{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:67JGF7TB7DK5BXS3S4F3HQ6JVJ","short_pith_number":"pith:67JGF7TB","canonical_record":{"source":{"id":"2605.27960","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T04:54:07Z","cross_cats_sorted":[],"title_canon_sha256":"a1877b1058ed5d4f4e80082f3fbe6e2eebd542eeff7a9d6b564206eeb78403b9","abstract_canon_sha256":"e04bc7c082472356930e7620317a364ce418776065ad57c37ff24ee895391230"},"schema_version":"1.0"},"canonical_sha256":"f7d262fe61f8d5d0de5b970bb3c3c9aa6b0ba3c70e8879e5ebda3d13df1c6883","source":{"kind":"arxiv","id":"2605.27960","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.27960","created_at":"2026-05-28T01:04:54Z"},{"alias_kind":"arxiv_version","alias_value":"2605.27960v1","created_at":"2026-05-28T01:04:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.27960","created_at":"2026-05-28T01:04:54Z"},{"alias_kind":"pith_short_12","alias_value":"67JGF7TB7DK5","created_at":"2026-05-28T01:04:54Z"},{"alias_kind":"pith_short_16","alias_value":"67JGF7TB7DK5BXS3","created_at":"2026-05-28T01:04:54Z"},{"alias_kind":"pith_short_8","alias_value":"67JGF7TB","created_at":"2026-05-28T01:04:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:67JGF7TB7DK5BXS3S4F3HQ6JVJ","target":"record","payload":{"canonical_record":{"source":{"id":"2605.27960","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T04:54:07Z","cross_cats_sorted":[],"title_canon_sha256":"a1877b1058ed5d4f4e80082f3fbe6e2eebd542eeff7a9d6b564206eeb78403b9","abstract_canon_sha256":"e04bc7c082472356930e7620317a364ce418776065ad57c37ff24ee895391230"},"schema_version":"1.0"},"canonical_sha256":"f7d262fe61f8d5d0de5b970bb3c3c9aa6b0ba3c70e8879e5ebda3d13df1c6883","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:04:54.251632Z","signature_b64":"zWdQIMN5sARhULwtCybypphyijphm5QQdU0WQLYZgtYPetwLS+qInO/seJLP7yOtWiOP4gSUAOFJ3JTnCD5sCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f7d262fe61f8d5d0de5b970bb3c3c9aa6b0ba3c70e8879e5ebda3d13df1c6883","last_reissued_at":"2026-05-28T01:04:54.251234Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:04:54.251234Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.27960","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":"2026-05-28T01:04:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2zDVxuk4O/2pJScmwmgJPMaGbHPVMlHREcBhGYPpO+66T4W4ZC7BWMcye1z544czwM4V7tSstpttUZ6D3cSrDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T21:49:05.295234Z"},"content_sha256":"56fff6597ea9b82377411d9e46d729241c775807af77b57ed6a8eaf850cb3539","schema_version":"1.0","event_id":"sha256:56fff6597ea9b82377411d9e46d729241c775807af77b57ed6a8eaf850cb3539"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:67JGF7TB7DK5BXS3S4F3HQ6JVJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mags-RL: Wearing Multimodal LLMs a Magnifying Glass via Agentic Reinforcement Learning For Complex Scene Reasoning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Gen Li, Hao Wang, Peijie Qiu, Prayag Tiwari, Shao Tang, Wenhui Zhu, Xiaobing Yu, Xin Li, Xiwen Chen, Xuanzhao Dong, Yalin Wang, Yanxi Chen, Yujian Xiong, Zhipeng Wang","submitted_at":"2026-05-27T04:54:07Z","abstract_excerpt":"Despite their popularity and success, Multimodal Large Language Models (MLLMs) often struggle to interpret images accurately, which limits their reasoning capability in complex scenarios (e.g., high object density and complex background clutter). Prior work mainly addresses this limitation by incorporating explicit visual cues like bounding boxes that require extra annotations. In addition, the resulting low-resolution crops often miss fine-grained details that MLLMs require for accurate reasoning. Therefore, we propose Mags-RL, an Agentic Reinforcement Learning (RL) framework that equips MLLM"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.27960","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/2605.27960/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":"2026-05-28T01:04:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VXGQ95esKi4Tgu3Ix1uc1rhv3Ls5eySKj6s2VoGZPNS1xWY6O7/e9DZSGFXShgOUqxX+mbchk8MlmWzTJeooDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T21:49:05.295624Z"},"content_sha256":"f2a769a4342578ed1a4ce3bbee588bcc2f69b36394a4b92148bb997c531f1840","schema_version":"1.0","event_id":"sha256:f2a769a4342578ed1a4ce3bbee588bcc2f69b36394a4b92148bb997c531f1840"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/67JGF7TB7DK5BXS3S4F3HQ6JVJ/bundle.json","state_url":"https://pith.science