{"as_of":"2026-08-10T13:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7a22a8abccaf3f62dc80c1e7148d7fac4974b25e3173171f91d4ebf856532be4","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:11:57.447422Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T16:14:26.057709Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T09:06:00.693051Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"cited_work":{"arxiv_id":"2505.19734","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.19734","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"ReChisel: Effective automatic chisel code generation by llm with reflection","venue":null,"work_id":"d17695dd-ed0f-45fd-ae8c-b7cf8c5e6598","year":2025},"citing_paper":{"arxiv_id":"2604.11044","last_updated":"2026-04-13T06:18:25Z","snapshot_observed_at":"2026-07-31T18:19:28.731184Z","submitted_at":"2026-04-13T06:18:25Z","title":"Automated SVA Generation with LLMs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-10T16:14:26.057709Z"},"links":{"cited_paper":"/paper/2505.19734","citing_paper":"/paper/2604.11044"},"observation_digest":"sha256:92918fb100369da9b02f5ad8d2d369ba286a97e045348faf2778e068f5f2517e","observation_id":"cfd935a6-50f2-4b9f-b6fa-7648de69e7db","resolution":{"observed_at":"2026-05-11T09:06:00.698428Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.19734/citation-record","integrity":"/paper/2505.19734/integrity","json":"/paper/2505.19734/citation-record.json","paper":"/paper/2505.19734"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T14:12:00.401248Z","title":"The rocket chip generator,","venue":null,"work_id":"f0d0cee4-33f8-455e-9bc7-92a623dd2e8c","year":2016},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:55.660057Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:34dacce8b8339cb54f1a82b1d8c20efce0de711b5ca8abe272d1ed8230df1caa","observation_id":"803f2506-75dd-4652-b51f-045459dbd6f9","resolution":{"observed_at":"2026-08-07T14:12:00.444799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:12:00.270775Z","title":"Chisel: constructing hardware in a scala embedded language,","venue":null,"work_id":"3347b378-c86b-486b-b46f-05fae7b8c651","year":2012},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:55.701360Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:c6be3c8098699e89562ca2aa03f0ba0c4e1e0e9936da9ee23f3853badc18c059","observation_id":"e35b2025-818e-4901-99fb-eff2f49c6081","resolution":{"observed_at":"2026-08-07T14:12:00.355315Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:11:55.748426Z","title":"Chip-chat: Chal- lenges and opportunities in conversational hardware design,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:55.748426Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:36ef4676bb8b3686b1a317457595042d027f934e68b84330dda6023db23e1b44","observation_id":"4756308f-db26-4b90-9292-417791d05101","resolution":{"observed_at":"2026-08-07T14:11:55.748426Z","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":"2026-08-07T14:11:55.794755Z","title":"Chipgpt: How far are we from natural language hardware design,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:55.794755Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:8dd048399d7e87071068ea5b769991e90733b58494f6cbe472317f6157649fdf","observation_id":"455478df-e45e-4db9-9203-3f07e4a83984","resolution":{"observed_at":"2026-08-07T14:11:55.794755Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-08-08T11:58:24.516369Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-07T14:11:55.849611Z","title":"Evaluating large language models trained on code,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:55.849611Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:1c88729932f4d156fcefc6b9ea22c822d1c73267d1e8b2cfcbf5f349a6db82eb","observation_id":"63c630c0-bb43-44f0-b280-80b72e96a9d4","resolution":{"observed_at":"2026-08-07T14:11:55.849611Z","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":"2026-08-07T14:12:00.053643Z","title":"Magma-si: A matrix accelerator generator,","venue":null,"work_id":"4a1936b0-153c-413c-beed-76715e4ec6cb","year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:55.916894Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:75fe86c4d53e7971e05611b2d75400363a49a18c3764498ffe585d1949b5b342","observation_id":"5d5b8094-3fc8-4b45-b4a9-c4a972b036b5","resolution":{"observed_at":"2026-08-07T14:12:00.137300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.00234","last_updated":"2024-10-05T11:47:02Z","snapshot_observed_at":"2026-07-06T14:36:25.690733Z","submitted_at":"2022-12-31T15:57:09Z","title":"A