{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:PUTD67ZFDKZJQ4DBR56VEFNB35","short_pith_number":"pith:PUTD67ZF","canonical_record":{"source":{"id":"2605.12213","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-12T14:51:02Z","cross_cats_sorted":[],"title_canon_sha256":"eed31c490fa286472976ea67cbb361e4237a28bc43fc351d12c6fa18490df991","abstract_canon_sha256":"598ddbab20a0861b3e2f75165ff7ee40827ac27d20fff9fd2ef45b8f725d122a"},"schema_version":"1.0"},"canonical_sha256":"7d263f7f251ab29870618f7d5215a1df53a229eda3c67e4253600f6c1abf2785","source":{"kind":"arxiv","id":"2605.12213","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12213","created_at":"2026-06-09T01:05:46Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12213v2","created_at":"2026-06-09T01:05:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12213","created_at":"2026-06-09T01:05:46Z"},{"alias_kind":"pith_short_12","alias_value":"PUTD67ZFDKZJ","created_at":"2026-06-09T01:05:46Z"},{"alias_kind":"pith_short_16","alias_value":"PUTD67ZFDKZJQ4DB","created_at":"2026-06-09T01:05:46Z"},{"alias_kind":"pith_short_8","alias_value":"PUTD67ZF","created_at":"2026-06-09T01:05:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:PUTD67ZFDKZJQ4DBR56VEFNB35","target":"record","payload":{"canonical_record":{"source":{"id":"2605.12213","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-12T14:51:02Z","cross_cats_sorted":[],"title_canon_sha256":"eed31c490fa286472976ea67cbb361e4237a28bc43fc351d12c6fa18490df991","abstract_canon_sha256":"598ddbab20a0861b3e2f75165ff7ee40827ac27d20fff9fd2ef45b8f725d122a"},"schema_version":"1.0"},"canonical_sha256":"7d263f7f251ab29870618f7d5215a1df53a229eda3c67e4253600f6c1abf2785","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:05:46.961881Z","signature_b64":"ykHhlJx0ISlSq0s49aj9amodd1ocGhh1d/B6oppz1AbUOZSt/+TfBNCLvh9bTkDXYxLrqMoMaSMmpQq/oR8lBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7d263f7f251ab29870618f7d5215a1df53a229eda3c67e4253600f6c1abf2785","last_reissued_at":"2026-06-09T01:05:46.961364Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:05:46.961364Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.12213","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":"2026-06-09T01:05:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nTd7GrVcFuWZRaUz0cryLEvF/X0AlTOsLfj0+GICajRUu6AbYhOlbyr0xqtpyvOouAh8ahEx7ilbKzjIRKFkDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T02:49:32.088989Z"},"content_sha256":"fe1e59bd6fca556288ffb18f1b3c63770034d559591b0333cd8d16c7c32bfa17","schema_version":"1.0","event_id":"sha256:fe1e59bd6fca556288ffb18f1b3c63770034d559591b0333cd8d16c7c32bfa17"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:PUTD67ZFDKZJQ4DBR56VEFNB35","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Goal-Mem improves RAG memory retrieval by decomposing user goals into atomic subgoals and applying backward chaining to fetch missing facts.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Armin Toroghi, Faeze Moradi Kalarde, Jiazhou Liang, Liam Gallagher, Scott Sanner, Yifan Simon Liu","submitted_at":"2026-05-12T14:51:02Z","abstract_excerpt":"LLM-based conversational AI agents struggle to maintain coherent behavior over long horizons due to limited context. While RAG-based approaches are increasingly adopted to overcome this limitation by storing interactions in external memory modules and performing retrieval from them, their effectiveness in answering challenging questions (e.g., multi-hop, commonsense) ultimately depends on the agent's ability to reason over the retrieved information. However, existing methods typically retrieve memory based on semantic similarity to the raw user utterance, which lacks explicit reasoning about m"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Through extensive experiments on two datasets and comparing to nine strong memory baselines, we show that Goal-Mem consistently improves performance, particularly on tasks requiring multi-hop reasoning and implicit inference.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That automatic decomposition of goals into atomic subgoals combined with targeted retrieval will reliably surface missing intermediate facts without introducing reasoning errors or requiring human intervention.