{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:TV36JKRY4ONOXO7QZKLT353KDM","short_pith_number":"pith:TV36JKRY","canonical_record":{"source":{"id":"2407.19619","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-07-29T00:41:48Z","cross_cats_sorted":["cs.SE"],"title_canon_sha256":"425efc8eef31be685a78587e5690c929e3defc45efa12b79309d8090e79bcd5b","abstract_canon_sha256":"baf484839d90223f400a7eda2b7c6787437d62825ce593f08ef30be219cd534c"},"schema_version":"1.0"},"canonical_sha256":"9d77e4aa38e39aebbbf0ca973df76a1b2cd05475fd414559f9446e40210317e8","source":{"kind":"arxiv","id":"2407.19619","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.19619","created_at":"2026-07-05T08:49:42Z"},{"alias_kind":"arxiv_version","alias_value":"2407.19619v1","created_at":"2026-07-05T08:49:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.19619","created_at":"2026-07-05T08:49:42Z"},{"alias_kind":"pith_short_12","alias_value":"TV36JKRY4ONO","created_at":"2026-07-05T08:49:42Z"},{"alias_kind":"pith_short_16","alias_value":"TV36JKRY4ONOXO7Q","created_at":"2026-07-05T08:49:42Z"},{"alias_kind":"pith_short_8","alias_value":"TV36JKRY","created_at":"2026-07-05T08:49:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:TV36JKRY4ONOXO7QZKLT353KDM","target":"record","payload":{"canonical_record":{"source":{"id":"2407.19619","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-07-29T00:41:48Z","cross_cats_sorted":["cs.SE"],"title_canon_sha256":"425efc8eef31be685a78587e5690c929e3defc45efa12b79309d8090e79bcd5b","abstract_canon_sha256":"baf484839d90223f400a7eda2b7c6787437d62825ce593f08ef30be219cd534c"},"schema_version":"1.0"},"canonical_sha256":"9d77e4aa38e39aebbbf0ca973df76a1b2cd05475fd414559f9446e40210317e8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:49:42.347412Z","signature_b64":"7YrJ1W0Zn4ihga+6qFsd2VC1H6gql4+EGFGxx0LyoSAqBLwxJDIMEbqkM3NaeXiH10CFcRMLeVU4fc4Y2SFpBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9d77e4aa38e39aebbbf0ca973df76a1b2cd05475fd414559f9446e40210317e8","last_reissued_at":"2026-07-05T08:49:42.346982Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:49:42.346982Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.19619","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:49:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"clDvCAkQUwDeF5tSFXqxkV0GIYw8amHRt00WFc/bqV08VBROdsYSz60BX2KWd0Tay0GAmYRZNGWzR4FSXFtdDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T21:47:16.451118Z"},"content_sha256":"f6b07d48e1037e95872d086b9408452fafa9d882f3e20a3d59763b1b029b58b1","schema_version":"1.0","event_id":"sha256:f6b07d48e1037e95872d086b9408452fafa9d882f3e20a3d59763b1b029b58b1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:TV36JKRY4ONOXO7QZKLT353KDM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.AI","authors_text":"Ayan Biswas, Boian Alexandrov, Daniel O'Malley, Javier E. Santos, Manish Bhattarai, Shawn Jones","submitted_at":"2024-07-29T00:41:48Z","abstract_excerpt":"The advent of large language models (LLMs) has significantly advanced the field of code translation, enabling automated translation between programming languages. However, these models often struggle with complex translation tasks due to inadequate contextual understanding. This paper introduces a novel approach that enhances code translation through Few-Shot Learning, augmented with retrieval-based techniques. By leveraging a repository of existing code translations, we dynamically retrieve the most relevant examples to guide the model in translating new code segments. Our method, based on Re"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.19619","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2407.19619/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:49:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BFCtvNVN2ARAynF1NDfmDzyjvhbRB6WplG9gFFFOVKReE6QZEm/+zJcqjERr8aD8oBP3Ljnm6Wr7qOnLBb/lBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T21:47:16.451610Z"},"content_sha256":"98a762a906e8c87eaa8507e1840e405f2b32e3d44a706819677cce378626496c","schema_version":"1.0","event_id":"sha256:98a762a906e8c87eaa8507e1840e405f2b32e3d44a706819677cce378626496c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TV36JKRY4ONOXO7QZKLT353KDM/bundle.json","state_url":"https://pith.science/pith/TV36JKRY4ONOXO7QZKLT353KDM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