{"as_of":"2026-08-23T21:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7aca659d74c0d0249757e15a58f1cc79d9e00ae9965f3c9c7c0d53def136aa3e","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-02T19:32:20.957294Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2603.02156/citation-record","integrity":"/paper/2603.02156/integrity","json":"/paper/2603.02156/citation-record.json","paper":"/paper/2603.02156"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:18.549966Z","title":"F-iran: Performance analysis of 6g fog intelli- gent radio access network,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:18.549966Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:ef6ba10a3d25d527aad2b9f0596e5b76b0a1c75b4c670d5cd2e93f54a76c271c","observation_id":"831ed6eb-0d8d-40a0-ab8a-eb9eced58183","resolution":{"observed_at":"2026-08-02T19:32:18.549966Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:18.669859Z","title":"Study on 6g use cases and service requirements,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:18.669859Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:adc60661697cf21c6d821dd188495a532a965247303e07b183bb8361676dc500","observation_id":"ad983a0e-cd85-475d-8672-903fa5edde07","resolution":{"observed_at":"2026-08-02T19:32:18.669859Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:18.873489Z","title":"Generative ai use cases and requirements on 6g network,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:18.873489Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:fd7e9d4cb54d0d1fc0a5191d85e6d13f26f2d603cb5a61f7e99fa35dde006122","observation_id":"2e6f318b-2a32-4882-ad2a-6b6a79b514d8","resolution":{"observed_at":"2026-08-02T19:32:18.873489Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:19.005850Z","title":"GS MEC 003 V3.1.1, Mar","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:19.005850Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:c2b9e78d1b28939dbdde70e33064f0d402a6086b8598a95f4387ff7b80156a35","observation_id":"da64b642-e2a0-4882-86c8-19c5d708364c","resolution":{"observed_at":"2026-08-02T19:32:19.005850Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:19.199895Z","title":"Requirements and enabling technologies of agent protocols for 6g networks,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:19.199895Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:0bca302bc751ada79621678db38856563fd35f8d22bb177e33873d8e29638fa5","observation_id":"15bc658e-7cdf-4d5a-8d7a-df1a4116a104","resolution":{"observed_at":"2026-08-02T19:32:19.199895Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:19.286866Z","title":"6g cellular networks: Mapping the land- scape for the imt-2030 framework,","venue":null,"work_id":null,"year":2030},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:19.286866Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:8f7a4fe3e418eeb9f4f0e74ccb87d135392dc5e5c65b923b689f8e29da48515a","observation_id":"c0b959d9-c356-4c93-9476-5bd7b1f71d24","resolution":{"observed_at":"2026-08-02T19:32:19.286866Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:19.402046Z","title":"Extremely large aperture array (elaa) communications: Foundations, research advances and challenges,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:19.402046Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:47f86726985f8afd4f61bbd76559d0e5c5b412e5c131c47c7fb78cbf4ef6d57d","observation_id":"e5e3f8ff-dbb7-4ca6-9674-913f341a7399","resolution":{"observed_at":"2026-08-02T19:32:19.402046Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:19.481366Z","title":"Energy-efficient ris-aided cell-free massive mimo systems: Application, opportunities, and challenges,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:19.481366Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:b902359894e87a09970cb961aac1da8ccf6435804de01c3c71196aea6570012b","observation_id":"245ab6cd-0092-4403-bb72-867ea97bf719","resolution":{"observed_at":"2026-08-02T19:32:19.481366Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:19.545714Z","title":"6g phy: Insights from 6g-anna research initiative,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:19.545714Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:4cdb1153485548d593e10875853ddda2957ead560d8e06d4283bef3e56b7569e","observation_id":"8870e6cd-71c8-4c7c-9324-53a3b964566e","resolution":{"observed_at":"2026-08-02T19:32:19.545714Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:19.635729Z","title":"Resource optimization for semantic communication in 6g networks: A