Paper Citation Record · LEDGER

Evaluating Vision-Language Models as Evaluators in Path Planning

As of 22 August 2026, this Paper Citation Record lists 100 of 100 outbound references and 3 inbound Pith citation observations for arXiv:2411.18711.

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

pith.paper-citation-record.v1
2411.18711 v4

Coverage vector

measured 100 of 100 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:04:44.887611Z

measured 103 of 103 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:49:07.509682Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T22:49:11.112158Z

Reference resolution

100 of 100 outbound references displayed

verified exact1
verified fuzzy59
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ade9c557-2325-44ab-8d0f-3a47a0aadaec · outbound

This paper cites Faithfulness vs.

Evaluating Vision-Language Models as Evaluators in Path Planning Faithfulness vs

Reference 1

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Observation 01e605a1-7603-43b4-a035-4aa5f33450ed · outbound

This paper cites Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoning.

Evaluating Vision-Language Models as Evaluators in Path Planning Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoning

Reference 2

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Observation 3761979d-6e9a-4f2d-a81c-6dd3208b7f74 · outbound

This paper cites Look Further Ahead: Testing the Limits of GPT-4 in Path Planning.

Evaluating Vision-Language Models as Evaluators in Path Planning Look Further Ahead: Testing the Limits of GPT-4 in Path Planning

Reference 3

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This paper cites Lawrence Zitnick, and Devi Parikh.

Evaluating Vision-Language Models as Evaluators in Path Planning Lawrence Zitnick, and Devi Parikh

Reference 4

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Observation 233da0ae-c88f-4c80-8869-17a93b546bb7 · outbound

This paper cites Vision- Language Models as a Source of Rewards, 2024.

Evaluating Vision-Language Models as Evaluators in Path Planning Vision- Language Models as a Source of Rewards, 2024

Reference 5

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Evaluating Vision-Language Models as Evaluators in Path Planning Unresolved cited work

Reference 6

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Observation f4f330c3-da50-4450-ae7f-513c72441a5d · outbound

This paper cites Rehg, and Chao Zheng.

Evaluating Vision-Language Models as Evaluators in Path Planning Rehg, and Chao Zheng

Reference 7

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Observation 32f0e98a-2ad7-40d1-932b-2028269b1a11 · outbound

This paper cites Emerg- ing properties in self-supervised vision transformers.

Evaluating Vision-Language Models as Evaluators in Path Planning Emerg- ing properties in self-supervised vision transformers

Reference 8

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Observation 7b5b0e98-dc65-4bf4-a698-34cf5f8e319d · outbound

This paper cites MapGPT: Map- guided prompting with adaptive path planning for vision- and-language navigation.

Evaluating Vision-Language Models as Evaluators in Path Planning MapGPT: Map- guided prompting with adaptive path planning for vision- and-language navigation

Reference 9

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This paper cites Multi-object hallucination in vision language models.

Evaluating Vision-Language Models as Evaluators in Path Planning Multi-object hallucination in vision language models

Reference 10

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This paper cites Autotamp: Autoregressive task and motion planning with llms as translators and check- ers.

Evaluating Vision-Language Models as Evaluators in Path Planning Autotamp: Autoregressive task and motion planning with llms as translators and check- ers

Reference 11

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Observation 5b82d06f-ced8-47e9-bdd9-159ceb70355f · outbound

This paper cites InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks.

Evaluating Vision-Language Models as Evaluators in Path Planning InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks

Reference 12

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Observation d99cc4f6-441a-4bd6-8385-0c5c2d47e72c · outbound

This paper cites Gonzalez, Ion Stoica, and Eric P.

Evaluating Vision-Language Models as Evaluators in Path Planning Gonzalez, Ion Stoica, and Eric P

Reference 13

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This paper cites Task and motion planning with large language models for ob- ject rearrangement.

