ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models | Paper Citation Record

Observation abecde08-14da-408c-9db3-58fc95cf88a4 · outbound

This paper cites Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:32:33.134725Z

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Observation 6297bc22-ada9-45c0-8a01-4c6b657e449e · outbound

This paper cites PDM-Lite: A rule-based planner for carla leaderboard 2.0.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models PDM-Lite: A rule-based planner for carla leaderboard 2.0

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.639392Z

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Observation 476908b7-2dda-4ee4-b16d-8ebb3f11cb64 · outbound

This paper cites Towards Synergistic, Generalized, and Efficient Dual-System for Robotic Manipulation.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Towards Synergistic, Generalized, and Efficient Dual-System for Robotic Manipulation

Reference 3

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Observation f8eb9768-9a47-4b0a-9168-a8690e6d88d5 · outbound

This paper cites End-to-end autonomous driving: Challenges and frontiers.IEEE TPAMI, 2024.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models End-to-end autonomous driving: Challenges and frontiers.IEEE TPAMI, 2024

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.625059Z

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Observation 1517738f-a213-4d79-8d7a-5f5bf0e117e6 · outbound

This paper cites InternVL: Scaling up vision foundation models and aligning for generic visual-linguistic tasks.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models InternVL: Scaling up vision foundation models and aligning for generic visual-linguistic tasks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.609043Z

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Observation 740aacbf-67a3-403b-b6bf-59c1de6ab790 · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.593946Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0dda3ce0-53bc-429f-8d42-df22199a39ad · outbound

This paper cites an unresolved cited work.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:32:33.579147Z

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Observation bc45f9d9-2972-454c-a152-288b49d676a3 · outbound

This paper cites Helix: A vision-language-action model for generalist humanoid control.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Helix: A vision-language-action model for generalist humanoid control

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.565187Z

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 6fd11281-0a5d-4ca4-b245-ce3e7ba42e98 · outbound

This paper cites Planning-oriented autonomous driv- ing.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Planning-oriented autonomous driv- ing

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.551709Z

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 60f30ab4-8f85-4877-85b1-0c7901727bf7 · outbound

This paper cites Hid- den biases of end-to-end driving models.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Hid- den biases of end-to-end driving models

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.537464Z

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 e1122433-99fd-4c3d-aab5-099766253add · outbound

This paper cites DriveAdapter: breaking the coupling barrier of perception and planning in end-to-end autonomous driving.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models DriveAdapter: breaking the coupling barrier of perception and planning in end-to-end autonomous driving

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.523299Z

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Observation 15488630-d38d-44bf-b683-8d83bff8868d · outbound

This paper cites Think twice before driv- ing: Towards scalable decoders for end-to-end autonomous driving.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Think twice before driv- ing: Towards scalable decoders for end-to-end autonomous driving

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.509472Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 23ecc1ad-dc8e-42bd-a552-a5e9e194e51e · outbound

This paper cites Bench2Drive: towards multi-ability bench- marking of closed-loop end-to-end autonomous driving.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Bench2Drive: towards multi-ability bench- marking of closed-loop end-to-end autonomous driving

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.494458Z

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Observation ed26de16-f777-4519-bc3d-db5fb7b4af59 · outbound

This paper cites DriveTransformer: unified transformer for scalable end-to- end autonomous driving.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models DriveTransformer: unified transformer for scalable end-to- end autonomous driving

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.480666Z

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 bd58fbb4-189f-4cc3-ad83-7b4f6d4d9b9a · outbound

This paper cites V AD: vectorized scene representation for efficient autonomous driving.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models V AD: vectorized scene representation for efficient autonomous driving

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.466198Z

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 5a33190f-af65-4c7d-a441-44973718cdcc · outbound

This paper cites Farrar, Straus and Giroux, 2011.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Farrar, Straus and Giroux, 2011

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.450292Z

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 30e1bfc6-3947-4897-894e-3d05b220087e · outbound

This paper cites Integrating End-to-End and Modular Driving Approaches for Online Corner Case Detection in Autonomous Driving.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Integrating End-to-End and Modular Driving Approaches for Online Corner Case Detection in Autonomous Driving

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:32:33.094914Z

Source-reported events for the cited work

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Observation 3360259f-5944-42b2-a11f-6ed5daa42ec6 · outbound

This paper cites An energy and gpu-computation efficient backbone network for real-time object detection.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models An energy and gpu-computation efficient backbone network for real-time object detection

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.435120Z

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 567108f4-8da7-4780-b8ad-a53b66128478 · outbound

This paper cites Think2Drive: efficient reinforcement learning by thinking in latent world model for quasi-realistic autonomous driving (in carla-v2).