Survey on In-context Learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.00234","snapshot_observed_at":"2026-08-07T14:11:55.953451Z","title":"A survey on in-context learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:55.953451Z"},"links":{"cited_paper":"/paper/2301.00234","citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:7a37f08056d148e6d5d8607f8a38be9c84239dccb232889f305f050eedac5fad","observation_id":"9ca0b916-b552-46e1-a7a4-99d1c417af65","resolution":{"observed_at":"2026-08-07T14:11:55.953451Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1911.09925","last_updated":"2021-07-09T06:53:12Z","snapshot_observed_at":"2026-08-10T04:37:22.313608Z","submitted_at":"2019-11-22T08:51:28Z","title":"Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1911.09925","snapshot_observed_at":"2026-08-07T14:11:56.001240Z","title":"Gemmini: An agile systolic array generator enabling systematic evaluations of deep-learning architec- tures,","venue":null,"work_id":null,"year":1911},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.001240Z"},"links":{"cited_paper":"/paper/1911.09925","citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:c65e0f674fd75d154bc0b4c23e66af8c48b3fcc628c2e703c6701464f96c49f7","observation_id":"840c5ecf-c4a6-46b4-bbd9-7fe68b00be86","resolution":{"observed_at":"2026-08-07T14:11:56.001240Z","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":"2026-08-07T14:11:59.857870Z","title":"Problem sets - hdlbits,","venue":null,"work_id":"556938f0-b82a-4cc7-99ff-369f84e5a211","year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.042681Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:eac29ad856051805f04a6e3845b7f9ed3e52c548973157bea55a1812d9ba4f95","observation_id":"d2a8bfa2-13dd-4ec8-a057-e444bb7740b9","resolution":{"observed_at":"2026-08-07T14:11:59.956174Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:11:59.671205Z","title":"White paper-investigate the high-level hdl chisel,","venue":null,"work_id":"6f5c37bd-9ee4-4c9a-869e-61ca63e9f072","year":2013},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.077513Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:2b5b4cd20ee953fc8c5e87636c33fb1a633d15cdb9f5ce93825863d51c1a118a","observation_id":"3387a31a-4882-4569-8348-17c947e3fffb","resolution":{"observed_at":"2026-08-07T14:11:59.773489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.08927","last_updated":"2025-03-05T06:23:52Z","snapshot_observed_at":"2026-08-08T07:52:46.013774Z","submitted_at":"2024-08-15T20:06:06Z","title":"VerilogCoder: Autonomous Verilog Coding Agents with Graph-based Planning and Abstract Syntax Tree (AST)-based Waveform Tracing Tool","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.08927","snapshot_observed_at":"2026-08-07T14:11:56.124275Z","title":"Verilogcoder: Autonomous verilog coding agents with graph-based planning and abstract syntax tree (ast)- based waveform tracing tool,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.124275Z"},"links":{"cited_paper":"/paper/2408.08927","citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:047e053275dc93801fd575c385a5ef0532601a9863ae7a23f6ff6a08160a3ac5","observation_id":"eb10efc6-382d-45b9-9d9a-64e472642abd","resolution":{"observed_at":"2026-08-07T14:11:56.124275Z","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":"2026-08-07T14:11:59.456736Z","title":"Reusability is firrtl ground: Hardware construction languages, compiler frameworks, and transformations,","venue":null,"work_id":"bf04885e-346c-4b4a-96c5-83990bffc4ed","year":2017},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.179196Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:8f964248ab257b9b209bba8e6a121857dfe6abb154ebfe2a19d08ee5ebbac8c6","observation_id":"c08b0cae-6b63-4ba8-90c2-a5f50540bb75","resolution":{"observed_at":"2026-08-07T14:11:59.518128Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:11:59.233695Z","title":"A comparative study of chisel for fpga