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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=sha256:953c8b4d262bfe5491e68139f877f453b8785ab64268692323dcdc3298b7e623

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=sha256:9234e3cfe4590def1047f1baee88d50421da74f0a8a82a5abcf528d557ea295d

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=sha256:47530923e3866df4e67897e863dd99b28f3a6d016c0340ef98452bd6f3a4dbbc

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:afb93f7b1839d6072a293f0f0081f052f36bfdd69b9792960f65d950bf860ae7

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:c777216e8a5ec3d82deb578a92b0e927bbe6926cc78579b6d51701b963a5e8f5

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=sha256:73b3beab8dee0cea0ca16a97df97d7b3cb72184306cf694d95a1076bd57d7244

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:caaecabd6cba5aa75f1d038d2810357e17a9ab39f6356274ee4a51047a37705f

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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raw_fallback, observed 2026-08-06T21:32:24.201369Z

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=sha256:41e47f21f8d8f8b1a446b46b63352c0684e42c94e701253f20e035007efeff86

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:32:23.787390Z digest=sha256:bceb782388bddb22e02845678b4cc25c3f83dbeee8b1dc399f1e88d2db10a493

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:ade76e40ed9cec376f7ba768f0c8b6e041972fa16c093dd20d19b8668acd274e

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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raw_fallback, observed 2026-08-06T21:32:24.185780Z

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:a5cca319065994df4fe32aa399cd2de8bce04d1750ef7a9092375f38103aebd7

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=pdf_text observed=2026-08-06T21:32:23.799228Z digest=sha256:08d045fd8f27d9babedf357c35e7bf2a0fae72fb04e347eacf4b196a033e5ccc

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:32:23.803598Z digest=sha256:897566ed577ca0c25489cb6f05e52ff3d832f4a85df242f7a9c7b63659efbf01

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=sha256:2cebb07ca4fc3e7dca4df31b70f85f83a86a447087726ff40f4f308b3649c91f

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:a27660a8e5bd676d462ddd30e82afceded9bfb7073e7861893438f20f938b236

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=sha256:412d9bc866c118f1c29217c3c2907d6c92247b385f083ec36da70b1f8eacfb95

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=sha256:787c55336f322d5157fea168f29941c7ee26486941686226713ab2c200328b15

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:f275e1e9e6e3529a8610db20f283db87e83f17f692f514a6254ee05dda61f002

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=sha256:527c2544ab57a1736e85287256bb6260fcd90d895c38189036c87284a5e04bde

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=sha256:4c0776aadfed48ee0f59a11aedf1c01c1cdd2bfbb58d190ee346ac49e1b16f91

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=sha256:101afc450fcc2bf5d2f671588bb54d0ee937d933509911b69e6bb982ddbe464c

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=sha256:12f621587f8f4ddfdb0c7f145b273d9e86560f54cac3e091f702bea17ca62a47

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=sha256:78540d8560684758ca53d0fefe97a5fcccd69908c403bcf13f4c1ce5fca171b2

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:c13d5d95784929aad4284876909e353f6fdfacc64b6e97ede5674d311db14aec

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=sha256:0bfd2d3556f34b620f95736832454560cdae9fcaa0fdfeb3df0f569873e37927

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

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=2026-08-06T21:32:23.732034Z digest=sha256:795653bae34aeafbe1d4a34835048cf95f1db2316002c905e23829c6481c8292

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:32:23.848820Z digest=sha256:885a500d7ac85b924359862bfa2b767ba02d7e7e8a6ed6c575785714f6fa7ec4

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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no resolver link, observed 2026-08-06T21:32:23.736753Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:32:23.736753Z digest=sha256:fb8a4e43e719c61ca518ac72ac331146eea10e980a5a54ff4ccabefb899e403f

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:32:23.749088Z digest=sha256:3ab6ddffac9b602f7cd154d67bd0e9fc3d477cf131743d6703618e40cc0df7ca

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:ef5941c165003f1c99d9da77c58e9d6108bf7b3fa2435486d8ea392d1c3c4b86

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=sha256:5bd418e8634106a3ee71d18f14418cb3a5e979c06074fce2330bb96c6d331e07

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=sha256:6bb458eca2e45a6cb570711d7bc1f99060ce93aa277047d23329628ec4bb9e1d

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=sha256:745eb1ac6a4c42bd3aeb7faa58a37da16d49309c5b072ab692dc2d85bbb2fc7e