Benchmark Convergence As Operational Confirmation Of Large Language Fields (LLFs)

Benchmarks for LLM agents are undergoing a visible design shift: from pooled single scores toward explicit regimes, explicit admissibility, and checkable closure. This paper argues that the shift is not merely incremental evaluation engineering; it is operational convergence toward the reliability unit implied by Large Language Fields (LLFs): a field-level contract layer above the model where meaning, evidence, and “done” are governed by reusable, versioned rules. We formalize this convergence as a move from impression-based scoring to contract-based verification: subfields become declared slices with distinct closure predicates, and progress becomes portable only when supported by receipts that an independent verifier can validate under a declared budget. We connect recent benchmark patterns — interactive research, controllable extreme context growth, state-diff outcome contracts, and construct-before-prove formal tasks — to a unified LLF reading: benchmarks are increasingly instrumenting the field contract rather than merely sampling model outputs. We then state falsifiable predictions: subfield-explicit benchmarks will predict operational failures better than pooled means, worst-subfield and dispersion will dominate mean scores as predictors of governance outcomes, and receipt completeness under audit sampling will explain a large fraction of false closure in agent deployments.