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Paper Citation Record · LEDGER

AI Flow: Perspectives, Scenarios, and Approaches

As of 7 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 2026-08-07T06:34:17.273281+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=sha256:5f7771c539da5999a9094086ec59a4dd119548c26dd4add46ec89be15528eb9e

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:c385c623a32cd4559b9ba33b642dde35bf30de775f1956a985b0afa19a7c1abf

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:ef7a33e1eac5acaf42365bf2b960470ee445f34298700931d5071d98c244185f

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=sha256:8050bcc4ee96a588016edcc46fb17f8f01eabfcb0737d2fcb5bbb41f5575c9ec

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:d9b59339b2c113451518f6db5d1daa409bd4c177e0cd27db06e0cbe2b1cc5039

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=sha256:851cbf03005e965b18393df8a5d38f39f7f782c5d6c5ed1b7b332ed723a568c5

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

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.194123Z digest=sha256:c1bbcf64f4796e6442b5afb2179118152a16a3f3b26fa435e5d833c5148a74d3

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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no resolver link, observed 2026-08-07T00:58:09.256380Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.256380Z digest=sha256:2b60cb874461c656a1b714b2b8f3060a72752cbd0eb6c7d326065db011a9475c

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=sha256:335b701a6f5776bba54c1ca5d5c81b4aa0c1a2bd2691c31abf80fb4d5e45e571

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:a00393384ecb9b77692557b91ca37f7047fa104bb7b8785e686f5933baee8aea

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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no resolver link, observed 2026-08-07T00:58:09.569997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:09.569997Z digest=sha256:b55d26c33fafa16e4f58adb1b79ade5de98558208ca3c45e2027e961bfe55456

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:b4cf2f519f4ea94093dc3aae313833aeefa2386ccfec49743b0ca4c7c8d01b3a

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:c284174ac6378f2a58970169996bc3aa695d1940bb71d27e9dfd9bae31805736

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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Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T00:58:09.830665Z digest=sha256:9c0fa178495340593f822dea9e0d519b8d39187872ce0fb45d985e6f68fd073e

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

Source-reported events for the cited work

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source=pdf_text observed=2026-08-07T00:58:09.977985Z digest=sha256:f0ab27b2c059c467111328701b0069c676f1838965baf56912f25016d43a6c7c

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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source=pdf_text observed=2026-08-07T00:58:10.072421Z digest=sha256:7b09a561b7628067b6f60061e1e97a9586169daf2b3e16ba8206f0a386e6c40e

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=sha256:681e8f858be54ee290dd6ae5425d19915009d8f3e42efc51bf25572370653281

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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source=pdf_text observed=2026-08-07T00:58:10.265776Z digest=sha256:e429a1a672faa719c0f3d65a1ce0412442e8f2c9c175a3abf09c401232f5f0af

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=sha256:266167e529139c480f8af52ff10d2937db741eb47ea183fe36c4a1d4e71d8268

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

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source=pdf_text observed=2026-08-07T00:58:10.481608Z digest=sha256:a000286f5daa3a3bc825f27f16e45f975fe8654bc641da730b2560f997015cad

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=sha256:0a4109998e8784d8206845e72a70b43ff73875a1ca56f0cac1c83e90a34fa92c

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:f5c1806b06c464e66845fd48b6d841dea529f4dcf6c278e744d23bd5059c3374

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:c196ec7ef4a81e65c4769b3b726d109c671b01ea26a2e65cd14c9ca3a9289455

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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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:10.801744Z digest=sha256:96fa1d9ec7e7077989c2caf03d4a6715e8faabe055c58052689bf14cff02ebb5

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:c4c3d4485a8d2599a314c326a4c5129ddc5a441b779db31ccb31cfb79703a240

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-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:10.967773Z digest=sha256:39fc74945e1583ba9c50fab27f0d17487a6686ac2500c604c44886d298a8c996

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=sha256:8abaf2dc4db88b97c0403d33e6e4393dc0471d22ccca31f7808d12d97350ae08

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=sha256:2d89c825f8236ec2f094685883b1c5228a5ab09cf56058351293343ff5ba12f8

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:11.244590Z digest=sha256:2c028d105bf1e082713b0ea56ccb4c341545b47ea75b8a5af08421307b184ef9

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:11.364130Z digest=sha256:674bae4d920e60cc409ee04a2307008fc69ed1daa12d999f7db99c66f0986f3f

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:11.458092Z digest=sha256:e706bdd19f0199ddbd464c4d2fca562665452f4734456ff65f06626bf91e9b33

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:11.520168Z digest=sha256:f5dc9bdf917c7de93f0491c9a8fb4ae7262021ef5b78d85aef16176a9064e525

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:11.619396Z digest=sha256:15631eb0253cabf4383dbad9255ebff31e206daf034577c9d64fa96eb8aa605f

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:f54267216126892770d86a835511544983cde55fc5d53fc60a27249dc216b3c3

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:11.803263Z digest=sha256:813c7686fd336e3fe795f46212f06c335381891159b794ad9d8a9273d8733286

