Paper Citation Record · LEDGER

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation

As of 9 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2505.23612.

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

pith.paper-citation-record.v1
2505.23612 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:45:49.995694Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

14 of 14 outbound references displayed

verified exact0
verified fuzzy3
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4633c0b5-4630-4f1d-b4f7-7593ad033ba8 · outbound

This paper cites GPT-4 Technical Report.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:48.690108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da863ee5-03be-4c98-b566-27b5ca3b4007 · outbound

This paper cites UniMM: A Unified Mixture Model Framework for Multi-Agent Simulation.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation UniMM: A Unified Mixture Model Framework for Multi-Agent Simulation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:48.950184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d74758c9-ffb7-4396-a06a-0f66af8ef916 · outbound

This paper cites SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation SparseAD: Sparse Query-Centric Paradigm for Efficient End-to-End Autonomous Driving

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:49.200980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5b4e1ca-042a-4c0d-b898-59a36eb9d121 · outbound

This paper cites DRoPE: Directional Rotary Position Embedding for Efficient Agent Interaction Modeling.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation DRoPE: Directional Rotary Position Embedding for Efficient Agent Interaction Modeling

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:49.270432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1709b3da-f5f3-4d8a-a446-5f4c3644a96e · outbound

This paper cites an unresolved cited work.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:45:51.193120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c3b4e7e8-11fa-4b3b-b147-ab15956bcb17 · outbound

This paper cites This significantly reduces the memory complexity by a factor of N, the number of agents in the scene, thereby accelerating training while maintaining strong performance.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation This significantly reduces the memory complexity by a factor of N, the number of agents in the scene, thereby accelerating training while maintaining strong performance

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:45:50.934455Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1fcbda9e-6872-4c7d-917c-5966a2ecc17d · outbound

This paper cites This adjustment ensures that the embedding retains the inherent2π-periodicity of directional angles.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation This adjustment ensures that the embedding retains the inherent2π-periodicity of directional angles

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:45:50.668570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 93671052-dedf-41a6-9c8b-63ce97ff2aad · outbound

This paper cites Both the action prediction module and the meta-action prediction module consist of three transformer layers for temporal aggregation.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation Both the action prediction module and the meta-action prediction module consist of three transformer layers for temporal aggregation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:45:50.411180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 18b42b33-ce04-46c0-8244-7d63b25bcdfb · outbound

This paper cites OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:49.132849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98a226a5-c546-4b57-973a-f71d66953428 · outbound

This paper cites MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:48.781737Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8d40546-da2f-49f0-afb5-52d6040ccebb · outbound

This paper cites Large Trajectory Models are Scalable Motion Predictors and Planners.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation Large Trajectory Models are Scalable Motion Predictors and Planners

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:49.069135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1664f1b8-9a79-46ab-8d2f-bc27a15d5c26 · outbound

This paper cites CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:49.004620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e537f5a7-12df-478e-b541-889395048913 · outbound

This paper cites Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation Gen-Drive: Enhancing Diffusion Generative Driving Policies with Reward Modeling and Reinforcement Learning Fine-tuning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:48.866794Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0bb50f07-48cf-4bc5-9269-215e999d15d9 · outbound

This paper cites QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction.

Autoregressive Meta-Actions for Unified Controllable Trajectory Generation QCNeXt: A Next-Generation Framework For Joint Multi-Agent Trajectory Prediction

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T12:45:49.396232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.