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Goal-Mem improves RAG memory retrieval in agentic LLMs by explicit goal decomposition and backward chaining via Natural Language Logic, outperforming nine baselines on multi-hop and implicit inference tasks.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Goal-Mem improves RAG memory retrieval by decomposing user goals into atomic subgoals and applying backward chaining to fetch missing facts.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d8759e0194c73e384c695c5c996afadf37eff89d022c214e25e6f5726cff4ce4"},"source":{"id":"2605.12213","kind":"arxiv","version":2},"verdict":{"id":"787985c7-887a-455f-a9cb-44dc591e45b1","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-13T05:14:59.969959Z","strongest_claim":"Through extensive experiments on two datasets and comparing to nine strong memory baselines, we show that Goal-Mem consistently improves performance, particularly on tasks requiring multi-hop reasoning and implicit inference.","one_line_summary":"Goal-Mem improves RAG memory retrieval in agentic LLMs by explicit goal decomposition and backward chaining via Natural Language Logic, outperforming nine baselines on multi-hop and implicit inference tasks.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That automatic decomposition of goals into atomic subgoals combined with targeted retrieval will reliably surface missing intermediate facts without introducing reasoning errors or requiring human intervention.","pith_extraction_headline":"Goal-Mem improves RAG memory retrieval by decomposing user goals into atomic subgoals and applying backward chaining to fetch missing facts."},"integrity":{"clean":false,"summary":{"advisory":2,"critical":0,"by_detector":{"doi_compliance":{"total":2,"advisory":2,"critical":0,"informational":0}},"informational":0},"endpoint":"/pith/2605.12213/integrity.json","findings":[{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.18653/v1/2020.emnlp-main.550.URLhttps://aclanthology.org/2020.emnlp-main.550/) was visible in the surrounding text but could not be confirmed against doi.org as printed.","detector":"doi_compliance","severity":"advisory","ref_index":12,"audited_at":"2026-05-19T07:48:08.677340Z","detected_doi":"10.18653/v1/2020.emnlp-main.550.URLhttps://aclanthology.org/2020.emnlp-main.550/","finding_type":"recoverable_identifier","verdict_class":"incontrovertible","detected_arxiv_id":null},{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.18653/v1/2024.emnlp-main.379.URLhttps://aclanthology.org/2024.emnlp-main.379/) was visible in the surrounding text but could not be confirmed against doi.org as printed.","detector":"doi_compliance","severity":"advisory","ref_index":31,"audited_at":"2026-05-19T07:48:08.677340Z","detected_doi":"10.18653/v1/2024.emnlp-main.379.URLhttps://aclanthology.org/2024.emnlp-main.379/","finding_type":"recoverable_identifier","verdict_class":"incontrovertible","detected_arxiv_id":null}],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-26T14:46:34.776534Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-20T14:31:25.532426Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-20T10:35:18.958647Z","status":"completed","version":"1.0.0","findings_count":2},{"name":"claim_evidence","ran_at":"2026-05-19T22:41:58.370207Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"a806257728bf330e0ff3c89e8c09f4f2b654a6ccf6fee1f32e78543d28bf4844"},"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":"787985c7-887a-455f-a9cb-44dc591e45b1"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-09T01:05:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Dpy+I33xbeXuIx/V23d5vAMGZ057GpmA26F5HKRP+APPjsoxNAi071776OcXstLQp4lN927z0ZU9AEu5sYBZBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T02:49:32.089591Z"},"content_sha256":"add1011e0ead1ea6575a859bcde0230444e21c8cd7af01f9d78c9dd1fc5c7bb6","schema_version":"1.0","event_id":"sha256:add1011e0ead1ea6575a859bcde0230444e21c8cd7af01f9d78c9dd1fc5c7bb6"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:PUTD67ZFDKZJQ4DBR56VEFNB35","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.18653/v1/2024.emnlp-main.379.URLhttps://aclanthology.org/2024.emnlp-main.379/) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Armin