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Paper Citation Record · LEDGER

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings

As of 7 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2508.13606.

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

pith.paper-citation-record.v1
2508.13606 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T19:02:50.040192Z

measured 36 of 36 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 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

36 of 36 outbound references displayed

  • verified exact1
  • verified fuzzy1
  • unresolved34
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f8b73b26-fd74-42bb-8af7-819aaee6e469 · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.850460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.850460Z digest=sha256:b94b9c932aa85a24b9d25ea3a8d2853aad537e40a03c3cde756b6ee83f409336

Observation ba9978f5-1e36-44bc-a63f-bd95b02b0069 · outbound

This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.857148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.857148Z digest=sha256:93e5d14c4c04f73f46cf0790e8cfb542fe9670147fbf8f3ba594214609e49396

Observation 1b3e63d3-cb80-4c35-b1b7-e335282c416f · outbound

This paper cites Qwen2.5-VL Technical Report.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Qwen2.5-VL Technical Report

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.864940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.864940Z digest=sha256:1b2bcbdb8b04a396733bcb01cc7f1be20333472c2fd875df31788a5f74f7bf96

Observation e55ab182-227b-4260-af2a-c2de0a4c4605 · outbound

This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.870719Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.870719Z digest=sha256:e1efb1454fe454409e919c1587d2588c11def1e0178840eb953df3525440794d

Observation 541f98a0-3a26-475c-b577-107e41fbff59 · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-05T19:02:50.641819Z

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=2026-08-05T19:02:49.877856Z digest=sha256:ff6b6ef127b71714544ed72c0b1e50dcc80dbea7ea6d2546ab298c8325062877

Observation b13bcf2d-c0c4-4c4a-ac4a-f3d768bd7941 · outbound

This paper cites UnitedQA: A Hybrid Approach for Open Domain Question Answering.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings UnitedQA: A Hybrid Approach for Open Domain Question Answering

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-08-05T19:02:50.360458Z

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=2026-08-05T19:02:49.882860Z digest=sha256:1bf9741b5ed8ec62fcf4bd4e47c775a5d20278f328dfc4dd26a88893ed27803a

Observation eb96d8d3-76cb-4b9a-8bcc-5f5af614cf8b · outbound

This paper cites The Faiss library.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings The Faiss library

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.890463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.890463Z digest=sha256:f978a409240d659d094cf9fcbfe2eddebc6f6ecd77685f9108c69db63a5c5b89

Observation 2c072c63-2f38-41ae-b617-bef8076e1766 · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.895279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.895279Z digest=sha256:428af828f23e98ace6e9dbbea59bf15905e66d42c709043dbabd4b0f3fb62b0e

Observation b57c4ce9-ea3a-43d1-901c-ce70c3a3a3fb · outbound

This paper cites HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings HyKGE: A Hypothesis Knowledge Graph Enhanced Framework for Accurate and Reliable Medical LLMs Responses

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.904460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.904460Z digest=sha256:572bc9c457b5f270108c5df505d6524e78635dafc364d7c6b84f3f0d169358e0

Observation a98f6308-16c7-495a-9f86-ca169297dca6 · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 10

Resolution
unresolved
raw_fallback, observed 2026-08-05T19:02:50.614605Z

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=2026-08-05T19:02:49.909250Z digest=sha256:0636e4d86dfe7293b6f38b1716f7e4f0b30aa73b32a54650dbca133af537055e

Observation 9ce3b769-99f4-428d-94a3-213515021610 · outbound

This paper cites Generalization through Memorization: Nearest Neighbor Language Models.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Generalization through Memorization: Nearest Neighbor Language Models

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.914262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.914262Z digest=sha256:a2fba2ad7609165873726b558ae29c929b9bd4cf3b5b8e06e846c94282234e9b

Observation cec09424-a7be-4bb8-93df-01e6a7ccd233 · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-05T19:02:50.599047Z

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=2026-08-05T19:02:49.919285Z digest=sha256:5605e83516d04288be51058aea4545d7d0e5781a3bef79ed00cbaf4b5725543e

Observation 466f0b64-f41b-4b27-bda6-63677381e5bb · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.924058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.924058Z digest=sha256:83c0e461fa503de60ef54697671f544e17ab1285cd94c64cab0b2ed4286277dd

Observation 5eaaec05-f53b-43aa-9ac0-424881140064 · outbound

This paper cites LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.929085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.929085Z digest=sha256:6c6980cd966467f527ca56613ac6b2de5680eea2f7704f082b758194d6ffd2a8

Observation b89a0590-d078-4ae1-a5fd-8d0f305e2e54 · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.934219Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.934219Z digest=sha256:a19b59b7306e6d2016f96be30cce0d5f2fe4278b4688327286c21e99382f68bf

Observation efc5abf7-74c8-4b0f-b00c-c215eacea9fb · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.939009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.939009Z digest=sha256:7eec02fd52cb1977d47ef8dece578804993c20b7d66bfcc97ad45c9677c31e49

Observation e4766ab8-d4cb-4151-b223-56de6af29205 · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.943555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.943555Z digest=sha256:d80eb05a715b5ef95fd828ea91fe705652a18946936508ad84dbb52ad306e0cf

Observation 2fb131f7-c372-4e21-9cf5-12fdef1362a2 · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 18

Resolution
unresolved
raw_fallback, observed 2026-08-05T19:02:50.538754Z

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=2026-08-05T19:02:49.948508Z digest=sha256:3e8dd4c38f6fa0c8031878b1704845f4b63f321b14d42b195984e6f69ed7d244

