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Paper Citation Record · LEDGER

AI Flow: Perspectives, Scenarios, and Approaches

As of 18 August 2026, this Paper Citation Record lists 100 of 102 outbound references and 9 inbound Pith citation observations for arXiv:2506.12479.

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

pith.paper-citation-record.v1
2506.12479 v3

Coverage vector

measured 100 of 102 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:58:15.421753Z

measured 109 of 109 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:21:09.426211Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

100 of 102 outbound references displayed

  • verified exact2
  • verified fuzzy45
  • unresolved53
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation c28d9103-1161-441d-bb09-f16d08a9f3ec · outbound

This paper cites an unresolved cited work.

AI Flow: Perspectives, Scenarios, and Approaches Unresolved cited work

Reference 1

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:08.782774Z digest=sha256:efdf17f4879fc2ee8c2f34ed23be892020fefc268fe4b75203977fa28213794a

Observation 770b25a7-99cf-4978-9e2c-9237a05d8c46 · outbound

This paper cites A mathematical theory of communication,.

AI Flow: Perspectives, Scenarios, and Approaches A mathematical theory of communication,

Reference 2

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source=pdf_text observed=2026-08-07T00:58:08.847076Z digest=sha256:9835851422662f89cba9ea558136b0fe25e4746c192c3ec312f8109debc4cd43

Observation 6b7c4b53-0607-4f56-908e-cec8aa948da5 · outbound

This paper cites A survey on information and communication technologies for industry 4.0: State- of-the-art, taxonomies, perspectives, and challenges,.

AI Flow: Perspectives, Scenarios, and Approaches A survey on information and communication technologies for industry 4.0: State- of-the-art, taxonomies, perspectives, and challenges,

Reference 3

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source=pdf_text observed=2026-08-07T00:58:08.931651Z digest=sha256:227e187f2acf81ba644b679a511978c4b6995adbf82f857a1050b756d5c7858b

Observation 08facc0b-36da-4087-b6d8-2f5ad0a492bc · outbound

This paper cites Language models are few-shot learners,.

AI Flow: Perspectives, Scenarios, and Approaches Language models are few-shot learners,

Reference 4

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source=pdf_text observed=2026-08-07T00:58:09.016029Z digest=sha256:2ba5ee598f4f09d4daa4d919b749044c9d6206917b125a61496c58326cee94c0

Observation 445ee830-fc4a-4b23-844a-fbfcae2922ad · outbound

This paper cites Latva-aho and K.

AI Flow: Perspectives, Scenarios, and Approaches Latva-aho and K

Reference 5

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source=pdf_text observed=2026-08-07T00:58:09.102570Z digest=sha256:8830941cfa4c571aa6d63f68cdd0141a3614b0f45b44475950615e7cb74cd937

Observation f6e97a8c-37a8-431f-8490-60fee4bb1549 · outbound

This paper cites AI flow at the network edge,.

AI Flow: Perspectives, Scenarios, and Approaches AI flow at the network edge,

Reference 6

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source=pdf_text observed=2026-08-07T00:58:09.167426Z digest=sha256:cf645b8071c0f5ee0fcfae8b10cef2ee5a9692810f567d27dd981b38cdcdeb9d

Observation c3bbb34b-23b7-4987-af4e-850afd881021 · outbound

This paper cites A survey on mobile edge computing: The communication perspective,.

AI Flow: Perspectives, Scenarios, and Approaches A survey on mobile edge computing: The communication perspective,

Reference 7

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source=pdf_text observed=2026-08-07T00:58:09.194123Z digest=sha256:fa18f8880f21b1c9a86faecca3fadd3b39b285b317fdeb0ffe96f839ec3e70a0

Observation 6922a760-87de-4b9e-b550-1e2dd114cf3c · outbound

This paper cites Communication-computation trade-off in resource-constrained edge inference,.

AI Flow: Perspectives, Scenarios, and Approaches Communication-computation trade-off in resource-constrained edge inference,

Reference 8

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.256380Z digest=sha256:246a385584bd949d1a37e56ed5201a6387cde27cb1216d68d8a86149e2d547ce

Observation a856debe-fe94-479d-b985-2e386ffbfebf · outbound

This paper cites The roadmap to 6G: AI empowered wireless networks,.

AI Flow: Perspectives, Scenarios, and Approaches The roadmap to 6G: AI empowered wireless networks,

Reference 9

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.337541Z digest=sha256:7a330be810443310528c7179e394d44223b64a56df30082df38ce8b588d67571

Observation fd463997-e7cb-4588-9195-d7e6e344e0ae · outbound

This paper cites Edge artificial intelligence for 6G: Vision, enabling technologies, and applications,.

AI Flow: Perspectives, Scenarios, and Approaches Edge artificial intelligence for 6G: Vision, enabling technologies, and applications,

Reference 10

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no resolver link, observed 2026-08-07T00:58:09.486665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.486665Z digest=sha256:9e0eaf242f0d480e7de775866dede52449178bfc0d1694c84d2d1873b47e1da2

Observation 66cc8e14-b0b8-4c98-b739-8aa4cbf00c52 · outbound

This paper cites Reconstructive sequence-graph network for video summarization,.

AI Flow: Perspectives, Scenarios, and Approaches Reconstructive sequence-graph network for video summarization,

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.569997Z digest=sha256:5d22c2fcc065a57f04ab9425c69ab191130962b6e7e1179c5338023b1898ef2f

Observation f8ea2ba5-b1c9-4a96-b476-4944ed2a4240 · outbound

This paper cites Two-stage learning to predict human eye fixations via sdaes,.

AI Flow: Perspectives, Scenarios, and Approaches Two-stage learning to predict human eye fixations via sdaes,

Reference 12

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T00:58:09.678910Z digest=sha256:4013982dc7cdecaff6100855920d2e8dd814ca981c54df60f4276361ec6e2b8d

Observation 3bf8684f-967a-4523-ab68-69c1a48642f3 · outbound

This paper cites A review of co-saliency detection algorithms: Fundamentals, applications, and challenges,.