/pith/67JGF7TB7DK5BXS3S4F3HQ6JVJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/67JGF7TB7DK5BXS3S4F3HQ6JVJ/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":"2026-06-27T21:49:05Z","links":{"resolver":"https://pith.science/pith/67JGF7TB7DK5BXS3S4F3HQ6JVJ","bundle":"https://pith.science/pith/67JGF7TB7DK5BXS3S4F3HQ6JVJ/bundle.json","state":"https://pith.science/pith/67JGF7TB7DK5BXS3S4F3HQ6JVJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/67JGF7TB7DK5BXS3S4F3HQ6JVJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:67JGF7TB7DK5BXS3S4F3HQ6JVJ","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":"e04bc7c082472356930e7620317a364ce418776065ad57c37ff24ee895391230","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T04:54:07Z","title_canon_sha256":"a1877b1058ed5d4f4e80082f3fbe6e2eebd542eeff7a9d6b564206eeb78403b9"},"schema_version":"1.0","source":{"id":"2605.27960","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.27960","created_at":"2026-05-28T01:04:54Z"},{"alias_kind":"arxiv_version","alias_value":"2605.27960v1","created_at":"2026-05-28T01:04:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.27960","created_at":"2026-05-28T01:04:54Z"},{"alias_kind":"pith_short_12","alias_value":"67JGF7TB7DK5","created_at":"2026-05-28T01:04:54Z"},{"alias_kind":"pith_short_16","alias_value":"67JGF7TB7DK5BXS3","created_at":"2026-05-28T01:04:54Z"},{"alias_kind":"pith_short_8","alias_value":"67JGF7TB","created_at":"2026-05-28T01:04:54Z"}],"graph_snapshots":[{"event_id":"sha256:f2a769a4342578ed1a4ce3bbee588bcc2f69b36394a4b92148bb997c531f1840","target":"graph","created_at":"2026-05-28T01:04:54Z","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/2605.27960/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite their popularity and success, Multimodal Large Language Models (MLLMs) often struggle to interpret images accurately, which limits their reasoning capability in complex scenarios (e.g., high object density and complex background clutter). Prior work mainly addresses this limitation by incorporating explicit visual cues like bounding boxes that require extra annotations. In addition, the resulting low-resolution crops often miss fine-grained details that MLLMs require for accurate reasoning. Therefore, we propose Mags-RL, an Agentic Reinforcement Learning (RL) framework that equips MLLM","authors_text":"Gen Li, Hao Wang, Peijie Qiu, Prayag Tiwari, Shao Tang, Wenhui Zhu, Xiaobing Yu, Xin Li, Xiwen Chen, Xuanzhao Dong, Yalin Wang, Yanxi Chen, Yujian Xiong, Zhipeng Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T04:54:07Z","title":"Mags-RL: Wearing Multimodal LLMs a Magnifying Glass via Agentic Reinforcement Learning For Complex Scene Reasoning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.27960","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":"sha256:56fff6597ea9b82377411d9e46d729241c775807af77b57ed6a8eaf850cb3539","target":"record","created_at":"2026-05-28T01:04:54Z","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":"e04bc7c082472356930e7620317a364ce418776065ad57c37ff24ee895391230","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T04:54:07Z","title_canon_sha256":"a1877b1058ed5d4f4e80082f3fbe6e2eebd542eeff7a9d6b564206eeb78403b9"},"schema_version":"1.0","source":{"id":"2605.27960","kind":"arxiv","version":1}},"canonical_sha256":"f7d262fe61f8d5d0de5b970bb3c3c9aa6b0ba3c70e8879e5ebda3d13df1c6883","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f7d262fe61f8d5d0de5b970bb3c3c9aa6b0ba3c70e8879e5ebda3d13df1c6883","first_computed_at":"2026-05-28T01:04:54.251234Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-28T01:04:54.251234Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zWdQIMN5sARhULwtCybypphyijphm5QQdU0WQLYZgtYPetwLS+qInO/seJLP7yOtWiOP4gSUAOFJ3JTnCD5sCg==","signature_status":"signed_v1","signed_at":"2026-05-28T01:04:54.251632Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.27960","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:56fff6597ea9b82377411d9e46d729241c775807af77b57ed6a8eaf850cb3539","sha256:f2a769a4342578ed1a4ce3bbee588bcc2f69b36394a4b92148bb997c531f1840"],"state_sha256":"58c70a811cb5d45c71ec5a54337d394879735b25860bc9a395d95a50f48227f3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ixKEdyODPejhtGC+fwQ0CTjP8wJTrAJDfOSe+P3cgXH+Uw9UFqjF4zwJS2ouHmWxTYiU/YlRjYEUIZeAuHUyAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-27T21:49:05.297586Z","bundle_sha256":"d413f20f9e141cab4a7825217b39728ae97d28e4683bfd5d26aa3050f6ec2ebf"}}