design,","venue":null,"work_id":"680e38bc-ca88-4b4c-bd16-09b54aee04ed","year":2018},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.221084Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:ed3c4131cf694018fc44cbcf164538ffc24199012c75a422e294511a64a59e57","observation_id":"a9869f77-1f31-4602-8a66-b7eb0ee41329","resolution":{"observed_at":"2026-08-07T14:11:59.359387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:11:58.965768Z","title":"Specification for the firrtl language,","venue":null,"work_id":"deeb50bd-8e4c-47c0-8d94-54da5ee56723","year":2016},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.266010Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:93e9ea55510d3693842ed646b666f7e65b441b6fbf07f5a4fcaedf2f31fbf09a","observation_id":"2fd4da26-2282-4082-bdaa-8f3aea1045fd","resolution":{"observed_at":"2026-08-07T14:11:59.046942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:11:56.298797Z","title":"Verilogeval: Evaluating large language models for verilog code generation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.298797Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:29696fa497fb2912b43086bea965ac50992a1fe1c65b6a04ddafd8dba07324fd","observation_id":"9b0bb55c-e93e-4c5a-8cfa-b27fb406c2ad","resolution":{"observed_at":"2026-08-07T14:11:56.298797Z","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":"2026-08-07T14:11:58.835737Z","title":"Rtl- coder: Fully open-source and efficient llm-assisted rtl code generation technique,","venue":null,"work_id":"7fc03642-48ee-479c-8c30-f9e510ec105d","year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.362880Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:17f25ac26f046d832e2b7c798a4fbb692ae61257fbea75ea054e947b2b9b5d53","observation_id":"9bf5c556-9d9f-458f-a556-3cb40e8614cc","resolution":{"observed_at":"2026-08-07T14:11:58.900361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:11:56.410206Z","title":"Openllm-rtl: Open dataset and benchmark for llm-aided design rtl generation(invited),","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.410206Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:579756fb2178cc63d9edf447e089df62d43a8d68eb46ef2ed8aced34be857a04","observation_id":"d29db9fe-f51f-44d5-a446-fb7e88c2b5ae","resolution":{"observed_at":"2026-08-07T14:11:56.410206Z","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":"2026-08-07T14:11:56.471637Z","title":"Chatchisel: Enabling agile hardware design with large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.471637Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:d4de414ce038a23bdde73d44556be7d5870e159ba5c532aa2627eabd9aa90021","observation_id":"a14fb395-6dc7-4e6b-a385-40d68f246a26","resolution":{"observed_at":"2026-08-07T14:11:56.471637Z","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":"2026-08-07T14:11:56.525549Z","title":"Rtllm: An open-source benchmark for design rtl generation with large language model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.525549Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:3a4a3ae4df4e176d821562bbeac76106f2eb4bccf57a8d57c9a4f4ccf0d994cb","observation_id":"9f2c7831-8fc5-439d-a62b-612aec9af08c","resolution":{"observed_at":"2026-08-07T14:11:56.525549Z","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":"2026-08-07T14:11:56.591664Z","title":"Dave: Deriving automatically verilog from english,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.591664Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:488ecbb09f6fc9f5f429439431e97e5f99d4734e79aa2dd1d1fc139ba515ed2f","observation_id":"5d2b85b6-32e1-4d91-a1cd-0fb47d8f6446","resolution":{"observed_at":"2026-08-07T14:11:56.591664Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.11053","last_updated":"2025-02-03T19:29:27Z","snapshot_observed_at":"2026-07-06T19:03:39.672411Z","submitted_at":"2024-08-20T17:58:56Z","title":"Revisiting VerilogEval: A Year of Improvements in Large-Language Models for Hardware Code Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.11053","snapshot_observed_at":"2026-08-07T14:11:56.644594Z","title":"Revisiting verilogeval: Newer llms, in-context learning, and specification-to-rtl tasks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.644594Z"},"links":{"cited_paper":"/paper/2408.11053","citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:868b778838133ffc0ad45f60ac72f40b72173980bb8f51a6e8b96876faf6cf87","observation_id":"31f7419a-37ad-44e6-bb27-cbf6df3999b4","resolution":{"observed_at":"2026-08-07T14:11:56.644594Z","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":"2026-08-07T14:11:58.601634Z","title":"Schoeberl,Digital