Pith. sign in

Paper Citation Record · LEDGER

Good Questions Help Zero-Shot Image Reasoning

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2312.01598.

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

pith.paper-citation-record.v1
2312.01598 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 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T18:11:54.593526Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T00:08:16.473526Z

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 9670f636-c851-4df2-a8c3-24a601df6408 · inbound

How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey cites this paper.

How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey Good Questions Help Zero-Shot Image Reasoning

Reference 92

Resolution
unresolved
no resolver link, observed 2026-08-11T18:11:54.593526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:11:54.593526Z digest=sha256:7adf0f53cc48698409d6ec94847c79421aae2b30a2f2ff61f7cd213271c9570b

Observation 9a25f1f2-6fef-4e35-9b4d-b4250a6a1fad · inbound

Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework cites this paper.

Boosting Private Domain Understanding of Efficient MLLMs: A Tuning-free, Adaptive, Universal Prompt Optimization Framework Good Questions Help Zero-Shot Image Reasoning

Reference 30

Resolution
verified exact
local_arxiv, observed 2026-08-11T00:08:16.498579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T00:08:16.286778Z digest=sha256:7b7fc66a44d1ec0462ed7bcfd75aaff7a1775171c62de026166926ff1d4db853