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Paper Citation Record · LEDGER

ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering

As of 22 July 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:1906.02467.

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

pith.paper-citation-record.v1
1906.02467 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-02T22:53:35.213874Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T22:57:25.556016Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 13ca805f-407b-4275-a2d2-9b61710f6dff · inbound

AVATAAR: Agentic Video Answering via Temporal Adaptive Alignment and Reasoning cites this paper.

AVATAAR: Agentic Video Answering via Temporal Adaptive Alignment and Reasoning ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering

Reference 30

Resolution
verified exact
local_arxiv, observed 2026-05-17T20:20:11.785472Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

source=pdf_text observed=2026-05-17T20:18:19.580156Z digest=sha256:5ef5959d4796368790e8d6836901fa6284b144d20b7149c1ed29717a5c1ac85e

Observation 6f72d124-ebe3-451f-82ea-8285a12d8b91 · inbound

IoT-Brain: Grounding LLMs for Semantic-Spatial Sensor Scheduling cites this paper.

IoT-Brain: Grounding LLMs for Semantic-Spatial Sensor Scheduling ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering

Reference 87

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T06:05:56.737749Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

source=pdf_text observed=2026-05-10T17:49:16.409834Z digest=sha256:eb475f4351186bbbf9acdb7dbe695f2fcc36564df9b70b5dd45491633075a09c

Observation 4aaf2ae7-4043-4fcf-b1b1-4bb81cdcfa6d · inbound

SVI-Bench: A Dynamic Microworld for Strategic Video Intelligence cites this paper.

SVI-Bench: A Dynamic Microworld for Strategic Video Intelligence ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering

Reference 90

Resolution
verified exact
local_arxiv, observed 2026-06-28T23:02:46.761272Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

source=pdf_text observed=2026-06-28T22:57:38.477065Z digest=sha256:20f29d936246bcd83a66fbf181210f22c5aa489df535a5b794beaa82307302a5

Observation e808f194-783a-4a4b-8e16-f28eb616e315 · inbound

SVI-Bench: A Dynamic Microworld for Strategic Video Intelligence cites this paper.

SVI-Bench: A Dynamic Microworld for Strategic Video Intelligence ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering

Reference 90

Resolution
verified exact
local_arxiv, observed 2026-07-02T22:57:25.557881Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

source=pdf_text observed=2026-07-02T22:53:35.213874Z digest=sha256:f14909e5dd5da7a5a6e678cbefca6e9bc25e521db5e508ec97bc3b3e33c03f2d

Observation 19360593-d7c1-40aa-894a-4799b2a49859 · inbound

MuseBench: Benchmarking Intent-Level Audiovisual Arts Understanding in MLLMs cites this paper.

MuseBench: Benchmarking Intent-Level Audiovisual Arts Understanding in MLLMs ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering

Reference 63

Resolution
verified exact
local_arxiv, observed 2026-06-30T06:34:19.474308Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

source=pdf_text observed=2026-06-30T06:25:38.593423Z digest=sha256:1170df89e260522e3621ee9ea31f043565f4998d81ea050b2e8e28efbc107d80