AI Flow: Perspectives, Scenarios, and Approaches A review of co-saliency detection algorithms: Fundamentals, applications, and challenges,

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.760145Z digest=sha256:cc0016a1f14c2a309af5bf4504ae1aae02746c9beabf9f904cb5c529727c5666

Observation 1d82856e-0fb2-4c99-a563-9c2450b1c69c · outbound

This paper cites Deep neural networks with elastic rectified linear units for object recognition,.

AI Flow: Perspectives, Scenarios, and Approaches Deep neural networks with elastic rectified linear units for object recognition,

Reference 14

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.830665Z digest=sha256:b6412530b6285ca1500ab3b3e045c8f62781d2978b1401b9563bfca5bfe9ce38

Observation 9182d403-4aa9-4a22-bac9-62a9be195907 · outbound

This paper cites Bayesian tensor approach for 3-d face modeling,.

AI Flow: Perspectives, Scenarios, and Approaches Bayesian tensor approach for 3-d face modeling,

Reference 15

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no resolver link, observed 2026-08-07T00:58:09.977985Z

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source=pdf_text observed=2026-08-07T00:58:09.977985Z digest=sha256:3ac13ffec2db4d55e02ba71255e7b8aa8304991a3d4868bce11e20e807f5f9b5

Observation 839ff41f-28b6-4dd5-acfa-8691240e3262 · outbound

This paper cites Attention is all you need,.

AI Flow: Perspectives, Scenarios, and Approaches Attention is all you need,

Reference 16

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source=pdf_text observed=2026-08-07T00:58:10.072421Z digest=sha256:b2b5c4b6025e3c19bc5a5fe09c1357797ae68c54424089f0c5550c79c7012b8b

Observation e3973ad9-1405-4d9c-8bad-d8d5620c10da · outbound

This paper cites GPT-4 Technical Report.

AI Flow: Perspectives, Scenarios, and Approaches GPT-4 Technical Report

Reference 17

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source=pdf_text observed=2026-08-07T00:58:10.165530Z digest=sha256:25b606d93f80e78c23560fc68f7f48a085f5eac03bde306c468d2ed52f9d897e

Observation d07af860-f7d8-4830-b317-ab066f737acd · outbound

This paper cites DeepSeek-V3 Technical Report.

AI Flow: Perspectives, Scenarios, and Approaches DeepSeek-V3 Technical Report

Reference 18

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source=pdf_text observed=2026-08-07T00:58:10.265776Z digest=sha256:4fcc5534466d47ce06a6b397f70a66fdb5471942e0cb194dc9092d984b3d361f

Observation dcc20b4a-0138-4061-a7f6-99cdcc7858f7 · outbound

This paper cites Qwen2.5 Technical Report.

AI Flow: Perspectives, Scenarios, and Approaches Qwen2.5 Technical Report

Reference 19

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source=pdf_text observed=2026-08-07T00:58:10.393350Z digest=sha256:95822cf226f5a66282a95824691618b1c0a5ed6c3f14470a2ac808a172c798fc

Observation 2fecd691-64e4-4d7b-bb58-c86715bc2ef9 · outbound

This paper cites Visual instruction tuning,.

AI Flow: Perspectives, Scenarios, and Approaches Visual instruction tuning,

Reference 20

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source=pdf_text observed=2026-08-07T00:58:10.481608Z digest=sha256:9d426130ff529167eed406d17d9bcce69872e76d454631cebf2e5e99ee61826d

Observation c23ea920-ad64-4333-b1cd-4dfd0b6f3a3e · outbound

This paper cites Positive-incentive noise,.

AI Flow: Perspectives, Scenarios, and Approaches Positive-incentive noise,

Reference 21

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source=pdf_text observed=2026-08-07T00:58:10.529572Z digest=sha256:5abc2fedfbb69f50c82f630a9eb361b5e1b8964b47c750509ffe41a7471930fc

Observation 40a2e46a-817d-4300-a49c-000acbc2ef81 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

AI Flow: Perspectives, Scenarios, and Approaches Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 22

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source=pdf_text observed=2026-08-07T00:58:10.582016Z digest=sha256:c040fd29df9a656176f458152aecca234e999cabb883c04ee020eb53ff79539d

Observation c2d2b2e4-5439-4378-87ba-69651390fa18 · outbound

This paper cites A survey on large language model based autonomous agents,.

AI Flow: Perspectives, Scenarios, and Approaches A survey on large language model based autonomous agents,

Reference 23

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:10.671303Z digest=sha256:5cac46a44d0e50bb0e37479a474a29850cc795dbe0774647a1c560a7c111f9b7

Observation a14bc073-83f6-434c-bc9e-9eeac06f6d1c · outbound

This paper cites WirelessLLM: Empowering large language models towards wireless intelligence,.

AI Flow: Perspectives, Scenarios, and Approaches WirelessLLM: Empowering large language models towards wireless intelligence,

Reference 24

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source=pdf_text observed=2026-08-07T00:58:10.801744Z digest=sha256:a52199cc6c9d9bcf678429a25ab00be9e107a4ee354e381d41b507517b1f224a

Observation 17c7b8be-c702-4583-b2fe-aee99ec69719 · outbound

This paper cites Task-oriented communication for edge video analytics,.

AI Flow: Perspectives, Scenarios, and Approaches Task-oriented communication for edge video analytics,

Reference 25

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source=pdf_text observed=2026-08-07T00:58:10.882205Z digest=sha256:3c2e796fd7ede696d246651f58fda3e891c56f22caefecdb6f8804edd5189771

Observation 746ff8a8-d58f-460a-9b52-a2518eb11773 · outbound

This paper cites Deep Residual Learning for Image Recognition.

AI Flow: Perspectives, Scenarios, and Approaches Deep Residual Learning for Image Recognition

Reference 26

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source=pdf_text observed=2026-08-07T00:58:10.967773Z digest=sha256:62e3e7866e0d806264d4e8fd529bad1dd07a9337026a7dec85c5303b2bf75c43

Observation 3f0d605d-a7b6-487d-b48d-98f6eccdd935 · outbound

This paper cites Large language models empowered autonomous edge AI for connected intelligence,.

