Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T17:52:17.434247Z
Paper Citation Record · LEDGER
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems
As of 22 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2507.09836.
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 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
25 of 25 outbound references displayed
- verified exact0
- verified fuzzy22
- unresolved3
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 95ea7fc2-feb0-401d-918d-eff7065b938e · outbound
This paper cites Flow: A modular learning framework for mixed autonomy traffic.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Flow: A modular learning framework for mixed autonomy traffic
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:20.104318Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 88f04d90-c18f-4985-8d25-187e1d2cadb7 · outbound
This paper cites Cooperation for scalable supervision of autonomy in mixed traffic.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Cooperation for scalable supervision of autonomy in mixed traffic
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:20.029293Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 30cb02aa-87e7-47ba-8a9f-d50a580f641b · outbound
This paper cites Eco-driving of autonomous vehicles for nonstop crossing of signalized intersections.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Eco-driving of autonomous vehicles for nonstop crossing of signalized intersections
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:19.914891Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation f54224ae-78bc-4c44-adb8-4b74be6b3ec7 · outbound
This paper cites Performance study of a green light optimized speed advisory (glosa) application using an integrated cooperative its simulation plat- form.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Performance study of a green light optimized speed advisory (glosa) application using an integrated cooperative its simulation plat- form
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:19.818637Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation e3578d94-8375-44e3-9b11-7def9397fe4e · outbound
This paper cites Nonlinear model predictive control for ecological driver assistance systems in electric vehicles.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Nonlinear model predictive control for ecological driver assistance systems in electric vehicles
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:19.692406Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 051c3e1a-52f7-4ae0-9c13-c525148b6c9d · outbound
This paper cites Residual Policy Learning.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Residual Policy Learning
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:52:16.230110Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 776ec024-144b-4331-84dd-8320d3d57384 · outbound
This paper cites Residual reinforcement learning for robot control.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Residual reinforcement learning for robot control
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:19.575240Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5c6c98c7-14d1-41c7-a637-5f9e79cff675 · outbound
This paper cites Generalizing cooperative eco-driving via multi-residual task learning.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Generalizing cooperative eco-driving via multi-residual task learning
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:19.465517Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7285ce77-86f1-45d4-aa9f-bd855e439b9b · outbound
This paper cites Endurl: Enhancing safety, stability, and efficiency of mixed traffic under real-world perturbations via reinforcement learning.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Endurl: Enhancing safety, stability, and efficiency of mixed traffic under real-world perturbations via reinforcement learning
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:19.283846Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 2c451a5d-5962-4715-ab1b-c4bc3339f289 · outbound
This paper cites Model-free learning of corridor clearance: A near-term deployment perspective.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Model-free learning of corridor clearance: A near-term deployment perspective
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:19.139893Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation fb80f4a2-8b66-4105-8b34-7de07cda1c84 · outbound
This paper cites Stabilizing traffic with autonomous vehicles.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Stabilizing traffic with autonomous vehicles
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:19.038738Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation f3fcbdd3-fba1-4598-b113-644c861539ba · outbound
This paper cites Reinforcement learning-based oscillation dampening: Scaling up single-agent reinforcement learning algorithms to a 100-autonomous-vehicle highway field operational test.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Reinforcement learning-based oscillation dampening: Scaling up single-agent reinforcement learning algorithms to a 100-autonomous-vehicle highway field operational test
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:18.882683Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation e3ccfab2-82f9-4b52-9b9f-1510f5fb8ca2 · outbound
This paper cites Task-driven autonomous driving: Balanced strategies integrating curriculum reinforcement learning and residual policy.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Task-driven autonomous driving: Balanced strategies integrating curriculum reinforcement learning and residual policy
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:18.766491Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 64b0ad44-8a56-471c-a985-5e2b2feabeba · outbound
This paper cites Residual policy learning facilitates efficient model-free autonomous racing.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Residual policy learning facilitates efficient model-free autonomous racing
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:18.669917Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 4e05169e-50d9-4f23-a3d4-5dd42eb70823 · outbound
This paper cites Meta-residual policy learning: Zero-trial robot skill adaptation via knowledge fusion.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Meta-residual policy learning: Zero-trial robot skill adaptation via knowledge fusion
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:18.526956Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 6b422650-7150-4eb8-a4d1-b4713f179987 · outbound
This paper cites Learning-based model predictive control: Toward safe learning in control.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Learning-based model predictive control: Toward safe learning in control
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:18.439659Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 5536808b-abb0-4a77-ae2a-7bdb8ce5fac0 · outbound
This paper cites Combining model-based policy search with online model learning for control of physical humanoids.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Combining model-based policy search with online model learning for control of physical humanoids
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:18.317714Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 0d897efd-7daf-449a-af96-d194862b506d · outbound
This paper cites Learning-based model predictive control for safe exploration.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Learning-based model predictive control for safe exploration
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:18.188515Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation bda8b36b-a485-43ab-a30d-efc282a0772d · outbound
This paper cites Learning continuous control policies by stochas- tic value gradients.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Learning continuous control policies by stochas- tic value gradients
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:18.046313Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 8b46c82f-4126-4254-bd88-eaf7379ed86a · outbound
This paper cites Mitigating metropolitan carbon emissions with dynamic eco-driving at scale.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Mitigating metropolitan carbon emissions with dynamic eco-driving at scale
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:17.945402Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 4f93eae9-4c6e-424d-8d19-94ed9ca89dbc · outbound
This paper cites Contextual Markov Decision Processes.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Contextual Markov Decision Processes
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:52:17.254533Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7d6c044b-a97e-49f5-a9f2-a50ee67caebe · outbound
This paper cites Contextualize me–the case for context in reinforcement learning.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Contextualize me–the case for context in reinforcement learning
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:17.840022Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 278526e7-0fa7-490d-b80d-3ca332eeadb5 · outbound
This paper cites Intersectionzoo: Eco-driving for bench- marking multi-agent contextual reinforcement learning.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Intersectionzoo: Eco-driving for bench- marking multi-agent contextual reinforcement learning
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:17.696333Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 265be27c-3eaf-4bba-88f2-3e2d5f0b3ea8 · outbound
This paper cites Traffic flow dynamics , volume 1.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Traffic flow dynamics , volume 1
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T17:52:17.606215Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation d3a3fba3-78bf-4c26-96d9-698ca957d31c · outbound
This paper cites Proximal Policy Optimization Algorithms.
Multi-residual Mixture of Experts Learning for Cooperative Control in Multi-vehicle Systems Proximal Policy Optimization Algorithms
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:52:17.434247Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.