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Paper Citation Record · LEDGER

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving

As of 9 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2603.00919.

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

pith.paper-citation-record.v1
2603.00919 v3

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T06:00:16.978845Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

47 of 47 outbound references displayed

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Outbound references

Observation c01e03a0-58c1-4b39-8781-20532db4781d · outbound

This paper cites The role of the hercules autonomous vehicle during the covid-19 pandemic: An autonomous logistic vehicle for contactless goods transportation,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving The role of the hercules autonomous vehicle during the covid-19 pandemic: An autonomous logistic vehicle for contactless goods transportation,

Reference 1

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Observation 3a67e19d-68c4-4630-80a3-c080c4e7e697 · outbound

This paper cites Hidden biases of end-to-end driving models,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Hidden biases of end-to-end driving models,

Reference 2

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Observation fc0d5c6d-dcd9-4459-87c1-5c49e8493488 · outbound

This paper cites Vad: Vectorized scene representation for efficient autonomous driving,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Vad: Vectorized scene representation for efficient autonomous driving,

Reference 3

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Observation c532b8ef-9641-411b-9d68-8fc01a1816f7 · outbound

This paper cites Planning-oriented autonomous driving,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Planning-oriented autonomous driving,

Reference 4

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source=pdf_text observed=2026-08-04T06:00:13.371386Z digest=sha256:122bf439adb023afc582dc059ee041ad95864e7bb393afa18cdcbe9eb6ec29a1

Observation a1b85e06-f3fc-4209-8cef-9d096e8b5a50 · outbound

This paper cites Drivegpt4-v2: Harnessing large language model capabilities for enhanced closed-loop autonomous driving,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Drivegpt4-v2: Harnessing large language model capabilities for enhanced closed-loop autonomous driving,

Reference 5

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Observation ea1ce92c-c84f-48ed-80ef-bd29a9fc2f5f · outbound

This paper cites TinyLlama: An Open-Source Small Language Model.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving TinyLlama: An Open-Source Small Language Model

Reference 6

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source=pdf_text observed=2026-08-04T06:00:13.639322Z digest=sha256:64fc25af55e56e4ff21467e68bed63c571cc895f3d1d6bec62da2469903496ec

Observation 538c3386-db2d-4199-a6de-a288c294489e · outbound

This paper cites Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality,

Reference 7

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source=pdf_text observed=2026-08-04T06:00:13.730653Z digest=sha256:e7a3d6d3fa59741e262d88b1339381390485124bfde2a7132eca074f7832d172

Observation 2aca17de-2287-4365-86bc-9fd10cbd662c · outbound

This paper cites Visual instruction tuning,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Visual instruction tuning,

Reference 8

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source=pdf_text observed=2026-08-04T06:00:13.834564Z digest=sha256:4efbeedf6eedf0ac2350905e5c97f8fcad2ebb4f392b0dbb07cf2b4ed493eb87

Observation 4f742e5a-4381-4585-9a4c-8c1fb17e5d33 · outbound

This paper cites Qwen2.5-VL Technical Report.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Qwen2.5-VL Technical Report

Reference 9

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source=pdf_text observed=2026-08-04T06:00:13.987470Z digest=sha256:9bc0460bf99c53c3ac8e90e944ec34002bb146e4645cdf9d16ab680a5fc1bc27

Observation 223ca307-9dd6-4c66-885f-8a2bd6210a3e · outbound

This paper cites Video- llava: Learning united visual representation by alignment before pro- jection,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Video- llava: Learning united visual representation by alignment before pro- jection,

Reference 10

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source=pdf_text observed=2026-08-04T06:00:14.058970Z digest=sha256:22dfdb2ac0ff8974687c14adc163b331ddc059e564c1308bd7b7e38ded99ee99

Observation 45052362-20d8-40b9-bdcd-86ad17b82690 · outbound

This paper cites Llava- next: Improved reasoning, ocr, and world knowledge,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Llava- next: Improved reasoning, ocr, and world knowledge,

Reference 11

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Observation 613fe55e-1e2a-4984-83f8-dfd8f1493235 · outbound

This paper cites Language models do not embed numbers continuously,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Language models do not embed numbers continuously,

Reference 12

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source=pdf_text observed=2026-08-04T06:00:14.224286Z digest=sha256:14df58808f4d27347fa3c1191287e5769d83b8f2c6bcfe263eb9b96867331b9b