Evaluating Vision-Language Models as Evaluators in Path Planning Task and motion planning with large language models for ob- ject rearrangement

Reference 14

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Evaluating Vision-Language Models as Evaluators in Path Planning Unresolved cited work

Reference 15

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This paper cites Tenenbaum, Leslie Pack Kaelbling, Andy Zeng, and Jonathan Tompson.

Evaluating Vision-Language Models as Evaluators in Path Planning Tenenbaum, Leslie Pack Kaelbling, Andy Zeng, and Jonathan Tompson

Reference 16

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Evaluating Vision-Language Models as Evaluators in Path Planning Unresolved cited work

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This paper cites Cric: A vqa dataset for compositional reasoning on vision 9 and commonsense.

Evaluating Vision-Language Models as Evaluators in Path Planning Cric: A vqa dataset for compositional reasoning on vision 9 and commonsense

Reference 18

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Observation fc792dc8-6b55-4f65-89b0-bfdb299ccfe9 · outbound

This paper cites Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing.

Evaluating Vision-Language Models as Evaluators in Path Planning Making the v in vqa matter: Elevating the role of image understanding in visual question answer- ing

Reference 19

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Observation a3cf708c-c8df-4f20-ab5b-fc86919d52ee · outbound

This paper cites Vision-and-language navigation: A survey of tasks, methods, and future directions.

Evaluating Vision-Language Models as Evaluators in Path Planning Vision-and-language navigation: A survey of tasks, methods, and future directions

Reference 20

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This paper cites Task success is not enough: Investigating the use of video-language models as behavior critics for catching undesirable agent behaviors.

Evaluating Vision-Language Models as Evaluators in Path Planning Task success is not enough: Investigating the use of video-language models as behavior critics for catching undesirable agent behaviors

Reference 21

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This paper cites Hal- lusionbench: An advanced diagnostic suite for entangled language hallucination & visual illusion in large vision- language models, 2023.

Evaluating Vision-Language Models as Evaluators in Path Planning Hal- lusionbench: An advanced diagnostic suite for entangled language hallucination & visual illusion in large vision- language models, 2023

Reference 22

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Observation 16a0fdbb-a7fb-440a-b308-e0cd982e0b36 · outbound

This paper cites Generating and evolving reward functions for highway driving with large language models, 2024.

Evaluating Vision-Language Models as Evaluators in Path Planning Generating and evolving reward functions for highway driving with large language models, 2024

Reference 23

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Observation da16eae5-a81d-4993-9316-ebe64dc9fb09 · outbound

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Evaluating Vision-Language Models as Evaluators in Path Planning Hudson and Christopher D

Reference 24

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Observation 751f3513-7d07-40c2-a2ec-6a1309b9c47e · outbound

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Evaluating Vision-Language Models as Evaluators in Path Planning Open- clip, 2021

Reference 25

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Observation 104ad04a-0b97-4ab9-97f6-ae2a612b739d · outbound

This paper cites What’s ”up” with vision-language models? Investigating their strug- gle with spatial reasoning.

Evaluating Vision-Language Models as Evaluators in Path Planning What’s ”up” with vision-language models? Investigating their strug- gle with spatial reasoning

Reference 26

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This paper cites Position: LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks.

Evaluating Vision-Language Models as Evaluators in Path Planning Position: LLMs Can’t Plan, But Can Help Planning in LLM-Modulo Frameworks

Reference 27

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Evaluating Vision-Language Models as Evaluators in Path Planning Kavraki, P

Reference 28

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Evaluating Vision-Language Models as Evaluators in Path Planning Kuffner and S.M

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Evaluating Vision-Language Models as Evaluators in Path Planning Unresolved cited work

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This paper cites Why i’m optimistic about our align- ment approach: Evaluation is easier than generation.