AI Flow: Perspectives, Scenarios, and Approaches Large language models empowered autonomous edge AI for connected intelligence,

Reference 27

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:11.081431Z digest=sha256:746e650adfaf5fb4e176a0e0a2c11a9a00e9fad5ad4f2653c5c00f4b0fffb3d4

Observation 43dc7d00-3200-46a3-b649-c30ab74213be · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

AI Flow: Perspectives, Scenarios, and Approaches Learning Transferable Visual Models From Natural Language Supervision

Reference 28

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:11.149104Z digest=sha256:6cf7312b4af94eb714aaa6c6235be946d679b1e93062b84e4b6784bfb38bbef1

Observation c6a31f2e-2d85-4593-a82c-b3967ddb4845 · outbound

This paper cites Task-oriented feature compression for multimodal understanding via device-edge co-inference,.

AI Flow: Perspectives, Scenarios, and Approaches Task-oriented feature compression for multimodal understanding via device-edge co-inference,

Reference 29

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verified exact
raw_fallback, observed 2026-08-07T00:58:16.238891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:11.244590Z digest=sha256:021718683c0dd570d03ea791512d26d4f0598df6543b2b48ea931c9f5bd16d63

Observation 469f2137-85bf-4d01-bb9a-a69ff4f08e7e · outbound

This paper cites Task-oriented communication for multidevice cooperative edge inference,.

AI Flow: Perspectives, Scenarios, and Approaches Task-oriented communication for multidevice cooperative edge inference,

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-07T00:58:17.073251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:11.364130Z digest=sha256:a92405556bf309bba75e1759d15bc6c56588323a589d12678c04cf98a4401070

Observation fb0cef92-b722-4f65-8c44-0ebcc87185f6 · outbound

This paper cites Study on density peaks clustering based on k-nearest neighbors and principal component analysis,.

AI Flow: Perspectives, Scenarios, and Approaches Study on density peaks clustering based on k-nearest neighbors and principal component analysis,

Reference 31

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verified fuzzy
raw_fallback, observed 2026-08-07T00:58:17.057960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:11.458092Z digest=sha256:ecb1293a010692332ec7064b511dd9a1ab702b15697fb7d0b1354f385fd6c2da

Observation 0d1fc641-c9e6-4b1a-848a-e2d741c4d57b · outbound

This paper cites Variational image compression with a scale hyperprior,.

AI Flow: Perspectives, Scenarios, and Approaches Variational image compression with a scale hyperprior,

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-07T00:58:17.043114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:11.520168Z digest=sha256:c1c66b12d42ee44c71ae662874745db5a8caed94957cc6454e396f46eaa86747

Observation a9b01d6e-9253-4e14-b701-cd356476537b · outbound

This paper cites Channel-wise autoregressive entropy models for learned image compression,.

AI Flow: Perspectives, Scenarios, and Approaches Channel-wise autoregressive entropy models for learned image compression,

Reference 33

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raw_fallback, observed 2026-08-07T00:58:17.027806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:11.619396Z digest=sha256:82016d0e3c1664a40daf920ac35463db20437461d63541c29a57c35bc2a234af

Observation f590f93a-580a-4b65-92e0-6dd5aee179fa · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

AI Flow: Perspectives, Scenarios, and Approaches LLaVA-OneVision: Easy Visual Task Transfer

Reference 34

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no resolver link, observed 2026-08-07T00:58:11.688742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:11.688742Z digest=sha256:3787c129c1588f7e778231ccb26453b1fb11703469cfc76bcc61fb56c26ab60a

Observation 24726006-afd2-4d7b-acea-4c9ab8fed95d · outbound

This paper cites LoRA: Low-rank adaptation of large language models,.

AI Flow: Perspectives, Scenarios, and Approaches LoRA: Low-rank adaptation of large language models,

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-07T00:58:17.012305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:11.803263Z digest=sha256:f19f767408310355c356d22793a527bd675853acc6ffe42f9fec872818f45d85

Observation 2d897c27-6158-4f69-b787-8650c9fa4eee · outbound

This paper cites Improved baselines with visual instruction tuning,.

AI Flow: Perspectives, Scenarios, and Approaches Improved baselines with visual instruction tuning,

Reference 36

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raw_fallback, observed 2026-08-07T00:58:16.996924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:11.865376Z digest=sha256:dfd1c158c220cd871da87b0c046415566028461fd1a3a240120a15d0e39b4a3d

Observation 8ea96dad-4f2f-4555-b6b8-37e1083f6a64 · outbound

This paper cites MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models.

AI Flow: Perspectives, Scenarios, and Approaches MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models

Reference 37

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:11.948904Z digest=sha256:b1f69614829bcb6afe39d98f44afc9feae7088dbcf88b9390bb4bcfaf3d0603d

Observation a1fcdf1c-b075-4d53-8726-51b1155350d6 · outbound

This paper cites The JPEG 2000 still image compression standard,.

AI Flow: Perspectives, Scenarios, and Approaches The JPEG 2000 still image compression standard,

Reference 38

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verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.981067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:12.024445Z digest=sha256:c07bb7e0daf9f13b12ef124fc79e19bec01b00b8d153f7b1dfe35f218c535fa9

Observation 4c230fa4-61cc-4ccb-a540-0706855c7216 · outbound

This paper cites Research on the WebP image format,.

AI Flow: Perspectives, Scenarios, and Approaches Research on the WebP image format,

Reference 39

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verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.965462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:12.173874Z digest=sha256:3cc5095cabba2a78e26eb70b71d477630b140af88a67babfaee562929c4b02de

Observation ed825d76-b21e-4613-9f36-c2c1ea341b8d · outbound

This paper cites Let's Verify Step by Step.

AI Flow: Perspectives, Scenarios, and Approaches Let's Verify Step by Step

Reference 40

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no resolver link, observed 2026-08-07T00:58:12.244680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:12.244680Z digest=sha256:b4fca3974713c1e534b37e8c56fdde8a1bd6a0381858308492876f5e6b6235e6

Observation 77356eaa-d091-4086-b05b-088674186ec9 · outbound

This paper cites LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code.

