{"as_of":"2026-08-07T21:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7b88a63b199b0cb9aede2ce9579ae6000a9f9293422226265d7f4dd77c38ce73","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T05:21:30.874144Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2509.05635/citation-record","integrity":"/paper/2509.05635/integrity","json":"/paper/2509.05635/citation-record.json","paper":"/paper/2509.05635"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T05:21:31.319581Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding,","venue":null,"work_id":"965c70d0-a146-4f98-9bd1-961588f670ed","year":2019},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.725300Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:9545fc545a4a24fa6340d66ab007e7ea83e647b993f9eb15a9aece988babd999","observation_id":"376d6c5c-c377-419e-a798-53f7601b4df3","resolution":{"observed_at":"2026-08-05T05:21:31.323420Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.310140Z","title":"Exploring the limits of transfer learning with a unified text-to-text transformer,","venue":null,"work_id":"263969b3-8c78-401c-ba32-ccea20c3f48d","year":2020},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.728539Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:7c5e4356bac2082f781b23b4d5d125a2371b665c5019f2c3950889974349222b","observation_id":"3108767f-0e74-45f2-b824-d958d8494913","resolution":{"observed_at":"2026-08-05T05:21:31.313370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-05T05:21:30.732046Z","title":"Language models are few-shot learners,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.732046Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:d2a26b788934f5fb7155c5bb09859206c2d18d8ec425be966a4676df4c9216d6","observation_id":"8e1faad9-cc1d-4e85-9cb5-59df1ca41f95","resolution":{"observed_at":"2026-08-05T05:21:30.732046Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-05T05:21:30.736252Z","title":"Llama: Open and efficient foundation language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.736252Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:0ab2a6bb3046a361da6239e99cffe02b6b87e283e240550f165006ed84d7cbcd","observation_id":"1545d2f7-2923-4b3e-b23b-57c42c9a1da9","resolution":{"observed_at":"2026-08-05T05:21:30.736252Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-05T05:21:30.740381Z","title":"Gpt-4 technical report,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.740381Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:f10979c007f9ab5290ababfac86dbdf63209df86b4bc10b1bca8d959c4431a67","observation_id":"d0bdbdfb-f6f5-4aef-9ece-0650e455d83d","resolution":{"observed_at":"2026-08-05T05:21:30.740381Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.11805","last_updated":"2025-05-09T21:04:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-19T02:39:27Z","title":"Gemini: A Family of Highly Capable Multimodal Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.11805","snapshot_observed_at":"2026-08-05T05:21:30.743754Z","title":"Gemini: a family of highly capable multimodal models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.743754Z"},"links":{"cited_paper":"/paper/2312.11805","citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:6a01c0f2fad203ac426722079ead69f4fe3ce1e242712c88e8430fa84d1958c9","observation_id":"511a0996-a6e3-440f-90b6-f22ad4f343f1","resolution":{"observed_at":"2026-08-05T05:21:30.743754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-05T05:21:30.747915Z","title":"Deepseek-r1: Incentivizing reason- ing capability in llms via reinforcement learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.747915Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:850a5852405de82adc653d3e4b5e65fb629d45f8897a2edba56aa0223804f986","observation_id":"f4be014b-3659-49d6-bd04-4899e894c0a7","resolution":{"observed_at":"2026-08-05T05:21:30.747915Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.03293","last_updated":"2023-10-05T03:45:54Z","snapshot_observed_at":"2026-07-06T16:27:58.898609Z","submitted_at":"2023-10-05T03:45:54Z","title":"A New Dialogue Response Generation Agent for Large Language Models by Asking Questions to Detect User's Intentions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.03293","snapshot_observed_at":"2026-08-05T05:21:30.751235Z","title":"A new dialogue response generation agent for large language models by asking questions to detect user’s