Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-20T09:19:06.011699Z
Paper Citation Record · LEDGER
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning
As of 4 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2605.19033.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-07-09T01:24:07.087802Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-09T01:25:49.407199Z
Reference resolution
45 of 45 outbound references displayed
- verified exact10
- verified fuzzy33
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 67aeed02-67c4-4f23-a0d4-1cf7706d865f · outbound
This paper cites Curb your attention: Causal attention gating for robust trajectory prediction in autonomous driving.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Curb your attention: Causal attention gating for robust trajectory prediction in autonomous driving
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.900807Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 7175c40b-b9eb-46f6-8cb1-e300f6c4b6bd · outbound
This paper cites Getting SMARTER for motion planning in autonomous driving systems.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Getting SMARTER for motion planning in autonomous driving systems
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.915695Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c3015937-4c55-45da-8b5f-70fc594bd2c5 · outbound
This paper cites Albrecht, Cillian Brewitt, John Wilhelm, Balint Gyevnar, Francisco Eiras, Mihai Dobre, and Subramanian Ramamoorthy.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Albrecht, Cillian Brewitt, John Wilhelm, Balint Gyevnar, Francisco Eiras, Mihai Dobre, and Subramanian Ramamoorthy
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.906784Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 42518bef-399a-4db4-a948-3c10db9fba84 · outbound
This paper cites Hind- sight experience replay.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Hind- sight experience replay
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.925046Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 36a7ee20-d439-4a01-9888-2486694ee29a · outbound
This paper cites NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
Reference 5
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T09:23:10.793010Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d2c8c826-d0c1-41eb-87ce-4cdd7ebc0e65 · outbound
This paper cites Reinforcement learning with human feedback for realistic traffic simulation.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Reinforcement learning with human feedback for realistic traffic simulation
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.929573Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b3b2ef53-8a81-4a68-968b-bc9ffa66659f · outbound
This paper cites Human-compatible driving agents through data-regularized self-play reinforce- ment learning.Reinforcement Learning Journal, 1.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Human-compatible driving agents through data-regularized self-play reinforce- ment learning.Reinforcement Learning Journal, 1
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.896650Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation aeb7f785-2cf2-422b-a0c8-5c3e96d45259 · outbound
This paper cites Causal confusion in imitation learning.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Causal confusion in imitation learning
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.902628Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 7882bd23-d480-453c-b679-30746d88dd66 · outbound
This paper cites Large scale in- teractive motion forecasting for autonomous driving : The Waymo open motion dataset.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Large scale in- teractive motion forecasting for autonomous driving : The Waymo open motion dataset
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.914006Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3bca1b20-a4c7-48cb-b622-c35ba0812c8c · outbound
This paper cites Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.885671Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a8743111-ce82-4b55-83a3-a28ff7722f23 · outbound
This paper cites Solv- ing motion planning tasks with a scalable generative model.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Solv- ing motion planning tasks with a scalable generative model
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.888986Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a14513f8-a156-43bf-9611-da19546ea986 · outbound
This paper cites Solv- ing motion planning tasks with a scalable generative model.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Solv- ing motion planning tasks with a scalable generative model
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.887150Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 94e31e66-8235-40f4-ae33-4433c7932c2d · outbound
This paper cites Versatile behavior diffusion for generalized traffic agent simulation.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Versatile behavior diffusion for generalized traffic agent simulation
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T09:23:10.790303Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 074d8604-fb84-46f9-b626-62bfdeb182d4 · outbound
This paper cites Sym- phony: Learning realistic and diverse agents for autonomous driving simulation.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Sym- phony: Learning realistic and diverse agents for autonomous driving simulation
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.893208Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e6cc69d0-48a5-4180-b29a-63ba06350ccd · outbound
This paper cites an unresolved cited work.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Unresolved cited work
Reference 15
- Resolution
- unresolved
- raw_fallback, observed 2026-05-20T09:33:25.941008Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 083456a9-fc4c-4e6e-bd66-db4c5f7733a6 · outbound