AI Flow: Perspectives, Scenarios, and Approaches LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Reference 41

Resolution
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no resolver link, observed 2026-08-07T00:58:12.358095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:12.358095Z digest=sha256:158511694b2b5a9aaa9fd7c1a135bdca3a08f3075a527a749884cfaa91d66483

Observation b1868d00-df62-4e58-a884-608df8b4778a · outbound

This paper cites Low-rank matrix factorization for deep neural network training with high-dimensional output targets,.

AI Flow: Perspectives, Scenarios, and Approaches Low-rank matrix factorization for deep neural network training with high-dimensional output targets,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.949234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:12.460084Z digest=sha256:f483012274a0da698c63283f68ee11c4f4d72240b39e144a66045762f56d2faf

Observation 4681b954-1b96-4ab8-8cdd-b3f987471e36 · outbound

This paper cites Learning low-rank deep neural networks via singular vector orthogonality regularization and singular value sparsification,.

AI Flow: Perspectives, Scenarios, and Approaches Learning low-rank deep neural networks via singular vector orthogonality regularization and singular value sparsification,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.933393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:12.577444Z digest=sha256:8903185b210ce7f4e6b0b2410ce52f5278a899c59d002e9dd47a0f56766ea120

Observation c440cf49-dcfb-48a1-bc01-ea18736ea259 · outbound

This paper cites LoRA+: Efficient Low Rank Adaptation of Large Models.

AI Flow: Perspectives, Scenarios, and Approaches LoRA+: Efficient Low Rank Adaptation of Large Models

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:12.645489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:12.645489Z digest=sha256:026ca0ac27d1e0864fbf21751948b3b4a13bb05caa85585c4d1bea89ad509971

Observation f3c1d2fa-64c4-4d4a-b38c-eef21de59fce · outbound

This paper cites Early-exit deep neural network-a comprehensive survey,.

AI Flow: Perspectives, Scenarios, and Approaches Early-exit deep neural network-a comprehensive survey,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.917186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:12.718065Z digest=sha256:5e58aa81b26e71f8de30cdd7f01dbcaa21afa9757b9d1ffacaddd1f92562c315

Observation 6317f728-1230-4bf9-893d-8fc319ce752f · outbound

This paper cites EE-LLM: large-scale training and inference of early-exit large language models with 3d parallelism,.

AI Flow: Perspectives, Scenarios, and Approaches EE-LLM: large-scale training and inference of early-exit large language models with 3d parallelism,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.902369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:12.852444Z digest=sha256:c14cf0c98ae98539e20aec6dd7dbeb4c49471b4ddc3e1d5118097133d15203b9

Observation f6fecf8a-7fa1-4848-8195-919d748f2536 · outbound

This paper cites HELIOS: Adaptive model and early-exit selection for efficient llm inference serving,.

AI Flow: Perspectives, Scenarios, and Approaches HELIOS: Adaptive model and early-exit selection for efficient llm inference serving,

Reference 47

Resolution
verified exact
raw_fallback, observed 2026-08-07T00:58:16.073817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:12.943628Z digest=sha256:93dafe0dad064373526ea78112b1741106ca9b615284daa30653b0edc6b729a6

Observation 48ced121-9e48-400a-abd4-69fa8af5ca3d · outbound

This paper cites Branchynet: Fast inference via early exiting from deep neural networks,.

AI Flow: Perspectives, Scenarios, and Approaches Branchynet: Fast inference via early exiting from deep neural networks,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.885408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:13.112517Z digest=sha256:a1aa63f457ba37164f599363835d64feb4cdc4aa4e55c70c66ffcc97479db593

Observation a8a4ea96-d0e1-4310-b284-7a89059a5366 · outbound

This paper cites Branchy-gnn: A device-edge co-inference framework for efficient point cloud processing,.

AI Flow: Perspectives, Scenarios, and Approaches Branchy-gnn: A device-edge co-inference framework for efficient point cloud processing,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.870473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:13.297017Z digest=sha256:204016167ceeaaed2053175028a3c370c42ab7cdbf1e1c9df4e5b919118958cf

Observation 01747405-c07b-44df-bc3c-ad66635e46a7 · outbound

This paper cites Anytime Dense Prediction with Confidence Adaptivity.

AI Flow: Perspectives, Scenarios, and Approaches Anytime Dense Prediction with Confidence Adaptivity

Reference 50

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:13.434912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:13.434912Z digest=sha256:e1a5fc296dd91bc77f2a29b8002fd47be2545fc9104d185f97a839b5c2b65c05

Observation ab9dd46f-43ff-45d7-baff-8d03419f4fa4 · outbound

This paper cites DeeBERT: Dynamic early exiting for accelerating BERT inference,.

AI Flow: Perspectives, Scenarios, and Approaches DeeBERT: Dynamic early exiting for accelerating BERT inference,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.854989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:13.525808Z digest=sha256:394e027ce9d2ee51dd0352bb01fb1f4528e187432fa47cf394417b9959fa54ef

Observation b8c0680e-8c3c-4c83-b7de-1da4e922f446 · outbound

This paper cites SkipBERT: Efficient inference with shallow layer skipping,.

AI Flow: Perspectives, Scenarios, and Approaches SkipBERT: Efficient inference with shallow layer skipping,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.839814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:13.609702Z digest=sha256:152883ec6b573e7234555dc5135ccde58db16f420e722affc73d9f8375537c14

Observation d8c965b9-9718-40f5-8331-052b4fe2aa7b · outbound

This paper cites Confident adaptive language modeling,.

AI Flow: Perspectives, Scenarios, and Approaches Confident adaptive language modeling,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.824204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:13.732905Z digest=sha256:ad0b279552ad2e054bdd36181d2a9141e25e4a9175f66f08f579502f457f5ce9

Observation 03448e97-d814-4afb-ac6f-b2401a2c65eb · outbound

This paper cites EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models.

