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Paper Citation Record · LEDGER

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner

As of 12 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2412.18086.

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

pith.paper-citation-record.v1
2412.18086 v2

Coverage vector

measured 52 of 52 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:06:16.554760Z

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 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

52 of 52 outbound references displayed

  • verified exact0
  • verified fuzzy8
  • unresolved43
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8cb3ab6a-fca9-41d8-954b-87ec79bc2c82 · outbound

This paper cites , " * write output.state after.block = add.period write newline.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner , " * write output.state after.block = add.period write newline

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-11T05:06:15.469246Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:06:15.469246Z digest=sha256:cd4571e9b15082f08e93b72f6293499f7dfaa5f6a320b3dad56a01f057f59e04

Observation ab6f8648-59c0-45c6-87dc-f70c696d4bab · outbound

This paper cites write newline.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner write newline

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-11T05:06:15.504751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:06:15.504751Z digest=sha256:3a3e25f567c10b648114b81c5b77d86a165ce7954f236300609a46db2c2f492a

Observation 67a99219-1fb5-4334-a9c2-ab48c8fea5b8 · outbound

This paper cites GPT-4 Technical Report.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner GPT-4 Technical Report

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-11T05:06:15.554758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:06:15.554758Z digest=sha256:df604e5eb2821671579a8ac11e16ea088f383c5f163b10c829ecf35123379b47

Observation 9f786546-1fab-4890-afc2-30cf8d7391dd · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:18.254285Z

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.

source=arxiv_source observed=2026-08-11T05:06:15.604760Z digest=sha256:d3097b6260c9d7c4bd747224877e52376280aaa2869758264cfb86252275247d

Observation 6280a0d2-369a-4d56-b141-698158849eae · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 5

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:18.235963Z

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.

source=arxiv_source observed=2026-08-11T05:06:15.664755Z digest=sha256:592e0657890c60dfbf91d03f4f363dd8149bc2e71e14ecd8e739b70f94c8f244

Observation efc033c2-175f-45d4-8764-28eb4edcb248 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 6

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:18.216322Z

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.

source=arxiv_source observed=2026-08-11T05:06:15.671658Z digest=sha256:ad22bd9ab7b28e7fcc85af3438d9e8742bf72a5a8d28345f78a6eb489e53884b

Observation dda5670a-590d-49dd-a3fd-cb65a3fe82e1 · outbound

This paper cites TRECVID 2019: An Evaluation Campaign to Benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & Retrieval.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner TRECVID 2019: An Evaluation Campaign to Benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & Retrieval

Reference 7

Resolution
metadata mismatch
local_arxiv, observed 2026-08-11T05:06:16.987714Z

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.

source=arxiv_source observed=2026-08-11T05:06:15.677624Z digest=sha256:c51aeb9cb524964993b45af6f8b939ec9e9459cb2b0f33b44f7324a6cfd022ad

Observation 603ca4ad-3bd8-48de-8b80-3d63a74efc0a · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 8

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:18.202496Z

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.

source=arxiv_source observed=2026-08-11T05:06:15.696088Z digest=sha256:20aba21bdf97ceb3cbdcc2a5593c0c6676a4171a411300b526b7954879bc5569

Observation 041dc67e-9b34-427d-82f4-8f2ebfa9f10d · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 9

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:18.187692Z

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.

source=arxiv_source observed=2026-08-11T05:06:15.719638Z digest=sha256:4584306c87e82d4ca845f0ddc8252913e6798af9f8ad6fd759b5693f2f0cfc51

Observation 0c7e3901-6d36-4985-9b36-29de59270189 · outbound

This paper cites H.; Vora, S.; Liong, V.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner H.; Vora, S.; Liong, V

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:06:18.157490Z

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.

source=arxiv_source observed=2026-08-11T05:06:15.756126Z digest=sha256:d9d4d914c71a039d3691e194e747d3efc91d7c9a240752e85f9c810a097bc250

Observation 82262fbf-c3d9-41a8-827d-801dbf27ff7a · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 11

