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Paper Citation Record · LEDGER

AI Flow: Perspectives, Scenarios, and Approaches

As of 14 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 2026-08-14T06:32:32.682623+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=sha256:2c4da4a95be877636636e6e55e66a031f07242f9e378412485090c791da86fbd

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=sha256:9bf4d71daa14fb7755fe9f27e77c958a78a7361147ee8715287f955ca26b1f4a

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:cbb6f6f8cff3543cf85408a523e456d325b65e1f48be9510807921999e5a5473

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:ee01715bb2dc04e46d685adfea92c8d81fd29740c9d6aa7d1bff115606239f2a

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:f248733f746e7ca4e5da916a5f7c2871feb43a01a7472629dbb365476d22f3d5

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.167426Z digest=sha256:8f4cd4317962b1617c0ca853996d7f5b782ee0062e79df514f3fbbfd7be79858

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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no resolver link, observed 2026-08-07T00:58:09.194123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.194123Z digest=sha256:c4a6efdffd106a581f8866671facaf2ecb685acbcc0dae68c4e8a1facd4d4aa2

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.256380Z digest=sha256:ba724c24f7bfd05bb3abe20809cc258a83765a79e66861d9915dc9b9c8247003

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.337541Z digest=sha256:90452096516750c803af6e347ef15524b53403d3b77589a20df5ab709f5b6056

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:ab71526a855df5283217bcb3f105bd2b95b1d4a1d339e604fe40686c3c22f7d7

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=sha256:0bec50e61ebd471889311604e085b1c2d88f1c3dda86f517c7531ff946ef4651

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

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.678910Z digest=sha256:1de220f0b02902d096c81753a9f10adc7119d72ba62b96980e29c59112c683a8

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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no resolver link, observed 2026-08-07T00:58:09.760145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.760145Z digest=sha256:4c6e96da916039654a66c92a34625fe4777e3aacc05523900f2d28ebce97617b

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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no resolver link, observed 2026-08-07T00:58:09.830665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.830665Z digest=sha256:5ae283a5b8b4ec1a82ef4a638c31cfadb3a73eaf8e1fbd86f2fa92bfd198c3dd

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.977985Z digest=sha256:27a67a30d725b1dfb1482c023ef5c558bca34806316c2874f1326824fe615b2a

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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no resolver link, observed 2026-08-07T00:58:10.072421Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:10.072421Z digest=sha256:c121aa88dc22fe72d703e978a5bb240bf1276f23ce9e87b12cc3561b7117d9fd

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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no resolver link, observed 2026-08-07T00:58:10.165530Z

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source=pdf_text observed=2026-08-07T00:58:10.165530Z digest=sha256:9def74db8d264f7b0e45177b4a2ef1cb78b840d8c4f220e605fd2c55e42f819a

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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no resolver link, observed 2026-08-07T00:58:10.265776Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:10.265776Z digest=sha256:37ff00a8d5057e3eaf3be8964547390dab9e849d4ae51f64ebf5b4cd382b56de

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=sha256:0563285f9af3e68de45632b1bb5556fe4afb678c0c08a6283bc42687ba0d80ce

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:10.481608Z digest=sha256:dc5564393ec0062931e78f684fddc3c5d89d0288a0d98332453b2cb8d75949bf

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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no resolver link, observed 2026-08-07T00:58:10.529572Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:10.529572Z digest=sha256:ee9a92c0371903cb9258934547f7355c6f6be38f4bfaabda1aa2fd4acc398c06

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:10.582016Z digest=sha256:68eed9c5cf8456d01b6845298e391a62c0393793065618d1647b98b589054de3

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=sha256:4ea94526443dc14912134d898768f5cc1a60a53079f3c0619a61080c9bb6dccd

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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no resolver link, observed 2026-08-07T00:58:10.801744Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:10.801744Z digest=sha256:f43bcec47ffb2e4dbe02a7b4ca8396dba461f59c9d30e309f168804de5dd3cae

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:10.882205Z digest=sha256:5f1b0998b4fc0d9fb2089482b276287afc57a169eb3fe32cfa7612b1950820a7

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:10.967773Z digest=sha256:6d2ad5a17184bac6b270b6c49637bdd1a5f8468ca7284485c00affa1d0177195

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:11.081431Z digest=sha256:55e43ab3b43dbeb06ab2d5d0ca69e18be9c97e1d159d8ea5dcf365a47060b49a

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=sha256:34428bceb6824e8931216a0461690a3131e803c71ddef1626cbb05d6ef93a616

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

source=pdf_text observed=2026-08-07T00:58:11.244590Z digest=sha256:66d4a9ab9f27056dbb872a3b56262755866bd4f700fbf6faad5f78b1f4a43f9e

