{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FW23QLCH2HUNWMUMWDE3HTX4B4","short_pith_number":"pith:FW23QLCH","schema_version":"1.0","canonical_sha256":"2db5b82c47d1e8db328cb0c9b3cefc0f1b16190b3869fd5be4c9b5c575d60a4a","source":{"kind":"arxiv","id":"2403.11958","version":1},"attestation_state":"computed","paper":{"title":"Language Evolution with Deep Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.CL","authors_text":"Emmanuel Dupoux, Florian Strub, Mathieu Rita, Olivier Pietquin, Paul Michel, Rahma Chaabouni","submitted_at":"2024-03-18T16:52:54Z","abstract_excerpt":"Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language within a simulated controlled environment. Several methods have been used to investigate the origin of our language, including agent-based systems, Bayesian agents, genetic algorithms, and rule-based systems. This chapter explores another class of computational models that have recently revolutionized the field of machine learning: deep learning models. The chapter introduces the basic concepts of "},"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":"2403.11958","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-18T16:52:54Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"247303f003706c3b6b55b87e0b23fd325aed91e9ab4dd0f2303facde3cc44660","abstract_canon_sha256":"925ec205643cca7f0493cdce999ceaab66efd0bdd8852dc25751220ab8c9f67c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:57:34.234072Z","signature_b64":"w6z7kpk6+C1OG75vwlLvptg3OtLullQroAFvR9CGa2yz+DaYLfpYCj/YbbFgSnD6Kww01W2glLZ+NTlyIX04Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2db5b82c47d1e8db328cb0c9b3cefc0f1b16190b3869fd5be4c9b5c575d60a4a","last_reissued_at":"2026-07-05T07:57:34.233597Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:57:34.233597Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Language Evolution with Deep Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.CL","authors_text":"Emmanuel Dupoux, Florian Strub, Mathieu Rita, Olivier Pietquin, Paul Michel, Rahma Chaabouni","submitted_at":"2024-03-18T16:52:54Z","abstract_excerpt":"Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language within a simulated controlled environment. Several methods have been used to investigate the origin of our language, including agent-based systems, Bayesian agents, genetic algorithms, and rule-based systems. This chapter explores another class of computational models that have recently revolutionized the field of machine learning: deep learning models. The chapter introduces the basic concepts of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.11958","kind":"arxiv","version":1},"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/2403.11958/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":"2403.11958","created_at":"2026-07-05T07:57:34.233656+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.11958v1","created_at":"2026-07-05T07:57:34.233656+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.11958","created_at":"2026-07-05T07:57:34.233656+00:00"},{"alias_kind":"pith_short_12","alias_value":"FW23QLCH2HUN","created_at":"2026-07-05T07:57:34.233656+00:00"},{"alias_kind":"pith_short_16","alias_value":"FW23QLCH2HUNWMUM","created_at":"2026-07-05T07:57:34.233656+00:00"},{"alias_kind":"pith_short_8","alias_value":"FW23QLCH","created_at":"2026-07-05T07:57:34.233656+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12748","citing_title":"Agent-based models for the evolution of morphological alternation patterns","ref_index":113,"is_internal_anchor":false},{"citing_arxiv_id":"2305.01626","citing_title":"Basic syntax from speech: Spontaneous concatenation in unsupervised deep neural networks","ref_index":59,"is_internal_anchor":false},{"citing_arxiv_id":"2410.21803","citing_title":"SimSiam Naming Game: A Unified Approach for Representation Learning and Emergent Communication","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FW23QLCH2HUNWMUMWDE3HTX4B4","json":"https://pith.science/pith/FW23QLCH2HUNWMUMWDE3HTX4B4.json","graph_json":"https://pith.science/api/pith-number/FW23QLCH2HUNWMUMWDE3HTX4B4/graph.json","events_json":"https://pith.science/api/pith-number/FW23QLCH2HUNWMUMWDE3HTX4B4/events.json","paper":"https://pith.science/paper/FW23QLCH"},"agent_actions":{"view_html":"https://pith.science/pith/FW23QLCH2HUNWMUMWDE3HTX4B4","download_json":"https://pith.science/pith/FW23QLCH2HUNWMUMWDE3HTX4B4.json","view_paper":"https://pith.science/paper/FW23QLCH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.11958&json=true","fetch_graph":"https://pith.science/api/pith-number/FW23QLCH2HUNWMUMWDE3HTX4B4/graph.json","fetch_events":"https://pith.science/api/pith-number/FW23QLCH2HUNWMUMWDE3HTX4B4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FW23QLCH2HUNWMUMWDE3HTX4B4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FW23QLCH2HUNWMUMWDE3HTX4B4/action/storage_attestation","attest_author":"https://pith.science/pith/FW23QLCH2HUNWMUMWDE3HTX4B4/action/author_attestation","sign_citation":"https://pith.science/pith/FW23QLCH2HUNWMUMWDE3HTX4B4/action/citation_signature","submit_replication":"https://pith.science/pith/FW23QLCH2HUNWMUMWDE3HTX4B4/action/replication_record"}},"created_at":"2026-07-05T07:57:34.233656+00:00","updated_at":"2026-07-05T07:57:34.233656+00:00"}