AI Flow: Perspectives, Scenarios, and Approaches EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:13.818664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:13.818664Z digest=sha256:b51eabb9cbe042f316c2e8c93298ca1417286d2af08662468cbcf80348a67e56

Observation 2d7b0b71-03e0-4109-8460-0e9a4bfece2e · outbound

This paper cites SVD-LLM: Truncation-aware singular value decomposition for large language model compression,.

AI Flow: Perspectives, Scenarios, and Approaches SVD-LLM: Truncation-aware singular value decomposition for large language model compression,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.808037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:13.905327Z digest=sha256:7f01f83cb569b82e48c0b9be21a14b247e67227864f7b790be2c46982f1421b2

Observation 47f8ab9d-0986-410f-a00a-71047a03cf60 · outbound

This paper cites LayerSkip: Enabling early exit inference and self-speculative decoding,.

AI Flow: Perspectives, Scenarios, and Approaches LayerSkip: Enabling early exit inference and self-speculative decoding,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.792770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:14.018014Z digest=sha256:8f3efb54655a7be3df7dbfa7d13ec1fe334f94d90d8ef648b0e863ad3449b5f9

Observation 5d53f9ea-7d4c-4188-a259-d041855b85d8 · outbound

This paper cites TeleChat Technical Report.

AI Flow: Perspectives, Scenarios, and Approaches TeleChat Technical Report

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:14.132023Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:14.132023Z digest=sha256:0c8482d5ba0246405beb39943ae51665b5bf488fc9f58533871265746dc656ca

Observation c3a73f2b-01d0-4b87-ba48-a1d92266d1b8 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

AI Flow: Perspectives, Scenarios, and Approaches Measuring Massive Multitask Language Understanding

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:14.238314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:14.238314Z digest=sha256:50fdfbf9be1b5eac47870cb1b431c57842fe7789bd4899ebc451cfc33ac7e84b

Observation 3c58b6da-bc5d-46ee-94c3-aa2a10efd123 · outbound

This paper cites CMMLU: Measuring massive multitask language understanding in Chinese.

AI Flow: Perspectives, Scenarios, and Approaches CMMLU: Measuring massive multitask language understanding in Chinese

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:14.352916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:14.352916Z digest=sha256:290e2c876ff2590060b890ab07cef3c8bfc89530a7dddd939d150785565d48ef

Observation bb066663-b1b1-43f0-acb7-bab36b5b58ce · outbound

This paper cites C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models.

AI Flow: Perspectives, Scenarios, and Approaches C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:14.456272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:14.456272Z digest=sha256:e4239e28788569c6be7ce166c3136f4dabe6af4d6c12777b4cfebfd07345f448

Observation 1a50353e-5d4e-47b2-8d3e-c38a19e3ba02 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

AI Flow: Perspectives, Scenarios, and Approaches Training Verifiers to Solve Math Word Problems

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:14.566547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:14.566547Z digest=sha256:90e65a8651e0a4f8c0d7d90d205fb8512f83a24b7715b7a65b58785fc1c8dc52

Observation 8c7bf44f-89ac-4609-96b7-447c7cd7d933 · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

AI Flow: Perspectives, Scenarios, and Approaches Measuring Mathematical Problem Solving With the MATH Dataset

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:14.673669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:14.673669Z digest=sha256:9d0362266457bf7ffe4768902acd1759305b5568bbd27484c61a46263e6878cc

Observation 26dc7054-565c-47ac-be98-4c64076e228c · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

AI Flow: Perspectives, Scenarios, and Approaches Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 63

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no resolver link, observed 2026-08-07T00:58:14.782234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:14.782234Z digest=sha256:16fcc1a37500cd7f4bfed7ea9528de6ee193dcdb1b38e6edcf691558f1cf6e05

Observation 4a30e3d6-03ca-4ed2-a9c8-3036123aaa9d · outbound

This paper cites A diagram is worth a dozen images,.

AI Flow: Perspectives, Scenarios, and Approaches A diagram is worth a dozen images,

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.777460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:14.853345Z digest=sha256:2af680530b262aa9d6ea646d3c7143fe1bcb9d32a55bd1f526e64c9e9c8e31e5

Observation fc99ef18-eaf0-479e-bd98-f3ea71ae6b0b · outbound

This paper cites MMBench: Is your multi-modal model an all-around player?.

AI Flow: Perspectives, Scenarios, and Approaches MMBench: Is your multi-modal model an all-around player?

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.761850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:14.989180Z digest=sha256:bcfb4bf14dfc9983c3ca3ebcb4c9135feb52597cd6c323c661d43086e08826f6

Observation cdb35325-f5ae-46e7-8556-33a58d0b9415 · outbound

This paper cites Are we on the right way for evaluating large vision-language models?.

AI Flow: Perspectives, Scenarios, and Approaches Are we on the right way for evaluating large vision-language models?

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.746949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.015126Z digest=sha256:faf60a5cd178a14c546f437a79320e0e74979ef642e910652465a44aaabbb0b9

Observation 7422f77a-f1f2-47bb-b839-7f61367f0221 · outbound

This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering,.

AI Flow: Perspectives, Scenarios, and Approaches Learn to explain: Multimodal reasoning via thought chains for science question answering,

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.731173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.084586Z digest=sha256:357517892425444ec747422f4b064787fc6ca7c43163b983d248137955c62fa7

Observation 8ceadc7f-8d66-4e0e-9115-d0b6ffded55a · outbound

This paper cites SEED-Bench: Benchmarking multimodal large language models,.

AI Flow: Perspectives, Scenarios, and Approaches SEED-Bench: Benchmarking multimodal large language models,

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.715569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.205577Z digest=sha256:9b37f994f90faa07d4f26d76ed003fd5d07920e610bba0e23aea6a41177459b1

Observation a5c87e50-4013-41ce-8947-66d91cd55942 · outbound

This paper cites ChartQA: A benchmark for question answering about charts with visual and logical reasoning,.

AI Flow: Perspectives, Scenarios, and Approaches ChartQA: A benchmark for question answering about charts with visual and logical reasoning,

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.698272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.264573Z digest=sha256:a9a5ed8cf7998a91a93be58f465a87d12ab41a464aeccebfaf22a1457b162596

Observation 45209dd0-1759-4c85-b014-c07c00486d19 · outbound

This paper cites DocVQA: A dataset for VQA on document images,.

