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Paper Citation Record · LEDGER

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs

As of 7 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 2 inbound Pith citation observations for arXiv:2506.23940.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.23940 v2

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:32:23.852683Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:06:48.889429Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-20T05:53:22.253104Z

Reference resolution

31 of 31 outbound references displayed

  • verified exact1
  • verified fuzzy6
  • unresolved22
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c2e55b09-be3f-4b28-b287-aa728940ea79 · outbound

This paper cites Qwen2.5-Coder Technical Report.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Qwen2.5-Coder Technical Report

Reference 7

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:32:23.756344Z digest=sha256:e942f7fac191e6df35793091a31ec30be2d662624d63125455781b449a2bfad3

Observation fefd5596-0887-4480-8620-f09adc20bb2f · outbound

This paper cites Think Twice, Click Once: Enhancing GUI Grounding via Fast and Slow Systems.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Think Twice, Click Once: Enhancing GUI Grounding via Fast and Slow Systems

Reference 8

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source=pdf_text observed=2026-08-06T21:32:23.760575Z digest=sha256:80c357467f67d4ce1099897e81d0b71bf113a10d69a66de91d38dc0574bb5cb5

Observation d67f1f5e-9cb9-4bb6-807d-c69d893e9978 · outbound

This paper cites Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities

Reference 9

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source=pdf_text observed=2026-08-06T21:32:23.764650Z digest=sha256:112436392c95640f73fe11031932888666714a1b8134b3b67d9def9e6d74ef16

Observation 441d0559-8928-43c9-a974-f4360e20a9da · outbound

This paper cites Deep Model Fusion: A Survey.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Deep Model Fusion: A Survey

Reference 10

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source=pdf_text observed=2026-08-06T21:32:23.768843Z digest=sha256:22a911bf5f819eb314ab99309c8b10c6b7f11093f1a6e04972c5a9f9467a06f8

Observation b3f93aa0-927c-4011-8c47-14b2835ec6f8 · outbound

This paper cites Optimize Incompatible Parameters through Compatibility-aware Knowledge Integration.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Optimize Incompatible Parameters through Compatibility-aware Knowledge Integration

Reference 11

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verified exact
local_arxiv, observed 2026-08-06T21:32:23.998053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T21:32:23.772900Z digest=sha256:5eb072d27a1b250c47fac53769ca1e95bf73473feed9f8af4081ab3fa2d02ccd

Observation 3aa019e0-e9c3-4517-82cc-2f3e43fb1aea · outbound

This paper cites Editing Models with Task Arithmetic.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Editing Models with Task Arithmetic

Reference 12

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T21:32:23.776181Z digest=sha256:677b251b0578d6b5a139eb763c8c1961edfc93a4d7597a239eb4c5642d6082e2

Observation 10fbc0c2-422a-4352-8ac0-6ce6b3ae4b89 · outbound

This paper cites Duet: A tuning-free device-cloud collaborative parameters generation framework for efficient device model generalization.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Duet: A tuning-free device-cloud collaborative parameters generation framework for efficient device model generalization

Reference 13

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raw_fallback, observed 2026-08-06T21:32:24.212625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T21:32:23.780104Z digest=sha256:141e71c6046e022279b625e212a4627c86d17a5e5fa2721a8e96edeb77c3a8f1

Observation 3cb8c70d-c601-46d4-8c88-b28b5a7708f1 · outbound

This paper cites Intelligent model update strategy for sequential recommendation.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Intelligent model update strategy for sequential recommendation

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T21:32:23.783469Z digest=sha256:64ac3e9a99cd01a1c1f2f7853b601bcdb97ec5ddf09088e500fc42ae14c59ed0

Observation f033b2ce-8990-4934-b19d-3da318089dd4 · outbound

This paper cites Bridging Local Details and Global Context in Text-Attributed Graphs.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Bridging Local Details and Global Context in Text-Attributed Graphs

Reference 15

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T21:32:23.787390Z digest=sha256:7d3a5214d896f1e4bd1387ff15c4e7291df248be2b420e97f1fb35462bbc2a63

Observation cd66d592-4414-4ccd-9d70-c434bfc77eee · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 16

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source=pdf_text observed=2026-08-06T21:32:23.791816Z digest=sha256:6e15143acaca671e69ac3042da722da0603440a64b8be2b8233ee3cd876349f2

Observation ef1d4479-9480-4626-adf0-f2f52c9b9949 · outbound

This paper cites Improving language understanding by generative pre-training.(2018),.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Improving language understanding by generative pre-training.(2018),