Toroghi, Willis Guo, Ali Pesaranghader, and Scott Sanner. Verifiable, debuggable, and repairable commonsense logical reasoning via LLM-based theory resolution. InProceedings of the 2024 Conference on Empirical Methods in Natural Langu","arxiv_id":"2605.12213","detector":"doi_compliance","evidence":{"ref_index":31,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"Armin Toroghi, Willis Guo, Ali Pesaranghader, and Scott Sanner. Verifiable, debuggable, and repairable commonsense logical reasoning via LLM-based theory resolution. InProceedings of the 2024 Conference on Empirical Methods in Natural Langu","reconstructed_doi":"10.18653/v1/2024.emnlp-main.379.URLhttps://aclanthology.org/2024.emnlp-main.379/"},"severity":"advisory","ref_index":31,"audited_at":"2026-05-19T07:48:08.677340Z","event_type":"pith.integrity.v1","detected_doi":"10.18653/v1/2024.emnlp-main.379.URLhttps://aclanthology.org/2024.emnlp-main.379/","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"d050da4a45750f0f5716ab83a838ee52a237398c20d28edad6f664e056aa23b4","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":345,"payload_sha256":"6857853b2a779e4b35b4882b11e18fb9fc5afb4ddb3ccbae613d3b4ee3547ad0","signature_b64":"48ZsHC0wChagT6FTbigra2s7V0OCwkBvMgx0RW3QkRrwcJI7HpKO9uQct78oyFMlnLTFK7OF/b1xPyxhhUtFAA==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-19T07:51:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i5TGQcgt0ZHKK+DiGvD4dyQcblSZ+KJJpKOVcVzKlqvFBt2ps6NSKMx2OZeUixSUwh3V/D9r8l3Tu/FGGbapBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T02:49:32.090439Z"},"content_sha256":"ac96cc8d547c04ddc35b2d59ea72612ef943262ff1a70d6d526535f4853134b9","schema_version":"1.0","event_id":"sha256:ac96cc8d547c04ddc35b2d59ea72612ef943262ff1a70d6d526535f4853134b9"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:PUTD67ZFDKZJQ4DBR56VEFNB35","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.18653/v1/2020.emnlp-main.550.URLhttps://aclanthology.org/2020.emnlp-main.550/) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. Dense passage retrieval for open-domain question answering. InProceedings of the 2020 Conference on Empirical Methods in Natur","arxiv_id":"2605.12213","detector":"doi_compliance","evidence":{"ref_index":12,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. Dense passage retrieval for open-domain question answering. InProceedings of the 2020 Conference on Empirical Methods in Natur","reconstructed_doi":"10.18653/v1/2020.emnlp-main.550.URLhttps://aclanthology.org/2020.emnlp-main.550/"},"severity":"advisory","ref_index":12,"audited_at":"2026-05-19T07:48:08.677340Z","event_type":"pith.integrity.v1","detected_doi":"10.18653/v1/2020.emnlp-main.550.URLhttps://aclanthology.org/2020.emnlp-main.550/","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"9fb76c6f7d0edd2996b340662e9a197ce647e85e08eb937208fbac0d6912f887","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":344,"payload_sha256":"fcd5e6d4f1166ee4dbbae341b4ed458d2b5b3c170ad2dc7ce21f171be2b21dcb","signature_b64":"WAyNj4/jNFk00Vtju2pQYUzUhCM3hsXxS6GgXjCU9E0NU6NI3x1YfBhGlTV/frNt/jhwcl4wYS/fM6YvQ1CGCg==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-19T07:51:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wZZepb/Zo9bYWIQfCtHXbdeRRO4QnJi0R0vOIb7+JxsTZa3uneAk+IMD/jD+Q5/1bvj7s6yN/bt8NzpR7wIfAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T02:49:32.090715Z"},"content_sha256":"d4ef8bb9a01a0fb2b0b0518e705a5b546549eb323eb970c5230fe6bd030be3e7","schema_version":"1.0","event_id":"sha256:d4ef8bb9a01a0fb2b0b0518e705a5b546549eb323eb970c5230fe6bd030be3e7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PUTD67ZFDKZJQ4DBR56VEFNB35/bundle.json","state_url":"https://pith.science/pith/PUTD67ZFDKZJQ4DBR56VEFNB35/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PUTD67ZFDKZJQ4DBR56VEFNB35/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":"2026-07-03T02:49:32Z","links":{"resolver":"https://pith.science/pith/PUTD67ZFDKZJQ4DBR56VEFNB35","bundle":"https://pith.science/pith/PUTD67ZFDKZJQ4DBR56VEFNB35/bundle.json","state":"https://