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

source=pdf_text observed=2026-08-07T00:58:11.364130Z digest=sha256:1b4b6f6912919b982f6c87353bf28039bebb6791f0818beef363e0e4d3aadd39

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

source=pdf_text observed=2026-08-07T00:58:11.458092Z digest=sha256:42af2e2b051ebf07a6dc087854b8028778372b3449104c66447587ffd9ece87f

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

source=pdf_text observed=2026-08-07T00:58:11.520168Z digest=sha256:7c8bb8357ec1991dfc3e32cf0315a85e22a6ebf33069a6ed9a0fc143c646f3d3

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

source=pdf_text observed=2026-08-07T00:58:11.619396Z digest=sha256:7324ea830f60a6c7b746350c837be4e46b712c60dae8ecfe2999e9c2d5626762

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=sha256:7ba1f6f2d14f11cd11ad2a0d74e8ef36f108d9f2f2ce259fe57757cadff741d0

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

source=pdf_text observed=2026-08-07T00:58:11.803263Z digest=sha256:ffafb21a730636d02d06392c31c55f94b7c06192e2e4b8b0d3eea6dfbf5366ca

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

source=pdf_text observed=2026-08-07T00:58:11.865376Z digest=sha256:63447f314960fdac46f87ecde39d5af94798ad5cfeba2a23a30e4a26cfdc89dd

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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no resolver link, observed 2026-08-07T00:58:11.948904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:11.948904Z digest=sha256:7d56c9fce7de62f066fe09c211e1fe5d2e510ec4fe9047fa7f7fb61c14dae911

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

source=pdf_text observed=2026-08-07T00:58:12.024445Z digest=sha256:9cf965dfd733c757800ff008a7588f5a5607ede5363211807294430e8c72c423

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

source=pdf_text observed=2026-08-07T00:58:12.173874Z digest=sha256:2cb047a6ba611f1270f9f2490208c7830f3930e10d3f9453d5d27f3526d35851

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=sha256:1541016461efeec950fcfa3c3243f1325dd20b7f69b2abd5877f18523c0e45cf

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=sha256:854b3abdd02dc1a2bd1e801f66110a61851dcac9e9c4caac252487e3b340fa0d

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

source=pdf_text observed=2026-08-07T00:58:12.460084Z digest=sha256:5b7f68232f92c8bd1fb466fed03712a142e51da53f9d46e832870d083b2507d0

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

source=pdf_text observed=2026-08-07T00:58:12.577444Z digest=sha256:535e09fe65aef039f03e9492f680c6b320b4b18e1ef9bd4017770cd0427bdc23

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=sha256:2b81339a0680d4581eb0635acd90d5caa5ecff80f78d338332794d4d8b69a652

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

source=pdf_text observed=2026-08-07T00:58:12.718065Z digest=sha256:79aa660dfb519b00ba79314ad194dbd72197811dc429e5dd6909018f17a6fd89

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

source=pdf_text observed=2026-08-07T00:58:12.852444Z digest=sha256:4bba071958973c1f2b422ab6ba0d849352d51fc464dbd64ace8312006ebceac7

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

source=pdf_text observed=2026-08-07T00:58:12.943628Z digest=sha256:f3a8cab2999319536472f115e553b0481441768f51501b75069575da9fc14f42

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

source=pdf_text observed=2026-08-07T00:58:13.112517Z digest=sha256:3420a4ef9ca552b8c81fb2897ec24e8bb3b953bd9e7a2f0ae3bcb75ab1f31b3d

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

source=pdf_text observed=2026-08-07T00:58:13.297017Z digest=sha256:15f8cd1386c42958ba0c8fd4e82528edac796b7b1a628c2eeef044bbc9848fc2

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:f60dc046dca4eaf285f59892f1b1b8e4544d73cbf144a727e4a80012154e7aa0

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

source=pdf_text observed=2026-08-07T00:58:13.525808Z digest=sha256:858106b4e27cc45b6529e4a880a1ae7d90c36904274a38d68a283b8de08c7d3e

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

source=pdf_text observed=2026-08-07T00:58:13.609702Z digest=sha256:255a9368378b529c1d2a21206d1ccafffdfe654084ed883c3458648eaab27649

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

source=pdf_text observed=2026-08-07T00:58:13.732905Z digest=sha256:97bc18d735af57d6bacede326ef97400077d552e9494a247a53756bd3cb9a648

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:b7fa8501e8137851e454b0100b64b52f0993904757b3c87583ceb089cc71925a