This paper cites Buy 4 REINFORCE samples, get a baseline for free! InDeepRL- StructPred@ICLR.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Buy 4 REINFORCE samples, get a baseline for free! InDeepRL- StructPred@ICLR
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.938789Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 7d945716-938a-4fa5-8cfe-8a91e7659855 · outbound
This paper cites UniMM: A Unified Mixture Model Framework for Multi-Agent Simulation.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning UniMM: A Unified Mixture Model Framework for Multi-Agent Simulation
Reference 17
- Resolution
- verified exact
- arxiv_id, observed 2026-06-19T17:11:51.260840Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c7811aeb-0663-4324-b7b1-daba00f32463 · outbound
This paper cites Goal- conditioned reinforcement learning: Problems and solutions.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Goal- conditioned reinforcement learning: Problems and solutions
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.945691Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a5f8dceb-2ba8-4e7d-b90c-0269cff15bb4 · outbound
This paper cites Imitation is not enough: Robustifying imitation with reinforcement learning for challenging driv- ing scenarios.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Imitation is not enough: Robustifying imitation with reinforcement learning for challenging driv- ing scenarios
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.943195Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 18352be0-c81a-4ef4-ae5d-9778943c8002 · outbound
This paper cites The Waymo Open Sim Agents Chal- lenge.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning The Waymo Open Sim Agents Chal- lenge
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.950068Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 51b9ada2-379c-4ed8-9664-4b71f34a4451 · outbound
This paper cites Improving agent behaviors with RL fine-tuning for autonomous driving.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Improving agent behaviors with RL fine-tuning for autonomous driving
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.920642Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 39b11e0b-b5aa-4e63-9c54-6b1e12e7ebae · outbound
This paper cites Trajeglish: Traffic modeling as next-token prediction.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Trajeglish: Traffic modeling as next-token prediction
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.923115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9bd9a183-1657-49cc-8851-f5e9efc49a71 · outbound
This paper cites Hindsight policy gradients.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Hindsight policy gradients
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.927146Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 180ad613-0d94-4212-be41-5dbe05f9060c · outbound
This paper cites A re- duction of imitation learning and structured prediction to no- regret online learning.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning A re- duction of imitation learning and structured prediction to no- regret online learning
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.931865Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation fc74133d-17ad-4511-a60c-1ad65620a813 · outbound
This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Reference 25
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T09:23:10.787009Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4a21a0f1-4140-4292-bd12-2c0de9f871cf · outbound
This paper cites TrafficSim: Learning to simulate realistic multi- agent behaviors.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning TrafficSim: Learning to simulate realistic multi- agent behaviors
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.916454Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation addd3954-1fd9-4c2b-a1c9-466e55c4c306 · outbound
This paper cites Promptable closed-loop traffic simulation.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Promptable closed-loop traffic simulation
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.912322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation df4730be-e401-4aa5-95a0-a557f1b2ee6e · outbound
This paper cites Understanding the performance gap between online and offline alignment algorithms.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Understanding the performance gap between online and offline alignment algorithms
Reference 28
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T09:23:10.778887Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e5b810d5-c306-4c44-854f-d2d68ce46cc6 · outbound
This paper cites Direct post-training prefer- ence alignment for multi-agent motion generation model us- ing implicit feedback from pre-training demonstrations.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Direct post-training prefer- ence alignment for multi-agent motion generation model us- ing implicit feedback from pre-training demonstrations
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.914255Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 7af707a1-57c7-4913-a57b-11a1e5e6f4ce · outbound
This paper cites Con- gested traffic states in empirical observations and micro- scopic simulations.Phys.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Con- gested traffic states in empirical observations and micro- scopic simulations.Phys
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.934293Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 505114d0-f972-491b-8d84-432c821486f9 · outbound
This paper cites Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Multiverse Transformer: 1st Place Solution for Waymo Open Sim Agents Challenge 2023
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T09:23:10.773544Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c4cf43c1-dc91-4452-81b4-eb31f9d2a204 · outbound