intentions,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.751235Z"},"links":{"cited_paper":"/paper/2310.03293","citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:c7e782584d53be7e7b91ec123917878a94b4a2eb5896ef8abdedacef2287a6aa","observation_id":"f9eab0c8-9c59-4acf-adcb-ad9d3001b822","resolution":{"observed_at":"2026-08-05T05:21:30.751235Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.09205","last_updated":"2024-02-15T09:59:52Z","snapshot_observed_at":"2026-08-04T07:21:16.090266Z","submitted_at":"2024-02-14T14:36:30Z","title":"Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.09205","snapshot_observed_at":"2026-08-05T05:21:30.754777Z","title":"Tell me more! towards implicit user intention understanding of language model driven agents,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.754777Z"},"links":{"cited_paper":"/paper/2402.09205","citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:695c413c64576d5c109101bef8adb372d9fb1844607f4c3b10da6776d3167aa3","observation_id":"2e91f4eb-0649-489c-90ea-612601b20981","resolution":{"observed_at":"2026-08-05T05:21:30.754777Z","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":"2026-08-05T05:21:31.300519Z","title":"Effectiveness of pre-training for few-shot intent classi- fication,","venue":null,"work_id":"7eebc99f-f301-4dfa-b48d-e4b45380e7c8","year":2021},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.758975Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:542d91c28a76848b13d5e03443b98f5dfbeb89524fe8d3d858aaa72251bb422e","observation_id":"db7198bf-5fc7-4981-bee5-c4dc28eab259","resolution":{"observed_at":"2026-08-05T05:21:31.303675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.290261Z","title":"Cluster & tune: Boost cold start performance in text classification,","venue":null,"work_id":"78c3fd12-f1b5-4162-88a3-7d4c5b976ffc","year":2022},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.762016Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:5b28b0f06e3c6bdd64a78589ac76fe8cc26b6e28e85739268d57f9d11716dd15","observation_id":"664e54d1-0ff7-44a1-a61c-5595569b4868","resolution":{"observed_at":"2026-08-05T05:21:31.293813Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.280251Z","title":"Fine-tuning pre-trained language models for few-shot intent detection: Supervised pre-training and isotropization,","venue":null,"work_id":"398ee309-d274-42a2-b5fd-5b75b2f192a2","year":2022},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.765402Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:709504287a04a821b80dda75517ab421005f0bf4ba30c8d881b7408d253653f8","observation_id":"7d1f689e-6ede-429d-a997-a26cca65aa74","resolution":{"observed_at":"2026-08-05T05:21:31.283983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.268684Z","title":"Few-shot intent detection via con- trastive pre-training and fine-tuning,","venue":null,"work_id":"1efb005d-68df-4100-aeeb-f0d59406d0fb","year":2021},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.768994Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:08fbe967d551f75a0104cde7468ce01bc014daba1044be8ba96818f07fb6f512","observation_id":"2fd4b76c-8790-45a0-8f76-597f8d13c5cd","resolution":{"observed_at":"2026-08-05T05:21:31.272850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-07-31T22:31:37.910868Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-08-05T05:21:30.772020Z","title":"Roberta: A robustly optimized bert pretraining approach,","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.772020Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:41472ccec7813203838a460111f25f8fe98c3dc6dbf0722a8bc15c602cfc4079","observation_id":"9de25778-d06e-43ab-8bb9-0f6b3777e71f","resolution":{"observed_at":"2026-08-05T05:21:30.772020Z","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":"2026-08-05T05:21:31.258335Z","title":"Exploring zero and few-shot techniques for intent classification,","venue":null,"work_id":"8dccf03d-e448-476b-ba22-91b8e2cfd311","year":2023},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.775834Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:c97c65e3a7844ddb3ff89249d4175e2f54b31acf6b0e58e63a4d7e58973287eb","observation_id":"27fa73d8-23d6-4dda-9554-6c078801a4cd","resolution":{"observed_at":"2026-08-05T05:21:31.261920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.247765Z","title":"Simcse: Simple contrastive learn- ing of sentence