AI Flow: Perspectives, Scenarios, and Approaches DocVQA: A dataset for VQA on document images,

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.682650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.269252Z digest=sha256:7bcfded0f9fbdb5c16ff110d772f67d43f86ab374c7e9552257cbb5ee3b9a8db

Observation 11b1155d-fc6c-4dbb-be44-26afd6e282a4 · outbound

This paper cites InfographicVQA,.

AI Flow: Perspectives, Scenarios, and Approaches InfographicVQA,

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.665962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.273401Z digest=sha256:9a870e98894360b12999095889d70b8e33f13e06c5bd8512527e4a23a8dd4197

Observation 965c4136-1d34-41aa-81ca-126c3a8931bb · outbound

This paper cites Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.

AI Flow: Perspectives, Scenarios, and Approaches Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

Reference 72

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:15.277655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:15.277655Z digest=sha256:e496bb37198fa8a0c7d800ab2c6705303cb690345fbce04dfcc4ffd215341abf

Observation 865c8c86-1197-4188-a3b3-cc76c4a526b2 · outbound

This paper cites Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators.

AI Flow: Perspectives, Scenarios, and Approaches Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:15.282543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:15.282543Z digest=sha256:abe2a45f10a686ab8b265a414183666aaeb69f484c2d6c43c84727fb90422d9f

Observation 0ebd55a3-2a46-419e-ade5-a04d60fee373 · outbound

This paper cites From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline.

AI Flow: Perspectives, Scenarios, and Approaches From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline

Reference 74

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unresolved
no resolver link, observed 2026-08-07T00:58:15.287360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:15.287360Z digest=sha256:d6d02c4bfc5b2e0b63e4dfe2d658590621a2ac54424b34fd0d40d1b2298ad82d

Observation 33b331d8-e283-4712-8c6f-58c769bc7516 · outbound

This paper cites Qwen2.5-VL Technical Report.

AI Flow: Perspectives, Scenarios, and Approaches Qwen2.5-VL Technical Report

Reference 75

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:15.292198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:15.292198Z digest=sha256:6b3d376777ecf29e8ec9872c8146d04d926115266574818d955a2540ea756ec7

Observation 22a539dd-561d-4e16-a9c4-6ec45a3773ee · outbound

This paper cites Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling.

AI Flow: Perspectives, Scenarios, and Approaches Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling

Reference 76

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no resolver link, observed 2026-08-07T00:58:15.296832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:15.296832Z digest=sha256:50fd7b36afcf86dbab3e327b2d380178b195205bd4ae6045610181c8ddca7dde

Observation 0afd4224-a0f2-4151-b259-0f06d1c3c87a · outbound

This paper cites Temos: Generating diverse human motions from textual descriptions,.

AI Flow: Perspectives, Scenarios, and Approaches Temos: Generating diverse human motions from textual descriptions,

Reference 77

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raw_fallback, observed 2026-08-07T00:58:16.650111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.301782Z digest=sha256:4470e7aea4d4af68924531850ae53b17dba95c1568ab6f4fdc0a2370c929a390

Observation dd3bfcc8-d189-4b46-b3c5-7560add9dfe8 · outbound

This paper cites Generating diverse and natural 3D human motions from text,.

AI Flow: Perspectives, Scenarios, and Approaches Generating diverse and natural 3D human motions from text,

Reference 78

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verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.634667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.307416Z digest=sha256:6fc95a9c6203bb6353c7e884eb0af98e8eaf78196903f3b79279975902f69b77

Observation f3af5c27-8111-4af9-8181-89c838c35226 · outbound

This paper cites Human motion diffusion model,.

AI Flow: Perspectives, Scenarios, and Approaches Human motion diffusion model,

Reference 79

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.619064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.312076Z digest=sha256:128e009ac463dcc485e5b0a1e25f810919f4f4b87ff177a60c38e5b9e795549c

Observation 13efc6a3-4d2b-4220-add6-fa365210360b · outbound

This paper cites Human Motion Diffusion as a Generative Prior.

AI Flow: Perspectives, Scenarios, and Approaches Human Motion Diffusion as a Generative Prior

Reference 80

Resolution
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no resolver link, observed 2026-08-07T00:58:15.316983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:15.316983Z digest=sha256:4aac7246c6eb1126140c58054b51b701f421f912361ffe043efc3fd9a2cef7e5

Observation 7b389243-a452-4a56-a781-c92e215793d7 · outbound

This paper cites Intergen: Diffusion-based multi-human motion generation under complex interactions,.

AI Flow: Perspectives, Scenarios, and Approaches Intergen: Diffusion-based multi-human motion generation under complex interactions,

Reference 81

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.603270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.322737Z digest=sha256:928104841827c9639c239839ce5a5790cb21e0f139a0d525cf03b639819af7f9

Observation 517f20ff-c3f2-475a-bd96-127391382cbb · outbound

This paper cites Freemotion: A unified framework for number-free text-to-motion synthesis,.

AI Flow: Perspectives, Scenarios, and Approaches Freemotion: A unified framework for number-free text-to-motion synthesis,

Reference 82

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.587704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.328295Z digest=sha256:f1bc3a6bbd80ea4a1716a4b750a2064c160e38e91f4293308dd4eba9f3c8c1ec

Observation 0dac3641-b705-45c0-9f75-1863e7c766e6 · outbound

This paper cites Metric-Solver: Sliding Anchored Metric Depth Estimation from a Single Image.

AI Flow: Perspectives, Scenarios, and Approaches Metric-Solver: Sliding Anchored Metric Depth Estimation from a Single Image

Reference 83

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no resolver link, observed 2026-08-07T00:58:15.333651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:15.333651Z digest=sha256:38f15d148c62add3baad709dd396e1ac3b114229382f2470011c517913e7d08c

Observation ad4fe4ee-192a-405c-8acf-ba1f8f0ba655 · outbound

This paper cites Adabins: Depth estimation using adaptive bins,.

