Paper Citation Record · LEDGER

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories?

As of 7 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 1 inbound Pith citation observation for arXiv:2506.11571.

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

pith.paper-citation-record.v1
2506.11571 v2

Coverage vector

measured 63 of 63 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:07:36.844173Z

measured 64 of 64 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T09:54:33.563266Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

63 of 63 outbound references displayed

verified exact0
verified fuzzy17
unresolved46
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0abaabf4-c43e-48f3-8d8c-ed11ae2f1b6c · outbound

This paper cites Hallucination of Multimodal Large Language Models: A Survey.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Hallucination of Multimodal Large Language Models: A Survey

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:31.587700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de2de84e-4f9d-4c43-bdca-87a18ce0ff6b · outbound

This paper cites Eagle 2.5: Boosting long-context post-training for frontier vision-language models.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Eagle 2.5: Boosting long-context post-training for frontier vision-language models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:31.660752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7637cb9b-dc85-4776-8ff2-9efccd7b9cd2 · outbound

This paper cites SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:31.703713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adb9539c-bbe9-4d46-bc61-7e061c22ee24 · outbound

This paper cites MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:31.778408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de2f71a6-6d3e-42c6-bc3f-951120f45647 · outbound

This paper cites M$^3$CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? M$^3$CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:31.820419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6eee8200-8caf-4adc-98e2-211589b90ad5 · outbound

This paper cites See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? See, Think, Confirm: Interactive Prompting Between Vision and Language Models for Knowledge-based Visual Reasoning

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:31.881851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbef4c86-23a9-4ba7-b7a3-cc1c5e026a43 · outbound

This paper cites Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:31.956173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90ecb6a8-ce9f-4510-9908-270fc34ff20f · outbound

This paper cites Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Video-of-Thought: Step-by-Step Video Reasoning from Perception to Cognition

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:32.061606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a649fa7-a297-4640-82a1-1d40ae8fa644 · outbound

This paper cites MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:32.177092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7ef88ad-7914-4c91-a6e4-0f7584eb693b · outbound

This paper cites Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:32.289278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa453ad4-0ad9-4b46-8019-78d046e0a629 · outbound

This paper cites Seed1.5-VL Technical Report.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Seed1.5-VL Technical Report

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:32.364515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0a20dea-c999-4157-9586-0d4a6ec543f5 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:32.480638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73d92c24-9872-40ad-a9a1-082c9a2b7a66 · outbound

This paper cites OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:32.555893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ff9ea26-f393-4506-9922-25216861501e · outbound

This paper cites CIEM: Contrastive Instruction Evaluation Method for Better Instruction Tuning.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? CIEM: Contrastive Instruction Evaluation Method for Better Instruction Tuning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:32.651474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57369070-914b-467a-9d5e-4de52cf965ce · outbound

This paper cites GPT-4o System Card.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? GPT-4o System Card

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:32.738113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80a94db2-df0c-498d-a628-6311cf99e159 · outbound

This paper cites OpenAI o1 System Card.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? OpenAI o1 System Card

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:32.813269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35009f1b-316b-4e07-a07c-a50ae55b903a · outbound

This paper cites LLaVA-OneVision: Easy Visual Task Transfer.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? LLaVA-OneVision: Easy Visual Task Transfer

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:32.918115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9876737f-06d9-4d91-8603-06bd20891a76 · outbound

This paper cites Evaluating Object Hallucination in Large Vision-Language Models.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Evaluating Object Hallucination in Large Vision-Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:33.024804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a2ee76d-092a-4c18-a14f-371f0b13bfe2 · outbound

This paper cites Visual-RFT: Visual Reinforcement Fine-Tuning.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Visual-RFT: Visual Reinforcement Fine-Tuning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:33.115300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b759e0a0-aa7d-4e6b-bd61-b01086fb387b · outbound

This paper cites Learn to explain: Multimodal reasoning via thought chains for science question answering.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Learn to explain: Multimodal reasoning via thought chains for science question answering

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:42.342580Z

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 70c288f9-a8eb-4fdc-b88e-b5b704dabdd5 · outbound