This paper cites Reinforcement Learning from Human Feedback for Lane Changing of Autonomous Vehicles in Mixed Traffic.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Reinforcement Learning from Human Feedback for Lane Changing of Autonomous Vehicles in Mixed Traffic
Reference 32
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T09:23:10.776127Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 341e51a4-0d43-4e9e-83af-9dd7eb7edc0d · outbound
This paper cites Argoverse 2: Next generation datasets for self-driving perception and fore- casting.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Argoverse 2: Next generation datasets for self-driving perception and fore- casting
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.908948Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9f5179f4-58ce-4bfa-bf1d-2a258e23be2a · outbound
This paper cites SMART: Scalable multi-agent real-time motion generation via next-token prediction.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning SMART: Scalable multi-agent real-time motion generation via next-token prediction
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.904642Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8a434942-1aef-4023-b1ab-e8d3e34329a8 · outbound
This paper cites BITS: Bi-level imitation for traffic simulation.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning BITS: Bi-level imitation for traffic simulation
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.907040Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation cbb85c13-6259-45dc-b7f4-0c356b2f8d04 · outbound
This paper cites TrafficBots: Towards world models for autonomous driving simulation and motion prediction.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning TrafficBots: Towards world models for autonomous driving simulation and motion prediction
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.918498Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 127552cc-79d9-4a3c-b55b-359ba95a7859 · outbound
This paper cites TrafficBots V1.5: Traffic Simulation via Conditional VAEs and Transformers with Relative Pose Encoding.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning TrafficBots V1.5: Traffic Simulation via Conditional VAEs and Transformers with Relative Pose Encoding
Reference 37
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T09:23:10.781624Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c72bd7fe-a074-4c1b-bebb-8aefa3817d46 · outbound
This paper cites TrajTok: Technical Report for 2025 Waymo Open Sim Agents Challenge.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning TrajTok: Technical Report for 2025 Waymo Open Sim Agents Challenge
Reference 38
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T09:23:10.771057Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation fd026e84-4137-4beb-87ea-8cb587c6523e · outbound
This paper cites Closed- loop supervised fine-tuning of tokenized traffic models.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Closed- loop supervised fine-tuning of tokenized traffic models
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.910606Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1c148aca-609a-4518-8caf-f6394fcf48ba · outbound
This paper cites TNT: Target-driven trajectory prediction.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning TNT: Target-driven trajectory prediction
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.936347Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 06a0a65b-4427-4d33-b500-7141e07f63e6 · outbound
This paper cites DRoPE: Directional Rotary Position Embedding for Efficient Agent Interaction Modeling.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning DRoPE: Directional Rotary Position Embedding for Efficient Agent Interaction Modeling
Reference 41
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T09:23:10.768062Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 33721343-178f-4e26-b1bb-017c27f05296 · outbound
This paper cites KiGRAS: Kinematic-driven generative model for realistic agent simulation.IEEE Robotics and Automa- tion Letters.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning KiGRAS: Kinematic-driven generative model for realistic agent simulation.IEEE Robotics and Automa- tion Letters
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.952629Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation a5c89baa-60b6-47b6-b590-3500004e6fd1 · outbound
This paper cites Guided conditional diffusion for controllable traffic simula- tion.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Guided conditional diffusion for controllable traffic simula- tion
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.889781Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 7c51b3f8-0c80-4edd-80dc-29a8e61597b3 · outbound
This paper cites BehaviorGPT: Smart agent simulation for autonomous driving with next-patch prediction.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning BehaviorGPT: Smart agent simulation for autonomous driving with next-patch prediction
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-20T09:33:25.883763Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e8a4e15b-4c16-4683-980d-5aaf6907ab99 · outbound
This paper cites Fine-Tuning Language Models from Human Preferences.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning Fine-Tuning Language Models from Human Preferences
Reference 45
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- local_arxiv, observed 2026-05-20T09:23:10.795678Z
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Pith citing papers
Observation fcc93778-cc1d-479e-bde2-35052dfdd81b · inbound
Flow-ERD: Agent-type Aware Flow Matching with Entropy-Regularized Distillation for Diverse Traffic Simulation cites this paper.
Flow-ERD: Agent-type Aware Flow Matching with Entropy-Regularized Distillation for Diverse Traffic Simulation RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning
Reference 23
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- local_arxiv, observed 2026-07-09T01:25:49.409195Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.