embeddings,","venue":null,"work_id":"5fe25e2b-e5f7-4d64-9882-8af6eceddc2f","year":2021},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.778759Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:e305a3d627d9453181ca26a961f07dae57ee4cb18e11367be534a5271dabc887","observation_id":"617b8648-e964-4618-a1a0-2f03f543fd64","resolution":{"observed_at":"2026-08-05T05:21:31.252003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.237221Z","title":"Efficient intent detection with dual sentence encoders,","venue":null,"work_id":"f72344a0-0911-4cc5-9f91-17d9490f3642","year":2020},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.781721Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:c0042fcb14659cb6181e3cf0846ffd6d85b9335af6dafbbceb9a4fb42a656755","observation_id":"01ac8e01-ced1-4ba7-a648-f98430f027d7","resolution":{"observed_at":"2026-08-05T05:21:31.241020Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2009.13570","last_updated":"2020-10-01T00:00:19Z","snapshot_observed_at":"2026-07-06T09:59:31.820485Z","submitted_at":"2020-09-28T18:36:23Z","title":"DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue","version":2},"cited_work":{"arxiv_id":"2009.13570","doi":null,"metadata_source":"pith","pith_arxiv_id":"2009.13570","snapshot_observed_at":"2026-08-05T05:21:30.941004Z","title":"DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue","venue":"cs.CL","work_id":"7b430cda-9a39-4116-9fc5-7ff5bdee087f","year":2020},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.784596Z"},"links":{"cited_paper":"/paper/2009.13570","citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:bbb2077965f77dd70a0ec0c3c1b7c050f0b3d5ba0dd8e5fb26c2f909197c9b5e","observation_id":"c4f217fe-dce6-4683-b842-8195a4180af4","resolution":{"observed_at":"2026-08-05T05:21:30.946197Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.227502Z","title":"Revisit few-shot intent classification with plms: Direct fine-tuning vs. con- tinual pre-training,","venue":null,"work_id":"d010732c-aa54-4734-be88-1a7ac2837ecf","year":2023},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.787880Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:9ce5de7dc1d94801e95d81bbc54588c353625a4602a3fde68c882cd1f39f36fe","observation_id":"1edcddeb-0703-4192-ad10-45c28a7e67f1","resolution":{"observed_at":"2026-08-05T05:21:31.231158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.217586Z","title":"Region embedding with intra and inter-view contrastive learning,","venue":null,"work_id":"1a5e3172-778d-4023-b438-d22776a1da67","year":2022},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.791024Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:7ee626efac8056a16011cc8b98018a9c0d6eb326185153394128205189b581a6","observation_id":"0828e531-9564-434f-acb5-07d356aa80d7","resolution":{"observed_at":"2026-08-05T05:21:31.221064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:30.794028Z","title":"A simple framework for contrastive learning of visual representations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.794028Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:ea54f21f8ef14276ed6a2ad87716d89407b197de971e8b1967958984005e5ef0","observation_id":"c7552dbc-5f87-430d-8b63-edf551902f2b","resolution":{"observed_at":"2026-08-05T05:21:30.794028Z","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":"2026-08-05T05:21:31.202030Z","title":"Discriminative nearest neighbor few- shot intent detection by transferring natural language inference,","venue":null,"work_id":"0f68f402-299c-4f33-bbed-33a1f226bbee","year":2020},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.796992Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:2fe992e40836df9737504ecef79face2ee1279650d9aa5403cd83de41c26bea9","observation_id":"a87b94a0-45a9-42e8-9033-bfd45d80dc91","resolution":{"observed_at":"2026-08-05T05:21:31.205997Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.192463Z","title":"Exploring the limits of transfer learning with a unified text-to-text transformer,","venue":null,"work_id":"20fcf748-2c2b-4d3b-b177-96efc426ae2f","year":2020},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.799904Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:b5b2e74363d5348971131144899cf16c15d4121e1ba4dc6f97fb2678e4f27f56","observation_id":"9e8fe313-26a4-4995-8821-a16ec70c4aaa","resolution":{"observed_at":"2026-08-05T05:21:31.196047Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:30.803259Z","title":"Language models are unsupervised