Evaluating Vision-Language Models as Evaluators in Path Planning Why i’m optimistic about our align- ment approach: Evaluation is easier than generation

Reference 31

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Evaluating Vision-Language Models as Evaluators in Path Planning LLaVA-OneVision: Easy Visual Task Transfer

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Evaluating Vision-Language Models as Evaluators in Path Planning Auto mc-reward: Automated dense reward design with large language models for minecraft

Reference 33

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This paper cites Evaluating object hallucination in large vision-language models.

Evaluating Vision-Language Models as Evaluators in Path Planning Evaluating object hallucination in large vision-language models

Reference 34

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This paper cites OmniBench: Towards The Future of Uni- versal Omni-Language Models, 2024.

Evaluating Vision-Language Models as Evaluators in Path Planning OmniBench: Towards The Future of Uni- versal Omni-Language Models, 2024

Reference 35

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This paper cites Chin, Shuhong Chai, Neil Bose, and Eonjoo Kim.

Evaluating Vision-Language Models as Evaluators in Path Planning Chin, Shuhong Chai, Neil Bose, and Eonjoo Kim

Reference 36

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Evaluating Vision-Language Models as Evaluators in Path Planning Visual instruction tuning

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Evaluating Vision-Language Models as Evaluators in Path Planning Improved Baselines with Visual Instruction Tuning, 2024

Reference 38

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Evaluating Vision-Language Models as Evaluators in Path Planning LLaV A-NeXT: Im- proved reasoning, OCR, and world knowledge, 2024

Reference 39

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This paper cites Mmbench: Is your multi-modal model an all-around player? In Computer Vi- sion – ECCV 2024 , pages 216–233, Cham, 2025.

Evaluating Vision-Language Models as Evaluators in Path Planning Mmbench: Is your multi-modal model an all-around player? In Computer Vi- sion – ECCV 2024 , pages 216–233, Cham, 2025

Reference 40

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Evaluating Vision-Language Models as Evaluators in Path Planning Unresolved cited work

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Evaluating Vision-Language Models as Evaluators in Path Planning Mathvista: Evaluating mathe- matical reasoning of foundation models in visual contexts

Reference 42

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Evaluating Vision-Language Models as Evaluators in Path Planning Ok-vqa: A visual question answering benchmark requiring external knowledge

Reference 43

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This paper cites LLM-a*: Large language model en- hanced incremental heuristic search on path planning.

Evaluating Vision-Language Models as Evaluators in Path Planning LLM-a*: Large language model en- hanced incremental heuristic search on path planning

Reference 44

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This paper cites Jmmmu: A japanese massive multi- discipline multimodal understanding benchmark for culture- aware evaluation, 2024.

Evaluating Vision-Language Models as Evaluators in Path Planning Jmmmu: A japanese massive multi- discipline multimodal understanding benchmark for culture- aware evaluation, 2024

Reference 45

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Evaluating Vision-Language Models as Evaluators in Path Planning GPT-4V(ision) System Card

Reference 46

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Evaluating Vision-Language Models as Evaluators in Path Planning GPT-4o System Card, 2024

Reference 47

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Evaluating Vision-Language Models as Evaluators in Path Planning GPT-4 Technical Report, 2024

Reference 48

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Evaluating Vision-Language Models as Evaluators in Path Planning Unresolved cited work

Reference 49

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Evaluating Vision-Language Models as Evaluators in Path Planning LangNav: Lan- guage as a perceptual representation for navigation

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.813938Z

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This paper cites VLP: Vision Language Planning for Autonomous Driving.

Evaluating Vision-Language Models as Evaluators in Path Planning VLP: Vision Language Planning for Autonomous Driving

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.800745Z

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Observation 99c8b953-1c46-4ad5-b9d8-5260e87b8c2e · outbound

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Evaluating Vision-Language Models as Evaluators in Path Planning Path planning for autonomous underwater vehicles

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.786723Z

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Observation c1538b26-955b-4445-88a4-12e2ce8e465c · outbound

This paper cites Clearance- driven motion planning for mobile robots with differential constraints.

