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Paper Citation Record · LEDGER

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

As of 14 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 2026-08-14T06:32:32.682623+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=sha256:08945d6bfbbceeced083eefa5fd303154d2e6eff9c023b6e51ca4babbce91fe8

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=sha256:5593b1d60a8bdfee5f8c80da6d72efb9e2ce236c018f096d148ba23db0d3ea27

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=sha256:990d91c3b9e03c6e722373abfb4e119bba24d2c349eba7b2d3bdcc8a28e52d5b

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=sha256:8084d8379eae6b88962d1cb84c5a9b0e4e5ea0c896117ebbed9d72b996a946bb

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T19:02:49.877856Z digest=sha256:2ca92ef8774b8401ec6b387093353b80af9fc9ad6333e3a9e95cf9c6728543fb

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T19:02:49.882860Z digest=sha256:f8d364975f251ff5b4994aeed6d456b3b0c86846bbeff8586041e293a47e8760

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=sha256:1ae6827f847995320a6b23c539e2853c9fd617055f0a2eddabe13e5e391be8e9

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:d3241ecb9304e02c63acd6d7432f39b80b3a19bd6a4af2b03ab9dab41c3833ec

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=sha256:6bf46dbfc35a1bea52144ef231480b00e0d4b39a3b5db5b867f56858a7206cdb

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T19:02:49.909250Z digest=sha256:0a63bc009a49fb294324ba0b5cb327b180766b3daaa2aa97148f5b60e62a8787

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=sha256:85e7ba90550d0d46c606bc9f1763af2ac4f28ca92c15d00da9b2d3f5de306b60

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T19:02:49.919285Z digest=sha256:356e8c47d3645de9399afc794f498b42dc0b1c08cacd17949aa6fda4935b6302

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=sha256:8d4cdd8d32edd1d5da0bed38247c37d1c7335dbc3f7371a6674d987c062169e6

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:f976e892cea398888df0f5c1b36334d2a25c891834bb5b3917034918039f8e29

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=sha256:67d168f1ad24d1ca4e4c0061fdd0766dd720f49ceca70c001f4b901fb4768cd8

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=sha256:529ce08efe7c2dc980f243eefad5815d85b849f4ecf78e15fc0dbed322183b2a

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=sha256:760b7e0f615b41cb1a6879cd3c72f5afc29ba3bbfc68cdb4119d1cc6c746c180

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T19:02:49.948508Z digest=sha256:a05e52c11aee5071bed624e06847375387f3f6aac35bc524c3085f849f7a60f1

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=sha256:9c94a821cdc9036577c5b843714b58a9c51e5bca8df49a9e8cf34d93915a50c7

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=sha256:375e04366041a230e3ee24f62c0890c7eeb6339357df87ad5eec04f900afd95c

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=sha256:267c8f15e973b705d596e57b6aafcc9f346aabace2791bea228540fbc57c76a8

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:a89f14ed89b3a2033d238af1d180792fb97126bf19d211958d443373bd22b165

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=sha256:27b3360d6b3f31f1271716adbc8da3bc56a84cd740672284c52a104379b9d4fc

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=sha256:73ae6ab639cc124729c6661cadaa80ba7caeab2dbe91e1669692886c72d21140

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=sha256:630fa96181874be58e3a5c68eae111c1fc1a34e43a56930b5a6597832034fb2a

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T19:02:49.994832Z digest=sha256:4d41d971fafb349c4c9586df65258c32b3df34bb5aea565ce00ef25f47a0c5f6

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T19:02:49.999884Z digest=sha256:699869e408867048c52f1060c00be259944528d0ede098a81263d3341d950759

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:bcf216a375e605e91cb0f006366945b4e7b5bdc08ac8027a6aa0d0e7d2b2170d

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T19:02:50.012226Z digest=sha256:290a15b054b71a7e91ac3a58aad2a63e24e6e4449a2bc61855d72ad442a5fcd9

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:ba70cf04a12cd6ee53d6b619d07192067b754395da18d0d2e8871c4e54d60936

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=sha256:0baa462ad9dd487c1b1faa9220ebd8e8f18879dae9ff5280aba48ef3e757fddf

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=sha256:61d41c696ae4ae2e6b0c636686f274ecf66d2527cfc1e663487df33e42aeab0e

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=sha256:679f2115a3cb8ae42880c368abb912905581dff3d346edeba81e3874c7dd5c0f

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:d6762f41502b139894308d2d6509906ace2ffb7705bf6258518eea37a26bf35f

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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-05T19:02:49.963423Z digest=sha256:c6d591d3d2201072c7821e0d189076175ec600441cea7c784f8763b363e34319

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=sha256:8a663e4d27cc82b7094077cbb6cd90f94149492ac64ef7c40a557ad1a43bc847

Pith citing papers

No inbound Pith citation observations are available.