Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:51:31.758682Z
Paper Citation Record · LEDGER
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?
As of 21 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 14 inbound Pith citation observations for arXiv:2505.11907.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T16:32:28.221452Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-08T14:44:59.676019Z
Reference resolution
63 of 63 outbound references displayed
- verified exact0
- verified fuzzy35
- unresolved28
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 4214ac27-6a40-462f-b73d-e87ebcebe72e · outbound
This paper cites DiMeR: Disentangled Mesh Reconstruction Model.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? DiMeR: Disentangled Mesh Reconstruction Model
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.466858Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 13c3f5ed-6459-4174-b27a-c63e3a76e60a · outbound
This paper cites Benchmarking Multi-modal Semantic Segmentation under Sensor Failures: Missing and Noisy Modality Robustness.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Benchmarking Multi-modal Semantic Segmentation under Sensor Failures: Missing and Noisy Modality Robustness
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.472307Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8fad7963-54e1-4a98-9828-eb9d623d3ff6 · outbound
This paper cites Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.477574Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 70ab66af-2035-4dca-8452-8ead588d9fb0 · outbound
This paper cites an unresolved cited work.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Unresolved cited work
Reference 4
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T20:51:32.832702Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0784b6be-8b51-4dff-a70b-90249b7e4bfa · outbound
This paper cites Realrag: Retrieval-augmented realistic image generation via self-reflective contrastive learning.arXiv preprint arXiv:2502.00848, 2025.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Realrag: Retrieval-augmented realistic image generation via self-reflective contrastive learning.arXiv preprint arXiv:2502.00848, 2025
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.488912Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 028b6d53-39c1-4865-99e6-28ca29b58ce1 · outbound
This paper cites Distilling efficient vision transformers from cnns for semantic segmentation.Pattern Recognition, 158:111029, 2025.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Distilling efficient vision transformers from cnns for semantic segmentation.Pattern Recognition, 158:111029, 2025
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.817168Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 121fc705-5832-4cee-af3c-84de5d094459 · outbound
This paper cites Open panoramic segmentation.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Open panoramic segmentation
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.802445Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation beb1a02b-cdc7-43f1-990e-0cd6a24b67a6 · outbound
This paper cites an unresolved cited work.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Unresolved cited work
Reference 8
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T20:51:32.787547Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7c636476-17af-4d46-ab90-d74adba73f63 · outbound
This paper cites Bending reality: Distortion-aware transformers for adapting to panoramic semantic segmentation.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Bending reality: Distortion-aware transformers for adapting to panoramic semantic segmentation
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.773275Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d8d1ac6c-5b6f-47d1-a115-7afb6215fd73 · outbound
This paper cites Both style and distortion matter: Dual-path unsupervised domain adaptation for panoramic semantic segmentation.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Both style and distortion matter: Dual-path unsupervised domain adaptation for panoramic semantic segmentation
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.760108Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 9843ecef-aee0-4fd2-8d84-185b9ae87b95 · outbound
This paper cites Look at the neighbor: Distortion-aware unsupervised domain adaptation for panoramic semantic segmentation.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Look at the neighbor: Distortion-aware unsupervised domain adaptation for panoramic semantic segmentation
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.744876Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0e76e5c1-375f-4509-bb81-41f4456f96f2 · outbound
This paper cites Semantics distortion and style matter: Towards source-free UDA for panoramic segmentation.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Semantics distortion and style matter: Towards source-free UDA for panoramic segmentation
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.728543Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 626b6e06-a49b-471e-8644-f9d1e75a95a0 · outbound
This paper cites 360SFUDA++: Towards source-free UDA for panoramic segmentation by learning reliable category prototypes.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? 360SFUDA++: Towards source-free UDA for panoramic segmentation by learning reliable category prototypes.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.709614Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 398d28f0-daa8-46a1-a793-53f9a5265b38 · outbound
This paper cites GoodSAM: Bridging domain and capacity gaps via segment anything model for distortion-aware panoramic semantic segmentation.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? GoodSAM: Bridging domain and capacity gaps via segment anything model for distortion-aware panoramic semantic segmentation
