Harness-IF: Evaluating Instruction Following Across Instruction Surfaces in Coding Agents
When a coding agent obeys a rule, it may simply have been going to do that anyway. Existing instruction-following benchmarks cannot tell the difference: they concentrate rules in the user turn, while coding-agent benchmarks emphasize final task success. We introduce Harness-IF, which scores operational rules one at a time from execution evidence: 60 realistic multi-turn coding items drawn from a 642-rule library, 256 rules receiving verdicts, placed on the five configurable surfaces a deployed agent reads. To separate compliance from coincidence we introduce Against-Prior Accuracy (AP-Acc), which scores only rules labeled as opposing unprompted defaults, observed by re-running tasks with ...
| Authors | Zining Huang, Haoran Que, Hong Zeng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 12 August 2026 |
| Last updated at source | 12 August 2026 |
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