Observation c39a92c7-d10e-4be3-a091-8e7702cc0662 · outbound

This paper cites Number representations in llms: A computational parallel to human perception,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Number representations in llms: A computational parallel to human perception,

Reference 13

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source=pdf_text observed=2026-08-04T06:00:14.314719Z digest=sha256:a24582b15244f74546b446806429954014035d6d7e7f7057e507cd61070cff4b

Observation 891aaf12-947c-4e04-8691-b3e5decb84ef · outbound

This paper cites GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models

Reference 14

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source=pdf_text observed=2026-08-04T06:00:14.387721Z digest=sha256:4ad8a2b71aecb84f52476d5cd869bbc0d46796e3ae05ec97deced6e7e58addcf

Observation 80033fc3-cc5d-45f3-a32f-17e001563af9 · outbound

This paper cites Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report

Reference 15

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source=pdf_text observed=2026-08-04T06:00:14.473178Z digest=sha256:0badbee4698aef7297cb549c8225f27f9d13b21424e359220246492a43a6033b

Observation ac0eb2a4-9d7c-49fe-987b-d74caa081594 · outbound

This paper cites Benchmarking the performance of large language models in uveitis: a comparative analysis of chatgpt- 3.5, chatgpt-4.0, google gemini, and anthropic claude3,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Benchmarking the performance of large language models in uveitis: a comparative analysis of chatgpt- 3.5, chatgpt-4.0, google gemini, and anthropic claude3,

Reference 16

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source=pdf_text observed=2026-08-04T06:00:14.565660Z digest=sha256:82380713d720a362cdbd8f4c0fc6dbbaef011de3f8b81c5e0eeb03f7e23541f4

Observation cd6a0b4a-c170-4fb8-b26b-899d5531d445 · outbound

This paper cites Llava-mini: Efficient image and video large multimodal models with one vision token,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Llava-mini: Efficient image and video large multimodal models with one vision token,

Reference 17

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source=pdf_text observed=2026-08-04T06:00:14.659002Z digest=sha256:9fe7a8a3debc4a13544e7d7292d032376420bc173eb2b96b99f310c31af11457

Observation 48fc25d4-9347-41c0-9db0-c77eeea07807 · outbound

This paper cites Llava-more: A comparative study of llms and visual backbones for enhanced visual instruction tuning,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Llava-more: A comparative study of llms and visual backbones for enhanced visual instruction tuning,

Reference 18

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source=pdf_text observed=2026-08-04T06:00:14.712513Z digest=sha256:5320982b56d22ee16ea419f5d0226aa3e41d0cae9490fc536d9c697dfa8782a3

Observation e7db801f-cc5c-4245-b14a-646db3a55f63 · outbound

This paper cites Omni-r1: Do you really need audio to fine-tune your audio llm?.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Omni-r1: Do you really need audio to fine-tune your audio llm?

Reference 19

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source=pdf_text observed=2026-08-04T06:00:14.799292Z digest=sha256:c1599fbf17cb059e26c4c1b42946dc276d3e02bf6811ed212d7303147c34e645

Observation a785ac36-2272-4989-91c3-17af25635df0 · outbound

This paper cites Exploring finetuned audio-llm on heart murmur features,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Exploring finetuned audio-llm on heart murmur features,

Reference 20

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source=pdf_text observed=2026-08-04T06:00:14.889140Z digest=sha256:21fbc9a9124c357524f522af1d7ac6261e210f37c8062aa8fb720ed9fa6bcbd3

Observation 95059a4c-a027-4815-b3f6-7fe867f63a7f · outbound

This paper cites Surveying the mllm landscape: A meta- review of current surveys,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Surveying the mllm landscape: A meta- review of current surveys,

Reference 21

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source=pdf_text observed=2026-08-04T06:00:14.973427Z digest=sha256:d7ec9668ee14b15e3d4359cf77ba7ebda651f895aae57da7d94a1c6a4cffdbf4

Observation c74b8d20-b91c-47f9-90b1-4ccae8f2c2eb · outbound

This paper cites Vtimellm: Empower llm to grasp video moments,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Vtimellm: Empower llm to grasp video moments,

Reference 22

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source=pdf_text observed=2026-08-04T06:00:15.037631Z digest=sha256:0a83c0cfd87de625a4a95b7e51c7b7a435f7e938f846f287748145323e8908b1

Observation c1189905-47cb-4df9-aebc-0e890cb884d2 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 23

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Observation cb1514fb-df2b-4cfa-80b4-7960078f81cd · outbound