Observation b458b242-038d-41c2-a7ac-159bdb84e615 · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.953645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.953645Z digest=sha256:a3369ef8572e7dc5f80cc4a5fa6ba9c671c68add2a2da5e8bb3b56f61d4a945f

Observation 87496f59-fd47-4946-a5d0-513af44bf171 · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.958563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.958563Z digest=sha256:20fef1ea6ae87c6255beaf9b7028b0de1aafd0803c8d414b5852b4a75feb0ad0

Observation 19557e47-b97d-482d-a573-b0ccde1569cf · outbound

This paper cites JDocQA: Japanese Document Question Answering Dataset for Generative Language Models.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings JDocQA: Japanese Document Question Answering Dataset for Generative Language Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.968303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.968303Z digest=sha256:a6713c7301daee003197cae6b3fa55174a90ea54fa18eb39cad825b1c3371e04

Observation 78053603-f408-4bd0-9e84-e0f34305b1eb · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.973654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.973654Z digest=sha256:64beaa24c7a417f7192d78624f8b03f537546cddfe56a11be59481b7479e914c

Observation 2378803e-9258-4962-82da-0653bf05e30d · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.978135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.978135Z digest=sha256:68810d915b981e4c032d9bcf591e7d1f692ca71f1a652998e1fefb277a8bccae

Observation 180ff8fb-be08-4983-b6a1-7bd1742f3885 · outbound

This paper cites Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.983261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.983261Z digest=sha256:d234274c19899de1eafc0ac4e71543f304ca972e863f5333bc7f79fb6b3dd95b

Observation d0b3673b-8e53-46e7-8bc5-e1bfff3796f3 · outbound

This paper cites DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.988039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.988039Z digest=sha256:fae8fd8451a4f8bd0f06e23147075d6b383f350e0ea78f29e79be7e1b1a7bb03

Observation 511b018a-860c-4d29-91c2-2cd50dfa0f70 · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 26

Resolution
unresolved
raw_fallback, observed 2026-08-05T19:02:50.475878Z

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=2026-08-05T19:02:49.994832Z digest=sha256:38f21968d5ee112f7b8c53632926bdd150f4d62166d9c99cfd26d92f60420ab4

Observation e626c19e-50f0-45fd-acc5-a65d0cd441a1 · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 27

Resolution
unresolved
raw_fallback, observed 2026-08-05T19:02:50.459641Z

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=2026-08-05T19:02:49.999884Z digest=sha256:c645adf05c9100f8cc627aeb4fc109e55f36342a26c413ba826b8ba63d9e7d5c

Observation 04889a51-f272-4100-8e6d-77740b9dc246 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings LLaMA: Open and Efficient Foundation Language Models

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:50.005570Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:50.005570Z digest=sha256:71e66d905837e9e25b0581b226b4c3f0b99ce9e288a940daaa28bc4cc9bf04e8

Observation 55966501-fd77-49f2-aeb2-c214b2c6cc8a · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 29

Resolution
unresolved
raw_fallback, observed 2026-08-05T19:02:50.444091Z

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=2026-08-05T19:02:50.012226Z digest=sha256:6c830f03cd3d187d833fa8be116392431411c088fad27cc607f995946abf87e6

Observation 69427795-bb3b-4e7c-adff-e355c7d09eb1 · outbound

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

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:50.017700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:50.017700Z digest=sha256:559cfd3456b5ec43fb888b5afd80432e55948da307add666b58f372180d7bc2b

Observation adc40953-6b25-486f-a26e-387f598e96be · outbound

This paper cites an unresolved cited work.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Unresolved cited work

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:50.023124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:50.023124Z digest=sha256:23bb71fc7510104c1f0c0f43678c5e05f9080a8da80b2774f62875bc6e9608af

Observation 84a138df-1bdf-4a74-b370-60cf12b524b5 · outbound

This paper cites Making Retrieval-Augmented Language Models Robust to Irrelevant Context.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Making Retrieval-Augmented Language Models Robust to Irrelevant Context

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:50.027327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:50.027327Z digest=sha256:ddc74f95081a995e9238435c063adf59e2c96e93e2aa148095f970f726c98c8b

Observation 62c8345a-357d-4c5d-98cd-eff89a44d8a2 · outbound

This paper cites Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Research

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:50.034446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:50.034446Z digest=sha256:252898b966cc27999f908b09f815b5cffad2c4273dea932515e917364bbb192c

Observation ae504764-c779-4e8e-ab7d-a6bbf660bae6 · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:50.040192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:50.040192Z digest=sha256:bf82b3d56dc512ff4ff1e142120268d107ec60a8c086c4bbcbbcd5304fe09e88

Observation e45c2fb1-0b44-455f-92d7-aa957b2acb4f · outbound

This paper cites In 2019 international conference on document analysis and recognition (ICDAR).

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings In 2019 international conference on document analysis and recognition (ICDAR)

Reference 2019

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T19:02:50.502405Z

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=2026-08-05T19:02:49.963423Z digest=sha256:b6e1dd9812fb3b9dc56b91161e2206816229ae15af1a5f42ba50fb339c8a68fc

Observation f5b083f9-c121-4b84-bc37-7d7359b0614e · outbound

This paper cites Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity.

AdaDocVQA: Adaptive Framework for Long Document Visual Question Answering in Low-Resource Settings Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:49.899858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:49.899858Z digest=sha256:c8d9c3411d5945be5b7ab8f561ca91e21adcec46524ec04dc5cb0ec217016d99

Pith citing papers

No inbound Pith citation observations are available.