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Paper Citation Record · LEDGER

AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2208.08084.

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

pith.paper-citation-record.v1
2208.08084 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:44:48.468564Z

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

0 of 0 outbound references displayed

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

External citation measurements

4
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 839dc7de-d203-4981-99cc-ca6522294d58 · inbound

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations cites this paper.

QuEST: Stable Training of LLMs with 1-Bit Weights and Activations AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-08T20:44:48.468564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T20:44:48.468564Z digest=sha256:a7d7c05a679abd46bcba2784aae2d7a1a57668a353e9c50343e3bbf8fe382b21

Observation 906dde0f-dd3b-4022-a4de-19a0f2590c91 · inbound

Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression cites this paper.

Vanishing Contributions: A Unified Framework for Smooth and Iterative Model Compression AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-18T08:41:08.051626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-18T08:38:52.367887Z digest=sha256:a5cea6f8e84b50e2810f807dac4157320ad69d7f65b2c9fe4bc22dc4ae306b32