Paper Citation Record · LEDGER

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries

As of 13 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 4 inbound Pith citation observations for arXiv:2412.10726.

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

pith.paper-citation-record.v1
2412.10726 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:43:51.799817Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T13:42:23.610697Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T21:18:59.491529Z

Reference resolution

48 of 48 outbound references displayed

verified exact1
verified fuzzy31
unresolved15
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9002ad96-b4e7-4987-88b8-c5a6d0085329 · outbound

This paper cites GPT-4 Technical Report.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T15:43:51.624285Z

Source-reported events for the cited work

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Observation 43fdccfb-08bb-4b47-897d-eb54215426cd · outbound

This paper cites Vqa: Visual question answering.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Vqa: Visual question answering

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.355787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8885a7bc-0cb9-493a-8fbd-77798f74669f · outbound

This paper cites Embodied question answer- ing.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Embodied question answer- ing

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.342885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3f2865eb-4930-4136-8966-102de8419532 · outbound

This paper cites Neural modular control for embodied question answering.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Neural modular control for embodied question answering

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.330838Z

Source-reported events for the cited work

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Observation 9ec7816c-393b-4db1-877d-e93c37f9c360 · outbound

This paper cites Is the House Ready For Sleeptime? Generating and Evaluating Situational Queries for Embodied Question Answering.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Is the House Ready For Sleeptime? Generating and Evaluating Situational Queries for Embodied Question Answering

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T15:43:51.639704Z

Source-reported events for the cited work

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Observation 0a74e696-e175-42e9-ba71-91f1f9f4b900 · outbound

This paper cites The eu ai act: a summary of its significance and scope.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries The eu ai act: a summary of its significance and scope

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.318636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e6ece127-1331-4bb7-8a77-71096ce9eace · outbound

This paper cites Dissecting dissonance: Benchmarking large mul- timodal models against self-contradictory instructions.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Dissecting dissonance: Benchmarking large mul- timodal models against self-contradictory instructions

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.306478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 70d95bb0-b7c6-4d83-b186-5bafa2b540fc · outbound

This paper cites ActiveLab: Active Learning with Re-Labeling by Multiple Annotators.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries ActiveLab: Active Learning with Re-Labeling by Multiple Annotators

Reference 8

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Source-reported events for the cited work

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Observation 4a6c8a58-7c69-4a44-9acb-f3b24deef545 · outbound

This paper cites Hallusionbench: an advanced diagnos- tic suite for entangled language hallucination and visual il- lusion in large vision-language models.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Hallusionbench: an advanced diagnos- tic suite for entangled language hallucination and visual il- lusion in large vision-language models

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.292548Z

Source-reported events for the cited work

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Observation 286ca3d3-b883-4771-8b2f-084d16c75008 · outbound

This paper cites Detecting and preventing hallucinations in large vision language models.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Detecting and preventing hallucinations in large vision language models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T15:43:51.659146Z

Source-reported events for the cited work

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Observation d40593ed-7383-4dcc-8235-8a7542d9694a · outbound

This paper cites Quantifying the uncertainty of llm hallucination spreading in complex adaptive social networks.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Quantifying the uncertainty of llm hallucination spreading in complex adaptive social networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.274121Z

Source-reported events for the cited work

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Observation f576f722-5548-4a00-8f90-efbfcea869c6 · outbound

This paper cites Hal-eval: A uni- versal and fine-grained hallucination evaluation framework for large vision language models.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Hal-eval: A uni- versal and fine-grained hallucination evaluation framework for large vision language models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.264517Z

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Observation aff39dd5-a833-4efc-9127-fc3efe493bd6 · outbound

This paper cites Hallucination detection in llm-enriched prod- uct listings.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Hallucination detection in llm-enriched prod- uct listings

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.253901Z

Source-reported events for the cited work

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Observation 2bab8fdc-5af5-4ef8-91af-c74de62827da · outbound

This paper cites Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models

Reference 14

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Source-reported events for the cited work

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Observation 9f3133f4-aa69-4f2e-9255-340f071b72ae · outbound

This paper cites Mitigating object hal- lucinations in large vision-language models through visual contrastive decoding.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Mitigating object hal- lucinations in large vision-language models through visual contrastive decoding

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.242879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1d9169c0-bf2f-4237-9b34-f6decee85b1e · outbound

This paper cites How to configure good in-context sequence for visual question answering.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries How to configure good in-context sequence for visual question answering

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.232060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation ec1375f7-3a97-4737-8dce-b4c46735fcf7 · outbound