design with chisel","venue":null,"work_id":"790b0e5c-0ccf-43e6-ae32-7f765aa02a2a","year":2019},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.677472Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:82ed057d38d88671cef3f864ee6ab84ea12a88cd233d99877fbc57f768e7bc03","observation_id":"18fd63b9-8842-4d2a-859c-1f6d4d85f684","resolution":{"observed_at":"2026-08-07T14:11:58.672237Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:11:56.727941Z","title":"Re- flexion: Language agents with verbal reinforcement learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.727941Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:f74187c2fd5909d977383de76a5b1c59c2b693bc98c0f92bd29c88fdcbb3d4b1","observation_id":"87f97bdc-1406-4c17-a1ac-fff40c401bcd","resolution":{"observed_at":"2026-08-07T14:11:56.727941Z","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":"2026-08-07T14:11:58.429288Z","title":"Benchmarking large language models for auto- mated verilog rtl code generation,","venue":null,"work_id":"7b2957e5-358d-4556-9232-60bb2d2b9bb9","year":2023},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.794528Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:477f85a447a2711ea6b62c7ea58a700d4cf554ee6b7834db4bf2455865967a4a","observation_id":"f409a677-c218-4e6f-ab32-6208f35b5dc8","resolution":{"observed_at":"2026-08-07T14:11:58.497036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:11:56.851537Z","title":"Verigen: A large language model for verilog code generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.851537Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:04bdd2ebe96c1a5456885afb609bed616043e82d4b629b7a232ec735a368900b","observation_id":"7ea820d9-f01f-43a0-8713-1cbadae826d7","resolution":{"observed_at":"2026-08-07T14:11:56.851537Z","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":"2026-08-07T14:11:58.283718Z","title":"Au- tochip: Automating hdl generation using llm feedback,","venue":null,"work_id":"6c4e0a8e-18be-49e5-ad11-51822d70c7ca","year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.900289Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:4a3e151b48764af3877111a69481d51cc440f906f4dc23949b56a659825245da","observation_id":"f28a7c21-20e0-47b4-b50a-13225673cea5","resolution":{"observed_at":"2026-08-07T14:11:58.336965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:11:56.936161Z","title":"Rtlfixer: Automatically fixing rtl syntax errors with large language model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.936161Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:668d5d150a60988a204fc172a1341667d362c4715f9db72846488bd3fff7f862","observation_id":"9875b5b5-c6f2-4275-8bd1-797450ff3939","resolution":{"observed_at":"2026-08-07T14:11:56.936161Z","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":"2026-08-07T14:11:58.131969Z","title":"Chatcpu: An agile cpu design & verification platform with llm,","venue":null,"work_id":"67e9d36f-dc9c-4458-8295-be55366d01f3","year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:56.991011Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:becbde287604a510b3087a09eee32c1fbc5e0963e9fbb4d6450d8cb12eb98379","observation_id":"ecb76ef4-c545-4a04-b32f-732d87c31ea6","resolution":{"observed_at":"2026-08-07T14:11:58.184554Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T14:11:57.052960Z","title":"Chain-of-thought prompting elicits reasoning in large language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:57.052960Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:84930623f39391d49612872374f2df080f6da829a2bf19305d5d298ee6610f16","observation_id":"fabc9413-e807-42fa-b1ae-c05810d19465","resolution":{"observed_at":"2026-08-07T14:11:57.052960Z","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":"2026-08-07T14:11:57.111182Z","title":"Vgv: Verilog generation using visual capabilities of multi-modal large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:57.111182Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:2ecf73197b3d7333042b869a23179cee754cc61b3a37d4f9c32b649bb952ba4c","observation_id":"7b72a98a-03a0-48e7-b0a8-dc30372cd148","resolution":{"observed_at":"2026-08-07T14:11:57.111182Z","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":"2026-08-07T14:11:57.998913Z","title":"Towards