Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 inbound Pith citation observations for arXiv:2401.06805.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T05:57:51.050490Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T09:59:44.777709Z
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 0b2da07f-e806-4b7e-a552-f13f425ffe47 · inbound
ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection cites this paper.
ErrorRadar: Benchmarking Complex Mathematical Reasoning of Multimodal Large Language Models Via Error Detection Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 65
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T20:13:24.651777Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 8847d598-5b09-49ee-9377-b84a0658d1b3 · inbound
Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning cites this paper.
Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 198
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T04:32:32.853992Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation c7117d9e-44c6-4bd9-9c55-afd3332f733c · inbound
LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL cites this paper.
LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 79
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T15:15:46.312789Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation e7c99b5f-2b23-4896-81d2-441100c7da1e · inbound
MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems cites this paper.
MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 65
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T22:57:13.410915Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 8e9ee500-0dcb-4a2f-906c-6f50032227b7 · inbound
Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning cites this paper.
Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T05:52:07.985526Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation b13c8292-0d2e-4eed-9c48-d58e99344d59 · inbound
PRISM: Programmatic Reasoning with Image Sequence Manipulation for LVLM Jailbreaking cites this paper.
PRISM: Programmatic Reasoning with Image Sequence Manipulation for LVLM Jailbreaking Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T03:37:01.119697Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 5ab7640e-addd-4be7-b2f4-2910d7d78817 · inbound
Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance cites this paper.
Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T05:57:51.050490Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 15ef3c24-902a-4c0f-a59c-05fa565f8774 · inbound
The Emotional Baby Is Truly Deadly: Does your Multimodal Large Reasoning Model Have Emotional Flattery towards Humans? cites this paper.
The Emotional Baby Is Truly Deadly: Does your Multimodal Large Reasoning Model Have Emotional Flattery towards Humans? Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T00:59:41.320388Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cc887b9b-36c5-4070-a7e2-26956cfdb73b · inbound
Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning cites this paper.
Large Language Models Show Signs of Alignment with Human Neurocognition During Abstract Reasoning Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 63
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T21:10:36.314261Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 77663c75-23bc-40cf-ae45-4fea7f918f0c · inbound
Large Model Empowered Embodied AI: A Survey on Decision-Making and Embodied Learning cites this paper.
Large Model Empowered Embodied AI: A Survey on Decision-Making and Embodied Learning Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 191
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T20:31:53.870257Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fa318581-36f8-4249-ba9b-7d7ec939165d · inbound
SATORI: Static Test Oracle Generation for REST APIs cites this paper.
SATORI: Static Test Oracle Generation for REST APIs Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T17:26:48.552058Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ca171eef-4fd3-420a-8051-9f6507910840 · inbound
Leveraging Vision-Language Large Models for Interpretable Video Action Recognition with Semantic Tokenization cites this paper.
Leveraging Vision-Language Large Models for Interpretable Video Action Recognition with Semantic Tokenization Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T05:12:54.499516Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5d91e788-d584-4756-a747-a78b5a31bf85 · inbound
SheetDesigner: MLLM-Powered Spreadsheet Layout Generation with Rule-Based and Vision-Based Reflection cites this paper.
SheetDesigner: MLLM-Powered Spreadsheet Layout Generation with Rule-Based and Vision-Based Reflection Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T22:14:15.550079Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e8d72ed2-dcf4-4090-b0f6-b37a42c9706b · inbound
AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives cites this paper.
AI Reasoning for Wireless Communications and Networking: A Survey and Perspectives Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T19:34:25.404351Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6d39f313-f117-4f59-8bd8-a882444ee909 · inbound
MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs cites this paper.
MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 56
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T19:54:20.248281Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 91346b78-69ce-4cd5-9fa8-964916bcd6b3 · inbound
MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs cites this paper.
MVI-Bench: A Comprehensive Benchmark for Evaluating Robustness to Misleading Visual Inputs in LVLMs Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T21:44:02.813473Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 91be9c46-15bf-4390-aee6-8426011432d5 · inbound
Dual Tuning for Reasoning Efficacy-Driven Data Curation in Multimodal LLM Training cites this paper.