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.693015Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 3142aa25-10d0-486a-ae87-5048ad68b0c4 · outbound
This paper cites GoodSAM++: Bridging Domain and Capacity Gaps via Segment Anything Model for Panoramic Semantic Segmentation.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? GoodSAM++: Bridging Domain and Capacity Gaps via Segment Anything Model for Panoramic Semantic Segmentation
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.540206Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eb6dbcc6-b488-4061-98af-5429ad2a95e6 · outbound
This paper cites Interact360: Interactive identity-driven text to 360° panorama generation.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Interact360: Interactive identity-driven text to 360° panorama generation
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.679012Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 570585d6-6026-4e15-8ee7-a09043b878ad · outbound
This paper cites OmniSAM: Omnidirectional segment anything model for UDA in panoramic semantic segmentation.arXiv preprint arXiv:2503.07098, 2025.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? OmniSAM: Omnidirectional segment anything model for UDA in panoramic semantic segmentation.arXiv preprint arXiv:2503.07098, 2025
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.549290Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6df75775-2aec-45fd-b1fb-a298f94f6a80 · outbound
This paper cites A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? A Survey of Mathematical Reasoning in the Era of Multimodal Large Language Model: Benchmark, Method & Challenges
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.553533Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 08e7849a-553b-40aa-8ccb-4b3ae5c41d3a · outbound
This paper cites MMUnlearner: Reformulating Multimodal Machine Unlearning in the Era of Multimodal Large Language Models.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? MMUnlearner: Reformulating Multimodal Machine Unlearning in the Era of Multimodal Large Language Models
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.558531Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eac0d469-bb35-4c0f-a042-2660fef78b6b · outbound
This paper cites Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.563568Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d5fca540-2f9e-4915-9589-d34fb13ffacb · outbound
This paper cites ManipLLM: Embodied multimodal large language model for object-centric robotic manipulation.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? ManipLLM: Embodied multimodal large language model for object-centric robotic manipulation
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.664657Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 07c0994c-d3dc-4572-96c4-6ff697c6052b · outbound
This paper cites PanoContext-Former: Panoramic total scene understanding with a transformer.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? PanoContext-Former: Panoramic total scene understanding with a transformer
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.649071Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 0635ce79-230d-4b04-99de-1e391dc4052c · outbound
This paper cites DeepPanoContext: Panoramic 3D scene understanding with holistic scene context graph and relation-based optimization.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? DeepPanoContext: Panoramic 3D scene understanding with holistic scene context graph and relation-based optimization
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.633053Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7c3e25e5-62da-46bd-92ad-97c3d1f54668 · outbound
This paper cites Evaluating object hallucination in large vision-language models.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Evaluating object hallucination in large vision-language models
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.580309Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a02d84da-aec8-44a2-8a7c-4edf2972f277 · outbound
This paper cites Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.584843Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9cf1cdc1-6641-4683-9450-9f27a3adffd1 · outbound
This paper cites Improved baselines with visual instruction tuning.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Improved baselines with visual instruction tuning
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.588941Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9aae7c2e-e0c8-4a49-83e9-8c91683df178 · outbound
This paper cites The Llama 3 Herd of Models.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? The Llama 3 Herd of Models
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.594178Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6e42aa9d-577f-474c-88c4-75a07bb12a9e · outbound
This paper cites Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.598531Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1cf03ce4-eff8-4aaf-81f4-04ba940ade45 · outbound
This paper cites DeepSeek-VL: Towards Real-World Vision-Language Understanding.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? DeepSeek-VL: Towards Real-World Vision-Language Understanding
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.603940Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 92036eef-d850-4dbf-a0a6-b39be2cca922 · outbound
This paper cites Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.608920Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 75f581e6-b00b-420f-b677-da9e5e3027ae · outbound
This paper cites Making the V in VQA matter: Elevating the role of image understanding in visual question answering.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Making the V in VQA matter: Elevating the role of image understanding in visual question answering