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:18.112036Z

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.

source=arxiv_source observed=2026-08-11T05:06:15.770049Z digest=sha256:6a6437638abdf5b99e16058dedca6465210cb0af2d42201dd4f924bef57e0d4f

Observation 01eb3003-48b9-4c79-b8dd-50018a993206 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 12

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:18.081337Z

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.

source=arxiv_source observed=2026-08-11T05:06:15.819457Z digest=sha256:083cc1741f2bcb4e18b492ee821ba9c45184fdab64ed3700a7e6f4a9e69f9d01

Observation 5aaac2cf-0b3b-46fe-bcd8-cc2e69e61707 · outbound

This paper cites Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-11T05:06:15.854754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:06:15.854754Z digest=sha256:b9c4f4dafc94ba906301460ce907798dd52e54d17d3357f00c1a2ca1c5be63be

Observation d0eb02f2-40d0-46d9-a264-994ec23e4b60 · outbound

This paper cites TARGET: Automated Scenario Generation from Traffic Rules for Testing Autonomous Vehicles via Validated LLM-Guided Knowledge Extraction.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner TARGET: Automated Scenario Generation from Traffic Rules for Testing Autonomous Vehicles via Validated LLM-Guided Knowledge Extraction

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-11T05:06:15.874829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:06:15.874829Z digest=sha256:f7157344200278c24e4e0796aa68e1f6ff5cf6173084aafa0a14ccc37cb3238b

Observation 1a6c60a1-f374-43de-97b0-ebfa145c8d1a · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 15

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:18.052446Z

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.

source=arxiv_source observed=2026-08-11T05:06:15.894780Z digest=sha256:6fc31fa8b17c74fe08b212f48207c3e717846fb5d3e1a9982138c6244cce0ada

Observation 4d94e31b-4a3c-4f5a-9ec3-dfaea5c7b8ba · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-11T05:06:15.917192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:06:15.917192Z digest=sha256:d2adbb58a9db0eef79423247714e01670e0ef570fc74612147bd475bfee788ad

Observation 2eccfa8d-c5dd-4cb6-9cdf-46b6fce1d65e · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 17

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:18.008041Z

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.

source=arxiv_source observed=2026-08-11T05:06:15.944750Z digest=sha256:df56a7b4eedd9fedd76f5a7498971a0d7e7ab2487cbfb51c0c9d430261a0c874

Observation e8643bbe-9073-4325-93b2-a3a9b1a63c9d · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 18

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.994135Z

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.

source=arxiv_source observed=2026-08-11T05:06:15.984836Z digest=sha256:5f334077c640062270c0a1c87f4dd064a60e703c3e0ac758599904bc7f39480b

Observation 26dd2979-a1c4-4fa9-949f-e7e090b0faa2 · outbound

This paper cites J.; Dreossi, T.; Ghosh, S.; Yue, X.; Sangiovanni-Vincentelli, A.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner J.; Dreossi, T.; Ghosh, S.; Yue, X.; Sangiovanni-Vincentelli, A

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:06:17.982258Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.024756Z digest=sha256:551e250086ff7f2d774c4542a3167f86a08aaaa87bfe9dcb6b6bdefaebb5279b

Observation a22d1f57-c758-472e-a18a-4dd219a05a02 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 20

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.969713Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.041921Z digest=sha256:9286b5d650fe9feec97c5bb2b2e02b768283ca367875f804f2100cd2412ea61d

Observation 6e05b2cf-b383-4a84-8c58-0c0dcd765ec8 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 21

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.948775Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.051755Z digest=sha256:35490e6de25d711d76b66e6bee12bd486677ee5ae5325e3bcd3b3e0239f472ce

Observation 45dd6860-7014-42a6-8d76-27422f208e0f · outbound

This paper cites Automatic Generation of Scenarios for System-level Simulation-based Verification of Autonomous Driving Systems.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Automatic Generation of Scenarios for System-level Simulation-based Verification of Autonomous Driving Systems

