RAC is a log-based recovery paradigm implemented as an architectural extension to agent frameworks, achieving 1.5-8X better latency and token economy than LLM-based recovery on τ-bench and REALM-Bench.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
BranchBench shows that existing branchable DBMSes face severe trade-offs between branching speed and read/write performance, with no system supporting representative agentic workloads at scale.
citing papers explorer
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Robust Agent Compensation (RAC): Teaching AI Agents to Compensate
RAC is a log-based recovery paradigm implemented as an architectural extension to agent frameworks, achieving 1.5-8X better latency and token economy than LLM-based recovery on τ-bench and REALM-Bench.
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BranchBench: Aligning Database Branching with Agentic Demands
BranchBench shows that existing branchable DBMSes face severe trade-offs between branching speed and read/write performance, with no system supporting representative agentic workloads at scale.