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:11.865376Z digest=sha256:be41be7aba2ad5c5e0a7e0c3473a0c09c1e2153dd3012cb5390258dc051038ce

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=sha256:35a94bfec45443f096ddd91d964f7e7dd505c22ff467ae09f3293abe41da3c6b

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:12.024445Z digest=sha256:a925544cbcf90abc2c33afb06aad1d40439fb1aefda68d192041c6bcafa64229

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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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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:12.173874Z digest=sha256:9887c49f132f51c1424a43c27ccf0bd02a2569791031d3f8f51116710f8f970e

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:d442e59382eeb718a8926c62882343e37834a109c9b677e0af917483aa4e7e47

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

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:12.358095Z digest=sha256:16d08d0f94fb27117f2ec25ae6020019c1835af1fd4f49fb997f0e6e60b380bf

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:12.460084Z digest=sha256:140bb54f5bc357076bce86ffd6e90186794173972fea39e449bcccf13b03692a

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:12.577444Z digest=sha256:4aba0fb964a26fe01d4bbaa90e2d0d2b6b7fddb0492be4be8961c2244965a770

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=sha256:62e9f17bdbe07546b23b7e782e4c7df3d87138bc113eb980cc49521d0028e222

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:12.718065Z digest=sha256:5340dcc97706748531ff1dd55afce2eb7d199a1a5391e23cf1289f618f7224bc

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:12.852444Z digest=sha256:600b608108dc3dd074284bc24d7f602577d3ab34592b70b186a970057c98ebd8

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:12.943628Z digest=sha256:eeb44a5486657d690028ee85ef4633eed5ef60b9ac12f592b3ffbc8fab41cfb6

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:13.112517Z digest=sha256:9135af4c397c136872c863432b5d67c762f13fe17f49cf29c1871b23943bc7da

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:13.297017Z digest=sha256:460c0da5c3be03a1c13c4e0bd554111073ddf29f77b8ecdd6c0ddeb5e888c32d

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:a67deb6bc212952c2c81f8d546ca8cfab141320830d9331bfe887fb41e3b093a

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:13.525808Z digest=sha256:7f88ffe87e6f5aaec65229b3d7917fafe197ef683c49467edf499a97d0574684

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:13.609702Z digest=sha256:fafde9a0b7ca9f158cbcd6c800893ec034da8874dc12bf4c4cddd110bcc6691d

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:13.732905Z digest=sha256:62813ba907efb20af243ae4d833748c07aae1757c02f5eca9b89afbe06aaf21c

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=sha256:8f7cfe15ff6d08309238b765454d30761800aafab5e254925b3caab7c35cae2d

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:13.905327Z digest=sha256:f82607e0281ed3544be7c074c38bea9ccbdf8d3b972b9bf493dc4aca4ce15b6b

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:14.018014Z digest=sha256:60dc2f4db7d6f21d13f473398b686f50b2d1e06eebf091f2bacb501536a0b5b8

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=sha256:322a60eb37f996a237d7ff8731cebc5b976c929f56a549d5eb131086794d452b

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=sha256:26ce8257ed0c6512c35d6073046f0d2c6e2ce77abda5b8a1e17a49bb10f5dbdd

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:c65b1b354d0bfac4222ae78b61a6cf8f676bc822bf43cfebe73bf0e8a10de997

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
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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=sha256:64c9ecf6055b9608bf5ca769d43d3ad816fe47fb78eaa121150c247de6a07938

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=sha256:02e112c0b4bc988259c4f4232455a8eafdbe142f1ebc63fe3f2e44dd3404924f

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=sha256:3a4b57c8964c2bdd210bee9733fb0e4aeba114bb3318601921d54675c7870da8

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=sha256:01c8835f39f6e3f28fd41f7d5f954e6c5cb3e9c117632d54b5d7e2e0dfae1bfd

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:14.853345Z digest=sha256:cf2dbaac7722c7bc88b3f3507906bc24cd128db21f155305b0360eb803b67670

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:14.989180Z digest=sha256:419b8269b95ae933e7e5236df1612b449b535a75515109b66e37afe9019d5463

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.015126Z digest=sha256:f76d1411c0678d99c741ca5ac2e610a61a7e1131fc0f191ed46854d4f87bbdee

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.084586Z digest=sha256:dc23599c3c1309d945e633c64b66f21a732cdadf68d7846c8ddb5e08188042fa

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.205577Z digest=sha256:4021e42f656d55222097273533baf791aad525de004608c8e73e195d4e064194

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.264573Z digest=sha256:993666b718fbe08715a9c2d92c50d254fa487270e39ae9f6b5764b8bdf6ab060

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.269252Z digest=sha256:12bd9bec7a700c6ea824df2edc367ef7faa6b5536ab6699c6db90d7abe404f38

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.273401Z digest=sha256:7e1b25888281871870785648a5b7b8d8986fb47cfd0c06bfd57b17b88b7f9f06

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:e21ad2732bb7d65201233aa16b1cd7055471317ac611b0768c36854a4b077071