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.613470Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7f905c13-fc30-4379-9e92-0a22c32257e8 · outbound
This paper cites Hudson and Christopher D.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Hudson and Christopher D
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.590353Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation c9b02bf9-8ced-45f6-940a-e782356d66e1 · outbound
This paper cites Towards VQA models that can read.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Towards VQA models that can read
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.575694Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7581812c-b3dc-4a77-8c4b-41c72ae610a2 · outbound
This paper cites SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.628793Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c3fbdf7c-4920-4435-bb66-f403d66fc6a9 · outbound
This paper cites MMBench: Is your multi-modal model an all-around player? InECCV, 2024.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? MMBench: Is your multi-modal model an all-around player? InECCV, 2024
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.560204Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2670ec3b-f06e-4451-91ea-fb3680d9d5d8 · outbound
This paper cites MM-Vet: Evaluating large multimodal models for integrated capabilities.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? MM-Vet: Evaluating large multimodal models for integrated capabilities
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.545683Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 89e0d854-ff64-4357-b169-3230df3c043a · outbound
This paper cites ConvBench: A multi-turn conversation evaluation benchmark with hierarchical ablation capability for large vision-language models.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? ConvBench: A multi-turn conversation evaluation benchmark with hierarchical ablation capability for large vision-language models
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.532148Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 4d151204-0300-428d-9c56-7d688f13ce34 · outbound
This paper cites MMDU: A multi-turn multi-image dialog understanding benchmark and instruction-tuning dataset for LVLMs.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? MMDU: A multi-turn multi-image dialog understanding benchmark and instruction-tuning dataset for LVLMs
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.517026Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 7cb02c7a-f0ab-472b-a916-b689cc2dd027 · outbound
This paper cites Negative object presence evaluation (NOPE) to measure object hallucination in vision-language models.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Negative object presence evaluation (NOPE) to measure object hallucination in vision-language models
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.499498Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 3edc5dcc-c52c-4eb8-a66a-47e2e2d5bc74 · outbound
This paper cites Is a picture worth a thousand words? Delving into spatial reasoning for vision language models.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Is a picture worth a thousand words? Delving into spatial reasoning for vision language models
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.485937Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 600d5c69-87e5-4f7b-9d4a-e8c202713e70 · outbound
This paper cites Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.662128Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ae96d098-aa7a-4242-a8f6-01602e25d62e · outbound
This paper cites PanoContext: A whole-room 3D context model for panoramic scene understanding.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? PanoContext: A whole-room 3D context model for panoramic scene understanding
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.471976Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation adb0e3b3-a19c-4190-9a70-c7d35c92f7ac · outbound
This paper cites PANDORA: A panoramic detection dataset for object with orientation.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? PANDORA: A panoramic detection dataset for object with orientation
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.456784Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation af5873e6-6efc-4e53-93dc-a8116a359538 · outbound
This paper cites 360+x: A panoptic multi-modal scene understanding dataset.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? 360+x: A panoptic multi-modal scene understanding dataset
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.441868Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2077dc25-a16d-427b-a09c-a2f8a7629596 · outbound
This paper cites an unresolved cited work.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Unresolved cited work
Reference 45
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T20:51:32.427967Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 219a45de-cd58-4170-a147-611527598695 · outbound
This paper cites KITTI-360: A novel dataset and benchmarks for urban scene understanding in 2D and 3D.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? KITTI-360: A novel dataset and benchmarks for urban scene understanding in 2D and 3D.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.413359Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 30fd200c-f404-4ea8-93a9-37c453595474 · outbound
This paper cites DensePASS: Dense panoramic semantic segmentation via unsupervised domain adaptation with attention-augmented context exchange.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? DensePASS: Dense panoramic semantic segmentation via unsupervised domain adaptation with attention-augmented context exchange