survey,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:19.635729Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:f37dd33796a4ecc8f1377de0180ea9324c4fe6fc7e199e4bce2663cfa446dbd1","observation_id":"7b1b38d8-541e-43f3-81a9-bf736d7da891","resolution":{"observed_at":"2026-08-02T19:32:19.635729Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:19.701501Z","title":"Towards 6g authen- tication and key agreement protocol: A survey on hybrid post quantum cryptography,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:19.701501Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:58e2ed69a9c4a46f311dd7a37e817ce28772f7f653ad16d6d55f50fd8eb1ee67","observation_id":"1474d37f-b14e-4174-a0e0-0cd092873fd8","resolution":{"observed_at":"2026-08-02T19:32:19.701501Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:19.773957Z","title":"Llms on a budget: System-level approaches to power-efficient and scalable fine-tuning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:19.773957Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:5b49666852bb152f91a524ee833bc2e9c61622c2a2240e04e3487d561a2b6dfb","observation_id":"9cde823f-44ca-4790-af18-d8585b65c7c8","resolution":{"observed_at":"2026-08-02T19:32:19.773957Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:19.835831Z","title":"α 3-bench: A unified bench- mark of safety, robustness, and efficiency for llm-based uav agents over 6g networks,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:19.835831Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:77a49f353b440435220fff35ba1fe0a597e095fcf70e024ed6c6f4322ca3fbc4","observation_id":"4860c0fc-e1e1-4e4b-bfc0-0eaa0c40145a","resolution":{"observed_at":"2026-08-02T19:32:19.835831Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.15556","last_updated":"2022-03-29T13:38:03Z","snapshot_observed_at":"2026-08-21T02:12:10.329412Z","submitted_at":"2022-03-29T13:38:03Z","title":"Training Compute-Optimal Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.15556","snapshot_observed_at":"2026-08-02T19:32:19.913142Z","title":"Training compute-optimal large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:19.913142Z"},"links":{"cited_paper":"/paper/2203.15556","citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:0a1c18cc06558bb8c1f3808049e7ffa6a3b6c4aa03712c215722ef4c5ef67533","observation_id":"dd6fabd5-fecd-4c63-8158-05d2ab3de859","resolution":{"observed_at":"2026-08-02T19:32:19.913142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-08-13T17:41:53.092611Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-02T19:32:19.952089Z","title":"Scaling laws for neural language models,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:19.952089Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:ac831fc2a43c869e03efa9c10f25a54cf0915ea15bcc61ac7892dd3e89c30fce","observation_id":"f48d27ec-f413-4bc6-96d6-a5c5025e62fa","resolution":{"observed_at":"2026-08-02T19:32:19.952089Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.07682","last_updated":"2022-10-26T05:06:24Z","snapshot_observed_at":"2026-08-17T00:14:01.413100Z","submitted_at":"2022-06-15T17:32:01Z","title":"Emergent Abilities of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.07682","snapshot_observed_at":"2026-08-02T19:32:20.112627Z","title":"Emergent abilities of large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:20.112627Z"},"links":{"cited_paper":"/paper/2206.07682","citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:36d4a3b8f10c39076800f38f43ee994c0568588d0b0c0c73e879ce4546098d25","observation_id":"9b7b649b-04ae-42b5-a531-feb2875e03ef","resolution":{"observed_at":"2026-08-02T19:32:20.112627Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:20.119453Z","title":"6g-bench: An open benchmark for semantic communication and network-level reasoning with founda- tion models in ai-native 6g networks,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:20.119453Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:7859012013b6daad281cf174f856c17b4e47ca8e30d46a39106bd3f94344ba7b","observation_id":"53a7d2c2-c97e-43e2-b6fb-b2d11e8587ab","resolution":{"observed_at":"2026-08-02T19:32:20.119453Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.11446","last_updated":"2022-01-21T18:39:38Z","snapshot_observed_at":"2026-08-09T14:52:01.161814Z","submitted_at":"2021-12-08T19:41:47Z","title":"Scaling Language Models: Methods, Analysis & Insights from Training Gopher","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.11446","snapshot_observed_at":"2026-08-02T19:32:20.183146Z","title":"Scaling language models: Methods, analysis & insights from training