{"as_of":"2026-08-18T14:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:759a66f0aaff080e06263ae4cdadc3fbfbecfa58fc4515e53fe7bd0e8388b5ce","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:43:08.713730Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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/2505.09659/citation-record","integrity":"/paper/2505.09659/integrity","json":"/paper/2505.09659/citation-record.json","paper":"/paper/2505.09659"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.355542Z","title":"Spikingbert: Distilling bert to train spiking language models using implicit differentiation","venue":null,"work_id":"2367e39a-3ea4-422f-aab1-ff2267d788c7","year":2024},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.514467Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:40c0220164f509f76b419b0329e298228cc894322b1e093066438341c1a5ab52","observation_id":"886e512a-e6d9-4a72-975f-7d8aef3b21a5","resolution":{"observed_at":"2026-08-15T21:43:09.360238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:08.519435Z","title":"Pythia: A suite for analyzing large language models across training and scaling","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.519435Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:1cded2928b90793c28658894f26fd976e1475175bb39bfc3a344ad1df310bfeb","observation_id":"6535676c-57b7-4716-aac3-a6828d00002a","resolution":{"observed_at":"2026-08-15T21:43:08.519435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.06745","last_updated":"2022-04-14T04:00:27Z","snapshot_observed_at":"2026-08-13T14:54:27.001192Z","submitted_at":"2022-04-14T04:00:27Z","title":"GPT-NeoX-20B: An Open-Source Autoregressive Language Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.06745","snapshot_observed_at":"2026-08-15T21:43:08.523808Z","title":"Gpt-neox-20b: An open-source autoregressive language model.arXiv preprint arXiv:2204.06745, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.523808Z"},"links":{"cited_paper":"/paper/2204.06745","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:4d28eb1acec6532efe8b1c8239e55e4f4720fcc39a5ad60572828b21e546293d","observation_id":"62a490c6-75e6-4446-be7e-f8d1e451b3b1","resolution":{"observed_at":"2026-08-15T21:43:08.523808Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.333050Z","title":"Spikeprop: backpropagation for networks of spiking neurons","venue":null,"work_id":"fb875333-4ddd-4f53-8a1d-4301bad08e8f","year":2000},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.528822Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:3b649cf41a9d3ec9e8ee7d1eba0b5e3cf71e9f40c18fab3d8eb3f4721ef5a503","observation_id":"22780db4-1356-4f04-87e8-b0fd640981d6","resolution":{"observed_at":"2026-08-15T21:43:09.337498Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.04347","last_updated":"2023-03-08T03:04:53Z","snapshot_observed_at":"2026-08-16T15:49:56.149882Z","submitted_at":"2023-03-08T03:04:53Z","title":"Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.04347","snapshot_observed_at":"2026-08-15T21:43:08.533198Z","title":"Optimal ann-snn conversion for high-accuracy and ultra-low-latency spiking neural networks.arXiv preprint arXiv:2303.04347, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.533198Z"},"links":{"cited_paper":"/paper/2303.04347","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:a606559e2b7c0a589c3118f2a22bd2761ed74d16be58a3a8b3125e60ff605f27","observation_id":"c0109412-902a-49cc-a75c-c2b7b7c69a78","resolution":{"observed_at":"2026-08-15T21:43:08.533198Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.319327Z","title":"Spiking deep convolutional neural networks for energy-efficient object recognition.International Journal of Computer Vision, 113:54–66, 2015","venue":null,"work_id":"d938c84a-6966-4248-8b84-3c5690360b59","year":2015},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.537845Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:378af54370fd9cb98cd98cc69798ece2a0408b8f1ac6a91a554c97ced0288ecd","observation_id":"62816357-126a-44a1-a0a7-bf7fb097d7a4","resolution":{"observed_at":"2026-08-15T21:43:09.323924Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.04405","last_updated":"2025-05-14T05:23:45Z","snapshot_observed_at":"2026-08-14T03:08:09.145073Z","submitted_at":"2025-02-06T09:08:12Z","title":"FAS: Fast ANN-SNN Conversion for Spiking Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.04405","snapshot_observed_at":"2026-08-15T21:43:08.542657Z","title":"Fas: Fast ann-snn conversion for spiking large language models.arXiv preprint arXiv:2502.04405, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.542657Z"},"links":{"cited_paper":"/paper/2502.04405","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:6d60035d353828bac46473189ca2ef3c8dec8768f1098df969bceef32915ac32","observation_id":"4b210b3f-5c90-4992-9435-6b24a7bd8c0c","resolution":{"observed_at":"2026-08-15T21:43:08.542657Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.305610Z","title":"Loihi: