{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:Q3ACPIUX37MVGP33ULG37KR2F2","short_pith_number":"pith:Q3ACPIUX","schema_version":"1.0","canonical_sha256":"86c027a297dfd9533f7ba2cdbfaa3a2e82a9b3b8156078377a10505ee1bf10fd","source":{"kind":"arxiv","id":"2601.01685","version":2},"attestation_state":"computed","paper":{"title":"Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.MA"],"primary_cat":"cs.CL","authors_text":"Jinwei Hu, Xiaowei Huang, Xinmiao Huang, Yi Dong, Youcheng Sun","submitted_at":"2026-01-04T22:50:23Z","abstract_excerpt":"As large language models (LLMs) transition to autonomous agents synthesizing real-time information, their reasoning capabilities introduce an unexpected attack surface. This paper introduces a novel threat where colluding agents steer victim beliefs using only truthful evidence fragments distributed through public channels, without relying on covert communications, backdoors, or falsified documents. By exploiting LLMs' overthinking tendency, we formalize the first cognitive collusion attack and propose Generative Montage: a Writer-Editor-Director framework that constructs deceptive narratives "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2601.01685","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-01-04T22:50:23Z","cross_cats_sorted":["cs.AI","cs.MA"],"title_canon_sha256":"74267043b576277f0d18d20700193e788bed8be65864a70fb2786daf7ddc2138","abstract_canon_sha256":"4957c608b8392e1347fbb6aab2987e576f3101238e8a0913120dd53e53c000d4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:05:39.874397Z","signature_b64":"vh7ibaxwZDmywywh/fQW7cjmyqrFdwah7dcDZMe+C5wDXiJOJP82T0y/INb3h4c5h8FaXRwPkc0PFMhkZadJBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"86c027a297dfd9533f7ba2cdbfaa3a2e82a9b3b8156078377a10505ee1bf10fd","last_reissued_at":"2026-05-20T00:05:39.873393Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:05:39.873393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Lying with Truths: Open-Channel Multi-Agent Collusion for Belief Manipulation via Generative Montage","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.MA"],"primary_cat":"cs.CL","authors_text":"Jinwei Hu, Xiaowei Huang, Xinmiao Huang, Yi Dong, Youcheng Sun","submitted_at":"2026-01-04T22:50:23Z","abstract_excerpt":"As large language models (LLMs) transition to autonomous agents synthesizing real-time information, their reasoning capabilities introduce an unexpected attack surface. This paper introduces a novel threat where colluding agents steer victim beliefs using only truthful evidence fragments distributed through public channels, without relying on covert communications, backdoors, or falsified documents. By exploiting LLMs' overthinking tendency, we formalize the first cognitive collusion attack and propose Generative Montage: a Writer-Editor-Director framework that constructs deceptive narratives "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.01685","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/2601.01685/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2601.01685","created_at":"2026-05-20T00:05:39.873527+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.01685v2","created_at":"2026-05-20T00:05:39.873527+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.01685","created_at":"2026-05-20T00:05:39.873527+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q3ACPIUX37MV","created_at":"2026-05-20T00:05:39.873527+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q3ACPIUX37MVGP33","created_at":"2026-05-20T00:05:39.873527+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q3ACPIUX","created_at":"2026-05-20T00:05:39.873527+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.08896","citing_title":"FragileFlow: Spectral Control of Correct-but-Fragile Predictions for Foundation Model Robustness","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q3ACPIUX37MVGP33ULG37KR2F2","json":"https://pith.science/pith/Q3ACPIUX37MVGP33ULG37KR2F2.json","graph_json":"https://pith.science/api/pith-number/Q3ACPIUX37MVGP33ULG37KR2F2/graph.json","events_json":"https://pith.science/api/pith-number/Q3ACPIUX37MVGP33ULG37KR2F2/events.json","paper":"https://pith.science/paper/Q3ACPIUX"},"agent_actions":{"view_html":"https://pith.science/pith/Q3ACPIUX37MVGP33ULG37KR2F2","download_json":"https://pith.science/pith/Q3ACPIUX37MVGP33ULG37KR2F2.json","view_paper":"https://pith.science/paper/Q3ACPIUX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.01685&json=true","fetch_graph":"https://pith.science/api/pith-number/Q3ACPIUX37MVGP33ULG37KR2F2/graph.json","fetch_events":"https://pith.science/api/pith-number/Q3ACPIUX37MVGP33ULG37KR2F2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q3ACPIUX37MVGP33ULG37KR2F2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q3ACPIUX37MVGP33ULG37KR2F2/action/storage_attestation","attest_author":"https://pith.science/pith/Q3ACPIUX37MVGP33ULG37KR2F2/action/author_attestation","sign_citation":"https://pith.science/pith/Q3ACPIUX37MVGP33ULG37KR2F2/action/citation_signature","submit_replication":"https://pith.science/pith/Q3ACPIUX37MVGP33ULG37KR2F2/action/replication_record"}},"created_at":"2026-05-20T00:05:39.873527+00:00","updated_at":"2026-05-20T00:05:39.873527+00:00"}