pith.science/pith/PUTD67ZFDKZJQ4DBR56VEFNB35/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PUTD67ZFDKZJQ4DBR56VEFNB35/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PUTD67ZFDKZJQ4DBR56VEFNB35","merge_version":"pith-open-graph-merge-v1","event_count":4,"valid_event_count":4,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"598ddbab20a0861b3e2f75165ff7ee40827ac27d20fff9fd2ef45b8f725d122a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-12T14:51:02Z","title_canon_sha256":"eed31c490fa286472976ea67cbb361e4237a28bc43fc351d12c6fa18490df991"},"schema_version":"1.0","source":{"id":"2605.12213","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.12213","created_at":"2026-06-09T01:05:46Z"},{"alias_kind":"arxiv_version","alias_value":"2605.12213v2","created_at":"2026-06-09T01:05:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.12213","created_at":"2026-06-09T01:05:46Z"},{"alias_kind":"pith_short_12","alias_value":"PUTD67ZFDKZJ","created_at":"2026-06-09T01:05:46Z"},{"alias_kind":"pith_short_16","alias_value":"PUTD67ZFDKZJQ4DB","created_at":"2026-06-09T01:05:46Z"},{"alias_kind":"pith_short_8","alias_value":"PUTD67ZF","created_at":"2026-06-09T01:05:46Z"}],"graph_snapshots":[{"event_id":"sha256:add1011e0ead1ea6575a859bcde0230444e21c8cd7af01f9d78c9dd1fc5c7bb6","target":"graph","created_at":"2026-06-09T01:05:46Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Through extensive experiments on two datasets and comparing to nine strong memory baselines, we show that Goal-Mem consistently improves performance, particularly on tasks requiring multi-hop reasoning and implicit inference."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That automatic decomposition of goals into atomic subgoals combined with targeted retrieval will reliably surface missing intermediate facts without introducing reasoning errors or requiring human intervention."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Goal-Mem improves RAG memory retrieval in agentic LLMs by explicit goal decomposition and backward chaining via Natural Language Logic, outperforming nine baselines on multi-hop and implicit inference tasks."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Goal-Mem improves RAG memory retrieval by decomposing user goals into atomic subgoals and applying backward chaining to fetch missing facts."}],"snapshot_sha256":"d8759e0194c73e384c695c5c996afadf37eff89d022c214e25e6f5726cff4ce4"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":false,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-26T14:46:34.776534Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-20T14:31:25.532426Z","status":"completed","version":"1.0.0"},{"findings_count":2,"name":"doi_compliance","ran_at":"2026-05-20T10:35:18.958647Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T22:41:58.370207Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.12213/integrity.json","findings":[{"audited_at":"2026-05-19T07:48:08.677340Z","detected_arxiv_id":null,"detected_doi":"10.18653/v1/2020.emnlp-main.550.URLhttps://aclanthology.org/2020.emnlp-main.550/","detector":"doi_compliance","finding_type":"recoverable_identifier","note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.18653/v1/2020.emnlp-main.550.URLhttps://aclanthology.org/2020.emnlp-main.550/) was visible in the surrounding text but could not be confirmed against doi.org as printed.","ref_index":12,"severity":"advisory","verdict_class":"incontrovertible"},{"audited_at":"2026-05-19T07:48:08.677340Z","detected_arxiv_id":null,"detected_doi":"10.18653/v1/2024.emnlp-main.379.URLhttps://aclanthology.org/2024.emnlp-main.379/","detector":"doi_compliance","finding_type":"recoverable_identifier","note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.18653/v1/2024.emnlp-main.379.URLhttps://aclanthology.org/2024.emnlp-main.379/) was visible in the surrounding text but could not be confirmed against doi.org as printed.","ref_index":31,"severity":"advisory","verdict_class":"incontrovertible"}],"snapshot_sha256":"a806257728bf330e0ff3c89e8c09f4f2b654a6ccf6fee1f32e78543d28bf4844","summary":{"advisory":2,"by_detector":{"doi_compliance":{"advisory":2,"critical":0,"informational":0,"total":2}},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"LLM-based