A neuromorphic manycore processor with on-chip learning.Ieee Micro, 38(1):82–99, 2018","venue":null,"work_id":"17f0b4d2-aae6-493f-b3a8-2ae083474347","year":2018},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.547008Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:dfe4b1c46a73c1d76df15cd072cf3cb771b696a06492dc24041061d359202cab","observation_id":"6e732d4d-d81c-451a-9aa6-1af1b8af90d6","resolution":{"observed_at":"2026-08-15T21:43:09.310063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.00476","last_updated":"2021-02-28T12:04:22Z","snapshot_observed_at":"2026-08-16T18:42:26.221811Z","submitted_at":"2021-02-28T12:04:22Z","title":"Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.00476","snapshot_observed_at":"2026-08-15T21:43:08.551245Z","title":"Optimal conversion of conventional artificial neural networks to spiking neural networks.ArXiv, abs/2103.00476, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.551245Z"},"links":{"cited_paper":"/paper/2103.00476","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:7c92246ac844e09e4f8200c744fd8aba618dc0b7251e77b11ab70198a9b7f8e0","observation_id":"4d1503a7-3ac3-4bf7-a272-886353e230a0","resolution":{"observed_at":"2026-08-15T21:43:08.551245Z","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":"2026-08-15T21:43:08.555875Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.555875Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:8b859b03569105969e01659bca4be814fd71cbdfc244c6504df25bfcf69dd67f","observation_id":"028bd473-ce2b-431d-b0b4-09e9fa0728e6","resolution":{"observed_at":"2026-08-15T21:43:08.555875Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.283585Z","title":"Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing.2015 International Joint Conference on Neural Networks (IJCNN), pages 1–8, 2015","venue":null,"work_id":"2351a9ff-fb0a-47e3-b315-c80a57f7cc44","year":2015},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.560326Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:c4b26fc486960e9f66871341b4795b912e537d6c29aa40f702f1acaad583913a","observation_id":"91c2ce1b-2e26-4548-a227-86de7a4e7c19","resolution":{"observed_at":"2026-08-15T21:43:09.288107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.269586Z","title":"Memristor-based neuromorphic chips.Advanced Materials, 36(14):2310704, 2024","venue":null,"work_id":"40936e33-318f-44bc-8ad6-17a1da7e95d0","year":2024},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.564905Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:b6c157c704ec4894253424dd8afb05d16e554c4bbe50024d5468e8ad62add92b","observation_id":"ca686685-e976-417f-9a66-bb8205b3b952","resolution":{"observed_at":"2026-08-15T21:43:09.274384Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:08.569182Z","title":"Cambridge University Press, 2014","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.569182Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:1b11cf34ce0d02e2c9ecafd7d797788c4aa0f2aeb2778e75fe1874261cf22a59","observation_id":"2dbd2051-4d67-4410-a2f4-1716810082e3","resolution":{"observed_at":"2026-08-15T21:43:08.569182Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.245114Z","title":"Reducing ann-snn conversion error through residual membrane potential","venue":null,"work_id":"9361d8ba-f144-420f-b131-1897d55634ce","year":2023},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.573366Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:39bd7db373a9cfda01ee4b708ff73f24f4f076bbb055ebaf42a47a8f8a2cca6e","observation_id":"b9d354d8-9671-4eb7-bc39-7aa0e1f02cf0","resolution":{"observed_at":"2026-08-15T21:43:09.250179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.10685","last_updated":"2023-02-21T14:10:56Z","snapshot_observed_at":"2026-08-16T15:53:49.552639Z","submitted_at":"2023-02-21T14:10:56Z","title":"Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.10685","snapshot_observed_at":"2026-08-15T21:43:08.578043Z","title":"Bridging the gap between anns and snns by calibrating offset spikes.ArXiv, abs/2302.10685, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.578043Z"},"links":{"cited_paper":"/paper/2302.10685","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:7f8865ee28ec6df01c437d4c7967b43aa1bb2787b9ccd5ea1179b85f6cff230a","observation_id":"54b463c5-d8bf-446b-82cb-9293184667f7","resolution":{"observed_at":"2026-08-15T21:43:08.578043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.00411","last_updated":"2024-10-09T02:56:46Z","snapshot_observed_at":"2026-08-16T14:22:36.376989Z","submitted_at":"2024-02-01T08:10:39Z","title":"LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.00411","snapshot_observed_at":"2026-08-15T21:43:08.582521Z","title":"Lm-ht