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Paper Citation Record · LEDGER

Per-Query Visual Concept Learning

As of 8 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2508.09045.

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

pith.paper-citation-record.v1
2508.09045 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:18:54.000846Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

39 of 39 outbound references displayed

  • verified exact0
  • verified fuzzy16
  • unresolved22
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b600f876-5fcc-4c24-8107-a773cd1375a8 · outbound

This paper cites Alignit: Enhancing prompt alignment in cus- tomization of text-to-image models.

Per-Query Visual Concept Learning Alignit: Enhancing prompt alignment in cus- tomization of text-to-image models

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 6b6bd397-e35e-491f-9a58-6dac766416e1 · outbound

This paper cites A Neural Space-Time Representation for Text-to-Image Personalization.

Per-Query Visual Concept Learning A Neural Space-Time Representation for Text-to-Image Personalization

Reference 2

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source=pdf_text observed=2026-08-05T21:18:49.405819Z digest=sha256:5f0f09efd718ac7c4a97710db318bccab367c7bd2ec2689a9a234bae4dd8bd80

Observation cae7c478-67c4-446b-aac3-fdca7c8fd567 · outbound

This paper cites PALP: Prompt Aligned Personalization of Text-to-Image Models.

Per-Query Visual Concept Learning PALP: Prompt Aligned Personalization of Text-to-Image Models

Reference 3

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source=pdf_text observed=2026-08-05T21:18:49.493940Z digest=sha256:3728a1673c13824ee7baa79b20b359f9417750fb5623542ec2cf1e5f380097ee

Observation df6d0f8f-e580-4c70-9558-304bbec9abe3 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Per-Query Visual Concept Learning Emerg- ing properties in self-supervised vision transformers

Reference 4

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source=pdf_text observed=2026-08-05T21:18:49.614139Z digest=sha256:019686425ed739897719e1ffe6a60e45e6fa9ab0c903e80f0009502320b5d870

Observation 9ac902e9-35e9-40d6-8b79-ac499906da9f · outbound

This paper cites Subject-driven text-to-image generation via apprenticeship learning.

Per-Query Visual Concept Learning Subject-driven text-to-image generation via apprenticeship learning

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:49.787201Z digest=sha256:650ea03dcba37c41b47df68bd71ed1ed2c30fe23478a5909996b0df9f066ddb8

Observation 12183a66-df21-4321-91f1-e9f04f95ccc6 · outbound

This paper cites Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models.

Per-Query Visual Concept Learning Custom-Edit: Text-Guided Image Editing with Customized Diffusion Models

Reference 6

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source=pdf_text observed=2026-08-05T21:18:49.965824Z digest=sha256:1dd33fc13546a2cba6c3bf7a1e9d4fc2b69cdbb3bd403e8b0a9b75e1fba3e203

Observation 8b176b2e-182e-419d-bf49-8480d865aa83 · outbound

This paper cites Scaling recti- fied flow transformers for high-resolution image synthesis.

Per-Query Visual Concept Learning Scaling recti- fied flow transformers for high-resolution image synthesis

Reference 7

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source=pdf_text observed=2026-08-05T21:18:50.077627Z digest=sha256:713e81daaf011824c3f5fd48b7c92c5ae6710fdb50dfe879db809ac70afcda64

Observation 53e4713b-014e-4e50-bc68-1516840a822c · outbound

This paper cites An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.

Per-Query Visual Concept Learning An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Reference 8

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source=pdf_text observed=2026-08-05T21:18:50.187095Z digest=sha256:74c3f84e5f3266c336198377a58bb050d2802a22a0e7ee36937a94a256b9b6ea

Observation ff68719d-ec7e-425c-ae12-82d99f67b092 · outbound

This paper cites Encoder-based domain tuning for fast personalization of text-to-image models.ACM Transactions on Graphics (TOG), 42(4):1–13, 2023.

Per-Query Visual Concept Learning Encoder-based domain tuning for fast personalization of text-to-image models.ACM Transactions on Graphics (TOG), 42(4):1–13, 2023

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:50.334752Z digest=sha256:d67f4b807e56efb9c93a5dfe7629ad212536b057dba3a4178257c69579635cb2

Observation 6ff0b0e3-d668-4d50-b97a-cebab1a74460 · outbound

This paper cites ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance.