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

source=pdf_text observed=2026-08-07T00:58:13.905327Z digest=sha256:79c48c3435fa2fa4760c0760d6a515911a287e101dc5a1b73d73e34dfe83f31f

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

source=pdf_text observed=2026-08-07T00:58:14.018014Z digest=sha256:036976c2850323f8ed4b84ab6ddcf0b19ff506140399443918cd61e3271895f1

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
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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:a678ac85e525a04e949edb44d2233cec2d332b330323318dceab1e187f4e4b6e

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:a21729dd7f3851c87942f284a1ee548df925525ed5147bf08cbf56e5a93f0110

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=sha256:55d6fe5a0c213538e922d8e6d151c5f6344f8ceadd7f103c1321f0593c343907

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:bb602a133095a6810af66ca2b2eacd6fed1e10fc67ae8e7a43ef01a6541d3584

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
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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=sha256:9a55c8335989c514c70e1f0ab999e2cd932a6565ac614ea517a36d50cbfe1db2

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
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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=sha256:15a2a41234d6d24513eacb96474de08f17be790e5272805b13988be8daf0a8e7

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

Resolution
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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:b466df19ab764a75d64691644504dc0c410f807f4116976b591e2485772ade71

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

source=pdf_text observed=2026-08-07T00:58:14.853345Z digest=sha256:aedbad0454c63435ded5b48ae89bcdf8489f97c1c79efd3029723b6c4a6fdae4

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

source=pdf_text observed=2026-08-07T00:58:14.989180Z digest=sha256:e988e90db0a6937432bacd65584398b6c29f242cee247dcb0d3e0786d7b2dfdf

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

source=pdf_text observed=2026-08-07T00:58:15.015126Z digest=sha256:9fe6b774c2e369054d860708bcf0976fb78d791dc8ca581a29f69f609d836444

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

source=pdf_text observed=2026-08-07T00:58:15.084586Z digest=sha256:c7d1bc90b278a18961f600010e3eac615df3ad53213feebe646c8db2cf0be911

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

source=pdf_text observed=2026-08-07T00:58:15.205577Z digest=sha256:6600fa5e6614dbd9307ce43aa0761d3703924995010d70fdc7b74e9da1c8da27

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

source=pdf_text observed=2026-08-07T00:58:15.264573Z digest=sha256:c9374b3e0f545418f57a4a2ff84bca086b501e14cd615657ad56f0d538478770

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

source=pdf_text observed=2026-08-07T00:58:15.269252Z digest=sha256:e2e636aaf5ad08cea10e867fb88a9be49a5c3ee48b7c1154f5ae993501d870de

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

source=pdf_text observed=2026-08-07T00:58:15.273401Z digest=sha256:543b927dea4742981427386ebaf75b7ecb7383d34ad32fc9c54c874ec4b50690

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=sha256:0c0a186d5ff2fbfcd2f709b72dfa472f6f0116ce55ee9298e320b654f08f190b

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:ffef3258a78ba8407b023336aacd2605432f62a9f60165f52041d3eb19d6c582

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:a5d539f5a3464895edd58d5f1b63ab3cbebac45d5d9e55074c84c7c61f9daa91

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:cf36ca9c968c3691143c3c5be7a544b052c5a33c6fb5323cbdae282d311d9ea4

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:e53aa8824e3334854479ee2d54e822c056499c204b781c70a270484fd740bbf3

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

Resolution
verified fuzzy
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 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T00:58:15.301782Z digest=sha256:56bab774e822a586186b0bf6c8c8d56a26d52db1554eef39f7b0c10ccaf71e25

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

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

source=pdf_text observed=2026-08-07T00:58:15.307416Z digest=sha256:d3e4d537494f26cb8ddc1f8f0a6896f33114a781e110f6066f1b8a6cc6cce321

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

source=pdf_text observed=2026-08-07T00:58:15.312076Z digest=sha256:ddd4b7bb1ce518b2030254624816fb552a094d3adcd5e473c78c54945bbec4da

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
unresolved
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=sha256:2150a2305dea40fa9fcb120dc7c2308f2d20fc922722faa994224e1004c11d91

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

source=pdf_text observed=2026-08-07T00:58:15.322737Z digest=sha256:0270457cef91d82427e1500fc5609bcfb856baa979f7fccb9fb8aba1bb2e7586

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

source=pdf_text observed=2026-08-07T00:58:15.328295Z digest=sha256:f5ddba39e3442e1ae11fa46de292abcae04ff3411573f8331e7341080147be99

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

Resolution
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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=sha256:7f15cc605c012982becab7ed02561c5ab41080254a5a9a5955fa3f5777074b04

