{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:7Y5E5DDVFWGB472VV6OTFM7CEY","short_pith_number":"pith:7Y5E5DDV","canonical_record":{"source":{"id":"2502.06854","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T17:23:48Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"ba694fbe39bb4073c60233aaf222e4d7abc16a18056a461be5c356c353609f5d","abstract_canon_sha256":"d1dd6d915750ef5d87843240e93c1ad88937665c4a3865dcad9e4e20a1f1f71e"},"schema_version":"1.0"},"canonical_sha256":"fe3a4e8c752d8c1e7f55af9d32b3e226265c6769c82d316c095e462d12cf554d","source":{"kind":"arxiv","id":"2502.06854","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.06854","created_at":"2026-07-05T11:16:31Z"},{"alias_kind":"arxiv_version","alias_value":"2502.06854v2","created_at":"2026-07-05T11:16:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.06854","created_at":"2026-07-05T11:16:31Z"},{"alias_kind":"pith_short_12","alias_value":"7Y5E5DDVFWGB","created_at":"2026-07-05T11:16:31Z"},{"alias_kind":"pith_short_16","alias_value":"7Y5E5DDVFWGB472V","created_at":"2026-07-05T11:16:31Z"},{"alias_kind":"pith_short_8","alias_value":"7Y5E5DDV","created_at":"2026-07-05T11:16:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:7Y5E5DDVFWGB472VV6OTFM7CEY","target":"record","payload":{"canonical_record":{"source":{"id":"2502.06854","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T17:23:48Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"ba694fbe39bb4073c60233aaf222e4d7abc16a18056a461be5c356c353609f5d","abstract_canon_sha256":"d1dd6d915750ef5d87843240e93c1ad88937665c4a3865dcad9e4e20a1f1f71e"},"schema_version":"1.0"},"canonical_sha256":"fe3a4e8c752d8c1e7f55af9d32b3e226265c6769c82d316c095e462d12cf554d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:16:31.805087Z","signature_b64":"Yko7p53suUBRsyZFcOtvjY8n8TpyEIniJqF7QKriAFLvl3Zeht7iJo0BILDztnlU9IeRtKBQ8lhbNYsk+Sh8CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe3a4e8c752d8c1e7f55af9d32b3e226265c6769c82d316c095e462d12cf554d","last_reissued_at":"2026-07-05T11:16:31.804506Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:16:31.804506Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.06854","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:16:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2IbjmnbzRQtn08VYP8jgd1/5I2Vi2z3Vr19ELpjBlgcNY1if2gMO+PQFGpAmjklQ8t45xb8Z7Z4oskTNbSXqAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T06:02:36.825825Z"},"content_sha256":"e3caedcd03cbecba8e2958d313df362bc1c17421c23ce73b3486f5ca3f9e0253","schema_version":"1.0","event_id":"sha256:e3caedcd03cbecba8e2958d313df362bc1c17421c23ce73b3486f5ca3f9e0253"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:7Y5E5DDVFWGB472VV6OTFM7CEY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Can Large Language Models Understand Intermediate Representations in Compilers?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Bo Fang, Hailong Jiang, Hongyu Zhang, Jianfeng Zhu, Qiang Guan, Ruoming Jin, Yao Wan","submitted_at":"2025-02-07T17:23:48Z","abstract_excerpt":"Intermediate Representations (IRs) play a critical role in compiler design and program analysis, yet their comprehension by Large Language Models (LLMs) remains underexplored. In this paper, we present an explorative empirical study evaluating the capabilities of six state-of-the-art LLMs: GPT-4, GPT-3, DeepSeek, Gemma 2, Llama 3, and Code Llama, in understanding IRs. Specifically, we assess model performance across four core tasks: control flow graph reconstruction, decompilation, code summarization, and execution reasoning. While LLMs exhibit competence in parsing IR syntax and identifying h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.06854","kind":"arxiv","version":2},"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/2502.06854/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":"2026-07-05T11:16:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"56q8+VLnJln9dxgG9anCsybrhC95TCBotWUaIrLlo6g9/IlqQq/Sy3tl3oIDTQu4ggVnJ+5bq8OZ/sBevsYtDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T06:02:36.826328Z"},"content_sha256":"8f3380503de91187a45d405e3cd79b7ccd92bfece95d3ef8bdf32ed022871ace","schema_version":"1.0","event_id":"sha256:8f3380503de91187a45d405e3cd79b7ccd92bfece95d3ef8bdf32ed022871ace"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7Y5E5DDVFWGB472VV6OTFM7CEY/bundle.json","state_url":"https://pith.