Evaluating Vision-Language Models as Evaluators in Path Planning Clearance- driven motion planning for mobile robots with differential constraints

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.774346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7005ad3b-978d-46f4-863b-f689888b76a5 · outbound

This paper cites Plonski, Pratap Tokekar, and V olkan Isler.Energy- Efficient Path Planning for Solar-Powered Mobile Robots , pages 717–731.

Evaluating Vision-Language Models as Evaluators in Path Planning Plonski, Pratap Tokekar, and V olkan Isler.Energy- Efficient Path Planning for Solar-Powered Mobile Robots , pages 717–731

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.761555Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 97acd0c8-db5d-457b-835e-ca4a44c14580 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Evaluating Vision-Language Models as Evaluators in Path Planning Learning transferable visual models from natural language supervision

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.748543Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 208c4b37-928b-43b2-9bb2-3b32290f0cb2 · outbound

This paper cites LAION-5b: An open large-scale dataset for train- ing next generation image-text models.

Evaluating Vision-Language Models as Evaluators in Path Planning LAION-5b: An open large-scale dataset for train- ing next generation image-text models

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.734649Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c8c31025-8e85-4d14-93d6-13da16e0cf44 · outbound

This paper cites Investigating the Limitation of CLIP Mod- els: The Worst-Performing Categories, 2023.

Evaluating Vision-Language Models as Evaluators in Path Planning Investigating the Limitation of CLIP Mod- els: The Worst-Performing Categories, 2023

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.719336Z

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Observation 1e0c459e-dbdb-4c73-b732-7123298430c9 · outbound

This paper cites Tenen- baum, Leslie Pack Kaelbling, and Michael Katz.

Evaluating Vision-Language Models as Evaluators in Path Planning Tenen- baum, Leslie Pack Kaelbling, and Michael Katz

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.705388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 13b5b4a4-0daa-4350-ad8e-11a710779da2 · outbound

This paper cites Sucan, Mark Moll, and Lydia E.

Evaluating Vision-Language Models as Evaluators in Path Planning Sucan, Mark Moll, and Lydia E

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.579131Z

Source-reported events for the cited work

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Observation 51a45461-d8de-48fc-8e17-dbde915b2d2c · outbound

This paper cites Explore the hallucination on low-level perception for mllms, 2024.

Evaluating Vision-Language Models as Evaluators in Path Planning Explore the hallucination on low-level perception for mllms, 2024

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.565304Z

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Observation 0c054946-0e83-4f26-b2a6-214da8be18a7 · outbound

This paper cites Learning to nav- igate unseen environments: Back translation with environ- mental dropout.

Evaluating Vision-Language Models as Evaluators in Path Planning Learning to nav- igate unseen environments: Back translation with environ- mental dropout

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.550962Z

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Observation 338e3d43-c4cd-4d4d-814a-ce43c9e89ffe · outbound

This paper cites Gemini: A family of highly capable multi- modal models, 2024.

Evaluating Vision-Language Models as Evaluators in Path Planning Gemini: A family of highly capable multi- modal models, 2024

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T11:04:44.738697Z

Source-reported events for the cited work

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Observation 8d596672-66fd-4258-8223-56030596b5dd · outbound

This paper cites Mass- producing failures of multimodal systems with language models.

Evaluating Vision-Language Models as Evaluators in Path Planning Mass- producing failures of multimodal systems with language models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.526822Z

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Observation 38fbf9ff-1a3a-4dee-87a6-5bdf75093e3f · outbound

This paper cites Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs, 2024.

Evaluating Vision-Language Models as Evaluators in Path Planning Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs, 2024

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.513386Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2e9a1d4a-dc7f-44f4-8186-41d648aa540e · outbound

This paper cites Llama 2: Open foundation and fine- tuned chat models, 2023.

