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Paper Citation Record · LEDGER

Figurative Language in Recognizing Textual Entailment

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2106.01195.

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

pith.paper-citation-record.v1
2106.01195 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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T15:35:36.450131Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T14:07:20.573663Z

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 8edcf6f6-4877-478c-b556-7ed68f35255c · inbound

Large Vision-Language Models for Knowledge-Grounded Data Annotation of Memes cites this paper.

Large Vision-Language Models for Knowledge-Grounded Data Annotation of Memes Figurative Language in Recognizing Textual Entailment

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-10T15:35:36.450131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T15:35:36.450131Z digest=sha256:8a56ff8e558f1419c352c4aa9661ecc3a5e2b0c63592efef755b52fcd9ba59c4

Observation 06ca614a-f2ca-4863-89fb-0424d39921c8 · inbound

BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook cites this paper.

BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook Figurative Language in Recognizing Textual Entailment

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-19T14:07:20.576161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-05-19T14:03:35.214840Z digest=sha256:137c84889ccd1771c0f63b4f46c3dcb70939c2461501ffe671e34caae8e7943c