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T21:32:23.794970Z digest=sha256:9570d5cf68f5bc3d260d29fc65f816565d454b464faa1ba560635397d4e735c4

Observation 7eeb99bc-9b95-4314-8b29-0d1ddf720893 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 18

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T21:32:23.799228Z digest=sha256:fbe685f57b5cc369f16fa2e1d85be059959c5ef520bc1dea048958ed25eabf0a

Observation 6fd8c473-3fda-48ae-81fc-ca8731ef683a · outbound

This paper cites P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

Reference 19

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source=pdf_text observed=2026-08-06T21:32:23.803598Z digest=sha256:6042fa3ca4ca67ba716a775950853a72871b231c0451976dfce0ceda311eda1b

Observation 70ea454b-bf9e-44c1-90aa-86359ba138ae · outbound

This paper cites Dataless Knowledge Fusion by Merging Weights of Language Models.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Dataless Knowledge Fusion by Merging Weights of Language Models

Reference 20

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no resolver link, observed 2026-08-06T21:32:23.806970Z

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source=pdf_text observed=2026-08-06T21:32:23.806970Z digest=sha256:7f2b4a602992a55e3e5a59621295a4eb42c435988e7b194d000c82530977a4ae

Observation 39e185ef-e9c3-4050-bc06-9cf17268d1e3 · outbound

This paper cites AdaMerging: Adaptive Model Merging for Multi-Task Learning.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs AdaMerging: Adaptive Model Merging for Multi-Task Learning

Reference 21

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source=pdf_text observed=2026-08-06T21:32:23.810086Z digest=sha256:4921d9055cb6572a41e7aa7c90ed002be0913e44397d2e097bf29278c4e816db

Observation aac5b850-3d41-4e71-88fa-f19810a17c00 · outbound

This paper cites Mart: Learning hierarchical music audio representations with part-whole transformer.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Mart: Learning hierarchical music audio representations with part-whole transformer

Reference 22

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raw_fallback, observed 2026-08-06T21:32:24.175130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T21:32:23.814293Z digest=sha256:593c496f8dfce28d015489104c653275f5b394b18995c9169ed83c7261dadcc8

Observation e4308293-216f-4ced-bbec-7e0242a34670 · outbound

This paper cites EOC-Bench: Can MLLMs Identify, Recall, and Forecast Objects in an Egocentric World?.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs EOC-Bench: Can MLLMs Identify, Recall, and Forecast Objects in an Egocentric World?

Reference 23

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source=pdf_text observed=2026-08-06T21:32:23.818358Z digest=sha256:67d08f10637af2c9d2a1f87cf10fc0e56b684f88dbc30f49947fa6787dfaac26

Observation 6c7b1b8a-6327-4ecd-b6dc-77ded13a4995 · outbound

This paper cites MAKIMA: Tuning-free Multi-Attribute Open-domain Video Editing via Mask-Guided Attention Modulation.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs MAKIMA: Tuning-free Multi-Attribute Open-domain Video Editing via Mask-Guided Attention Modulation

Reference 24

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source=pdf_text observed=2026-08-06T21:32:23.821548Z digest=sha256:f653f8e14dcbefc8cc43d6bf01bc4c9d5fb578e53d177be479bb3829c4485ef0

Observation 29eb50a3-4367-4532-94f5-39646381daf0 · outbound

This paper cites Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework

Reference 25

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source=pdf_text observed=2026-08-06T21:32:23.825194Z digest=sha256:01510a92fab7a93da1d170f99c1dabd8d80cf68f51b475f6677f92c18885d129

Observation 06b4bad3-1ee3-441d-a3ea-6e65ad239507 · outbound

This paper cites Denoising Multi-modal Sequential Recommenders with Contrastive Learning.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Denoising Multi-modal Sequential Recommenders with Contrastive Learning

Reference 26

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local_arxiv, observed 2026-08-06T21:32:23.916950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T21:32:23.829484Z digest=sha256:83bd278f9970ed9006581be2b410fd28a0d95544443e8adf99b2a3d832b1ead0

Observation 9d0eeaf6-c37a-4a00-a772-42ebfd60eaee · outbound

This paper cites Multimodal large language models: A survey.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Multimodal large language models: A survey

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T21:32:23.832964Z digest=sha256:bb5390596c8793124d400a369d439bd7b78a7e87bc05cf2b3ebb89d8fbd81ead

Observation 84c9c7fb-6905-4346-9de0-ef4d6511f7f9 · outbound

This paper cites MM-LLMs: Recent Advances in MultiModal Large Language Models.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs MM-LLMs: Recent Advances in MultiModal Large Language Models

Reference 28

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source=pdf_text observed=2026-08-06T21:32:23.836684Z digest=sha256:76bba3294cae8be96262b218b146c4ae162c6cd68b9ec15c13a5b22119cebdac

Observation 24963130-d5b9-47ec-8dd2-c8c0d851dafa · outbound

This paper cites The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision).