snn: Enhancing the performance of snn to ann counterpart through learnable multi-hierarchical threshold model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.582521Z"},"links":{"cited_paper":"/paper/2402.00411","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:6b2ef474fceabdcabd6980c1a82b56fda49bcb499fad4691eebceecd06085bf7","observation_id":"9672458f-61d1-41f4-8321-a35ea054cf57","resolution":{"observed_at":"2026-08-15T21:43:08.582521Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.231126Z","title":"Towards high-performance spiking transformers from ann to snn conversion","venue":null,"work_id":"4bc39c4a-65ba-46a7-889f-14f4c8c4ab35","year":2024},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.586271Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:aa448ddd7a88dd60accee9b344755c6324b411dccc8d54777119bfa6430bcdfd","observation_id":"f6f94385-26f4-4e6d-b30e-33c185a6c53c","resolution":{"observed_at":"2026-08-15T21:43:09.235792Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.217523Z","title":"The information pathways hypothesis: Transformers are dynamic self- ensembles","venue":null,"work_id":"6147ec14-7b55-417b-a4da-aab201d1cbe0","year":2023},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.590057Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:ebbfd606e10064593c6171d41554f7cc6c8e72007a580115de8f8cca23276276","observation_id":"8bda04ee-34e6-4d64-87fc-dc1a354dbcae","resolution":{"observed_at":"2026-08-15T21:43:09.222124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.202623Z","title":null,"venue":null,"work_id":"246e6947-e74e-4c7e-af45-db44e8b68233","year":2024},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.593836Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:454ebb953ab08399679284dd4435a9ea92e0926697b686c1c4bccd87dcf3800b","observation_id":"9964460f-cfbb-4713-8330-440923b447b8","resolution":{"observed_at":"2026-08-15T21:43:09.207495Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.188213Z","title":"Spatio-temporal approximation: A training-free snn conversion for transformers","venue":null,"work_id":"ecadc7c9-b621-402d-a3ae-b231a467a9b9","year":2024},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.597670Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:8795a8c9c6eefd540fdd0e89267b8b0673c388a4a557e40797e933c35f3fc795","observation_id":"7071773f-8e8b-446a-93c9-4af8fc2fdbe0","resolution":{"observed_at":"2026-08-15T21:43:09.192723Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:08.601501Z","title":"Bloom: A 176b-parameter open-access multilingual language model","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.601501Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:6408fee0bee29382747dd661a9d9210523714d3cbd459e8b47a65c06db5234d2","observation_id":"35068dbc-731d-4958-aae9-eb1c76fc0e9b","resolution":{"observed_at":"2026-08-15T21:43:08.601501Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.162043Z","title":"Efficient and accurate conversion of spiking neural network with burst spikes","venue":null,"work_id":"e2fec7e0-a8ea-40ef-be6c-8097ac4c98c1","year":2022},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.605161Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:01aa3d5656d865f835ebf4def87e20f630530397d6d396956e78350383bc3378","observation_id":"d02c0d7f-f76d-4821-8a9d-fbf20e603e7c","resolution":{"observed_at":"2026-08-15T21:43:09.167751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-08-15T17:27:11.980940Z","submitted_at":"2024-12-27T04:03:16Z","title":"DeepSeek-V3 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19437","snapshot_observed_at":"2026-08-15T21:43:08.608825Z","title":"Deepseek-v3 technical report.arXiv preprint arXiv:2412.19437, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.608825Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:643399c0d3432d79f5ede9727e51870a41ae9cbceb8f0cc9d1a4312978f196d0","observation_id":"4f22a1e7-456b-4b1d-be93-5eaec827f516","resolution":{"observed_at":"2026-08-15T21:43:08.608825Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.148316Z","title":"Power efficient division and square root unit.IEEE Transactions on Computers, 61(8):1059–1070, 2012","venue":null,"work_id":"9b32ea35-61a5-451c-86d9-7ea82290c164","year":2012},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.613136Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:34474b1d6258ba823d5d3f194da47f3d55b48d2adfe7a276c54c381e39376016","observation_id":"1adba8e6-1373-4743-bc34-1c06359d4fec","resolution":{"observed_at":"2026-08-15T21:43:09.152836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:08.617269Z","title":"Spikebert: A language spikformer learned from bert with knowledge