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-11T05:06:16.057427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:06:16.057427Z digest=sha256:f96614b995ccae895f0b2b1fee6ae76de904123d4a255b7813e47cb571764e43

Observation 72eed1a7-734f-4f6a-a317-ded2fe211312 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 23

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.927161Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.062978Z digest=sha256:f518164098c672ec05ba4e580608e87b4ceaf12540d022ca8cf60210fdf670ab

Observation ba03d4c6-f78f-45fe-a6fd-6f91e34af94e · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 24

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.912688Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.072480Z digest=sha256:99b43bed4e80dd3eb30e0710d0eda4d6ece0dd9909303c3f30a116361a21a8a8

Observation c0ce6a87-de51-482f-b198-feaa0836cdab · outbound

This paper cites E.; Schiegg, F.; and Z \"o llner, J.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner E.; Schiegg, F.; and Z \"o llner, J

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:06:17.898351Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.080160Z digest=sha256:f063c656f9aaa2a97b31febe3d019e6f03196f1c6c2936e55d1116f217a24940

Observation 2f7c97d9-9d3e-4402-b028-8c9216d43c23 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 26

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.878783Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.086165Z digest=sha256:84a64e4602699f17bcdfd10ffffd7cecf23852b784040cbb3110fd3a4cfb4500

Observation e02f7ad0-a5e6-4bdb-83bb-73ec5f575226 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 27

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.861611Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.093436Z digest=sha256:57605f49a5bae48d48ef514fc384b7ab3f9310142b37ea9d52fa4dfeba2c5a02

Observation 91c1dc26-12f8-4f42-88e7-07e854cefb67 · outbound

This paper cites G.; Alexiadis, V.; and Zhang PE, L.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner G.; Alexiadis, V.; and Zhang PE, L

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:06:17.838159Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.099992Z digest=sha256:024f42dc67d8fd5a3bbd8c5dd8fa6b951ff15a06fec11302373fa7250fa43d38

Observation 1dc7ccd7-c1db-4c09-a19e-e37734f797ac · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 29

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.786113Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.104629Z digest=sha256:aaf89a3077fb1e2aeba6eb98201c60a0334ffe43a8fd0bb3849626abaa9bd97c

Observation 55d44aaf-556d-405f-9586-1db26fd67c7d · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 30

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.722138Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.109552Z digest=sha256:ff5f5351ac3e1e35dd1f7a148937c18d89256a2c36d4c25c7184ffe8492ad5a0

Observation 7de94dcb-850b-4b42-91ab-e62bdc1ab491 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 31

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.697836Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.113216Z digest=sha256:c4b49fd462231cbe2146b3f51fdef84a167de5d9a15d5dcb0ed662bd7867a936

Observation 3bf88f70-47b8-4c8f-8ec4-191abb57cda6 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 32

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.680074Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.122337Z digest=sha256:4ea247fdeea44871fd6b91c322484793022ca041a9d18d6ccf3192563d7e9492

Observation f2d49de0-84cc-449c-ad73-dc12b9c0a1ea · outbound

This paper cites M.; Feng, L.; Liu, Z.; Duan, C.; Mo, W.; and Zhou, B.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner M.; Feng, L.; Liu, Z.; Duan, C.; Mo, W.; and Zhou, B

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:06:17.661145Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.129642Z digest=sha256:b55cc88f47c58ee73b5e6b9197c661473f1b60a6d62948c5090ec96a924167f3

Observation 71187d2e-af30-4092-8930-19fcf9e50d38 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 34

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.643227Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.134063Z digest=sha256:6a1c2c88a38d02e10151edcc2ffec80a72b40e48c0060c24fa7c7b2cba034a29

Observation 831938bb-b875-414a-9cc4-9411f70d2789 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 35

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.626415Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.139656Z digest=sha256:b880c663539b9cd2db4b3e699896692863213f419adce50699c5023ca34b4903

Observation 09774412-34e4-429e-95ce-5a764c9c2234 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 36

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.605134Z

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.