gopher,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:20.183146Z"},"links":{"cited_paper":"/paper/2112.11446","citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:10e0a22ee491889f590ff0ac2dcd30e17f1e56421a12af66996eac337a1f24db","observation_id":"83cf7c41-c198-4a0c-be80-51d412d2ab83","resolution":{"observed_at":"2026-08-02T19:32:20.183146Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.02737","last_updated":"2025-02-04T21:43:16Z","snapshot_observed_at":"2026-08-14T05:03:05.661970Z","submitted_at":"2025-02-04T21:43:16Z","title":"SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.02737","snapshot_observed_at":"2026-08-02T19:32:20.299756Z","title":"Smollm2: When smol goes big–data-centric training of a small language model,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:20.299756Z"},"links":{"cited_paper":"/paper/2502.02737","citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:52da9cbb684a325f9ea364be7521a290b631f11f42a5a903c6f9527e7cd8526e","observation_id":"64db1a58-7936-4b18-a0ba-b6cc89d0daf2","resolution":{"observed_at":"2026-08-02T19:32:20.299756Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:20.400438Z","title":"Granite 4.0 nano language models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:20.400438Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:7330454b12c8a4eb3c59279b4cb62468d580dc5b2de2cd30bcc47139fd1095e6","observation_id":"bbe254f0-88f1-4316-b233-a05e2e814667","resolution":{"observed_at":"2026-08-02T19:32:20.400438Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:20.469031Z","title":"Lfm2 technical report,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:20.469031Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:85a85255d9beae4cffd6a5bc66f121e96a6d50c7f2d0c1fb4e30a5b6d590af06","observation_id":"c7572fa5-33c7-478a-b055-075ca7286968","resolution":{"observed_at":"2026-08-02T19:32:20.469031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-02T19:32:20.641158Z","title":"The llama 3 herd of models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:20.641158Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:6964fb9e32ce9ded54035b5745af173974c446f97aad209fd65ebd45c4d5bfec","observation_id":"c957cfdb-b132-440d-b352-92813db95a41","resolution":{"observed_at":"2026-08-02T19:32:20.641158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-02T19:32:20.736007Z","title":"Qwen2.5: A party of foundation models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:20.736007Z"},"links":{"citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:f774b4865cead21c3aacb6c4b8729512cb432b68d0414f76c4d5c6decc373606","observation_id":"3dadd429-4bdc-45bc-94f3-a25e8edde773","resolution":{"observed_at":"2026-08-02T19:32:20.736007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10671","last_updated":"2024-09-10T13:25:53Z","snapshot_observed_at":"2026-08-17T11:08:48.802438Z","submitted_at":"2024-07-15T12:35:42Z","title":"Qwen2 Technical Report","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10671","snapshot_observed_at":"2026-08-02T19:32:20.834963Z","title":"Qwen2 technical report,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:20.834963Z"},"links":{"cited_paper":"/paper/2407.10671","citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:026b17468c73c9b4cb2626443669d9ca2a25e9775eb80b8ed24b8296d7f97354","observation_id":"24461beb-cab8-4f3c-8581-111c36abd4bd","resolution":{"observed_at":"2026-08-02T19:32:20.834963Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2512.13961","last_updated":"2026-04-14T15:12:44Z","snapshot_observed_at":"2026-08-16T16:18:35.731432Z","submitted_at":"2025-12-15T23:41:48Z","title":"Olmo 3","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2512.13961","snapshot_observed_at":"2026-08-02T19:32:20.957294Z","title":"Olmoet al., “Olmo 3,”arXiv preprint arXiv:2512.13961, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-02T19:32:20.957294Z"},"links":{"cited_paper":"/paper/2512.13961","citing_paper":"/paper/2603.02156"},"observation_digest":"sha256:92cbf6a2200ec5aba7e6be1647ec4b4ef5d684129322f9c49f4be4cefaf9a9fb","observation_id":"2a993439-84e5-4fea-830f-82b45bfe8f20","resolution":{"observed_at":"2026-08-02T19:32:20.957294Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2603.02156","last_updated":"2026-06-23T18:51:46Z","latest_version":2,"primary_category":"cs.NI","snapshot_observed_at":"2026-08-19T12:54:58.522505Z","submitted_at":"2026-03-02T18:19:49Z","title":"How Small Can 6G Reason? Scaling Tiny-to-Small Language Models for AI-Native Networks"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":25,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":25},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2603.02156."}