multitask learners,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.803259Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:ed06de11bf591a3e348fccd4e6aba8559cf1f2a380b3c84abbbff5741559d671","observation_id":"f4dc5e7c-5b1e-4636-86c7-886552f8222c","resolution":{"observed_at":"2026-08-05T05:21:30.803259Z","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":"2026-08-05T05:21:31.176233Z","title":"Commonsense knowl- edge mining from pretrained models,","venue":null,"work_id":"028b2376-506b-45ba-99af-c05bf4ec2cea","year":2019},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.806076Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:6fbb8e13b326f7cd2d9291bbaae92069d4aa64ac93f76390672f7c00be532a6f","observation_id":"658cc680-5028-440f-a4a6-4da2a369b60b","resolution":{"observed_at":"2026-08-05T05:21:31.180192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.166298Z","title":"Ppt: Pre-trained prompt tuning for few-shot learning,","venue":null,"work_id":"75e05a58-09c2-443a-adf0-cb4d8b89587b","year":2022},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.809039Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:0936a60bcff65df5904b25ce95df8ad3ca021b09633cd5335835612925719766","observation_id":"fc22915c-6166-405b-93a5-d9dc640c7324","resolution":{"observed_at":"2026-08-05T05:21:31.169721Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.156472Z","title":"Learning to compose soft prompts for compositional zero-shot learning,","venue":null,"work_id":"8f48da26-6c56-4377-981e-46f1cc58bb39","year":null},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.812493Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:8d65e896c3efe128cbe3b71227451a003c9eae455dae7bc712cca0839ef901de","observation_id":"385923f0-6d05-4aa6-a0a9-75e7b6eec8eb","resolution":{"observed_at":"2026-08-05T05:21:31.159822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.146390Z","title":"Exploiting cloze-questions for few-shot text classification and natural language inference,","venue":null,"work_id":"b2d07211-a511-4082-a7d0-5f0fa71270df","year":2021},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.816676Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:654c86474e717209e06831496d433de47b6ee057e10cc593a697c6713929ad0c","observation_id":"480045ef-4191-4d3a-b88f-72228ddcb600","resolution":{"observed_at":"2026-08-05T05:21:31.150190Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.136975Z","title":"The power of scale for parameter-efficient prompt tuning,","venue":null,"work_id":"07a713d9-2dff-4c42-ae17-592debc10404","year":2021},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.819583Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:cb51e859892328ca67e1598f8ea4af88698bcf5c8f51830acfeb2e66560443aa","observation_id":"84e35ff8-24d7-4c00-955c-0779b7142b96","resolution":{"observed_at":"2026-08-05T05:21:31.140244Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.126573Z","title":"P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks,","venue":null,"work_id":"8074e772-04dc-4c73-8342-417df576a460","year":2022},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.822609Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:410a0d1bcf1c98e89e38612ed3636acf37c7c20c6587a5366c794cb32584aee0","observation_id":"00cc17d8-0507-4fe7-903b-f210dfb6ef89","resolution":{"observed_at":"2026-08-05T05:21:31.130141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.115450Z","title":"Pre- train, prompt, and predict: A systematic survey of prompting methods in natural language processing,","venue":null,"work_id":"eb846555-9eec-46ce-ac60-7855123a16a7","year":2023},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.825572Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:eb84aefbcb51f097401e0f3a4c314b753835e43b3e62b5577fed8abecded9b40","observation_id":"1cf6f25b-336e-4aed-be0b-4f25d2bc6365","resolution":{"observed_at":"2026-08-05T05:21:31.118879Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.07927","last_updated":"2025-03-16T06:23:34Z","snapshot_observed_at":"2026-08-05T17:55:26.008016Z","submitted_at":"2024-02-05T19:49:13Z","title":"A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.07927","snapshot_observed_at":"2026-08-05T05:21:30.828689Z","title":"A systematic survey of prompt engineering in large language models: Techniques and