{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:Q7BELYXTG2I2BPKHJ5UJGMAZPR","short_pith_number":"pith:Q7BELYXT","canonical_record":{"source":{"id":"2507.02626","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MM","submitted_at":"2025-07-03T13:52:24Z","cross_cats_sorted":[],"title_canon_sha256":"8b6c9ca4062c576118c8f0ea4c392dd2a2c5fc129827c4903439393efc3843ba","abstract_canon_sha256":"a66c27575e0b8de2007e5c6ad1846f5f84a4fdb818d162a1ea4fed22a7e16e00"},"schema_version":"1.0"},"canonical_sha256":"87c245e2f33691a0bd474f689330197c5d95954c2c0c44ab1e2c68342288dc6a","source":{"kind":"arxiv","id":"2507.02626","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.02626","created_at":"2026-07-05T11:31:31Z"},{"alias_kind":"arxiv_version","alias_value":"2507.02626v1","created_at":"2026-07-05T11:31:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.02626","created_at":"2026-07-05T11:31:31Z"},{"alias_kind":"pith_short_12","alias_value":"Q7BELYXTG2I2","created_at":"2026-07-05T11:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"Q7BELYXTG2I2BPKH","created_at":"2026-07-05T11:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"Q7BELYXT","created_at":"2026-07-05T11:31:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:Q7BELYXTG2I2BPKHJ5UJGMAZPR","target":"record","payload":{"canonical_record":{"source":{"id":"2507.02626","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MM","submitted_at":"2025-07-03T13:52:24Z","cross_cats_sorted":[],"title_canon_sha256":"8b6c9ca4062c576118c8f0ea4c392dd2a2c5fc129827c4903439393efc3843ba","abstract_canon_sha256":"a66c27575e0b8de2007e5c6ad1846f5f84a4fdb818d162a1ea4fed22a7e16e00"},"schema_version":"1.0"},"canonical_sha256":"87c245e2f33691a0bd474f689330197c5d95954c2c0c44ab1e2c68342288dc6a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:31:31.958790Z","signature_b64":"tP8jY+zdqc4XjnVpdsMTPwJS63uwUcZkPF3FQoHIsZLsyntAOetaXmCraLx4+0D4V+T3mKn4gAGT+naEEWD6BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"87c245e2f33691a0bd474f689330197c5d95954c2c0c44ab1e2c68342288dc6a","last_reissued_at":"2026-07-05T11:31:31.958305Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:31:31.958305Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.02626","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":"2026-07-05T11:31:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1scUbvxPKX5TM8fV+f8HVdRwC/RT6MVWG/H/iBbI/U1jgNhAa9RSZFDhiLt0EGEcm47y1whTSd9DhawO8akwDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-27T11:34:34.460669Z"},"content_sha256":"847a9ac2b2b33f5ea0d6b3e9ecae5f9e9fe11503e2da1f3f4edfe87c7ec3f7f6","schema_version":"1.0","event_id":"sha256:847a9ac2b2b33f5ea0d6b3e9ecae5f9e9fe11503e2da1f3f4edfe87c7ec3f7f6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:Q7BELYXTG2I2BPKHJ5UJGMAZPR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.MM","authors_text":"Boyu Chen, Chengxiang Zhuo, Chenyun Yu, Lei Cheng, Ouyang Yi, Siran Chen, Yali Wang, Yuxiao Luo, Zang Li","submitted_at":"2025-07-03T13:52:24Z","abstract_excerpt":"Owing to powerful natural language processing and generative capabilities, large language model (LLM) agents have emerged as a promising solution for enhancing recommendation systems via user simulation. However, in the realm of video recommendation, existing studies predominantly resort to prompt-based simulation using frozen LLMs and encounter the intricate challenge of multimodal content understanding. This frequently results in suboptimal item modeling and user preference learning, thereby ultimately constraining recommendation performance. To address these challenges, we introduce VRAgent"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.02626","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/2507.02626/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":"2026-07-05T11:31:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rIp0Bqr+erpbQF+7U2LvvCsFWeFWNpTuGo3DxK/p27SllUQZ0vkx/hQwRWUiyi0BU5pJzp/siR8fpeYLUkecAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-27T11:34:34.461038Z"},"content_sha256":"ff506d169743866704d744d9d5d2866b7675dd50c281f2bf5afeb5cd9dddc628","schema_version":"1.0","event_id":"sha256:ff506d169743866704d744d9d5d2866b7675dd50c281f2bf5afeb5cd9dddc628"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Q7BELYXTG2I2BPKHJ5UJGMAZPR/bundle.json","state_url":"https://pith.science/pith/Q7BELYXTG2I2BPKHJ5