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.399002Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation c8a77633-2c0d-4a9c-ad96-c3e1df8835a0 · outbound
This paper cites Waymo open dataset: Panoramic video panoptic segmentation.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Waymo open dataset: Panoramic video panoptic segmentation
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.385373Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation fa630c73-4294-4d64-9245-51bebdc91ddc · outbound
This paper cites Matterport3D: Learning from RGB-D data in indoor environments.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Matterport3D: Learning from RGB-D data in indoor environments
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.372905Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e2761595-c591-484d-bc73-7c1f1be9feef · outbound
This paper cites Joint 2D-3D-Semantic Data for Indoor Scene Understanding.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Joint 2D-3D-Semantic Data for Indoor Scene Understanding
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.700107Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aa6ac5ed-d5e8-413f-a500-b3c9ffaac082 · outbound
This paper cites Pano-A VQA: Grounded audio-visual question answering on 360° videos.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Pano-A VQA: Grounded audio-visual question answering on 360° videos
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.357532Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d807fb79-af2c-4d0c-ab4f-f156358a3020 · outbound
This paper cites Visual question answering on 360° images.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Visual question answering on 360° images
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.341341Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d5e6b545-85d8-4e2c-b233-f8d7dd2c8a1d · outbound
This paper cites Karen Liu, Hyowon Gweon, Jiajun Wu, Li Fei-Fei, and Silvio Savarese.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Karen Liu, Hyowon Gweon, Jiajun Wu, Li Fei-Fei, and Silvio Savarese
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.326278Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d80ecb80-6266-4bbb-ac13-1ca8b84e11e9 · outbound
This paper cites Video question answering for people with visual impairments using an egocentric 360-degree camera.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Video question answering for people with visual impairments using an egocentric 360-degree camera
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.311488Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 3bb88956-953d-4f05-a67f-bcd29659e3ce · outbound
This paper cites Xing, Hao Zhang, Joseph E.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Xing, Hao Zhang, Joseph E
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.296586Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 217aec4a-e930-457d-a9e2-17dca4a54a01 · outbound
This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
Reference 56
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.728159Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation de5ea926-adf9-46ab-8fad-1903fde67e1c · outbound
This paper cites GPT-4 Technical Report.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? GPT-4 Technical Report
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.732763Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 87ad5648-def1-49e3-b02a-7cd1f3e3a691 · outbound
This paper cites Focus ONLY on these categories.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Focus ONLY on these categories
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.282222Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation e9785a36-596e-456e-94ee-c5a27f1faf84 · outbound
This paper cites an unresolved cited work.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Unresolved cited work
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.742672Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9723daed-ab6e-42c5-8fdc-42d47941d9cb · outbound
This paper cites an unresolved cited work.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Unresolved cited work
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:51:31.746930Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5285f894-7fb9-4a2d-aad6-9f9daeba6d50 · outbound
This paper cites an unresolved cited work.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Unresolved cited work
Reference 61
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T20:51:32.247369Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 38b4334b-acc8-497f-a704-7a11033745d0 · outbound
This paper cites Closer objects should be placed near the center of the grid, while distant objects should be placed toward the edges.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? Closer objects should be placed near the center of the grid, while distant objects should be placed toward the edges
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.232321Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 529e2f2f-8085-41b3-85d7-3439d0be5ec7 · outbound
This paper cites cate- gory name.
Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning? cate- gory name
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:51:32.214292Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Pith citing papers
Observation 5abb6290-bf85-4906-895c-34d5a20f2458 · inbound
Omnidirectional Spatial Modeling from Correlated Panoramas cites this paper.
Omnidirectional Spatial Modeling from Correlated Panoramas Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:56:37.717534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 43e4b6e8-1baf-4d16-a8c0-8bb6bfbead22 · inbound
One Flight Over the Gap: A Survey from Perspective to Panoramic Vision cites this paper.