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=sha256:975361e246f5c2e9692b269fe64e922f68c41b1541df57a31e2ceb929812dc55

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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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=sha256:3e3b47d2c78f4c770d12ab6d7c227869266d540a60e26bb302c6e8041c8aa886

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

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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=sha256:9d5ce3932daea805457d23371f4c904abb6784150924fb8f6781c68ae7a36616

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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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:58:15.296832Z digest=sha256:c91738a27a28477fbc8f2298717505ecac29807ad250f06b2268424dd39b5456

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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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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.301782Z digest=sha256:fe95e624a7873bb4410c70ae47263c38afdeba2b4b2953bf4a008599bc9af085

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.307416Z digest=sha256:6992930d77897daef79042d4bc14dce59c1896b50117947dfa83c206cefadb0d

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.312076Z digest=sha256:65259e9b64e0c8429bda240f5dfa7ba486a656b1a2ef2bccc0405e51b5d1f67c

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=sha256:0502250dced7794034d52abf1d06686272ac760eaaa121a6184941d3795d785c

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.322737Z digest=sha256:9de8e1aaaae36dfeede1bb5c34193d38954f9009b531360b559b829e99078c35

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.328295Z digest=sha256:bb287e381d1de275a000ff2c827f1b2f377c2eab01f6242b9d8de3ca1647ae1f

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=sha256:2a1cea4b39684504e46d24c7c205b5ad357b3ddbe404667f778bff9f9322a5a1

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.338793Z digest=sha256:a5e458f086e61f4471752b6860cde225f6081ae23ca18e691a08555310d402e6

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:c035479f1f1df6b4c15cc06f934ca3c428749b325599b3990239930a7084653c

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.349548Z digest=sha256:d0367bccf776ba7a75cf5bb38ad02ca80225f4f401a32fe5c571e50af18c10b8

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.354950Z digest=sha256:e1bd7031f808f78aa4b12784011ebdab83e2321db66dd76b0b59fca233fa5be9

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.361067Z digest=sha256:28ea09eecd112f2615ce5aa0c3e8eb480365ceb341bd821d3ea8cfdf521b1dc6

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.365858Z digest=sha256:e1e53ab8b3ffb107dbe251aca4f6221a8d5c75be1d930afed0692d2e799b90c1

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.371561Z digest=sha256:481768456573fef69cd80d8e30d7c8a62d9cd28ef45a47d4d89f35dfefd16efa

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.376522Z digest=sha256:04b55f5e1d8489d10398205fcf33b8ce8dd4abc8bfbff212901d795a67b48c4d

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=sha256:54a83780a7ee929fade8899f1ec769c12bb4f466bc3e03e787b61021a6b10c9d

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.386383Z digest=sha256:79475fdfd39cdc232533f646e7c855c6cbf620aa0c9afd6b9d25fcf1372cfc0f

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:beb344a6c3e51b608b52e1ae5e88267342467bfab077b8cde3138b0841c25788

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=sha256:4dc3c217da6ec6e57806977ade5a6266c4ce2305a4bbf5e1a483047a3295b6cc

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.401556Z digest=sha256:8629880ed25dc80dbdeedc48bb5d5ba0929aab073872ed69614693d9799a1dd1

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.406103Z digest=sha256:70859a0a0d3b6bffbfe0780247c2dc563a218a06ffc5c38a606eacd51d8d152b

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.411095Z digest=sha256:2dec4dc82cd110b4a23b03b0b5be2782240cd135b30a351d26badb1fb284123b

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.416948Z digest=sha256:1af67cdb66022afe3f0b84f8813c34761152dfd4c1ad43b2393be0bd8b523318

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-07T00:58:15.421753Z digest=sha256:c58b953241494273accc3b6339fdf2832e1d2c3d0fca1a7bf5e5ed2c4d9ff21c

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:f3f7767b360fff669ba266b0eafd0ac325e9828462ef1ddef8b39b77b167b805

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:f9fa7f7b55927a1dfe0a84b2bfc4119130cf7779808fb7554ce2c2b81ba971be

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-12T02:28:14.734682Z digest=sha256:0ff5c7b73b33707ac2c3c31f00c399af4456bbf3e3621d00c5d627c971c99ae6

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-30T23:26:46.077894Z digest=sha256:7f0fcab53e96c74bfc648814be658cdd958fa418337db3095f3a820db3eadeda

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-27T23:49:05.871422Z digest=sha256:19d9897279d10f2f0b857be533feaff00fdaabef165fb98585eb2f5b7ac13411

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-27T16:49:46.634302Z digest=sha256:e94e6ebe32e51204837d06c185e8b152f6cb780594b4424c57c40f9a6b82b623

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-26T09:09:06.925645Z digest=sha256:08a98de63b21d48ab9f4c89b5d3b80e3728416706461b07e7387efb0ec1741a4

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 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-07-01T07:00:53.496569Z digest=sha256:1bd4f5fd2b74a330d786885ee871a38cee02d82f0915154854c31adf12428dfa

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:a2c0ab00e11183655798d7ea4668e403d7b39c9262e74d1c9f51bfbf27a4ef13