{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4XMIDL3PN64WAFXELFDUIDF4YZ","short_pith_number":"pith:4XMIDL3P","schema_version":"1.0","canonical_sha256":"e5d881af6f6fb96016e45947440cbcc6553d6f65c153a2c43224d1b6555c31b0","source":{"kind":"arxiv","id":"2403.19489","version":2},"attestation_state":"computed","paper":{"title":"Evolving Assembly Code in an Adversarial Environment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Achiya Elyasaf, Gera Weiss, Irina Maliukov, Oded Margalit","submitted_at":"2024-03-28T15:21:23Z","abstract_excerpt":"In this work, we evolve Assembly code for the CodeGuru competition. The goal is to create a survivor -- an Assembly program that runs the longest in shared memory, by resisting attacks from adversary survivors and finding their weaknesses. For evolving top-notch solvers, we specify a Backus Normal Form (BNF) for the Assembly language and synthesize the code from scratch using Genetic Programming (GP). We evaluate the survivors by running CodeGuru games against human-written winning survivors. Our evolved programs found weaknesses in the programs they were trained against and utilized them. To "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2403.19489","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2024-03-28T15:21:23Z","cross_cats_sorted":[],"title_canon_sha256":"7643c0314363889f34092abb72074ed7d890d7123b4a58b75ec3de0b22961612","abstract_canon_sha256":"df08a78c8af4f37928b5063bdd397b0f4d79d2be3b95c0bb0411ec7d0f079dfd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:29:24.894881Z","signature_b64":"rT+r0gN1MByO1gfEYSrAdMqOzoZ89qASpSxUw6AiWSAmdP41mChczWZNuTrQtxhbPCO+aAEuoZCDR8en1IpKBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e5d881af6f6fb96016e45947440cbcc6553d6f65c153a2c43224d1b6555c31b0","last_reissued_at":"2026-07-05T08:29:24.894319Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:29:24.894319Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evolving Assembly Code in an Adversarial Environment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Achiya Elyasaf, Gera Weiss, Irina Maliukov, Oded Margalit","submitted_at":"2024-03-28T15:21:23Z","abstract_excerpt":"In this work, we evolve Assembly code for the CodeGuru competition. The goal is to create a survivor -- an Assembly program that runs the longest in shared memory, by resisting attacks from adversary survivors and finding their weaknesses. For evolving top-notch solvers, we specify a Backus Normal Form (BNF) for the Assembly language and synthesize the code from scratch using Genetic Programming (GP). We evaluate the survivors by running CodeGuru games against human-written winning survivors. Our evolved programs found weaknesses in the programs they were trained against and utilized them. To "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.19489","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2403.19489/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2403.19489","created_at":"2026-07-05T08:29:24.894376+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.19489v2","created_at":"2026-07-05T08:29:24.894376+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.19489","created_at":"2026-07-05T08:29:24.894376+00:00"},{"alias_kind":"pith_short_12","alias_value":"4XMIDL3PN64W","created_at":"2026-07-05T08:29:24.894376+00:00"},{"alias_kind":"pith_short_16","alias_value":"4XMIDL3PN64WAFXE","created_at":"2026-07-05T08:29:24.894376+00:00"},{"alias_kind":"pith_short_8","alias_value":"4XMIDL3P","created_at":"2026-07-05T08:29:24.894376+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.05534","citing_title":"Evolutionary and Coevolutionary Multi-Agent Design Choices and Dynamics","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4XMIDL3PN64WAFXELFDUIDF4YZ","json":"https://pith.science/pith/4XMIDL3PN64WAFXELFDUIDF4YZ.json","graph_json":"https://pith.science/api/pith-number/4XMIDL3PN64WAFXELFDUIDF4YZ/graph.json","events_json":"https://pith.science/api/pith-number/4XMIDL3PN64WAFXELFDUIDF4YZ/events.json","paper":"https://pith.science/paper/4XMIDL3P"},"agent_actions":{"view_html":"https://pith.science/pith/4XMIDL3PN64WAFXELFDUIDF4YZ","download_json":"https://pith.science/pith/4XMIDL3PN64WAFXELFDUIDF4YZ.json","view_paper":"https://pith.science/paper/4XMIDL3P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.19489&json=true","fetch_graph":"https://pith.science/api/pith-number/4XMIDL3PN64WAFXELFDUIDF4YZ/graph.json","fetch_events":"https://pith.science/api/pith-number/4XMIDL3PN64WAFXELFDUIDF4YZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4XMIDL3PN64WAFXELFDUIDF4YZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4XMIDL3PN64WAFXELFDUIDF4YZ/action/storage_attestation","attest_author":"https://pith.science/pith/4XMIDL3PN64WAFXELFDUIDF4YZ/action/author_attestation","sign_citation":"https://pith.science/pith/4XMIDL3PN64WAFXELFDUIDF4YZ/action/citation_signature","submit_replication":"https://pith.science/pith/4XMIDL3PN64WAFXELFDUIDF4YZ/action/replication_record"}},"created_at":"2026-07-05T08:29:24.894376+00:00","updated_at":"2026-07-05T08:29:24.894376+00:00"}