UJGMAZPR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Q7BELYXTG2I2BPKHJ5UJGMAZPR/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":"2026-07-27T11:34:34Z","links":{"resolver":"https://pith.science/pith/Q7BELYXTG2I2BPKHJ5UJGMAZPR","bundle":"https://pith.science/pith/Q7BELYXTG2I2BPKHJ5UJGMAZPR/bundle.json","state":"https://pith.science/pith/Q7BELYXTG2I2BPKHJ5UJGMAZPR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Q7BELYXTG2I2BPKHJ5UJGMAZPR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:Q7BELYXTG2I2BPKHJ5UJGMAZPR","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":"a66c27575e0b8de2007e5c6ad1846f5f84a4fdb818d162a1ea4fed22a7e16e00","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MM","submitted_at":"2025-07-03T13:52:24Z","title_canon_sha256":"8b6c9ca4062c576118c8f0ea4c392dd2a2c5fc129827c4903439393efc3843ba"},"schema_version":"1.0","source":{"id":"2507.02626","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.02626","created_at":"2026-07-05T11:31:31Z"},{"alias_kind":"arxiv_version","alias_value":"2507.02626v1","created_at":"2026-07-05T11:31:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.02626","created_at":"2026-07-05T11:31:31Z"},{"alias_kind":"pith_short_12","alias_value":"Q7BELYXTG2I2","created_at":"2026-07-05T11:31:31Z"},{"alias_kind":"pith_short_16","alias_value":"Q7BELYXTG2I2BPKH","created_at":"2026-07-05T11:31:31Z"},{"alias_kind":"pith_short_8","alias_value":"Q7BELYXT","created_at":"2026-07-05T11:31:31Z"}],"graph_snapshots":[{"event_id":"sha256:ff506d169743866704d744d9d5d2866b7675dd50c281f2bf5afeb5cd9dddc628","target":"graph","created_at":"2026-07-05T11:31:31Z","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/2507.02626/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Owing to powerful natural language processing and generative capabilities, large language model (LLM) agents have emerged as a promising solution for enhancing recommendation systems via user simulation. However, in the realm of video recommendation, existing studies predominantly resort to prompt-based simulation using frozen LLMs and encounter the intricate challenge of multimodal content understanding. This frequently results in suboptimal item modeling and user preference learning, thereby ultimately constraining recommendation performance. To address these challenges, we introduce VRAgent","authors_text":"Boyu Chen, Chengxiang Zhuo, Chenyun Yu, Lei Cheng, Ouyang Yi, Siran Chen, Yali Wang, Yuxiao Luo, Zang Li","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MM","submitted_at":"2025-07-03T13:52:24Z","title":"VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.02626","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":"sha256:847a9ac2b2b33f5ea0d6b3e9ecae5f9e9fe11503e2da1f3f4edfe87c7ec3f7f6","target":"record","created_at":"2026-07-05T11:31:31Z","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":"a66c27575e0b8de2007e5c6ad1846f5f84a4fdb818d162a1ea4fed22a7e16e00","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.MM","submitted_at":"2025-07-03T13:52:24Z","title_canon_sha256":"8b6c9ca4062c576118c8f0ea4c392dd2a2c5fc129827c4903439393efc3843ba"},"schema_version":"1.0","source":{"id":"2507.02626","kind":"arxiv","version":1}},"canonical_sha256":"87c245e2f33691a0bd474f689330197c5d95954c2c0c44ab1e2c68342288dc6a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"87c245e2f33691a0bd474f689330197c5d95954c2c0c44ab1e2c68342288dc6a","first_computed_at":"2026-07-05T11:31:31.958305Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:31:31.958305Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tP8jY+zdqc4XjnVpdsMTPwJS63uwUcZkPF3FQoHIsZLsyntAOetaXmCraLx4+0D4V+T3mKn4gAGT+naEEWD6BA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:31:31.958790Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.02626","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:847a9ac2b2b33f5ea0d6b3e9ecae5f9e9fe11503e2da1f3f4edfe87c7ec3f7f6","sha256:ff506d169743866704d744d9d5d2866b7675dd50c281f2bf5afeb5cd9dddc628"],"state_sha256":"19351db60b1e9a5a9b824d93228b19d4a984316d25592f7b6c33651f1010aa86"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AjYKvZk3H+5lqPxHp/IRLA5En5S7dDxUMbz0jNKx67aRfUkSwlqiNrEHeHovBAgUDqK9VA63Lng1YhxcLB9aDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-27T11:34:34.463526Z","bundle_sha256":"4b6e4f993b5f6a6bdef006cc122039ce1b9494694b9927dc3bb8332ac19d4d41"}}