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Think2Drive: efficient reinforcement learning by thinking in latent world model for quasi-realistic autonomous driving (in carla-v2)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.420059Z

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 0ff9f5bb-5ad5-4407-afdb-79b35ae531e5 · outbound

This paper cites CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:32.640391Z

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Observation b3d98036-c704-4517-abb8-f7fbfd9f11ee · outbound

This paper cites Enhancing end-to-end autonomous driving with latent world model.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Enhancing end-to-end autonomous driving with latent world model

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.403888Z

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 091c07ec-f83b-404c-982a-d5a86c9240fb · outbound

This paper cites Visual instruction tuning.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Visual instruction tuning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.388604Z

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 d01848b7-80e7-4872-ae37-22016d0d6b7c · outbound

This paper cites Improved baselines with visual instruction tuning.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Improved baselines with visual instruction tuning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:32.654008Z

Source-reported events for the cited work

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Observation 385dbd66-2b6b-4f26-bab1-585f839db411 · outbound

This paper cites GPT-Driver: Learning to Drive with GPT.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models GPT-Driver: Learning to Drive with GPT

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:32.658236Z

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Observation c088d2bb-d01a-48ab-b0fb-ccf431e06c94 · outbound

This paper cites Continuously learning, adapting, and improving: A dual- process approach to autonomous driving.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Continuously learning, adapting, and improving: A dual- process approach to autonomous driving

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.362479Z

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 8f72cc88-6dbd-4d95-beda-4a3fd97569ac · outbound

This paper cites OpenAI: Introducing ChatGPT.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models OpenAI: Introducing ChatGPT

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.348536Z

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 e251d1f3-544b-4a8c-b2b3-ba443336ee23 · outbound

This paper cites VLP: vision language planning for autonomous driving.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models VLP: vision language planning for autonomous driving

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.334181Z

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 db19ec5a-0383-4d55-927e-91ddafaea5de · outbound

This paper cites CarLLaVA: Vision language models for camera-only closed-loop driving.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models CarLLaVA: Vision language models for camera-only closed-loop driving

Reference 28

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Observation 180bd9ff-7eb3-41cd-aa37-98559e015ae1 · outbound

This paper cites Waslander, Yu Liu, and Hongsheng Li.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Waslander, Yu Liu, and Hongsheng Li

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.318666Z

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 b0f3555d-9afa-43bc-b43f-797e8bebeb0c · outbound

This paper cites From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:32.685145Z

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Observation 7bd7c2d4-f26f-43df-94ec-938287230611 · outbound

This paper cites Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:32.689627Z

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Unavailable: canonical work link unavailable.

Observation 2bb74b8e-d99a-48c6-8d53-442a381308ff · outbound

This paper cites DriveLM: Driving with graph visual ques- tion answering.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models DriveLM: Driving with graph visual ques- tion answering

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.302766Z

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 557dbaba-8459-4651-91ec-58dd7feeafab · outbound

This paper cites Centaur: Robust End-to-End Autonomous Driving with Test-Time Training.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Centaur: Robust End-to-End Autonomous Driving with Test-Time Training

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:32.697992Z

Source-reported events for the cited work

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Observation 22587161-380b-4260-bfb3-9c2f043456e2 · outbound

This paper cites Probing Multimodal LLMs as World Models for Driving.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Probing Multimodal LLMs as World Models for Driving

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:32:32.976005Z

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 86a20143-64de-4924-b18e-87c6dc6155ec · outbound

