mlsanityData to 14 Oct 2026

SaltBench: A Referee-Gated Protocol for Measuring Method Effects in Machine-Checked Software Work

arXiv 2609.11076 ↗First posted 10 Sep 2026cs.SE cs.LOcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

SaltBench is a benchmark protocol for one question: How does a machine referee change the way a coding agent works? A machine referee --- a proof kernel, a program verifier, or a withheld test suite --- decides what an agent's work is worth, and the agent cannot argue with it. Here we report a protocol that makes the referee's effect measurable and whose answers cannot be narrated afterwards: every outcome is decided outside the agent's own toolchain; the agent is walled off from the network, the reference solutions and the harness itself, and the wall is tested by probes that try to breach it before any scored run, so the isolation is observed rather than assumed; every run is authorized by a dated freeze with its predictions registered; and a budget stop is a halt, never a failure. In this study, the subject of the benchmark is a ``seat'', meaning an agent session in its standard harness. We tested five systems components, all authored in Rust under a pinned Verus toolchain, with a withheld test suite as the referee for each. Four arms are tested: a plain agent; an agent that is also instructed to create a specification and verify the code against it, in a reduced rendering of the method, as registered; and two arms where the specification is provided a priori, extended under a dated amendment to k=4, where the registered sign test reached no verdict (3 of 4, p = 0.3125, every premium below the resolvable floor). We found that the arm instructed to specify and verify cost more on all five components, and by a practical margin: across these five components no premium exceeded 2.8879× under either reading of the declared set, and the three cheapest sat below 1.4×. That bound is a property of this population and not a promise about larger ones: the premium runs near 1 on the smallest components and rises with size. We publish the complete record.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. First posted on arXiv 10 Sep 2026.

Builds on

No reference list yet.

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026TestJack: Should you trust the results in coding benchmarks? Agentic Coding Benchmarks Auditing via Evaluator Evolution
  2. 2026SWE-Bench Pro Verified: A Reliable Benchmark for Software Engineering Agents
  3. 2026SpecBench: Measuring Reward Hacking in Long-Horizon Coding Agents
  4. 2026Breaking, Stale, or Missing? Benchmarking Coding Agents on Project-Level Test Evolution
  5. 2026CheckerBench: Can Long-Horizon Agents Synthesize Static-Analysis Checkers?
  6. 2026AgentCheck: A Reproduce-Intervene-Mitigate Workbench for LLM Agents over MCP
  7. 2026PBT-Bench: Benchmarking AI Agents on Property-Based Testing
  8. 2026AEVAL: From Anecdotal to Deterministic Testing for Agentic Skill Workflows