This paper cites Visual-O1: Understanding Ambiguous Instructions via Multi-modal Multi-turn Chain-of-thoughts Reasoning.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Visual-O1: Understanding Ambiguous Instructions via Multi-modal Multi-turn Chain-of-thoughts Reasoning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:33.318843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3e949cd-5a64-4d0f-8c61-d0672c991c69 · outbound

This paper cites We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:33.434821Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f578a40-6dee-4156-92a5-2592e763fa5b · outbound

This paper cites VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:33.560796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6091be3-7e48-42a5-8e95-8f6c51e6a581 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Gemini: A Family of Highly Capable Multimodal Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:33.622232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9024d70c-e031-4c04-90c2-80499c3da584 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:33.722511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c4133bd-d41a-42d7-8080-f873a69e81af · outbound

This paper cites Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language Models.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language Models

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:33.839250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d38dc04e-cd60-4b4d-9a3f-f55c27fbb8e2 · outbound

This paper cites Measuring multimodal mathematical reasoning with math-vision dataset.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Measuring multimodal mathematical reasoning with math-vision dataset

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:42.037028Z

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 2edb0dae-c5a2-4436-b1b9-2868fed6b484 · outbound

This paper cites Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:34.024686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0793f6ff-3665-447c-b54b-3f85c43aa745 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Chain-of-thought prompting elicits reasoning in large language models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:34.166022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2642020f-54ce-45c5-b412-27e43624b0ba · outbound

This paper cites DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:34.276313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1165e788-0faf-4ce7-b2d3-b886ed25850a · outbound

This paper cites Valley2: Exploring Multimodal Models with Scalable Vision-Language Design.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Valley2: Exploring Multimodal Models with Scalable Vision-Language Design

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:34.362773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b10a50c3-9317-44c1-8011-5dd93ad91905 · outbound

This paper cites LLaVA-CoT: Let Vision Language Models Reason Step-by-Step.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? LLaVA-CoT: Let Vision Language Models Reason Step-by-Step

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:34.464471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64848bed-8331-43f6-a7f8-50025735980f · outbound

This paper cites R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:34.579200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f848019f-2edc-47ec-a455-f1c34aded2ed · outbound

This paper cites Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:34.668498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab18994c-87b4-42b1-a5fe-574d3fcc4437 · outbound

This paper cites Tree of Thoughts: Deliberate Problem Solving with Large Language Models.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:34.753888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 907799d7-17df-4531-9005-ab4ee4715382 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:34.797292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3cbee560-f79c-4c3e-850a-a752a3295f19 · outbound

This paper cites InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T04:07:34.864725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20999872-3733-4c93-b6a4-1859a808f77e · outbound

This paper cites an unresolved cited work.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:07:41.794677Z

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 5f681d69-358f-4b4a-bfa6-f689294ea924 · outbound

This paper cites an unresolved cited work.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:07:41.652440Z

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 98dcbe5b-8481-4df7-a5c6-8d29ffa9a212 · outbound

This paper cites an unresolved cited work.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:07:41.516516Z

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 f842e7a6-5627-4b8f-b46b-b6a0001c49c9 · outbound

This paper cites an unresolved cited work.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:07:41.348976Z

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 31826df4-9b47-4a54-976c-26c18e42758d · outbound

This paper cites an unresolved cited work.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:07:41.133657Z

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 aeb8bcfc-5433-4087-b4dc-e468a5675797 · outbound

This paper cites The screen visible in the image is small and not a touch screen.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? The screen visible in the image is small and not a touch screen

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:40.932629Z

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 3176c547-2f17-4fcb-bf8b-c8dfe69166b4 · outbound

This paper cites This option is speculative and cannot be confirmed from the image alone.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? This option is speculative and cannot be confirmed from the image alone

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:40.667712Z

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 85f92ca6-d4c5-419e-9a24-4d2b0a23fd56 · outbound

This paper cites This option is likely correct.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? This option is likely correct

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:40.401958Z

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 223a4946-abc7-4129-9639-9c05df960629 · outbound

This paper cites The image does not show any indication of this technology.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? The image does not show any indication of this technology