Evaluating Vision-Language Models as Evaluators in Path Planning Llama 2: Open foundation and fine- tuned chat models, 2023

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.500389Z

Source-reported events for the cited work

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Observation e3c0274e-1378-4ba9-8692-4305b46a184d · outbound

This paper cites an unresolved cited work.

Evaluating Vision-Language Models as Evaluators in Path Planning Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:04:45.487491Z

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Observation 8218cfe2-8240-4893-beb4-ca100ef9707a · outbound

This paper cites Large Language Models Still Can’t Plan (A Benchmark for LLMs on Planning and Reasoning about Change).

Evaluating Vision-Language Models as Evaluators in Path Planning Large Language Models Still Can’t Plan (A Benchmark for LLMs on Planning and Reasoning about Change)

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.474491Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a5caeb81-5348-4536-9ab7-fec37bbde83b · outbound

This paper cites LLMs Still Can’t Plan; Can LRMs? A Preliminary Evaluation of OpenAI’s o1 on PlanBench, 2024.

Evaluating Vision-Language Models as Evaluators in Path Planning LLMs Still Can’t Plan; Can LRMs? A Preliminary Evaluation of OpenAI’s o1 on PlanBench, 2024

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.461224Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a70f9c6a-7463-475c-9399-92e807d2b61c · outbound

This paper cites Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution.

Evaluating Vision-Language Models as Evaluators in Path Planning Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-12T11:04:44.768564Z

Source-reported events for the cited work

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Observation e6ef1e45-00f8-4e60-bb4d-7d12c52c19e6 · outbound

This paper cites Reinforced cross-modal matching and self- supervised imitation learning for vision-language navigation.

Evaluating Vision-Language Models as Evaluators in Path Planning Reinforced cross-modal matching and self- supervised imitation learning for vision-language navigation

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.446679Z

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Observation a92fed30-c6c7-4975-9a0d-0e9b45721379 · outbound

This paper cites Chi, Quoc V Le, and Denny Zhou.

Evaluating Vision-Language Models as Evaluators in Path Planning Chi, Quoc V Le, and Denny Zhou

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.430518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 61302c78-1f1e-406c-8c9b-caa0d484fed0 · outbound

This paper cites Clip-dinoiser: Teaching clip a few dino tricks for open- vocabulary semantic segmentation.

Evaluating Vision-Language Models as Evaluators in Path Planning Clip-dinoiser: Teaching clip a few dino tricks for open- vocabulary semantic segmentation

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.416581Z

Source-reported events for the cited work

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Observation 6545e40f-d0ed-4e12-9072-8eb767b47fe9 · outbound

This paper cites Text2Reward: Reward Shaping with Language Models for Reinforcement Learning.

Evaluating Vision-Language Models as Evaluators in Path Planning Text2Reward: Reward Shaping with Language Models for Reinforcement Learning

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.402513Z

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Observation 34eddb6d-510e-41dd-9b97-90abb6b76ff3 · outbound

This paper cites Evaluating spatial understanding of large language models.

Evaluating Vision-Language Models as Evaluators in Path Planning Evaluating spatial understanding of large language models

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.387997Z

Source-reported events for the cited work

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Observation bd391b2f-403e-4ef6-8dd1-074afec3a009 · outbound

This paper cites Guiding long-horizon task and motion planning with vision language models, 2024.

Evaluating Vision-Language Models as Evaluators in Path Planning Guiding long-horizon task and motion planning with vision language models, 2024

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.373817Z

Source-reported events for the cited work

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Observation 606d6a23-387b-4cc5-b586-367b13321856 · outbound

This paper cites Coca: Contrastive captioners are image-text foundation models.

Evaluating Vision-Language Models as Evaluators in Path Planning Coca: Contrastive captioners are image-text foundation models

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-12T11:04:44.797911Z

Source-reported events for the cited work

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Observation b60f5fd1-affd-40a6-9a94-6ddabd629ecd · outbound

This paper cites Mm-vet: Evaluating large multimodal models for integrated capabilities.