AI Flow: Perspectives, Scenarios, and Approaches Adabins: Depth estimation using adaptive bins,

Reference 84

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.570901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.338793Z digest=sha256:d61bc8794485702a6c870f60cecfcf7a66a44dc8efd308693bda9bf563142058

Observation bebc1258-8268-4c44-ac6f-255edf806fc0 · outbound

This paper cites NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation.

AI Flow: Perspectives, Scenarios, and Approaches NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth Estimation

Reference 85

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:15.344217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:15.344217Z digest=sha256:f33768f974797244eb259f6a848eb423517857efd270707e0b1e12333da77187

Observation 85d4e241-ec59-4310-9862-b5438dd57234 · outbound

This paper cites Vision transformers for dense prediction,.

AI Flow: Perspectives, Scenarios, and Approaches Vision transformers for dense prediction,

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.555260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.349548Z digest=sha256:a46600fd31118fb9bd609c390dbfbbc5f8b637915ac90044d3a50a1f41508415

Observation d15b08b1-9c1b-4496-b243-65c72cb9ced7 · outbound

This paper cites P3depth: Monocular depth estimation with a piecewise planarity prior,.

AI Flow: Perspectives, Scenarios, and Approaches P3depth: Monocular depth estimation with a piecewise planarity prior,

Reference 87

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.539421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.354950Z digest=sha256:3a5ca2c0c0fcc05edd295db3e3a9c10335330b95731191c9465bcac1a3111ff4

Observation ba4e07b3-119c-40ab-9c8f-68d751f7fc65 · outbound

This paper cites Swin transformer v2: Scaling up capacity and resolution,.

AI Flow: Perspectives, Scenarios, and Approaches Swin transformer v2: Scaling up capacity and resolution,

Reference 88

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.523742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.361067Z digest=sha256:70cbc8bc6d0d7c983d41072866f7fc95f9f9b62a18e1d1986b9b9935b63c8a6e

Observation 4a694291-6cb9-4f96-b54a-5812b3e445ec · outbound

This paper cites All in tokens: Unifying output space of visual tasks via soft token,.

AI Flow: Perspectives, Scenarios, and Approaches All in tokens: Unifying output space of visual tasks via soft token,

Reference 89

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.505966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.365858Z digest=sha256:f13b33a8e39655cbec3ee5d7f4c1322bb6ad4db7efeb4a77624399be6da6815c

Observation 3a93dd5b-64ed-49f2-a91d-8475ee69467f · outbound

This paper cites Unleashing text-to-image diffusion models for visual perception,.

AI Flow: Perspectives, Scenarios, and Approaches Unleashing text-to-image diffusion models for visual perception,

Reference 90

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.490085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.371561Z digest=sha256:5f201d3d6f51f88a72984a519ba18ac63af1f07705c25684377fa5a349a851cd

Observation 590fc2eb-f25f-40c2-9cdb-8e79f5bb5c39 · outbound

This paper cites Iebins: Iterative elastic bins for monocular depth estimation,.

AI Flow: Perspectives, Scenarios, and Approaches Iebins: Iterative elastic bins for monocular depth estimation,

Reference 91

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.471339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.376522Z digest=sha256:d24e78fdedc7bb73c5439560ee6332133ffde7f2f6cf7ebaf7dee1c7b133f9ce

Observation 14afafde-34a7-4452-a463-ddc9c7f14226 · outbound

This paper cites ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth.

AI Flow: Perspectives, Scenarios, and Approaches ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth

Reference 92

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:15.381352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:15.381352Z digest=sha256:de93c9f8fa494360b007be34225f6a9ce9253522e10882ebd562dfbb1c08de08

Observation 432f9c79-33b3-437d-8ffc-2b1d37ff60f4 · outbound

This paper cites Depth anything: Unleashing the power of large-scale unlabeled data,.

AI Flow: Perspectives, Scenarios, and Approaches Depth anything: Unleashing the power of large-scale unlabeled data,

Reference 93

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.456167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.386383Z digest=sha256:c9174c8b5cf2f6a01efd5d70de7cf3b064ccd4e07767213280cfc80466776cc4

Observation f2f143a5-94a8-4b40-a690-52d702f4c191 · outbound

This paper cites OmniVDiff: Omni controllable video diffusion for generation and understanding,.

AI Flow: Perspectives, Scenarios, and Approaches OmniVDiff: Omni controllable video diffusion for generation and understanding,

Reference 94

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:15.391152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:15.391152Z digest=sha256:d6b5d3f8afd0a997b30a2b3e5346991b028dccb2227aa7a5d74cd008bc64b556

Observation 1ec6fba2-47c3-47aa-942d-fe44c6ebe1eb · outbound

This paper cites Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI.

AI Flow: Perspectives, Scenarios, and Approaches Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI

Reference 95

Resolution
unresolved
no resolver link, observed 2026-08-07T00:58:15.396770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:15.396770Z digest=sha256:f6d4ea4f02c92fed58c29368d5364df40ceba859b923cc01742002cf1a047ccb

Observation e525bddf-281d-4a0f-b12b-aaa207fc65fe · outbound

This paper cites Embodied-AI with large models: research and challenges,.

AI Flow: Perspectives, Scenarios, and Approaches Embodied-AI with large models: research and challenges,

Reference 96

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.440566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.401556Z digest=sha256:295fe3c79fabf10504b1518d121fbc9b15015623f6f0ea4d731ed152708427b3

Observation 076dcd3c-efe0-4fa7-9e5a-be0c64ae3eee · outbound

This paper cites Learning task-oriented communication for edge inference: An information bottleneck approach,.

AI Flow: Perspectives, Scenarios, and Approaches Learning task-oriented communication for edge inference: An information bottleneck approach,

Reference 97

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.422667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.406103Z digest=sha256:1792042d10daddc0826eedf3f390bd1f63a2a8931afa062d93ed09cda5568b96

Observation 24646e0b-c3d0-4fdd-a68a-0c3052bb52d3 · outbound

This paper cites Empowering smart glasses with large language models: Towards ubiquitous AGI,.