conversational AI agents struggle to maintain coherent behavior over long horizons due to limited context. While RAG-based approaches are increasingly adopted to overcome this limitation by storing interactions in external memory modules and performing retrieval from them, their effectiveness in answering challenging questions (e.g., multi-hop, commonsense) ultimately depends on the agent's ability to reason over the retrieved information. However, existing methods typically retrieve memory based on semantic similarity to the raw user utterance, which lacks explicit reasoning about m","authors_text":"Armin Toroghi, Faeze Moradi Kalarde, Jiazhou Liang, Liam Gallagher, Scott Sanner, Yifan Simon Liu","cross_cats":[],"headline":"Goal-Mem improves RAG memory retrieval by decomposing user goals into atomic subgoals and applying backward chaining to fetch missing facts.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-12T14:51:02Z","title":"Goal-Oriented Reasoning for RAG-based Memory in Conversational Agentic LLM Systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.12213","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-13T05:14:59.969959Z","id":"787985c7-887a-455f-a9cb-44dc591e45b1","model_set":{"reader":"grok-4.3"},"one_line_summary":"Goal-Mem improves RAG memory retrieval in agentic LLMs by explicit goal decomposition and backward chaining via Natural Language Logic, outperforming nine baselines on multi-hop and implicit inference tasks.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Goal-Mem improves RAG memory retrieval by decomposing user goals into atomic subgoals and applying backward chaining to fetch missing facts.","strongest_claim":"Through extensive experiments on two datasets and comparing to nine strong memory baselines, we show that Goal-Mem consistently improves performance, particularly on tasks requiring multi-hop reasoning and implicit inference.","weakest_assumption":"That automatic decomposition of goals into atomic subgoals combined with targeted retrieval will reliably surface missing intermediate facts without introducing reasoning errors or requiring human intervention."}},"verdict_id":"787985c7-887a-455f-a9cb-44dc591e45b1"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fe1e59bd6fca556288ffb18f1b3c63770034d559591b0333cd8d16c7c32bfa17","target":"record","created_at":"2026-06-09T01:05:46Z","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":"598ddbab20a0861b3e2f75165ff7ee40827ac27d20fff9fd2ef45b8f725d122a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-12T14:51:02Z","title_canon_sha256":"eed31c490fa286472976ea67cbb361e4237a28bc43fc351d12c6fa18490df991"},"schema_version":"1.0","source":{"id":"2605.12213","kind":"arxiv","version":2}},"canonical_sha256":"7d263f7f251ab29870618f7d5215a1df53a229eda3c67e4253600f6c1abf2785","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7d263f7f251ab29870618f7d5215a1df53a229eda3c67e4253600f6c1abf2785","first_computed_at":"2026-06-09T01:05:46.961364Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T01:05:46.961364Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ykHhlJx0ISlSq0s49aj9amodd1ocGhh1d/B6oppz1AbUOZSt/+TfBNCLvh9bTkDXYxLrqMoMaSMmpQq/oR8lBg==","signature_status":"signed_v1","signed_at":"2026-06-09T01:05:46.961881Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.12213","source_kind":"arxiv","source_version":2}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:ac96cc8d547c04ddc35b2d59ea72612ef943262ff1a70d6d526535f4853134b9","sha256:d4ef8bb9a01a0fb2b0b0518e705a5b546549eb323eb970c5230fe6bd030be3e7"]}],"invalid_events":[],"applied_event_ids":["sha256:fe1e59bd6fca556288ffb18f1b3c63770034d559591b0333cd8d16c7c32bfa17","sha256:add1011e0ead1ea6575a859bcde0230444e21c8cd7af01f9d78c9dd1fc5c7bb6"],"state_sha256":"b74ea6209f8badb343ac7f6944c62e0abc0107e8325b603e5724ba0417dbaf4d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RCQtV+0ix7QVeR/0BCaPpK5dz/lAKMM61j+zkDD6wJ4r+fcnsn3stoATUDYetAervTaWo/g84cUn+lU4mdPXAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T02:49:32.093080Z","bundle_sha256":"99bc01f046c51bb327a1f7652f860c35933ff2bee3a6f353a53fb604eac96237"}}