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

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Evaluating Object Hallucination in Large Vision-Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T15:43:51.685868Z

Source-reported events for the cited work

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Observation 18a73720-6bac-41a1-95e4-bea929b9c593 · outbound

This paper cites A Survey on Hallucination in Large Vision-Language Models.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries A Survey on Hallucination in Large Vision-Language Models

Reference 18

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Source-reported events for the cited work

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Observation 816ef6f5-e498-4fb8-889b-909d7b76940d · outbound

This paper cites Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T15:43:51.693745Z

Source-reported events for the cited work

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Observation ed784eed-ed0f-4b78-87c9-676c64e3fe34 · outbound

This paper cites Robust-eqa: robust learning for embodied question answering with noisy labels.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Robust-eqa: robust learning for embodied question answering with noisy labels

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.221217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e21d37cc-8cd7-4298-8ba7-7674f125bd40 · outbound

This paper cites SQA3D: Situated Question Answering in 3D Scenes.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries SQA3D: Situated Question Answering in 3D Scenes

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T15:43:51.702124Z

Source-reported events for the cited work

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Observation 6610fab9-5177-4db5-b133-00fbdb71ae01 · outbound

This paper cites Openeqa: Embodied question answering in the era of foun- dation models.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Openeqa: Embodied question answering in the era of foun- dation models

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.210934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8c902f07-0e6d-45b5-a3ed-1e83f8b89633 · outbound

This paper cites Confident learning: Estimating uncertainty in dataset labels.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Confident learning: Estimating uncertainty in dataset labels

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.200396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dcc17800-0d18-4e8c-903f-300e8e852c09 · outbound

This paper cites Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks

Reference 24

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Source-reported events for the cited work

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Observation 7b111485-eacb-4906-bf7e-6af5e4ddffb0 · outbound

This paper cites Dataset shift in ma- chine learning.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Dataset shift in ma- chine learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.189634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f9885328-8576-4165-86d9-3fec7be4fce5 · outbound

This paper cites Explore until Confident: Efficient Exploration for Embodied Question Answering.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Explore until Confident: Efficient Exploration for Embodied Question Answering

Reference 26

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Source-reported events for the cited work

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Observation 26740b3d-d414-48d2-bde5-f1958bcb586c · outbound

This paper cites Prompt- ing large language models with answer heuristics for knowledge-based visual question answering.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Prompt- ing large language models with answer heuristics for knowledge-based visual question answering

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.179340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0728884a-f72c-4dec-a48c-5ef6799ca7c2 · outbound

This paper cites Adaptive integration of par- tial label learning and negative learning for enhanced noisy label learning.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Adaptive integration of par- tial label learning and negative learning for enhanced noisy label learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.168694Z

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Observation 81a47e3d-dc8f-4587-a1b2-69219c60cfc7 · outbound

This paper cites Mind the Error! Detection and Localization of Instruction Errors in Vision-and-Language Navigation.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Mind the Error! Detection and Localization of Instruction Errors in Vision-and-Language Navigation

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-11T15:43:51.858671Z

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Observation db5d53ea-193f-4c23-957e-6a9797a632bd · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 30

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Observation e9a7ae0e-5165-443f-9fbc-6c31d2d8b4c2 · outbound

This paper cites an unresolved cited work.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Unresolved cited work

Reference 31

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:43:52.157808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 365caa1b-2f00-46cd-85fb-cb5cdfec5912 · outbound

This paper cites Unlocking the power of open set: A new perspective for open-set noisy label learn- ing.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Unlocking the power of open set: A new perspective for open-set noisy label learn- ing

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.147135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f590ccc3-9de6-45a6-a472-1cecc3c9895e · outbound

This paper cites 3d-aware visual question answering about parts, poses and occlusions.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries 3d-aware visual question answering about parts, poses and occlusions

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.137575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2bd043aa-2272-4304-a6a0-ae2a897068a7 · outbound

This paper cites Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Mitigating Hallucinations in Large Vision-Language Models with Instruction Contrastive Decoding

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-11T15:43:51.746584Z

Source-reported events for the cited work

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Observation 103c9a2f-8fa0-48ae-ab83-1ae723f9d6e9 · outbound

This paper cites Multi-target embodied question answering.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Multi-target embodied question answering

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.127062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cabe6006-e13c-4d43-9e04-30845289e777 · outbound

This paper cites Hallucidoctor: Mitigating hallucinatory toxicity in visual instruction data.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Hallucidoctor: Mitigating hallucinatory toxicity in visual instruction data