distillation, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.617269Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:e009d37e3f41892d0f5bf6fbf6c726c59f7ee1c4b56a58437f115e45af7eb014","observation_id":"4716667b-3bd9-433a-adc0-4b65fd81f1d5","resolution":{"observed_at":"2026-08-15T21:43:08.617269Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.123663Z","title":"Spiking convolutional neural networks for text classification","venue":null,"work_id":"6c17d4e8-200b-4ab4-a2ed-0105ceac2f75","year":2023},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.621709Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:dbd3d966f6dbfc44d964d695217d8e7ccd63d25788a3837c09a9ce5967097137","observation_id":"ac371c32-3019-4cb7-b009-680d25b3126d","resolution":{"observed_at":"2026-08-15T21:43:09.128559Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:08.626014Z","title":"Neftci, Hesham Mostafa, and Friedemann Zenke","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.626014Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:80ecdb16a268b92b15942b95c9cc16075bc3a5b027da74c53bd2c15b0093a811","observation_id":"2b873698-bfcd-4002-849a-98820d96511d","resolution":{"observed_at":"2026-08-15T21:43:08.626014Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.099114Z","title":"Hardware implementation of the exponential function using taylor series","venue":null,"work_id":"daea581a-6a48-432e-94a1-0c265dd2c5d0","year":2014},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.630489Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:157cff44d0c15be945a0a32c4dd0150e64ead6b64a29e50475e03211f3bbcde2","observation_id":"adb64981-5630-4403-9eff-ee270cb22727","resolution":{"observed_at":"2026-08-15T21:43:09.103810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:08.634823Z","title":"Language models are unsupervised multitask learners.OpenAI blog, 1(8):9, 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.634823Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:8b464cca9c761473ce8cafa094fa2c1fff6123bf6c2e46e102590d88c930fec0","observation_id":"2131aa5d-71a3-45d2-b8a2-8cb53acdef44","resolution":{"observed_at":"2026-08-15T21:43:08.634823Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.03658","last_updated":"2020-12-02T02:55:31Z","snapshot_observed_at":"2026-08-12T21:09:21.335646Z","submitted_at":"2020-08-09T05:07:17Z","title":"DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.03658","snapshot_observed_at":"2026-08-15T21:43:08.639008Z","title":"Diet-snn: Direct input encoding with leakage and threshold optimization in deep spiking neural networks.arXiv preprint arXiv:2008.03658, 2020","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.639008Z"},"links":{"cited_paper":"/paper/2008.03658","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:4a823e233975fe3d4a7f09dbf6bd4c65df6a7ce0b3e395630cb0a9043dd2fb83","observation_id":"5e4cd774-751a-4231-9bc6-03787d9a8196","resolution":{"observed_at":"2026-08-15T21:43:08.639008Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1612.04052","last_updated":"2016-12-13T07:58:34Z","snapshot_observed_at":"2026-08-14T21:25:48.313300Z","submitted_at":"2016-12-13T07:58:34Z","title":"Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1612.04052","snapshot_observed_at":"2026-08-15T21:43:08.644436Z","title":"Theory and tools for the conversion of analog to spiking convolutional neural networks.arXiv preprint arXiv:1612.04052, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.644436Z"},"links":{"cited_paper":"/paper/1612.04052","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:d763ced1ec43d3dbd285b0855df4abe1682e76f1673e90ff9c985a8375c134ab","observation_id":"bc8c4e06-1f43-47e0-9a12-344311043e32","resolution":{"observed_at":"2026-08-15T21:43:08.644436Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.073975Z","title":"Conversion of continuous-valued deep networks to efficient event-driven networks for image classification.Frontiers in neuroscience, 11:294078, 2017","venue":null,"work_id":"0d6aa04a-94c0-417d-b031-597ee115f70a","year":2017},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.648837Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:88d3e5e5052fce39232f597e8ecfc1c72d5e180ee58648a3a1a4ae53a08ff098","observation_id":"a37979f8-5cb2-4e31-84ad-c3716007423d","resolution":{"observed_at":"2026-08-15T21:43:09.078614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.059611Z","title":"Multitask prompted training enables zero-shot task