science/pith/7Y5E5DDVFWGB472VV6OTFM7CEY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7Y5E5DDVFWGB472VV6OTFM7CEY/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":"2026-08-13T06:02:36Z","links":{"resolver":"https://pith.science/pith/7Y5E5DDVFWGB472VV6OTFM7CEY","bundle":"https://pith.science/pith/7Y5E5DDVFWGB472VV6OTFM7CEY/bundle.json","state":"https://pith.science/pith/7Y5E5DDVFWGB472VV6OTFM7CEY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7Y5E5DDVFWGB472VV6OTFM7CEY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:7Y5E5DDVFWGB472VV6OTFM7CEY","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":"d1dd6d915750ef5d87843240e93c1ad88937665c4a3865dcad9e4e20a1f1f71e","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T17:23:48Z","title_canon_sha256":"ba694fbe39bb4073c60233aaf222e4d7abc16a18056a461be5c356c353609f5d"},"schema_version":"1.0","source":{"id":"2502.06854","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.06854","created_at":"2026-07-05T11:16:31Z"},{"alias_kind":"arxiv_version","alias_value":"2502.06854v2","created_at":"2026-07-05T11:16:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.06854","created_at":"2026-07-05T11:16:31Z"},{"alias_kind":"pith_short_12","alias_value":"7Y5E5DDVFWGB","created_at":"2026-07-05T11:16:31Z"},{"alias_kind":"pith_short_16","alias_value":"7Y5E5DDVFWGB472V","created_at":"2026-07-05T11:16:31Z"},{"alias_kind":"pith_short_8","alias_value":"7Y5E5DDV","created_at":"2026-07-05T11:16:31Z"}],"graph_snapshots":[{"event_id":"sha256:8f3380503de91187a45d405e3cd79b7ccd92bfece95d3ef8bdf32ed022871ace","target":"graph","created_at":"2026-07-05T11:16:31Z","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/2502.06854/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Intermediate Representations (IRs) play a critical role in compiler design and program analysis, yet their comprehension by Large Language Models (LLMs) remains underexplored. In this paper, we present an explorative empirical study evaluating the capabilities of six state-of-the-art LLMs: GPT-4, GPT-3, DeepSeek, Gemma 2, Llama 3, and Code Llama, in understanding IRs. Specifically, we assess model performance across four core tasks: control flow graph reconstruction, decompilation, code summarization, and execution reasoning. While LLMs exhibit competence in parsing IR syntax and identifying h","authors_text":"Bo Fang, Hailong Jiang, Hongyu Zhang, Jianfeng Zhu, Qiang Guan, Ruoming Jin, Yao Wan","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T17:23:48Z","title":"Can Large Language Models Understand Intermediate Representations in Compilers?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.06854","kind":"arxiv","version":2},"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:e3caedcd03cbecba8e2958d313df362bc1c17421c23ce73b3486f5ca3f9e0253","target":"record","created_at":"2026-07-05T11:16:31Z","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":"d1dd6d915750ef5d87843240e93c1ad88937665c4a3865dcad9e4e20a1f1f71e","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-07T17:23:48Z","title_canon_sha256":"ba694fbe39bb4073c60233aaf222e4d7abc16a18056a461be5c356c353609f5d"},"schema_version":"1.0","source":{"id":"2502.06854","kind":"arxiv","version":2}},"canonical_sha256":"fe3a4e8c752d8c1e7f55af9d32b3e226265c6769c82d316c095e462d12cf554d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fe3a4e8c752d8c1e7f55af9d32b3e226265c6769c82d316c095e462d12cf554d","first_computed_at":"2026-07-05T11:16:31.804506Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:16:31.804506Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Yko7p53suUBRsyZFcOtvjY8n8TpyEIniJqF7QKriAFLvl3Zeht7iJo0BILDztnlU9IeRtKBQ8lhbNYsk+Sh8CA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:16:31.805087Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.06854","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e3caedcd03cbecba8e2958d313df362bc1c17421c23ce73b3486f5ca3f9e0253","sha256:8f3380503de91187a45d405e3cd79b7ccd92bfece95d3ef8bdf32ed022871ace"],"state_sha256":"a620ff9d94e5080f14de20950a86f7e0636b10aeab7bbd7294e01d5101598dda"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hZKBH4WekIKHLNgbORtALrGbstAk61veDbdkJPkug4F77RkrflzaerNy28AY2Z+mAZzCDRCmlXsCK7wJPXPmAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T06:02:36.831553Z","bundle_sha256":"bf8624249e937fde8d556a23cb23e86dd6d1443d42e1718be8aef48b446e1870"}}