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.117370Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2c0e55aa-ecce-4539-aa64-5b3ebd337926 · outbound

This paper cites Early stopping against label noise without validation data.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Early stopping against label noise without validation data

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.106704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 59e4239d-c414-4162-a5e6-3bbdd873baff · outbound

This paper cites Learning visual question an- swering on controlled semantic noisy labels.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Learning visual question an- swering on controlled semantic noisy labels

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.095123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a209d94b-2bd5-4c98-a177-f9ccbba4b7ab · outbound

This paper cites Badlabel: A robust perspective on evaluating and enhancing label-noise learn- ing.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Badlabel: A robust perspective on evaluating and enhancing label-noise learn- ing

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.083601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4cd41b2b-908d-408b-be3b-20d8709d55ee · outbound

This paper cites AI Transparency in NoisyEQA Transparency in AI systems is essential for reliable [6] and interpretable decision-making, especially in noisy scenarios addressed in NoisyEQA.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries AI Transparency in NoisyEQA Transparency in AI systems is essential for reliable [6] and interpretable decision-making, especially in noisy scenarios addressed in NoisyEQA

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.071836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cf45a1da-abd8-436e-8eea-ca2c3f1e8200 · outbound

This paper cites Posi- tion.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Posi- tion

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.060745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 35c553bc-c316-4745-8d2a-20c8642f1683 · outbound

This paper cites an unresolved cited work.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:43:52.049250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9445f308-fcd1-4824-a849-84fcb7d03d1c · outbound

This paper cites This indicates a fun- damental misunderstanding for both the noise and the question’s core intent.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries This indicates a fun- damental misunderstanding for both the noise and the question’s core intent

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.036422Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 73911842-fc64-479b-9547-81a14e0f58e1 · outbound

This paper cites This suggests that the agent achieves the correct answer by coincidence, but fails to handle the noise in the question.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries This suggests that the agent achieves the correct answer by coincidence, but fails to handle the noise in the question

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.023287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0b9ee3c7-514f-459a-9b10-cbc22724b8d8 · outbound

This paper cites No, it’s not mentioned.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries No, it’s not mentioned

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:52.011613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a6c1a956-a62f-43c5-85a3-db76476b46ff · outbound

This paper cites It shows that the agent fully understands the question but not achieves a completely correct answer.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries It shows that the agent fully understands the question but not achieves a completely correct answer

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:51.999884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation de802415-f243-43d9-8858-6e4c15cddb00 · outbound

This paper cites It demonstrates that the agent has a full understanding of the question and can provide a high-quality answer.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries It demonstrates that the agent has a full understanding of the question and can provide a high-quality answer

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:43:51.988253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 41010130-4d89-4ec9-82e3-924efed057c6 · outbound

This paper cites Impact of Noise on Response Confidence Figure 2 in the manuscript shows that the noise in the ques- tion will significantly decrease the generation accuracy.

NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries Impact of Noise on Response Confidence Figure 2 in the manuscript shows that the noise in the ques- tion will significantly decrease the generation accuracy

Reference 48

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T15:43:51.976894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

Observation 46060bfd-e59e-485d-9da4-81ab370387ec · inbound

DarkQA: Benchmarking Vision-Language Models on Visual-Primitive Question Answering in Low-Light Indoor Scenes cites this paper.

DarkQA: Benchmarking Vision-Language Models on Visual-Primitive Question Answering in Low-Light Indoor Scenes NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-16T18:43:16.505575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b2ab6abe-37ea-4a80-a0c9-01f7331e0eea · inbound

Extending Embodied Question Answering from Perception to Decision cites this paper.

Extending Embodied Question Answering from Perception to Decision NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-06-29T21:33:58.966103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f8bfa398-bf03-4152-92de-22614aef5712 · inbound

ERQA-Plus: A Diagnostic Benchmark for Reasoning in Embodied AI cites this paper.

ERQA-Plus: A Diagnostic Benchmark for Reasoning in Embodied AI NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-07-03T21:18:59.493163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8b2825cb-e159-4977-958f-d5338ee6b098 · inbound

ActiveFly-Bench: Aligning Embodied Question Answering with Vision-Language-Action for Aerial Embodied Perception cites this paper.

ActiveFly-Bench: Aligning Embodied Question Answering with Vision-Language-Action for Aerial Embodied Perception NoisyEQA: Benchmarking Embodied Question Answering Against Noisy Queries

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-14T13:42:23.610697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.