{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:LZJMXACJ2QOP7HZXTFKINNIWCF","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9ddbe9ead40d1e75fbf7d5c1bd1d2688dc8c2b4a350c36f98988ecc1294a6228","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2026-04-13T07:10:01Z","title_canon_sha256":"9e767301e3cdcde0e6a2b375adcccddcc3020265586dd650c38257304c39e7ca"},"schema_version":"1.0","source":{"id":"2604.11088","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.11088","created_at":"2026-05-29T01:05:09Z"},{"alias_kind":"arxiv_version","alias_value":"2604.11088v2","created_at":"2026-05-29T01:05:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.11088","created_at":"2026-05-29T01:05:09Z"},{"alias_kind":"pith_short_12","alias_value":"LZJMXACJ2QOP","created_at":"2026-05-29T01:05:09Z"},{"alias_kind":"pith_short_16","alias_value":"LZJMXACJ2QOP7HZX","created_at":"2026-05-29T01:05:09Z"},{"alias_kind":"pith_short_8","alias_value":"LZJMXACJ","created_at":"2026-05-29T01:05:09Z"}],"graph_snapshots":[{"event_id":"sha256:4bf7b7e6594fc0c46bb6863b8017d9b75246654b421e28afea684fcbe861f0e6","target":"graph","created_at":"2026-05-29T01:05:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Rules improve performance by 7--14 percentage points, but random rules help as much as expert-curated ones -- suggesting rules work through context priming rather than specific instruction. Negative constraints are the only individually beneficial rule type, while positive directives actively hurt."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That differences in agent success rates are caused by the rule types themselves rather than unmeasured factors in how rules are inserted into prompts or variations across agent runs."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Negative constraints in agent rules improve coding performance via context priming while positive directives degrade it, with collective rules remaining helpful up to 50 rules."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Negative constraints raise AI coding agent success rates while positive instructions lower them, and random rules match expert ones."}],"snapshot_sha256":"e7dd00151feaf1be5a651358c4b6a755fc576a6d4ca45b0f75e2f20f231d11e7"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.11088/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Random rules improve a coding agent's task performance as much as expert-curated ones (both $+13.8$pp on a discriminative subset of SWE-bench Verified), and in our data every individually beneficial rule is a negative constraint (\"do not refactor unrelated code\"), while every individually harmful one is a positive directive (\"follow code style\"). We arrive at these findings through the first large-scale controlled study of agent rule files (\\texttt{CLAUDE.md}, \\texttt{.cursorrules}, and the broader family of agent skills, plugin manifests, and persona definitions): we scrape 679 rule files (25","authors_text":"Bing Zhu, Guanghui Wang, Peiyang He, Wei Qiu, Xing Zhang, Yanwei Cui, Ziyuan Li","cross_cats":["cs.CL"],"headline":"Negative constraints raise AI coding agent success rates while positive instructions lower them, and random rules match expert ones.","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2026-04-13T07:10:01Z","title":"Guardrails Beat Guidance: A Large-Scale Study of Rules, Skills, and Persistent Configuration for Coding Agents"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.11088","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-10T15:33:36.613027Z","id":"a2e26d41-360e-4016-8608-abd8319657b3","model_set":{"reader":"grok-4.3"},"one_line_summary":"Negative constraints in agent rules improve coding performance via context priming while positive directives degrade it, with collective rules remaining helpful up to 50 rules.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Negative constraints raise AI coding agent success rates while positive instructions lower them, and random rules match expert ones.","strongest_claim":"Rules improve performance by 7--14 percentage points, but random rules help as much as expert-curated ones -- suggesting rules work through context priming rather than specific instruction. Negative constraints are the only individually beneficial rule type, while positive directives actively hurt.","weakest_assumption":"That differences in agent success rates are caused by the rule types themselves rather than unmeasured factors in how rules are inserted into prompts or variations across agent runs."}},"verdict_id":"a2e26d41-360e-4016-8608-abd8319657b3"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b0b39f4139c45382163e51a6e8574f6d79479d6f29445fa7366564afd21ef898","target":"record","created_at":"2026-05-29T01:05:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9ddbe9ead40d1e75fbf7d5c1bd1d2688dc8c2b4a350c36f98988ecc1294a6228","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2026-04-13T07:10:01Z","title_canon_sha256":"9e767301e3cdcde0e6a2b375adcccddcc3020265586dd650c38257304c39e7ca"},"schema_version":"1.0","source":{"id":"2604.11088","kind":"arxiv","version":2}},"canonical_sha256":"5e52cb8049d41cff9f37995486b516115c78fdf867d1b9c76f5964c7ca721b64","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5e52cb8049d41cff9f37995486b516115c78fdf867d1b9c76f5964c7ca721b64","first_computed_at":"2026-05-29T01:05:09.360267Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-29T01:05:09.360267Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"frFbqo4DjE5cPvigwi9VrOaBCuTF3ZA+9Mumiw1j7LjhRmjGjN+xbqtrOtfSY747pBGGJl/VnYaJpqrFCBhODw==","signature_status":"signed_v1","signed_at":"2026-05-29T01:05:09.361213Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.11088","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b0b39f4139c45382163e51a6e8574f6d79479d6f29445fa7366564afd21ef898","sha256:4bf7b7e6594fc0c46bb6863b8017d9b75246654b421e28afea684fcbe861f0e6"],"state_sha256":"48e7fdb43d54c90f437e3816219adce8aa5ebadbe062317d67d1c592ed20bf14"}