Evaluating Vision-Language Models as Evaluators in Path Planning Mm-vet: Evaluating large multimodal models for integrated capabilities

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.349494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1faf5b72-fe6e-4f91-8347-7cf89c35e0b0 · outbound

This paper cites MMMU: A Massive Multi-discipline Multimodal Under- standing and Reasoning Benchmark for Expert AGI.

Evaluating Vision-Language Models as Evaluators in Path Planning MMMU: A Massive Multi-discipline Multimodal Under- standing and Reasoning Benchmark for Expert AGI

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.335634Z

Source-reported events for the cited work

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Observation 913e0fea-1979-4c77-8c4f-94073a936305 · outbound

This paper cites MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark.

Evaluating Vision-Language Models as Evaluators in Path Planning MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-12T11:04:44.809641Z

Source-reported events for the cited work

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Observation 42b58dfc-dacc-4079-8477-7d891a1bbed2 · outbound

This paper cites Sigmoid loss for language image pre-training.

Evaluating Vision-Language Models as Evaluators in Path Planning Sigmoid loss for language image pre-training

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.321121Z

Source-reported events for the cited work

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Observation 92df6538-c39a-4af8-b197-da54b87e7d31 · outbound

This paper cites CMMMU: A Chinese Massive Multi-discipline Multi- modal Understanding Benchmark, 2024.

Evaluating Vision-Language Models as Evaluators in Path Planning CMMMU: A Chinese Massive Multi-discipline Multi- modal Understanding Benchmark, 2024

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.307576Z

Source-reported events for the cited work

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Observation ee29249f-ac69-4f5b-9198-d65cce5efafb · outbound

This paper cites Multimodal chain-of-thought rea- soning in language models, 2024.

Evaluating Vision-Language Models as Evaluators in Path Planning Multimodal chain-of-thought rea- soning in language models, 2024

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.295221Z

Source-reported events for the cited work

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Observation b9ed1ede-e31e-49eb-95b6-cc626a4cc5cf · outbound

This paper cites Policy Improvement using Language Feed- back Models, 2024.

Evaluating Vision-Language Models as Evaluators in Path Planning Policy Improvement using Language Feed- back Models, 2024

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.282719Z

Source-reported events for the cited work

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Observation 3898e54f-21ac-40e0-97ea-31d3d2feb1a5 · outbound

This paper cites Visual7W: Grounded Question Answering in Images.

Evaluating Vision-Language Models as Evaluators in Path Planning Visual7W: Grounded Question Answering in Images

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.269706Z

Source-reported events for the cited work

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Observation 30afc4de-d523-4a54-938e-65a6753c89cb · outbound

This paper cites Min Clearance = min pj ∈P min Oi∈O D(pj, Oi).

Evaluating Vision-Language Models as Evaluators in Path Planning Min Clearance = min pj ∈P min Oi∈O D(pj, Oi)

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.256300Z

Source-reported events for the cited work

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Observation bba82f7d-5a91-44ef-a3e9-71d7261c0db1 · outbound

This paper cites Max Clearance = max pj ∈P min Oi∈O D(pj, Oi).

Evaluating Vision-Language Models as Evaluators in Path Planning Max Clearance = max pj ∈P min Oi∈O D(pj, Oi)

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.243179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7ba76492-d207-4345-a47e-c5540837a059 · outbound

This paper cites an unresolved cited work.

Evaluating Vision-Language Models as Evaluators in Path Planning Unresolved cited work

Reference 89

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:04:45.229295Z

Source-reported events for the cited work

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Observation 71a11df0-45e8-480c-b18d-79f385857564 · outbound

This paper cites Path Length = nX j=2 D(pj−1, pj).

Evaluating Vision-Language Models as Evaluators in Path Planning Path Length = nX j=2 D(pj−1, pj)

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.215081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 1482156a-5a18-4662-bd23-dfddcca7b0f6 · outbound

This paper cites Smoothness = nX j=3 θj where θj is the angle between the vectors − − − − − − →pj−2pj−1 and− − − − →pj−1pj.