developing high performance risc-v processors using agile methodology,","venue":null,"work_id":"9ea964c2-e6bd-4e0b-8a11-9a879c77b2e9","year":2022},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:57.159275Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:3c8f857df70e91e8064ec536c76a5a4058e7de9aa65856262b7100ff3a63fccd","observation_id":"0f75b9a2-f502-450f-9a74-132252daa582","resolution":{"observed_at":"2026-08-07T14:11:58.030911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.03629","last_updated":"2023-03-10T01:00:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-10-06T01:00:32Z","title":"ReAct: Synergizing Reasoning and Acting in Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.03629","snapshot_observed_at":"2026-08-07T14:11:57.223973Z","title":"React: Synergizing reasoning and acting in language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:57.223973Z"},"links":{"cited_paper":"/paper/2210.03629","citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:1b89ebec5264a9b6df56700d817dd298091a4848b01a5e598091eb12df48a0e6","observation_id":"88a88599-9e5a-464c-8d1f-a09e2445cc4c","resolution":{"observed_at":"2026-08-07T14:11:57.223973Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.11671","last_updated":"2024-03-18T11:19:37Z","snapshot_observed_at":"2026-08-03T23:45:53.300961Z","submitted_at":"2024-03-18T11:19:37Z","title":"HDLdebugger: Streamlining HDL debugging with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.11671","snapshot_observed_at":"2026-08-07T14:11:57.275703Z","title":"Hdldebugger: Streamlining hdl debugging with large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:57.275703Z"},"links":{"cited_paper":"/paper/2403.11671","citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:1875f94b22390fae57a1eb880832f8b593a5731e313fee9e99543de75424df3a","observation_id":"92678aa1-b9ed-472e-8007-06f4deb640b1","resolution":{"observed_at":"2026-08-07T14:11:57.275703Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.11414","last_updated":"2024-09-04T09:59:37Z","snapshot_observed_at":"2026-08-03T17:47:44.483939Z","submitted_at":"2024-09-04T09:59:37Z","title":"RTLRewriter: Methodologies for Large Models aided RTL Code Optimization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.11414","snapshot_observed_at":"2026-08-07T14:11:57.333888Z","title":"Rtlrewriter: Methodologies for large models aided rtl code optimization,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:57.333888Z"},"links":{"cited_paper":"/paper/2409.11414","citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:d8adf6168d8951afc0413f1acd427d8d886f7f0d1a96326d1a50f232a62cb5eb","observation_id":"07a7521a-01d5-4ff9-8b2f-b3d3512c4ad5","resolution":{"observed_at":"2026-08-07T14:11:57.333888Z","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":"2026-08-07T14:11:57.387301Z","title":"Mg-verilog: Multi- grained dataset towards enhanced llm-assisted verilog generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:57.387301Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:dfc23157279b6f1840dd0dceafae773c98af2c383d33c31cd01cc77eb15e6678","observation_id":"61b10745-c445-4e5d-9b54-443d6c851c3c","resolution":{"observed_at":"2026-08-07T14:11:57.387301Z","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":"2026-08-07T14:11:57.862545Z","title":"Sonicboom: The 3rd generation berkeley out-of-order machine,","venue":null,"work_id":"b91ab85d-b2ba-4072-a874-b7ddd2330537","year":2020},"citing_paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T14:11:57.447422Z"},"links":{"citing_paper":"/paper/2505.19734"},"observation_digest":"sha256:2c87b59f8b9efe43372e6bf05a81f3805f4ab6378758a12760613b25340baff4","observation_id":"ed878ae0-8890-4e3c-b822-69d1f8cb30ac","resolution":{"observed_at":"2026-08-07T14:11:57.921461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.19734","last_updated":"2025-05-26T09:20:07Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-10T04:37:32.474951Z","submitted_at":"2025-05-26T09:20:07Z","title":"ReChisel: Effective Automatic Chisel Code Generation by LLM with Reflection"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":21,"verified_exact":0,"verified_fuzzy":15},"total_outbound_references":36},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 1 inbound Pith citation observation for arXiv:2505.19734."}