{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/semantics-and-spatiality-of-emergent","title":"Semantics and Spatiality of Emergent Communication","arxiv_id":"2411.10173","date":"2024-11-15","proceeding":null,"authors":["Rotem Ben Zion","Boaz Carmeli","Orr Paradise","Yonatan Belinkov"],"abstract":"When artificial agents are jointly trained to perform collaborative tasks using a communication channel, they develop opaque goal-oriented communication protocols. Good task performance is often considered sufficient evidence that meaningful communication is taking place, but existing empirical results show that communication strategies induced by common objectives can be counterintuitive whilst solving the task nearly perfectly. In this work, we identify a goal-agnostic prerequisite to meaningful communication, which we term semantic consistency, based on the idea that messages should have similar meanings across instances. We provide a formal definition for this idea, and use it to compare the two most common objectives in the field of emergent communication: discrimination and reconstruction. We prove, under mild assumptions, that semantically inconsistent communication protocols can be optimal solutions to the discrimination task, but not to reconstruction. We further show that the reconstruction objective encourages a stricter property, spatial meaningfulness, which also accounts for the distance between messages. Experiments with emergent communication games validate our theoretical results. These findings demonstrate an inherent advantage of distance-based communication goals, and contextualize previous empirical discoveries.","url_abs":"https://arxiv.org/abs/2411.10173v1","url_pdf":"https://arxiv.org/pdf/2411.10173v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"semantics-and-spatiality-of-emergent","repo_url":"https://github.com/Rotem-BZ/SemanticConsistency","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2411.10173","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.10173"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Rotem-BZ/SemanticConsistency","reach":{"status":"ok"}}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"370948eb899a342d","entry":"DoubleConv","repo":"Rotem-BZ/SemanticConsistency","repo_kind":"official","path":"game_parts/vision_architectures.py","file_url":"https://github.com/Rotem-BZ/SemanticConsistency/blob/HEAD/game_parts/vision_architectures.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"370948eb899a342d"}},{"code_sha256_prefix":"81cb88f7fd4bc04a","entry":"get_loss_function","repo":"Rotem-BZ/SemanticConsistency","repo_kind":"official","path":"game_parts/loss.py","file_url":"https://github.com/Rotem-BZ/SemanticConsistency/blob/HEAD/game_parts/loss.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"81cb88f7fd4bc04a"}},{"code_sha256_prefix":"b3b7235ce396323e","entry":"get_weighted_loss_function","repo":"Rotem-BZ/SemanticConsistency","repo_kind":"official","path":"game_parts/loss.py","file_url":"https://github.com/Rotem-BZ/SemanticConsistency/blob/HEAD/game_parts/loss.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"b3b7235ce396323e"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}