generalization","venue":null,"work_id":"e41818d9-f127-4ba2-86eb-e5b7fec74485","year":2022},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.652938Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:107cccb501b7520d224ab4f3f3eb4d2e904419e981c2247af1a996d83352a5e2","observation_id":"0ce99825-1a54-4329-98fc-517388d877f4","resolution":{"observed_at":"2026-08-15T21:43:09.064303Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.07625","last_updated":"2023-12-26T02:54:29Z","snapshot_observed_at":"2026-08-18T10:17:54.794605Z","submitted_at":"2023-12-12T06:56:31Z","title":"Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.07625","snapshot_observed_at":"2026-08-15T21:43:08.657182Z","title":"Astrocyte-enabled advancements in spiking neural networks for large language modeling.ArXiv, abs/2312.07625, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.657182Z"},"links":{"cited_paper":"/paper/2312.07625","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:f3a55d9b729aa31516c3efd618f2bd487d88c8bb37e5b1035d00e4bb17f42d81","observation_id":"a041fa91-d6ba-4e11-8c27-ee2e35b9fbed","resolution":{"observed_at":"2026-08-15T21:43:08.657182Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.045981Z","title":"One-step spiking transformer with a linear complexity","venue":null,"work_id":"9c026cbf-cbc9-4d24-a4ac-4b0520956ef8","year":2024},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.662435Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:9e509c47d7e36293b060ebdd2bfd4426fe12f03d1fd7955e34f69263abe11434","observation_id":"1f273228-0995-440d-95f6-3359511ce099","resolution":{"observed_at":"2026-08-15T21:43:09.050035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.031820Z","title":"Optimized spiking neurons can classify images with high accuracy through temporal coding with two spikes.Nature Machine Intelligence, 3(3):230–238, 2021","venue":null,"work_id":"ec84c076-67a2-4c1e-b427-c0320b575139","year":2021},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.666846Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:93f09a8becc0df520b52c71f852778dfd70045b201ca104386380434062d777a","observation_id":"027f0ed1-3901-4a83-8ae3-6480519a1fdd","resolution":{"observed_at":"2026-08-15T21:43:09.036493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:09.017668Z","title":"Learning general purpose distributed sentence representations via large scale multi-task learning","venue":null,"work_id":"0890a206-4459-469f-917c-d7d34e7ac381","year":2018},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.671243Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:cfa685bef9bf97d2423bd72ea7391af0eefa1296cafba7f67953e7ade8b4abc6","observation_id":"a9dc1115-3ad5-4162-aca3-b23aff5f0a87","resolution":{"observed_at":"2026-08-15T21:43:09.022167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:08.675369Z","title":"Generalized leaky integrate-and-fire models classify multiple neuron types.Nature communications, 9(1):709, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.675369Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:36c4c99bd3fc558a393ed55aef62b2c86f4c90a97b0d56e885b22a18c596504f","observation_id":"f5aa10dc-d404-4ed1-b18c-2a12ff0037eb","resolution":{"observed_at":"2026-08-15T21:43:08.675369Z","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":"2026-08-15T21:43:08.679533Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.679533Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:a2334508107d428f8d9bbc63ab8bb254e5215d7ca4e7d4288a0da43b693fe4e3","observation_id":"275560d2-1a38-41db-a81c-84fba0bbd9a8","resolution":{"observed_at":"2026-08-15T21:43:08.679533Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12191","last_updated":"2024-10-03T15:54:49Z","snapshot_observed_at":"2026-08-06T05:35:29.109022Z","submitted_at":"2024-09-18T17:59:32Z","title":"Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12191","snapshot_observed_at":"2026-08-15T21:43:08.683567Z","title":"Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution.arXiv preprint arXiv:2409.12191, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.683567Z"},"links":{"cited_paper":"/paper/2409.12191","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:e365edb6176976e680b910d7410ee241ada3f80661531c44ae70d2b7a8886172","observation_id":"71f8741a-868d-4b0f-8195-baeb8683790c","resolution":{"observed_at":"2026-08-15T21:43:08.683567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.03287","last_updated":"2024-06-05T13:59:03Z","snapshot_observed_at":"2026-08-16T13:45:52.875611Z","submitted_at":"2024-06-05T13:59:03Z","title":"SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.03287","snapshot_observed_at":"2026-08-15T21:43:08.687904Z","title":"Spikelm: Towards general spike-driven language modeling via elastic bi-spiking mechanisms.arXiv preprint arXiv:2406.03287, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.687904Z"},"links":{"cited_paper":"/paper/2406.03287","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:71277727a6cb3be998bdd20fa32056f62c7ddcb07d38b06911b9efc41e38ce0b","observation_id":"8f7a9e18-5a6c-45a9-8b23-560261be4658","resolution":{"observed_at":"2026-08-15T21:43:08.687904Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:43:08.984630Z","title":"Spike-based