Paper Citation Record · LEDGER

Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation

As of 7 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2605.25036.

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

pith.paper-citation-record.v1
2605.25036 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-30T12:03:41.268375Z

measured 12 of 12 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact0
verified fuzzy7
unresolved2
parse uncertain0
malformed identifier3
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e4de0cd5-b69e-4176-8f66-dc0b1fbdaefb · outbound

This paper cites OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models.

Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models

Reference 1

Resolution: malformed identifier; local_arxiv, observed 2026-06-30T12:04:38.653273Z

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.

Observation 3e30ffba-497c-49be-9d82-7dbc9572466d · outbound

This paper cites The intuition is to regularize the average language bias per token rather than the cumulative language bias of the entire sequence.

Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation The intuition is to regularize the average language bias per token rather than the cumulative language bias of the entire sequence

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T07:26:03.994007Z

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.

Observation 40be7b75-be9c-4128-9947-00db28fec9a9 · outbound

This paper cites Instead of the standard KL divergence, we use a penalty function derived from a Taylor approximation of the reverse KL divergence.

Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation Instead of the standard KL divergence, we use a penalty function derived from a Taylor approximation of the reverse KL divergence

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T07:26:03.991337Z

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.

Observation 92ab2fcb-281b-4c44-8e8f-c1c93fea7bf5 · outbound

This paper cites It aims to ensure that the gain from adding visual information is maximized.

Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation It aims to ensure that the gain from adding visual information is maximized

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T07:26:03.979513Z

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.

Observation a70ae916-f947-4c84-b303-52c17871c02c · outbound

This paper cites Please help me describe the image in detail.

Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation Please help me describe the image in detail

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T07:26:03.988993Z

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.

Observation da5c9465-6bf5-4eea-b9c8-517941c0ae03 · outbound

This paper cites •LBR (ours):The baseline model trained with our Language Bias Regularization.

Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation •LBR (ours):The baseline model trained with our Language Bias Regularization

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T07:26:03.986299Z

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.

Observation 52e74448-35d7-4391-86de-226d19e19683 · outbound

This paper cites open” vs. “closed.

Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation open” vs. “closed

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T07:26:03.971785Z

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.

Observation e4c6d1e6-b5d0-4651-8994-3c011791da35 · outbound

This paper cites an unresolved cited work.

Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-07-09T07:26:03.977102Z

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.

Observation 7177a532-6723-4d14-9227-baaae30d384c · outbound

This paper cites it seems like.

Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation it seems like

Reference 9

Resolution: malformed identifier; raw_fallback, observed 2026-07-09T07:26:03.983948Z

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.

Observation c4ce506e-744e-44c1-8e6f-632aeafa5ea1 · outbound

This paper cites an unresolved cited work.

Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-07-09T07:26:03.969425Z

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.

Observation ffc818cd-6b1f-4480-9850-8092b81d58bf · outbound

This paper cites stove” and a “bottle.

Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation stove” and a “bottle

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-09T07:26:03.981742Z

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.

Observation 27f27d24-6a20-4099-a942-5457192f9a5f · outbound

This paper cites orange",.

Language Bias in LVLMs: From In-Depth Analysis to Simple and Effective Mitigation orange",

Reference 12

Resolution: malformed identifier; raw_fallback, observed 2026-07-09T07:26:03.974436Z

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.

Pith citing papers

No inbound Pith citation observations are available.