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Observation 1254a778-22cd-4ef7-9b1d-0e6365044a80 · outbound

This paper cites Language Models are Few-Shot Learners.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Language Models are Few-Shot Learners

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-11T05:06:16.187281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3aea2830-c810-4887-8341-e49c6feb874f · outbound

This paper cites Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-11T05:06:16.197154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b7bdf07c-3f8f-440f-b05a-57cdacaacafc · outbound

This paper cites T.; ; et al.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner T.; ; et al

Reference 39

Resolution
verified fuzzy
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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.

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Observation f9bcac21-58be-4b96-9e42-6451f86eb6b4 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 40

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.544699Z

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.

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Observation a9fbc72a-927e-4e8a-af5d-a6f4b98d9ea6 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 41

Resolution
unresolved
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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.

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Observation 0debe625-62cc-4df7-acb5-f53d8eb22d07 · outbound

This paper cites C.; Mirje, M.; Bikkannavar, K.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner C.; Mirje, M.; Bikkannavar, K

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:06:17.424767Z

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.

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Observation 326b3f3c-4f5b-4c18-8116-cc0516b2177c · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 43

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.382775Z

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.

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Observation ca36c992-c4ce-4959-92bf-408f00ead9b7 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 44

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.314769Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.362239Z digest=sha256:0b087a5ddc4b7118c90e077e8853be07459354d73d978821958882d8c926f170

Observation b2880603-b679-408f-9f5c-5d33ee98960e · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 45

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.237685Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.382255Z digest=sha256:48649cf29880072635309ca3209ab340bd440f84e71fe89d3c6cc66bef89db42

Observation 802ecce5-3aa2-4890-b4e2-0f59d0adc73e · outbound

This paper cites Do Prompt-Based Models Really Understand the Meaning of their Prompts?.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Do Prompt-Based Models Really Understand the Meaning of their Prompts?

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-11T05:06:16.415532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:06:16.415532Z digest=sha256:a583e74c3be95a2dc468e66b19e513cc1d86c1a9a917bf80ad65130f9084d6d2

Observation a7689427-e8f4-4cac-b1b7-15978dfba9d3 · outbound

This paper cites Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-11T05:06:16.425482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:06:16.425482Z digest=sha256:5b14676d777c98b0e2ff8929e25874e77335d5a0dcb4892ea4952ff4be213ae5

Observation 86f1de92-1bbb-4fa5-ab28-4b985a626417 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 48

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.175624Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.454751Z digest=sha256:65c1eb51aaa7ac0ab6f696e255d26bdbac56296cbe7788258d62d8a55189d9e9

Observation ba4f96f6-d447-4075-9e77-926e127ffe13 · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 49

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.146930Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.472994Z digest=sha256:9b97e87e97bfa7a83a9081149d3c1b7622df18b5a72b4751947275a071c33169

Observation c4662c06-bdd9-4aea-bbec-0cb070264f64 · outbound

This paper cites J.; Luo, X.; and Wang, M.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner J.; Luo, X.; and Wang, M

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T05:06:17.106642Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.504753Z digest=sha256:815b269f2eadc0d9e0e22897a1a1c68868c92835f5ebc54dc8e061adf8aef746

Observation 7d089bbc-f8b7-4c3e-baec-53503094270a · outbound

This paper cites an unresolved cited work.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Unresolved cited work

Reference 51

Resolution
unresolved
raw_fallback, observed 2026-08-11T05:06:17.032376Z

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.

source=arxiv_source observed=2026-08-11T05:06:16.515942Z digest=sha256:8c2097bda5a7452ce1dc6ddfb41d7ce3ecedc29399e5494105aa45afa450ed6d

Observation 1602ceae-c879-4047-ad33-da2cc42e697a · outbound

This paper cites Automatic Chain of Thought Prompting in Large Language Models.

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner Automatic Chain of Thought Prompting in Large Language Models

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-11T05:06:16.554760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:06:16.554760Z digest=sha256:645d4cd852ed063470b27cb0b4c68b5ad71421d3a07dc2b41583b5c7b03178a6

Pith citing papers

No inbound Pith citation observations are available.