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:40.194944Z

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 9ef69d52-90a2-4fa2-aef9-a1de3c5d9348 · outbound

This paper cites <vcues_2>The screen visible in the image is small and not a touch screen</vcues_2>.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? <vcues_2>The screen visible in the image is small and not a touch screen</vcues_2>

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:39.964892Z

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 ca1fb7ff-5e97-4513-9931-84e7dfb958d2 · outbound

This paper cites </vcues_3>.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? </vcues_3>

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:39.782624Z

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 03bc4ae7-ed92-4abf-b66d-ce84abb40fbf · outbound

This paper cites This option is likely correct.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? This option is likely correct

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:39.570953Z

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 93890703-e101-4190-9a39-d083b9789e5e · outbound

This paper cites <vcues_5>The image does not show any indication of this technology</vcues_5>.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? <vcues_5>The image does not show any indication of this technology</vcues_5>

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:39.399955Z

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 86ef2ce4-a475-454c-a7bc-9a658381ebf6 · outbound

This paper cites <vcues_2>The screen visible in the image is very small and not a touch screen</vcues_2>.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? <vcues_2>The screen visible in the image is very small and not a touch screen</vcues_2>

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:39.293227Z

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 a5ad5350-509d-4b95-a5f8-da23aa4ca87d · outbound

This paper cites Therefore, this option is incorrect.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Therefore, this option is incorrect

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:39.113386Z

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 47154189-fc59-433a-811a-67dce248275b · outbound

This paper cites This option is correct.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? This option is correct

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:38.971452Z

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 c073b7ea-f187-44a0-936e-fbcc93b2c171 · outbound

This paper cites new_option.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? new_option

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:38.796243Z

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 c9f65da7-5d69-429c-b231-0e8f3e28b1f1 · outbound

This paper cites an unresolved cited work.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:07:38.666183Z

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 b7d41924-d39f-4128-ac98-7de16039e3d0 · outbound

This paper cites The presence of dirt could be from the environment it has traveled through, such as dust, debris, or even road salt in some areas.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? The presence of dirt could be from the environment it has traveled through, such as dust, debris, or even road salt in some areas

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:38.554796Z

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 b4f9552f-97e2-435e-adae-3957608376dd · outbound

This paper cites an unresolved cited work.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:07:38.421824Z

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 72575578-c4d6-4e54-93ac-36c4e9d7c5c3 · outbound

This paper cites The train was caught in a rainstorm: While rain can cause dirt to accumulate, the image does not show signs of recent rain, such as wet surfaces or water streaks.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? The train was caught in a rainstorm: While rain can cause dirt to accumulate, the image does not show signs of recent rain, such as wet surfaces or water streaks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:38.195822Z

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 655077ef-3a7b-479a-b7ff-2410614e6255 · outbound

This paper cites an unresolved cited work.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:07:37.953934Z

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 f339e64e-a48b-4402-ab33-5d9271709600 · outbound

This paper cites an unresolved cited work.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:07:37.681408Z

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 d9a69d1d-bb67-4fcd-9848-5b7df5cd8ab2 · outbound

This paper cites an unresolved cited work.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:07:37.501653Z

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 885b32cd-a831-483a-838a-0b01f99498e6 · outbound

This paper cites an unresolved cited work.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-07T04:07:37.393645Z

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 958fcb89-84a5-4a7d-8d98-5019d2c34456 · outbound

This paper cites Given these observations, the most reasonable inference is that the setting is a small residential room, likely a bedroom in an apartment or a small house.

VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories? Given these observations, the most reasonable inference is that the setting is a small residential room, likely a bedroom in an apartment or a small house

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:07:37.321432Z

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

Observation 9f88081b-4638-42e2-99c6-e46901bbed90 · inbound

Position, Not Provenance: Separating Reasoning Mediation from Sycophancy in Medical Vision-Language Models cites this paper.

Position, Not Provenance: Separating Reasoning Mediation from Sycophancy in Medical Vision-Language Models VFaith: Do Large Multimodal Models Really Reason on Seen Images Rather than Previous Memories?

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T09:54:33.563266Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.