applications,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.828689Z"},"links":{"cited_paper":"/paper/2402.07927","citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:8fd515a7239fc2aadaddcdd59923bfd7c336b8933dee465712129ff0f7aaa47e","observation_id":"a762b94b-84dc-4f9e-9dbd-63e97f01e7ca","resolution":{"observed_at":"2026-08-05T05:21:30.828689Z","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":"2026-08-05T05:21:30.832014Z","title":"Visual prompt tuning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.832014Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:866844edcad3c5fdb70e9794ac0ac4cb57ddda6ae28a670f4e71c029fe0bd3de","observation_id":"c79543d6-17e6-43c7-9407-fed1e11f6866","resolution":{"observed_at":"2026-08-05T05:21:30.832014Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.17274","last_updated":"2022-06-03T17:52:04Z","snapshot_observed_at":"2026-07-06T12:55:27.843060Z","submitted_at":"2022-03-31T17:59:30Z","title":"Exploring Visual Prompts for Adapting Large-Scale Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.17274","snapshot_observed_at":"2026-08-05T05:21:30.834999Z","title":"Explor- ing visual prompts for adapting large-scale models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.834999Z"},"links":{"cited_paper":"/paper/2203.17274","citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:454ae54392ad42593854359cb50175832c56b04f06138b7656d5ab4f63cbf045","observation_id":"1128d73f-a5d9-4deb-8e6a-35935c20d013","resolution":{"observed_at":"2026-08-05T05:21:30.834999Z","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":"2026-08-05T05:21:31.099466Z","title":"Graphprompt: Unifying pre-training and downstream tasks for graph neural networks,","venue":null,"work_id":"754b874e-71b8-4ba4-bba5-c59e575b408d","year":2023},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.838121Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:47a97265b6ddb56afeb662e1c131e269f6bf28915dd4a0b2340cf382429dc26a","observation_id":"4f872833-15a0-4699-a9a4-bcf81c02afca","resolution":{"observed_at":"2026-08-05T05:21:31.102742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.088676Z","title":"Gppt: Graph pre- training and prompt tuning to generalize graph neural networks,","venue":null,"work_id":"02131c4c-47a3-4280-9441-15e966068605","year":2022},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.841149Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:979fd7cd07b5a1ca48c36375146c194894475139e53565735840de76f1d4e429","observation_id":"6926a400-ee8f-42df-9623-0ec5069462d4","resolution":{"observed_at":"2026-08-05T05:21:31.092794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.078373Z","title":"Convert: Efficient and accurate conversational rep- resentations from transformers,","venue":null,"work_id":"b71076b8-562c-416d-b7d5-cc1189356417","year":2020},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.844686Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:dfd522b327b7ccdbc1b48f50fa1bed312a6ee0254689847166b0449268308eea","observation_id":"9a1882fd-ccfd-470e-adfb-d4c5a53d97f6","resolution":{"observed_at":"2026-08-05T05:21:31.081986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.068592Z","title":"Dialogpt: Large-scale generative pre- training for conversational response generation,","venue":null,"work_id":"299bca21-7d6c-4cfd-963a-f7b7fe3ed5a4","year":2020},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.847533Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:8b6ea73a74efe5dd58863b22530153014210de573385693ec6029e4908af2516","observation_id":"dcc616ef-4d09-4a2f-a93a-73acbe110aa3","resolution":{"observed_at":"2026-08-05T05:21:31.071984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.058837Z","title":"Tod-bert: Pre-trained natural language understanding for task-oriented dialogue,","venue":null,"work_id":"3266ef64-264a-42bc-a590-1f8adbf388e8","year":2020},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.851085Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:f953ce029d5b98b2be9b861026b96b430ed1924b60d36dec11ed4948fdb0b72e","observation_id":"a8e82fc1-c7cf-4bf8-b171-68b9805d23ee","resolution":{"observed_at":"2026-08-05T05:21:31.062170Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.048973Z","title":"Wildchat: 1m chatgpt interaction logs in the wild,","venue":null,"work_id":"29f166fc-b8e3-4335-86b3-2ca02a894f24","year":null},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.854263Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:a11033b5cb1a8bade3ab5a4d42bf93bbd95b3a1967540f33575a7e4db65e79da","observation_id":"f215ed98-43b4-47bf-80b5-1a9da9a9663b","resolution":{"observed_at":"2026-08-05T05:21:31.052320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.039253Z","title":"Learn