TV36JKRY4ONOXO7QZKLT353KDM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T21:47:16Z","links":{"resolver":"https://pith.science/pith/TV36JKRY4ONOXO7QZKLT353KDM","bundle":"https://pith.science/pith/TV36JKRY4ONOXO7QZKLT353KDM/bundle.json","state":"https://pith.science/pith/TV36JKRY4ONOXO7QZKLT353KDM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TV36JKRY4ONOXO7QZKLT353KDM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:TV36JKRY4ONOXO7QZKLT353KDM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"baf484839d90223f400a7eda2b7c6787437d62825ce593f08ef30be219cd534c","cross_cats_sorted":["cs.SE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-07-29T00:41:48Z","title_canon_sha256":"425efc8eef31be685a78587e5690c929e3defc45efa12b79309d8090e79bcd5b"},"schema_version":"1.0","source":{"id":"2407.19619","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.19619","created_at":"2026-07-05T08:49:42Z"},{"alias_kind":"arxiv_version","alias_value":"2407.19619v1","created_at":"2026-07-05T08:49:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.19619","created_at":"2026-07-05T08:49:42Z"},{"alias_kind":"pith_short_12","alias_value":"TV36JKRY4ONO","created_at":"2026-07-05T08:49:42Z"},{"alias_kind":"pith_short_16","alias_value":"TV36JKRY4ONOXO7Q","created_at":"2026-07-05T08:49:42Z"},{"alias_kind":"pith_short_8","alias_value":"TV36JKRY","created_at":"2026-07-05T08:49:42Z"}],"graph_snapshots":[{"event_id":"sha256:98a762a906e8c87eaa8507e1840e405f2b32e3d44a706819677cce378626496c","target":"graph","created_at":"2026-07-05T08:49:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2407.19619/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The advent of large language models (LLMs) has significantly advanced the field of code translation, enabling automated translation between programming languages. However, these models often struggle with complex translation tasks due to inadequate contextual understanding. This paper introduces a novel approach that enhances code translation through Few-Shot Learning, augmented with retrieval-based techniques. By leveraging a repository of existing code translations, we dynamically retrieve the most relevant examples to guide the model in translating new code segments. Our method, based on Re","authors_text":"Ayan Biswas, Boian Alexandrov, Daniel O'Malley, Javier E. Santos, Manish Bhattarai, Shawn Jones","cross_cats":["cs.SE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-07-29T00:41:48Z","title":"Enhancing Code Translation in Language Models with Few-Shot Learning via Retrieval-Augmented Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.19619","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f6b07d48e1037e95872d086b9408452fafa9d882f3e20a3d59763b1b029b58b1","target":"record","created_at":"2026-07-05T08:49:42Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"baf484839d90223f400a7eda2b7c6787437d62825ce593f08ef30be219cd534c","cross_cats_sorted":["cs.SE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2024-07-29T00:41:48Z","title_canon_sha256":"425efc8eef31be685a78587e5690c929e3defc45efa12b79309d8090e79bcd5b"},"schema_version":"1.0","source":{"id":"2407.19619","kind":"arxiv","version":1}},"canonical_sha256":"9d77e4aa38e39aebbbf0ca973df76a1b2cd05475fd414559f9446e40210317e8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9d77e4aa38e39aebbbf0ca973df76a1b2cd05475fd414559f9446e40210317e8","first_computed_at":"2026-07-05T08:49:42.346982Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:49:42.346982Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7YrJ1W0Zn4ihga+6qFsd2VC1H6gql4+EGFGxx0LyoSAqBLwxJDIMEbqkM3NaeXiH10CFcRMLeVU4fc4Y2SFpBA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:49:42.347412Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.19619","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f6b07d48e1037e95872d086b9408452fafa9d882f3e20a3d59763b1b029b58b1","sha256:98a762a906e8c87eaa8507e1840e405f2b32e3d44a706819677cce378626496c"],"state_sha256":"73839c4c4733066adbb6cc2e00bc386f11b0631cd3e9c2976c62277647c9790e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+QvWtv7JEDB/i7mcWEE5C2cDTDLtvjcJC41T1QfivIz5xTKbVt6DiQ+0hzkx8S059POCXyj1rQ2OOtjS8CiUBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T21:47:16.454855Z","bundle_sha256":"216810c1bf9855061138f39bdc1b6a9d07d97ff1123f995a97652a6ad0554c49"}}