Per-Query Visual Concept Learning ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance

Reference 10

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source=pdf_text observed=2026-08-05T21:18:50.456059Z digest=sha256:c00b9136f36c5d8ed00b93e468376757b8450c04e38abaf7cf8d3b81e95d67ad

Observation d0cdf684-3332-43a2-a90f-7a0faaea50cc · outbound

This paper cites Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models.

Per-Query Visual Concept Learning Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models

Reference 11

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source=pdf_text observed=2026-08-05T21:18:50.551651Z digest=sha256:1e9d07e45060717766e835ddb2e6a5681cac1fccbc4b8c1e086220ac98881b36

Observation 6e6b087d-5619-4e4c-b7dd-aefe88d111b1 · outbound

This paper cites Multi-concept customization of text-to-image diffusion.

Per-Query Visual Concept Learning Multi-concept customization of text-to-image diffusion

Reference 12

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:50.702039Z digest=sha256:9291fcb3cfcef0e36b93db80ea83107e2e78ed37298158f4b0815c5911ff3c31

Observation 8a0ffc2c-75e9-48ba-b26f-f0acbd2dfece · outbound

This paper cites Flux.1 kontext: Flow matching for in-context image generation and editing in latent space,.

Per-Query Visual Concept Learning Flux.1 kontext: Flow matching for in-context image generation and editing in latent space,

Reference 13

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source=pdf_text observed=2026-08-05T21:18:50.815620Z digest=sha256:90a61257767d0cfe2cd32348efb8ee3443c24763a54dcf7deb55e076f6937d62

Observation 4e5ba9d5-d938-4d67-a42c-df748802fe17 · outbound

This paper cites Blip-diffusion: Pre- trained subject representation for controllable text-to-image generation and editing.

Per-Query Visual Concept Learning Blip-diffusion: Pre- trained subject representation for controllable text-to-image generation and editing

Reference 14

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:50.982741Z digest=sha256:88df3306ec23c603a5800190fdf667eba9f974cc99a8c01cf9f12e8d11d76f97

Observation 007a6380-8c15-4304-939c-2ff5926b4840 · outbound

This paper cites Flow Matching for Generative Modeling.

Per-Query Visual Concept Learning Flow Matching for Generative Modeling

Reference 15

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source=pdf_text observed=2026-08-05T21:18:51.102575Z digest=sha256:8025f046af2e3d0da805990568567ecae349be2acc7842a69f3012f108daa119

Observation 558dfdbc-4843-44e3-af97-ed938a68fc12 · outbound

This paper cites Low-rank adaptation for fast text-to-image diffusion fine-tuning.

Per-Query Visual Concept Learning Low-rank adaptation for fast text-to-image diffusion fine-tuning

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:51.285559Z digest=sha256:ccc7131241ddf7fb5d7304497f057633a26d5a83f7281f0f9e5be08f9fc62777

Observation 9fd020f2-265d-4aa5-8e34-447f3d42f4ff · outbound

This paper cites Unified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation.

Per-Query Visual Concept Learning Unified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation

Reference 17

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source=pdf_text observed=2026-08-05T21:18:51.407931Z digest=sha256:2d6cc148d2e816be8e089e623c536591aec2395b54c75c4b824a02357071c644

Observation fef6d9d2-3825-4699-8560-32620471ffb3 · outbound

This paper cites Locating and editing factual associations in gpt.

Per-Query Visual Concept Learning Locating and editing factual associations in gpt

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:51.504749Z digest=sha256:ed907c279d9fde399d026765c2dd639ab24cd0d1ba8efd936b0706517be97bd4

Observation dc51f67e-0376-4766-b7df-09f99549657b · outbound

This paper cites Attndreambooth: To- wards text-aligned personalized text-to-image generation.

Per-Query Visual Concept Learning Attndreambooth: To- wards text-aligned personalized text-to-image generation

Reference 19

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:51.644320Z digest=sha256:aa4a8d2aa7487b6e105e77c3d24b53fc3f0bd9cb5c3fe5758aab77cce3ba10a8

Observation 7f01ebc8-2401-4761-9f6e-a14fd2c68922 · outbound

This paper cites SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis.