This paper cites DriveVLM: The convergence of autonomous driving and large vision-language models.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models DriveVLM: The convergence of autonomous driving and large vision-language models

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.285578Z

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 05d80fa4-a91c-44a3-9d49-203f7477713b · outbound

This paper cites DriveMLM: Aligning multi- modal large language models with behavioral planning states for autonomous driving.arXiv preprint arXiv:2312.09245,.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models DriveMLM: Aligning multi- modal large language models with behavioral planning states for autonomous driving.arXiv preprint arXiv:2312.09245,

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:32.713802Z

Source-reported events for the cited work

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Observation 00119a68-8ca8-409a-90eb-d6586ba11db5 · outbound

This paper cites Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong base- line.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong base- line

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.269611Z

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 d8847b70-eb5f-497f-a57f-a5451daeadb5 · outbound

This paper cites DriveGPT4: Interpretable end-to-end autonomous driving via large language model.RA-L, 2024.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models DriveGPT4: Interpretable end-to-end autonomous driving via large language model.RA-L, 2024

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.254912Z

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 9fbe1897-f7f4-4d4f-bc51-f85558a54d09 · outbound

This paper cites Hard cases detection in motion prediction by vision-language foundation models.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Hard cases detection in motion prediction by vision-language foundation models

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.239750Z

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 bc454bb5-6bd7-4bab-991f-a9323952bf34 · outbound

This paper cites Distilling System 2 into System 1.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Distilling System 2 into System 1

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:32.730908Z

Source-reported events for the cited work

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Observation d43d0f68-22f7-4963-8e44-a617eac5cb47 · outbound

This paper cites Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:32.736825Z

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Observation c69132e8-eb5b-4fd0-8fb8-44f76673bac2 · outbound

This paper cites HiRT: En- hancing robotic control with hierarchical robot transformers.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models HiRT: En- hancing robotic control with hierarchical robot transformers

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.224855Z

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 3a56dd8f-38fd-4e0f-8786-e986ce6e7c9a · outbound

This paper cites Feedback-guided autonomous driving.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Feedback-guided autonomous driving

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:32.745124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 878ce20a-ecbd-4afe-8a4f-1371b6aa2875 · outbound

This paper cites AD-H: Language-guided Autonomous Driving with Hierarchical Agents.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models AD-H: Language-guided Autonomous Driving with Hierarchical Agents

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:32.749494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27dd1914-33da-45be-b6b4-ea19a19c80b8 · outbound

This paper cites Hidden Biases of End-to-End Driving Datasets.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Hidden Biases of End-to-End Driving Datasets

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T05:32:32.753867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 719e7f21-dfd3-4e2c-a1e9-6230c7775c57 · outbound

This paper cites Sanketi, Grecia Salazar, Michael S.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Sanketi, Grecia Salazar, Michael S

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.197031Z

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 2c8c88ca-3fc0-46a0-9b2b-ef81a4fba4db · outbound

This paper cites A line of work, represented by approaches like DriveMLM [36], LLM4AD/CarLLaV A [28], and FeD [43], uses LLMs for initialization to benefit from their pre-training on large datasets.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models A line of work, represented by approaches like DriveMLM [36], LLM4AD/CarLLaV A [28], and FeD [43], uses LLMs for initialization to benefit from their pre-training on large datasets

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.182027Z

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 0c861e67-3726-451c-b6bf-34c0f140d1aa · outbound

This paper cites Small Model:For the Small Model, we use the first 8 layers of the same CLIP-ViT-L-336px encoder as the Base Large Model.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Small Model:For the Small Model, we use the first 8 layers of the same CLIP-ViT-L-336px encoder as the Base Large Model

Reference 48

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T05:32:33.165741Z

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 f0ed052b-13cd-43b8-a4a1-83ab4f8dd750 · outbound

This paper cites Ability Scores We include the per-ability scores for all models in Table 4.

ETA: Efficiency through Thinking Ahead, A Dual Approach to Self-Driving with Large Models Ability Scores We include the per-ability scores for all models in Table 4

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:32:33.150474Z

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.

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