Credo proposes representing LLM agent state as beliefs and regulating pipeline behavior with declarative policies stored in a database for adaptive, auditable control.
Parameswaran, and Eugene Wu
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CADENZA introduces TxRA and dual planners to compile semantic operator intents into optimized task DAGs, claiming large gains in quality, latency, and cost on SemBench.
AvalancheBench introduces a benchmark for data agents based on recovering a known latent world from observations, reporting that the best coding agent recovers only 26% on an e-commerce case.
AADvark extends agent-aided CAD design to dynamic 3D assemblies with movable parts by integrating constraint solvers and visual feedback to create a verification signal for the agent.
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