One Flight Over the Gap: A Survey from Perspective to Panoramic Vision Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?
Reference 272
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T16:32:28.221452Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cc70dddd-d1b8-4cf1-9a8b-80502f10d403 · inbound
Beyond Thinking: Imagining in 360$^\circ$ for Humanoid Visual Search cites this paper.
Beyond Thinking: Imagining in 360$^\circ$ for Humanoid Visual Search Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?
Reference 10
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-12T03:01:18.289687Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2c2bf1a0-1fb2-42d4-ac68-d17823b0c0fa · inbound
Beyond Localization: A Comprehensive Diagnosis of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images cites this paper.
Beyond Localization: A Comprehensive Diagnosis of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T06:57:27.391208Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2f122681-9e17-437f-833e-f78c75ec0125 · inbound
Beyond Localization: A Comprehensive Diagnosis of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images cites this paper.
Beyond Localization: A Comprehensive Diagnosis of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T21:39:29.060872Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2a4ae096-c122-466e-8e03-116dbae3ed40 · inbound
Beyond Localization: A Comprehensive Diagnosis of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images cites this paper.
Beyond Localization: A Comprehensive Diagnosis of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?
Reference 11
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T22:29:09.857085Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation a2a1c787-b80a-49f8-9dae-5e07b9cc7707 · inbound
PanoWorld: Towards Spatial Supersensing in 360$^\circ$ Panorama World cites this paper.
PanoWorld: Towards Spatial Supersensing in 360$^\circ$ Panorama World Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T20:42:57.553435Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation bf4e4261-fd0e-4fd9-b8fd-ddea000b385f · inbound
PanoWorld: Towards Spatial Supersensing in 360$^\circ$ Panorama World cites this paper.
PanoWorld: Towards Spatial Supersensing in 360$^\circ$ Panorama World Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T16:57:40.120350Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 77cc1285-2155-41fa-9d5e-fe6602069f07 · inbound
Eliciting Complex Spatial Reasoning in MLLMs through Wide-Baseline Matching cites this paper.
Eliciting Complex Spatial Reasoning in MLLMs through Wide-Baseline Matching Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T02:36:27.097894Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 1162cc6d-6e32-4727-b1c5-185155f95162 · inbound
Panoramic Scene Understanding: A Survey from Distortion-Aware Engineering to Sphere-Native Modeling cites this paper.
Panoramic Scene Understanding: A Survey from Distortion-Aware Engineering to Sphere-Native Modeling Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?
Reference 25
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T20:03:56.622701Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d6159810-5c67-4fa8-9da0-f4b16dd4cc83 · inbound
Panoramic Scene Understanding: A Survey from Distortion-Aware Engineering to Sphere-Native Modeling cites this paper.
Panoramic Scene Understanding: A Survey from Distortion-Aware Engineering to Sphere-Native Modeling Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T09:58:47.727146Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fc623953-8777-4802-9eaf-ef25c84ff2d4 · inbound
OmniCoT: A Benchmark for Global and Multi-Step Panoramic Reasoning cites this paper.
OmniCoT: A Benchmark for Global and Multi-Step Panoramic Reasoning Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T06:14:19.082359Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 3f274e56-1d19-4a06-a910-12c2785f3bcb · inbound
Seek to Segment: Active Perception for Panoramic Referring Segmentation cites this paper.
Seek to Segment: Active Perception for Panoramic Referring Segmentation Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?
Reference 60
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T14:48:32.618936Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 86bf2789-2a3d-4959-8d59-7f1a160e6572 · inbound
EAGOR: Embodied Reasoning in Omni-direction cites this paper.
EAGOR: Embodied Reasoning in Omni-direction Are Multimodal Large Language Models Ready for Omnidirectional Spatial Reasoning?
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-07-08T14:44:59.678569Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.