This paper cites Attention is all you need,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Attention is all you need,

Reference 24

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source=pdf_text observed=2026-08-04T06:00:15.160968Z digest=sha256:e7e20901d64f7e11e60bf6840f98d678eab4ed83c7e49f6dfc571d517617945b

Observation d1751097-dd33-4088-87d3-7442a1068190 · outbound

This paper cites Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Blip: Bootstrapping language- image pre-training for unified vision-language understanding and generation,

Reference 25

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Observation d6bd6a50-6ac6-4993-8885-757926591c1c · outbound

This paper cites Vadv2: End-to-end vectorized autonomous driving via probabilistic planning,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Vadv2: End-to-end vectorized autonomous driving via probabilistic planning,

Reference 26

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Observation 9252beae-e54e-44a7-8ee7-dfc15e3e4c64 · outbound

This paper cites Lmdrive: Closed-loop end-to-end driving with large language models,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Lmdrive: Closed-loop end-to-end driving with large language models,

Reference 27

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Observation abdb94d6-19c4-4ef9-bd2b-304216ab8a51 · outbound

This paper cites Drivegpt4: Interpretable end-to-end autonomous driving via large language model,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Drivegpt4: Interpretable end-to-end autonomous driving via large language model,

Reference 28

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Observation a347ee98-c49c-42e5-981f-d9789b1de02c · outbound

This paper cites Insmapper: Exploring inner- instance information for vectorized hd mapping,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Insmapper: Exploring inner- instance information for vectorized hd mapping,

Reference 29

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Observation 46700c65-3034-4e92-985e-5c74921c472b · outbound

This paper cites Invdriver: Intra-instance aware vectorized query-based autonomous driving trans- former,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Invdriver: Intra-instance aware vectorized query-based autonomous driving trans- former,

Reference 30

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Observation 4bed009d-263e-4c56-955e-882f8e8a56cc · outbound

This paper cites Recent advancements in end-to-end au- tonomous driving using deep learning: A survey,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Recent advancements in end-to-end au- tonomous driving using deep learning: A survey,

Reference 31

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source=pdf_text observed=2026-08-04T06:00:15.541456Z digest=sha256:0dfb8dae6d6f93c411e0385c3f3ccb0b6fd0bab401f5ebe8855a3a3795e0a7f5

Observation 0a068470-221b-4aa7-9608-5aa8d8833b1e · outbound

This paper cites Lc-llm: Explainable lane-change intention and trajectory predictions with large language models,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Lc-llm: Explainable lane-change intention and trajectory predictions with large language models,

Reference 32

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Observation da1f690b-be9c-4ef9-afd1-e395937e33c3 · outbound

This paper cites sam-llm: interpretable lane change trajectoryprediction via parametric finetuning.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving sam-llm: interpretable lane change trajectoryprediction via parametric finetuning

Reference 33

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Observation b6a552bd-712a-4c71-8622-6a3720e3bf36 · outbound

This paper cites Autovla: A vision-language-action model for end-to-end autonomous driving with adaptive reasoning and reinforcement fine- tuning,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Autovla: A vision-language-action model for end-to-end autonomous driving with adaptive reasoning and reinforcement fine- tuning,

Reference 34

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source=pdf_text observed=2026-08-04T06:00:15.722489Z digest=sha256:8196c740fe239da48a28dde4cee687fe535d9635ff2e983c0273a6eb653e2172

Observation 3bc640cd-eeb5-4df5-815b-2c26357b4836 · outbound

This paper cites Applications of large language models and multimodal large models in autonomous driving: A comprehensive review,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Applications of large language models and multimodal large models in autonomous driving: A comprehensive review,

Reference 35

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source=pdf_text observed=2026-08-04T06:00:15.778168Z digest=sha256:3b8fc73c9be6c20d1985395b5e1f28eac67fffd8544231acb714e26f923065fe

Observation b1ae1868-364c-481b-8216-fdd79e70a5f0 · outbound

This paper cites A survey on large language model-powered autonomous driving,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving A survey on large language model-powered autonomous driving,

Reference 36

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Observation dbe5c12e-d192-4b6d-b174-a466b682101d · outbound

This paper cites Hallucination reduction and optimization for large language model-based autonomous driving,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Hallucination reduction and optimization for large language model-based autonomous driving,

Reference 37

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source=pdf_text observed=2026-08-04T06:00:15.904473Z digest=sha256:0d5f31a2845ad3d4ea70f347b046226798e759d7c77f05900fc89f5c8d8cb155

