Paper Citation Record · LEDGER

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving

As of 21 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2606.08684.

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

pith.paper-citation-record.v1
2606.08684 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-27T18:30:16.458971Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

12 of 12 outbound references displayed

verified exact2
verified fuzzy0
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch7

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 62a92c1d-5c72-4b91-aa5b-529531aec90c · outbound

This paper cites arXiv preprint arXiv:2603.14972 (2026) 4.

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving arXiv preprint arXiv:2603.14972 (2026) 4

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T23:07:26.638749Z

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 56a7167c-2698-46f3-bc77-0b110e83a779 · outbound

This paper cites EMMA: End-to-End Multimodal Model for Autonomous Driving.

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving EMMA: End-to-End Multimodal Model for Autonomous Driving

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T23:07:26.621374Z

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 1523c6d1-4904-4ae8-8d2d-be27527ad9a4 · outbound

This paper cites Finetuning generative trajectory model with reinforcement learning from human feedback.

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving Finetuning generative trajectory model with reinforcement learning from human feedback

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T23:07:26.629602Z

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 609b71e7-8837-44f9-a7f3-0f6f6a3fc54f · outbound

This paper cites Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models.

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models

Reference 4

Resolution: metadata mismatch; local_arxiv, observed 2026-07-02T23:07:26.642044Z

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 225566e8-7bc7-45d1-818d-605fba9b7d42 · outbound

This paper cites O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning.

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving O1-Pruner: Length-Harmonizing Fine-Tuning for O1-Like Reasoning Pruning

Reference 5

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T23:07:26.620633Z

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 aa1edf55-8508-4be8-a8c5-638d1b46ef91 · outbound

This paper cites FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedback.

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving FASIONAD : FAst and Slow FusION Thinking Systems for Human-Like Autonomous Driving with Adaptive Feedback

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T23:07:26.649904Z

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 5e12ebda-c9df-44f7-8a45-b8844b101945 · outbound

This paper cites Yingqi Tang, Zhuoran Xu, Zhaotie Meng, and Erkang Cheng.

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving Yingqi Tang, Zhuoran Xu, Zhaotie Meng, and Erkang Cheng

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-07-02T23:07:26.642849Z

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 29daec80-2bc5-4654-a281-df4a3977f1e7 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-07-02T23:07:26.625151Z

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 000411e1-6bb4-4d83-bd7a-5110d461431c · outbound

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

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving Hidden Biases of End-to-End Driving Datasets

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T23:07:26.646386Z

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 14fa489e-2112-4807-ad5c-8a0a72e61d81 · outbound

This paper cites We provide a detailed comparison between BLUE and these methods in §B.5.1.

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving We provide a detailed comparison between BLUE and these methods in §B.5.1

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-27T18:30:16.458971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65251be8-b8ae-4bac-9983-383b2b36dc4c · outbound

This paper cites CoT-Valve (Ma et al., 2025) progressively mixes parameters of long-reasoning and non-reasoning models to gen- erate variable-length training data.

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving CoT-Valve (Ma et al., 2025) progressively mixes parameters of long-reasoning and non-reasoning models to gen- erate variable-length training data

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-27T18:30:16.458971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 201920b9-3ac5-415c-b3b9-03b1231d682e · outbound

This paper cites We will fully open-source our code, trained gate checkpoints, training data, and evaluation logs to support reproducibility and future research.

BLUE: Toward Better Language Use in Efficient Vision-Language-Action Models for Autonomous Driving We will fully open-source our code, trained gate checkpoints, training data, and evaluation logs to support reproducibility and future research

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-27T18:30:16.458971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.