Per-Query Visual Concept Learning SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis

Reference 20

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source=pdf_text observed=2026-08-05T21:18:51.742349Z digest=sha256:6690b228edd33ca5cc8b244e20d8da4c681a5940499382d15ca6ce50e51ce6a4

Observation ba3b467e-7931-405b-9323-4dc68a72dd92 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Per-Query Visual Concept Learning Learning transferable visual models from natural language supervi- sion

Reference 21

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source=pdf_text observed=2026-08-05T21:18:51.886066Z digest=sha256:9f0f6cb68948802f3d12fd05358aca1a2a8f4f87b5a18e30a8a4f46f499bdebc

Observation a6b0dfe8-45a5-404b-8f4e-a3e0ee7706d5 · outbound

This paper cites Dreamblend: Advancing person- alized fine-tuning of text-to-image diffusion models.

Per-Query Visual Concept Learning Dreamblend: Advancing person- alized fine-tuning of text-to-image diffusion models

Reference 22

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:52.012238Z digest=sha256:875f39079f32af9ac796600dc08b0b112dbcdb7beb29c8da4980bc3749acb41e

Observation 119e9095-c6e2-4ebf-bac2-cd06e2809837 · outbound

This paper cites Zero-shot text-to-image generation.

Per-Query Visual Concept Learning Zero-shot text-to-image generation

Reference 23

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:52.169113Z digest=sha256:c87b58b5f93355cefdd345a8fcef2f24290af93a5502bd25dd10d7964008e58d

Observation 5ca61a2c-7303-41c8-9435-964d3c0b9a86 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Per-Query Visual Concept Learning Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 24

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source=pdf_text observed=2026-08-05T21:18:52.270993Z digest=sha256:f3acf1282f453713c551589b66cc6af6b897b8ada6d4dcb1df1a5131cd7f7164

Observation d3544090-1628-41a7-b8a1-56bd21cdcb08 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Per-Query Visual Concept Learning High-resolution image synthesis with latent diffusion models

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:52.388711Z digest=sha256:d7a9453f3275be5a2f836af41f31d85e90d41d3d430d18076e7878a771ec461c

Observation 5991865d-09c6-4d40-92f1-273b0908a574 · outbound

This paper cites Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation.

Per-Query Visual Concept Learning Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 26

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:52.547811Z digest=sha256:394486f1ca9513e8a11d33080259937ad321118e42c5d1a0a76bc5a7c5ecf42f

Observation 6e62030f-547a-4a1c-8873-d668a8ea8de1 · outbound

This paper cites Photorealistic text-to-image diffusion models with deep language understanding.

Per-Query Visual Concept Learning Photorealistic text-to-image diffusion models with deep language understanding

Reference 27

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source=pdf_text observed=2026-08-05T21:18:52.708760Z digest=sha256:f105605ed2cac7e0085a1d6ed8c80605df7b41dd8b26fc42b719965aa22048d7

Observation e7cb52eb-c3f7-4cab-bde4-5151233c78ee · outbound

This paper cites Where’s waldo: Diffusion features for person- alized segmentation and retrieval.

Per-Query Visual Concept Learning Where’s waldo: Diffusion features for person- alized segmentation and retrieval

Reference 28

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raw_fallback, observed 2026-08-05T21:19:12.754947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:52.810818Z digest=sha256:7074c1f1663b4382ca093815deb4c36174074094fbcf17b0345319fc44996680

Observation 702e18fd-dae9-44bc-b007-ce91847744a1 · outbound

This paper cites In- stantbooth: Personalized text-to-image generation without test-time finetuning.

Per-Query Visual Concept Learning In- stantbooth: Personalized text-to-image generation without test-time finetuning

Reference 29

Resolution
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raw_fallback, observed 2026-08-05T21:19:12.532647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:52.919274Z digest=sha256:cee6ac04243686e6c96c42dee0a12e0bf10756924566a03de7ed8e86989da269

Observation fae550a1-9bc8-42cf-91d7-06fefa4c8180 · outbound

This paper cites Emu: Generative Pretraining in Multimodality.