Observation 07ec1912-e1bf-4650-a49c-90afa3e32fc6 · outbound

This paper cites Teaching arithmetic to small transformers,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Teaching arithmetic to small transformers,

Reference 38

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source=pdf_text observed=2026-08-04T06:00:15.940509Z digest=sha256:ae7c40dec067648e67d7baa9f78728205bb6f82c8e647cbc2f6d81bfce5a7a84

Observation a4980c63-d8d7-4e6a-b0a6-8a5abb2b0da9 · outbound

This paper cites Positional Description Matters for Transformers Arithmetic.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Positional Description Matters for Transformers Arithmetic

Reference 39

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source=pdf_text observed=2026-08-04T06:00:16.039812Z digest=sha256:6bda0a1d39dec2e5e4cf0f5ab5550af99afc168343530335ba2f730a6c98c3d6

Observation bf718275-7559-4e74-9bb7-56eba5e6d508 · outbound

This paper cites Numerologic: Number encoding for enhanced llms’ numerical reasoning,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Numerologic: Number encoding for enhanced llms’ numerical reasoning,

Reference 40

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source=pdf_text observed=2026-08-04T06:00:16.154175Z digest=sha256:895d36d183f9cb9e572f486ace8a771f5d2344b3fde735d02159495818648ac1

Observation 04250933-6454-4e9f-82c6-9e4e49c7a28a · outbound

This paper cites Safeauto: Knowledge-enhanced safe autonomous driving with multi- modal foundation models,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Safeauto: Knowledge-enhanced safe autonomous driving with multi- modal foundation models,

Reference 41

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source=pdf_text observed=2026-08-04T06:00:16.266498Z digest=sha256:582bd58866369bc70a1c48419def858f64958baaedecf149556cc23e501a2720

Observation eecca896-5675-4f40-bdfc-cf49b08f8029 · outbound

This paper cites xval: A continuous number encoding for large language models,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving xval: A continuous number encoding for large language models,

Reference 42

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source=pdf_text observed=2026-08-04T06:00:16.422341Z digest=sha256:c2b9e4d9a33f74b41449c453bc08d16bf1e6de3f29c08f9781bf02bbb3c3282e

Observation 2ef12bff-0f17-4378-bee5-c97c65725356 · outbound

This paper cites Interleaving text and number embeddings to solve mathemathics problems,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Interleaving text and number embeddings to solve mathemathics problems,

Reference 43

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source=pdf_text observed=2026-08-04T06:00:16.550379Z digest=sha256:d0941352082796cafb889a1bbe42cd29e52c04f92fdb50924b4d871045443a7d

Observation 4ecff6bc-f766-499c-8c63-4e2d336ac742 · outbound

This paper cites The geometry of numerical reasoning: Language models compare numeric properties in linear subspaces,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving The geometry of numerical reasoning: Language models compare numeric properties in linear subspaces,

Reference 44

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source=pdf_text observed=2026-08-04T06:00:16.669336Z digest=sha256:eb333bf618b9d8e357bfe25683cd97f5f2db6eba02d95a36d8de40516b4af8b4

Observation 6e12dae9-719a-4795-9d2c-6d537fa25558 · outbound

This paper cites Language models encode the value of numbers linearly,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Language models encode the value of numbers linearly,

Reference 45

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source=pdf_text observed=2026-08-04T06:00:16.757839Z digest=sha256:171e67c0dd4143f0dfd13a95911782e7f40f72971afc5021c7639c0f5e4a9da6

Observation c6b27bec-4a15-49df-83ba-2773375cf1b3 · outbound

This paper cites Pre- trained language models learn remarkably accurate representations of numbers,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Pre- trained language models learn remarkably accurate representations of numbers,

Reference 46

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source=pdf_text observed=2026-08-04T06:00:16.916152Z digest=sha256:f7bb5a0da925342d932db50e4e0f9d8b4ffc3de8d40b445a0b4f7f1ea3eedb47

Observation 53b591db-167a-426d-aa29-49c0bfd0820c · outbound

This paper cites Adapt: Action-aware driving caption transformer,.

DriveCode: Domain Specific Numerical Encoding for LLM-Based Autonomous Driving Adapt: Action-aware driving caption transformer,

Reference 47

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source=pdf_text observed=2026-08-04T06:00:16.978845Z digest=sha256:36b518f23e6a11e3eed13976eae2c21d53acc8f9c47ac4c5ad3b21d543f5a9c5

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