Evaluating Vision-Language Models as Evaluators in Path Planning Smoothness = nX j=3 θj where θj is the angle between the vectors − − − − − − →pj−2pj−1 and− − − − →pj−1pj

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.201026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 585fbad9-0e2f-4a7f-a1d7-0bafe47bd97c · outbound

This paper cites Sharp Turns = nX j=3 δj, where δj = ( 1 if θj > 90◦ 0 otherwise.

Evaluating Vision-Language Models as Evaluators in Path Planning Sharp Turns = nX j=3 δj, where δj = ( 1 if θj > 90◦ 0 otherwise

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.187140Z

Source-reported events for the cited work

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Observation a07297e6-002d-4573-b2b1-06743bbd9168 · outbound

This paper cites Maximum angle = n max j=3 θj C.

Evaluating Vision-Language Models as Evaluators in Path Planning Maximum angle = n max j=3 θj C

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.173624Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f1989607-864e-4dc0-b6d3-9f660d93f832 · outbound

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Evaluating Vision-Language Models as Evaluators in Path Planning Unresolved cited work

Reference 94

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:04:45.160650Z

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Observation 8d3f074f-d807-4773-a14e-7c25a4875a3e · outbound

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Evaluating Vision-Language Models as Evaluators in Path Planning Unresolved cited work

Reference 95

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:04:45.146507Z

Source-reported events for the cited work

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Observation 1a7f99d8-acec-4c38-ae02-b07a8bf41e79 · outbound

This paper cites an unresolved cited work.

Evaluating Vision-Language Models as Evaluators in Path Planning Unresolved cited work

Reference 96

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:04:45.130946Z

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Observation 9c553200-9c59-4854-a2ec-4cd40bffa397 · outbound

This paper cites Smoother paths have a lower smoothness value.

Evaluating Vision-Language Models as Evaluators in Path Planning Smoother paths have a lower smoothness value

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.117915Z

Source-reported events for the cited work

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Observation 90985cf5-5f42-43b0-988f-9f1e9e4d1cb7 · outbound

This paper cites an unresolved cited work.

Evaluating Vision-Language Models as Evaluators in Path Planning Unresolved cited work

Reference 98

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:04:45.104257Z

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Observation 252f6b04-5df5-4c9a-8607-d1f62ad1f11d · outbound

This paper cites an unresolved cited work.

Evaluating Vision-Language Models as Evaluators in Path Planning Unresolved cited work

Reference 99

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:04:45.091181Z

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Observation d23d5a32-8b22-4c8a-bb54-0cfab950f2be · outbound

This paper cites Path 1 has a smaller value for the given met- ric.

Evaluating Vision-Language Models as Evaluators in Path Planning Path 1 has a smaller value for the given met- ric

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:04:45.077702Z

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Observation a058d5c1-a1b5-4368-b40c-1362551359d5 · outbound

This paper cites an unresolved cited work.

Evaluating Vision-Language Models as Evaluators in Path Planning Unresolved cited work

Reference 2022

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Observation da70871a-eec8-4526-9040-f859f5465cf5 · outbound

This paper cites an unresolved cited work.

Evaluating Vision-Language Models as Evaluators in Path Planning Unresolved cited work

Reference 2024

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Pith citing papers

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HRIBench: Benchmarking Vision-Language Models for Real-Time Human Perception in Human-Robot Interaction Evaluating Vision-Language Models as Evaluators in Path Planning

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-06T22:49:11.200446Z

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Observation 78728900-4e2e-484e-b0cf-c68cb9e86c6b · inbound

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Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T16:52:49.914971Z

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Observation 26b5b30f-c90a-4d2c-aaad-dd4e026f029c · inbound

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Resolution: unresolved; no resolver link, observed 2026-07-14T07:49:44.998466Z

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