Per-Query Visual Concept Learning Emu: Generative Pretraining in Multimodality

Reference 30

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source=pdf_text observed=2026-08-05T21:18:53.017729Z digest=sha256:576eb8c9e2d0f6bcfe3a595b1510cadd726342a0061e76de978f8b070b42a9f0

Observation d442070f-d690-49ba-8594-9221fc0e0e20 · outbound

This paper cites Emergent correspondence from image diffusion.

Per-Query Visual Concept Learning Emergent correspondence from image diffusion

Reference 31

Resolution
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raw_fallback, observed 2026-08-05T21:19:12.225805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:53.180948Z digest=sha256:9e7e851ff147c5d203f6cffc4ed0bda424e906e26f4bddef3998b437f6526ae6

Observation 1c50c29c-4bc6-4137-88be-3eacf81d50f2 · outbound

This paper cites Key-locked rank one editing for text-to-image personaliza- tion.

Per-Query Visual Concept Learning Key-locked rank one editing for text-to-image personaliza- tion

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-05T21:19:12.004920Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:53.319805Z digest=sha256:e8eb7d2cf421a04fb85d4449138c7d4ce84ed2c9e9d041f0a5a30209f970c762

Observation a9eef136-f256-43e5-bef0-804c0436359a · outbound

This paper cites P+: Extended Textual Conditioning in Text-to-Image Generation.

Per-Query Visual Concept Learning P+: Extended Textual Conditioning in Text-to-Image Generation

Reference 33

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T21:18:53.421539Z digest=sha256:f9a203c21ea48941a49827a6af51230e5f4a198badcecdc126562c0c8b625908

Observation 04183b0b-f6d4-4780-b59f-fa310cfd28e2 · outbound

This paper cites InstantID: Zero-shot Identity-Preserving Generation in Seconds.

Per-Query Visual Concept Learning InstantID: Zero-shot Identity-Preserving Generation in Seconds

Reference 34

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T21:18:53.556335Z digest=sha256:c0a5f733811c04afb9855f0ac335ed6bb86805c18ed70ffb2014cf1b3ddf1cd2

Observation c83fd7c6-d58d-43db-9c44-a5f15606ec65 · outbound

This paper cites Elite: Encoding visual con- cepts into textual embeddings for customized text-to-image generation.

Per-Query Visual Concept Learning Elite: Encoding visual con- cepts into textual embeddings for customized text-to-image generation

Reference 35

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T21:18:53.685741Z digest=sha256:a78f940de2833193d8e38cb0fba03b7a4ae6cdc140f96d40013eb68a97571b26

Observation fb221ba0-8c41-4aab-b112-2b52d0f5059a · outbound

This paper cites IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models.

Per-Query Visual Concept Learning IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T21:18:53.784755Z digest=sha256:c65d248634bed26465b61cf2b0fc8eb45c861b907fc9e397f16bdf1588790de3

Observation 18cde8cf-8778-47db-b12c-1cf1955b78c1 · outbound

This paper cites Scaling Autoregressive Models for Content-Rich Text-to-Image Generation.

Per-Query Visual Concept Learning Scaling Autoregressive Models for Content-Rich Text-to-Image Generation

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-05T21:18:53.874431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T21:18:53.874431Z digest=sha256:9bd2596aeb96d3fda5da199d632946002826f0be33e087967dba0b5cd579f9f6

Observation 8b47ca9d-bfd7-4c25-ac1e-f832e183a6b1 · outbound

This paper cites A survey on personalized content synthesis with diffusion models.

Per-Query Visual Concept Learning A survey on personalized content synthesis with diffusion models

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-05T21:18:54.000846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T21:18:54.000846Z digest=sha256:57e55742a410ae1860af6e69c36febc039ece6f85f0526954d930c449acd6a52

Observation 1f745ed6-69a1-4498-8245-5ed0a6f5274b · outbound

This paper cites an unresolved cited work.

Per-Query Visual Concept Learning Unresolved cited work

Reference 2025

Resolution
parse uncertain
raw_fallback, observed 2026-08-05T21:19:15.582195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T21:18:50.864177Z digest=sha256:6d1a18ad9538c9db75f4e1523d296d0337535056b8f9f3e64f95cb01a96abd8a

Pith citing papers

No inbound Pith citation observations are available.