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)

Reference 29

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source=pdf_text observed=2026-08-06T21:32:23.840773Z digest=sha256:17a880a1e43353cbf6f6e9bbfc3c1e0831625d1f3abc5b61a94fc5d4109ea266

Observation e92b9496-f065-4349-a926-08d18810bccf · outbound

This paper cites Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models

Reference 30

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source=pdf_text observed=2026-08-06T21:32:23.844440Z digest=sha256:88c1c3e116e85008f72886bb3da7b140455ac17e7a02fae6867eb0e0ca504836

Observation 4abe0bf8-b72c-42d2-8bff-02e4827374a1 · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 32

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source=pdf_text observed=2026-08-06T21:32:23.852683Z digest=sha256:2ccffad69ef128b0cb9ee08be5638e4dcd35328cebe5e0896a03aff6ae56555c

Observation 0055d29d-44b1-4e8c-8dfa-46b75906f816 · outbound

This paper cites End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding

Reference 2019

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local_arxiv, observed 2026-08-06T21:32:24.152856Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T21:32:23.732034Z digest=sha256:50115e1d69067c5586838044f984ba224c96b67c9e8ec418b3a9fd0ac95adedf

Observation a937b6f7-2bc8-4723-96c2-2aa5d0727d44 · outbound

This paper cites PathVQA: 30000+ Questions for Medical Visual Question Answering.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs PathVQA: 30000+ Questions for Medical Visual Question Answering

Reference 2020

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source=pdf_text observed=2026-08-06T21:32:23.848820Z digest=sha256:45afe745663083f1664f573c2aaedb70c9f680bf818b00215f848568a1e309a1

Observation 515a5ab5-4716-4b28-bc7f-247924f1171a · outbound

This paper cites METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection

Reference 2021

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source=pdf_text observed=2026-08-06T21:32:23.736753Z digest=sha256:a45c1ecb0cdd90542cbdf1ca35002197a6b5803af0421841b78153d268416bc3

Observation bd69d292-3edb-4c3a-bc79-a689ea57871e · outbound

This paper cites HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs HealthGPT: A Medical Large Vision-Language Model for Unifying Comprehension and Generation via Heterogeneous Knowledge Adaptation

Reference 2022

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source=pdf_text observed=2026-08-06T21:32:23.749088Z digest=sha256:331bb6b66988fdb449cd3cfc04a1c20368793401c86174bb7af41b735af335c9

Observation 5f0da28e-8ecc-46ae-b530-63a178e8b090 · outbound

This paper cites Minerva: Solving quantitative reasoning problems with language models.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Minerva: Solving quantitative reasoning problems with language models

Reference 2024

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verified fuzzy
raw_fallback, observed 2026-08-06T21:32:24.226426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T21:32:23.744747Z digest=sha256:1ac88cde0d71905b5cbca3224aaed4f3da69bc95b9df8c024a4fe37884f1c31c

Observation 9f2213b6-d442-46db-8de6-44a9160e38da · outbound

This paper cites Fin-r1: A large language model for financial reasoning through reinforcement learning.

Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs Fin-r1: A large language model for financial reasoning through reinforcement learning

Reference 2025

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:32:23.752982Z digest=sha256:77e2ba774cf31217c3a634af616e94128853bb527ed4b6efd7f0c9c28b28f0c2

Pith citing papers

Observation 1dee66f4-3534-4c46-8d85-686f1c3457e2 · inbound

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges cites this paper.

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs

Reference 277

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:06:48.889429Z digest=sha256:01263a847601a32b397c17d45833d285076e0e841c23b6bb640db084cab42a60

Observation 25a26b1f-bdf5-4676-8aea-b04e09b847ba · inbound

EgoCoT-Bench: Benchmarking Grounded and Verifiable Operation-Centric Chain of Thought Reasoning for MLLMs cites this paper.

EgoCoT-Bench: Benchmarking Grounded and Verifiable Operation-Centric Chain of Thought Reasoning for MLLMs Graft: Integrating the Domain Knowledge via Efficient Parameter Synergy for MLLMs

Reference 10

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arxiv_id, observed 2026-05-20T05:53:22.255505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-20T05:53:05.450946Z digest=sha256:2f40553e3a20e0e63321f4517dd50db5d2ca0ed78f9f71fb0861eaa167377d43