Dual Tuning for Reasoning Efficacy-Driven Data Curation in Multimodal LLM Training Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T08:12:35.420031Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 22ce4050-fbb7-42bb-bc97-7b4ed160d06d · inbound
Decompose, Look, and Reason: Reinforced Latent Reasoning for VLMs cites this paper.
Decompose, Look, and Reason: Reinforced Latent Reasoning for VLMs Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 9
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T07:21:00.732474Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 17be4f44-b564-4c49-b36b-7894eb917c0e · inbound
3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding cites this paper.
3D-VCD: Hallucination Mitigation in 3D-LLM Embodied Agents through Visual Contrastive Decoding Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 41
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T05:56:00.234454Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 050fd947-b622-4700-91a6-a22fec9d2dde · inbound
Learning Preference-Based Objectives from Clinical Narratives for Dynamic Sepsis Treatment cites this paper.
Learning Preference-Based Objectives from Clinical Narratives for Dynamic Sepsis Treatment Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T22:22:06.385856Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation be0cbbab-f994-47b4-adba-cbadba729324 · inbound
All in One: A Unified Synthetic Data Pipeline for Multimodal Video Understanding cites this paper.
All in One: A Unified Synthetic Data Pipeline for Multimodal Video Understanding Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 90
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T10:31:03.847437Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 57ead0d9-aa04-4116-951c-43c33b0e8f39 · inbound
Mol-Debate: Multi-Agent Debate Improves Structural Reasoning in Molecular Design cites this paper.
Mol-Debate: Multi-Agent Debate Improves Structural Reasoning in Molecular Design Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 51
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T00:49:49.012626Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation de2faed8-6e47-418c-b11a-458575130d60 · inbound
Learn to Think: Improving Multimodal Reasoning through Vision-Aware Self-Improvement Training cites this paper.
Learn to Think: Improving Multimodal Reasoning through Vision-Aware Self-Improvement Training Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T06:22:23.494875Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation d98ab346-77fb-4b13-9544-93c49b75b348 · inbound
GRIP-VLM: Group-Relative Importance Pruning for Efficient Vision-Language Models cites this paper.
GRIP-VLM: Group-Relative Importance Pruning for Efficient Vision-Language Models Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T19:17:50.811951Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 5f725fde-596d-44d7-aec4-537f102a3482 · inbound
Focus-then-Context: Subject-Centric Progressive Visual Token Reduction for Vision-Language Models cites this paper.
Focus-then-Context: Subject-Centric Progressive Visual Token Reduction for Vision-Language Models Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 46
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-21T05:23:58.507985Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation fce0f2f0-498d-4079-955a-b7992d695fbd · inbound
Mags-RL: Wearing Multimodal LLMs a Magnifying Glass via Agentic Reinforcement Learning For Complex Scene Reasoning cites this paper.
Mags-RL: Wearing Multimodal LLMs a Magnifying Glass via Agentic Reinforcement Learning For Complex Scene Reasoning Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 39
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T13:43:29.006421Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 86fc9560-a05a-46b4-9af2-0a8545c80a21 · inbound
Investigating Adversarial Robustness of Multi-modal Large Language Models cites this paper.
Investigating Adversarial Robustness of Multi-modal Large Language Models Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 60
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T02:06:27.607029Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 7e4be221-1973-4068-8fe2-80caf02295d6 · inbound
SPICE: Synergy and Partial Information Based Curriculum Evolution cites this paper.
SPICE: Synergy and Partial Information Based Curriculum Evolution Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T02:09:36.267326Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 845c266d-a0c7-4114-b019-d98d8ccc6040 · inbound
SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning cites this paper.
SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 279
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-04T09:59:44.779117Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 6002f94b-0bf0-40d4-84a0-a215daebff55 · inbound
SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning cites this paper.
SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 278
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-01T18:55:59.608772Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 95753894-36ad-4324-96fe-abc79050a56b · inbound
HalluScope: Fine-grained Hallucination Diagnosis for Multimodal Large Language Models cites this paper.
HalluScope: Fine-grained Hallucination Diagnosis for Multimodal Large Language Models Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T08:33:10.377116Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.