AI Flow: Perspectives, Scenarios, and Approaches Empowering smart glasses with large language models: Towards ubiquitous AGI,

Reference 98

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.406808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.411095Z digest=sha256:f89c5b1e50f10b424c63f47ff5963d336f96bb0b4a0df008fbf908f3238b878f

Observation 74d56998-f2fd-4fc9-9efa-ff663408fc1b · outbound

This paper cites Dres-FL: Dropout-resilient secure federated learning for non-IID clients via secret data sharing,.

AI Flow: Perspectives, Scenarios, and Approaches Dres-FL: Dropout-resilient secure federated learning for non-IID clients via secret data sharing,

Reference 99

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.391074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.416948Z digest=sha256:05201da962c103445ce25b32484be07a5f11c3306b308cedbc0d680f9543ba96

Observation 9a9c75de-44f4-4013-a307-81f27a1bf141 · outbound

This paper cites Federated machine learning: Concept and applications,.

AI Flow: Perspectives, Scenarios, and Approaches Federated machine learning: Concept and applications,

Reference 100

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T00:58:16.373443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-08-07T00:58:15.421753Z digest=sha256:4ac86087b4fe0b0077f7cbf54c5f3fd3eb6f0c767ea54986b6f466082c768967

Pith citing papers

Observation d92fb929-c2f8-4854-8bfe-5a777bcbb5ec · inbound

Skill-Nav: Enhanced Navigation with Versatile Quadrupedal Locomotion via Waypoint Interface cites this paper.

Skill-Nav: Enhanced Navigation with Versatile Quadrupedal Locomotion via Waypoint Interface AI Flow: Perspectives, Scenarios, and Approaches

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T22:21:09.426211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:21:09.426211Z digest=sha256:f733428f3e57b8bc971682f08e7adb921eebf08e1e0c644102b53a6070d67285

Observation 5f8deec3-be83-4e38-9069-182357c66482 · inbound

Technical Report of TeleChat2, TeleChat2.5 and T1 cites this paper.

Technical Report of TeleChat2, TeleChat2.5 and T1 AI Flow: Perspectives, Scenarios, and Approaches

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-06T14:43:22.098290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:43:22.098290Z digest=sha256:7a193e0c858b5909d70c42f2089562a1d7b27e58a05f771aa0118469366d5f81

Observation 74726bfd-03bc-4a3d-b355-81f16a3e9409 · inbound

Unison: Harmonizing Motion, Speech, and Sound for Human-Centric Audio-Video Generation cites this paper.

Unison: Harmonizing Motion, Speech, and Sound for Human-Centric Audio-Video Generation AI Flow: Perspectives, Scenarios, and Approaches

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:36:44.030300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-12T02:28:14.734682Z digest=sha256:5e1b9dd219622c6d199397794e64365b8f60e4f276615904539ce43d8c773953

Observation f124ec71-ba9f-4aa1-a7bb-b72510a474fd · inbound

Unison: Harmonizing Motion, Speech, and Sound for Human-Centric Audio-Video Generation cites this paper.

Unison: Harmonizing Motion, Speech, and Sound for Human-Centric Audio-Video Generation AI Flow: Perspectives, Scenarios, and Approaches

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-06-30T23:35:07.798707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-30T23:26:46.077894Z digest=sha256:ebe548d8a35a4dd0e11bf2fd17c983e867ee2eb1d6dfa392a6dbed2a5e2a4bd3

Observation 3f639b45-7dac-40df-81c5-0932f9096acf · inbound

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement cites this paper.

DBHN-Net: Dual-Branch Hybrid Neural Network For Low-Complexity Monaural Speech Enhancement AI Flow: Perspectives, Scenarios, and Approaches

Reference 10

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T15:37:06.108421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-27T23:49:05.871422Z digest=sha256:e2a5f476274c8a763b269b32f2ac4980894f50f9a0261098e5b0881eaf26581a

Observation 4d46bc25-db86-419b-90ad-e91fb3e0103f · inbound

SpaceVLN: A Zero-Shot Vision-and-Language Navigation Agent with Online Spatial Cognitive Memory and Reasoning cites this paper.

SpaceVLN: A Zero-Shot Vision-and-Language Navigation Agent with Online Spatial Cognitive Memory and Reasoning AI Flow: Perspectives, Scenarios, and Approaches

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-06-27T16:51:05.508049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-27T16:49:46.634302Z digest=sha256:d39b744d6027161a17d3795b792b33b860abc4a3aaad8adfa5ef133b55d74474

Observation 457fa5dd-b7a5-450e-837c-31a6d8f6753c · inbound

InteractiveAvatar: Real-Time Streaming Video Generation for Consistent and Intent-Aware Avatars cites this paper.

InteractiveAvatar: Real-Time Streaming Video Generation for Consistent and Intent-Aware Avatars AI Flow: Perspectives, Scenarios, and Approaches

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-04T10:09:44.576001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-26T09:09:06.925645Z digest=sha256:010cec6df27bdca44fc33d756fbb5516c71fe65845dc60566b757f6a1cbfe8c4

Observation fd772f35-a873-4a95-9fec-eb6e11bd4f04 · inbound

InteractiveAvatar: Real-Time Streaming Video Generation for Consistent and Intent-Aware Avatars cites this paper.

InteractiveAvatar: Real-Time Streaming Video Generation for Consistent and Intent-Aware Avatars AI Flow: Perspectives, Scenarios, and Approaches

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-07-01T07:05:29.093466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-07-01T07:00:53.496569Z digest=sha256:66d7b8c936f3ca5dff47e70368a11f1851ddb8766bacb103a146836a0b414abe

Observation 89f6f0cb-1f9d-4efd-baf7-381e734b4b29 · inbound

OmniMate: Open-Ended Real-Time Streaming Audio-Visual Generation for Interactive Avatars cites this paper.

OmniMate: Open-Ended Real-Time Streaming Audio-Visual Generation for Interactive Avatars AI Flow: Perspectives, Scenarios, and Approaches

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-01T03:52:55.335046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T03:52:55.335046Z digest=sha256:1c5e1f7e0f4f20c4db5552f1537218584bce6f62722ea372709c7ac71e57f057