and a 2.3x speedup in policy synthesis. The paper was accepted at ICML 2026 and submitted to arXiv in June 2026.
What's missing
The paper does not discuss how FCGraft performs as the validated function library grows in size or ages with API changes. Generalization to real-world (non-simulated) deployment and the cost of building and maintaining the function skeleton library are not addressed in the abstract.
What different sources said
- arXiv cs.AICenter
Functional Cache Grafting: Robust and Rapid Code-Policy Synthesis for Embodied Agents
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