dynamic computing with asynchronous sensing-computing neuromorphic chip.Nature Communications, 15(1):4464, 2024","venue":null,"work_id":"974a030f-ae9e-4a42-b08c-7033a4f1e043","year":2024},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.692272Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:37dc5adb54c7d216c954d841c1e408c7ad5a929c06a05751de4efa289b393b99","observation_id":"56b07f8f-aa85-4d51-a705-33e77f40bab8","resolution":{"observed_at":"2026-08-15T21:43:08.989822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02969","last_updated":"2023-06-06T11:03:31Z","snapshot_observed_at":"2026-08-16T16:26:24.006113Z","submitted_at":"2022-10-06T15:00:47Z","title":"Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners","version":4},"cited_work":{"arxiv_id":"2210.02969","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.02969","snapshot_observed_at":"2026-08-15T21:43:08.791102Z","title":"Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners","venue":"cs.CL","work_id":"f8d1340c-d0ff-48b6-8ede-dd465668cc49","year":2022},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.696520Z"},"links":{"cited_paper":"/paper/2210.02969","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:81ada65fc89048f794b6a2f09ae995188417477bd2d18b698a2bcc502d231bb8","observation_id":"757e4372-a2eb-445f-885a-b54552407b0f","resolution":{"observed_at":"2026-08-15T21:43:08.797324Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.03470","last_updated":"2024-06-05T17:24:07Z","snapshot_observed_at":"2026-08-16T13:45:47.987536Z","submitted_at":"2024-06-05T17:24:07Z","title":"SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.03470","snapshot_observed_at":"2026-08-15T21:43:08.701130Z","title":"Spikezip-tf: Conversion is all you need for transformer-based snn.arXiv preprint arXiv:2406.03470, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.701130Z"},"links":{"cited_paper":"/paper/2406.03470","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:ef2433fa98a092d90997ff797a2b4c9adb62bc712f533dfbdab7931f2c9af125","observation_id":"16160aab-b9f0-4466-8015-4885d162ad6a","resolution":{"observed_at":"2026-08-15T21:43:08.701130Z","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":"2026-08-15T21:43:08.705468Z","title":"The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks.Neural computation, 33(4):899–925, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.705468Z"},"links":{"citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:5c8a3a644ad2119b4ea4fd7ea0ef3135849ad0e8e25ff0337fe190d8243f7ad0","observation_id":"9eb69945-cd47-4df2-8bfb-e4803e8ee0c0","resolution":{"observed_at":"2026-08-15T21:43:08.705468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.01068","last_updated":"2022-06-21T17:04:40Z","snapshot_observed_at":"2026-08-06T03:13:37.403059Z","submitted_at":"2022-05-02T17:49:50Z","title":"OPT: Open Pre-trained Transformer Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.01068","snapshot_observed_at":"2026-08-15T21:43:08.709480Z","title":"Opt: Open pre-trained transformer language models.arXiv preprint arXiv:2205.01068, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.709480Z"},"links":{"cited_paper":"/paper/2205.01068","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:fcd5bfe7d12570e76b69bb3e1c7e21375f1af63d7076764404b9e9b2f9c1e317","observation_id":"b2770edf-ec6e-4ef0-913b-97418bd0cd88","resolution":{"observed_at":"2026-08-15T21:43:08.709480Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13939","last_updated":"2024-07-11T10:16:12Z","snapshot_observed_at":"2026-08-16T15:52:22.493008Z","submitted_at":"2023-02-27T16:43:04Z","title":"SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13939","snapshot_observed_at":"2026-08-15T21:43:08.713730Z","title":"Spikegpt: Generative pre-trained language model with spiking neural networks.arXiv preprint arXiv:2302.13939, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T21:43:08.713730Z"},"links":{"cited_paper":"/paper/2302.13939","citing_paper":"/paper/2505.09659"},"observation_digest":"sha256:1fdbb93ef693435d02952196a1d5eb9666db6cadc18266f4b877e6cd69d6e86e","observation_id":"d1f2b0ed-19c2-4956-9905-a04137307e8a","resolution":{"observed_at":"2026-08-15T21:43:08.713730Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.09659","last_updated":"2025-05-14T06:18:08Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T21:35:37.242102Z","submitted_at":"2025-05-14T06:18:08Z","title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":26,"verified_exact":1,"verified_fuzzy":20},"total_outbound_references":47},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2505.09659."}