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

source=pdf_text observed=2026-08-07T00:58:15.338793Z digest=sha256:c3655f94ee4b10006adc8d53bd862e2d97f1a9c30c9fc7f7a3ac75f51262186e

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:c7b40948dfaa19acbf2c7269d21d85afa1aa576bb4f515a5077638a1981b19b5

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

source=pdf_text observed=2026-08-07T00:58:15.349548Z digest=sha256:2c19887fc3482babcd060357ea6634348969645bff3803a2bc0becbb6ab92eaa

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

source=pdf_text observed=2026-08-07T00:58:15.354950Z digest=sha256:a915cc615d1aedfd303d4b6752af640bda3ec9f5bae66a3b959b6902e081db64

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

source=pdf_text observed=2026-08-07T00:58:15.361067Z digest=sha256:734ff2c2fa0779d6f70f74a7d4bba00619e7877d2ce14123d26b6c2e18e77c19

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

source=pdf_text observed=2026-08-07T00:58:15.365858Z digest=sha256:a499d97b4a27b3dda8be203c99661304f60172d0d949bc61dec128e110083574

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

source=pdf_text observed=2026-08-07T00:58:15.371561Z digest=sha256:1d3e357c21e59f41565f813367d4f08cf5d72895d84129323d0e71b0eb72c7c7

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

source=pdf_text observed=2026-08-07T00:58:15.376522Z digest=sha256:b9ad45609ef74f9c2aa1a505108cfb0dd77b2e9f5ae27c3463c7d9ad7eca1b57

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=sha256:8f9e5b6ae0f588ce31d357e22a8125106758b1699ccd6acc41a83fcfb34a502b

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

source=pdf_text observed=2026-08-07T00:58:15.386383Z digest=sha256:254d351a10a6e05bde4318a33bf56e3f5d774ef598f045004b6a1e15d106b06b

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=sha256:679a73b3701dff6f32a7fbf741978e2cb09024284cec2b857e4ce1a9b7006a2f

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:fd54ccd1a60a6556fe8bcfd069df9c414b41e63f36f894865253161da3ebf898

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

source=pdf_text observed=2026-08-07T00:58:15.401556Z digest=sha256:f0fd6ce65925723bc39e6cb28a8d54b7f55a72810bb4f3ad9c0f1092110de326

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

source=pdf_text observed=2026-08-07T00:58:15.406103Z digest=sha256:a3063a49c25e968fa84dfb2e628aed5d135910688acdddc04aed5a7df9436ae1

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

source=pdf_text observed=2026-08-07T00:58:15.411095Z digest=sha256:ee8310bdfad7081c5cbbb6c5f282cb47b606fa7de5c3a70380d29113837eeeb8

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

source=pdf_text observed=2026-08-07T00:58:15.416948Z digest=sha256:e1662ef4ab120090c5efd9ee9dd0d49cad5fd707acb761f87904d8c85b2e4455

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

source=pdf_text observed=2026-08-07T00:58:15.421753Z digest=sha256:79c4f3057557a24eb738a9b70c10c41f322811a54d81470e0325d02eacf6e055

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=sha256:08120456e3fce3eac324fa8e04de9a5a22b3ef3486ab0e412999aca9485e9c89

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:f398d2c99e1ddd21fb003b3a18c4836aca758a461eab341f305a32c76e707859

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

source=pdf_text observed=2026-05-12T02:28:14.734682Z digest=sha256:da27c8873dbf1996b1e8ae4309fca95132ce0d39851e9879d3c2de8c443aa6df

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

source=pdf_text observed=2026-06-30T23:26:46.077894Z digest=sha256:d180bc01891caaf2a240785f3b62014e6a2b49cf3b79748464dddfdf88ac21b8

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

source=pdf_text observed=2026-06-27T23:49:05.871422Z digest=sha256:a9dc4293f1b6038b7c835f39c429214e766bfad04ea6a43efbbf1d93ca9e7fb8

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

source=pdf_text observed=2026-06-27T16:49:46.634302Z digest=sha256:db14b142f092352879913d391f0d0a3f2855bbb7ebdcb8f89d2441e1ad503879

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

source=pdf_text observed=2026-06-26T09:09:06.925645Z digest=sha256:c6f92ca188f314280caea5b7b23346fd9050ac79a7072b3b75e36869688e8bcb

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

source=pdf_text observed=2026-07-01T07:00:53.496569Z digest=sha256:f228b6440a94f23df345ed524130256ccb28d17bd5edae0dd65ee60738bcc782

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=sha256:790d05419f60141d237789f90d5d343107763b54484339196cc7a92222d05ae1