to adapt for generalized zero-shot text classification,","venue":null,"work_id":"2d067215-a937-4c5a-a7a9-98e204f55316","year":2022},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.857382Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:d83ef9b43252cdee20ab32d803c13c80f9fc1480399a413250ebfc321f96ae9d","observation_id":"f4b4bf6d-3d3e-4119-afda-63f00db38a32","resolution":{"observed_at":"2026-08-05T05:21:31.042509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.01108","last_updated":"2020-03-01T02:57:50Z","snapshot_observed_at":"2026-08-07T19:07:36.327251Z","submitted_at":"2019-10-02T17:56:28Z","title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.01108","snapshot_observed_at":"2026-08-05T05:21:30.860374Z","title":"Distilbert, a distilled version of bert: Smaller, faster, cheaper and lighter,","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.860374Z"},"links":{"cited_paper":"/paper/1910.01108","citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:a8d23dd73e01d55ce571f852e400b5f247966e7e6ef6328dc6501cd606bf5ca3","observation_id":"bbbe839c-a6cb-45ae-af72-a93f273671ff","resolution":{"observed_at":"2026-08-05T05:21:30.860374Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.11942","last_updated":"2020-02-09T03:00:18Z","snapshot_observed_at":"2026-07-06T08:24:44.631342Z","submitted_at":"2019-09-26T07:06:13Z","title":"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.11942","snapshot_observed_at":"2026-08-05T05:21:30.864248Z","title":"Albert: A lite bert for self-supervised learning of language representations,","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.864248Z"},"links":{"cited_paper":"/paper/1909.11942","citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:74b760ad8efb6c583c0d6aade63c1e36953c4e1f1e47a3ad9cb033a20e26bc5b","observation_id":"5106fd86-f8ee-4604-b633-57335602653b","resolution":{"observed_at":"2026-08-05T05:21:30.864248Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.11055","last_updated":"2022-09-22T14:48:11Z","snapshot_observed_at":"2026-08-05T06:29:33.316428Z","submitted_at":"2022-09-22T14:48:11Z","title":"Efficient Few-Shot Learning Without Prompts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.11055","snapshot_observed_at":"2026-08-05T05:21:30.867771Z","title":"Efficient few-shot learning without prompts,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.867771Z"},"links":{"cited_paper":"/paper/2209.11055","citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:ebc9fcfceb06492a635d39aaba79c4332f8e187ebae5109ec35855231303ca1b","observation_id":"a0c62731-83bc-4780-8e52-00421f36e5f0","resolution":{"observed_at":"2026-08-05T05:21:30.867771Z","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":"2026-08-05T05:21:31.029566Z","title":"Beyond similarity: Relation-based collaborative filtering,","venue":null,"work_id":"94d976d9-78ea-45c5-a942-54fd67aeec24","year":2021},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.871026Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:861cbc016de5b0ba43df0f22773c8bbb17c987eac31a7e4968ecde6013afbced","observation_id":"51b72bf8-f4fd-4426-a985-27ac8d1ed1e9","resolution":{"observed_at":"2026-08-05T05:21:31.033175Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-05T05:21:31.019183Z","title":"Visualizing data using t-sne,","venue":null,"work_id":"49d23491-e2da-4a7e-a7e4-4ad9dcac58c6","year":2008},"citing_paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T05:21:30.874144Z"},"links":{"citing_paper":"/paper/2509.05635"},"observation_digest":"sha256:002c65df7fba0f240f2a6733dfea6bebbab4d80502c6721014eda7d2216baa03","observation_id":"609b8f48-01bf-4006-bb79-7ec558de38cd","resolution":{"observed_at":"2026-08-05T05:21:31.022383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.05635","last_updated":"2025-09-06T07:41:47Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-05T05:21:29.230518Z","submitted_at":"2025-09-06T07:41:47Z","title":"Few-Shot Query Intent Detection via Relation-Aware Prompt Learning"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":16,"verified_exact":1,"verified_fuzzy":29},"total_outbound_references":46},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2509.05635."}