Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-07-13T17:19:02.982053Z
Paper Citation Record · LEDGER
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching
As of 7 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 1 inbound Pith citation observation for arXiv:2603.27044.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-27T10:27:47.896922Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T09:17:48.486940Z
Reference resolution
15 of 15 outbound references displayed
- verified exact2
- verified fuzzy0
- unresolved13
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c6f2957b-b364-48a5-898e-e78144beab08 · outbound
This paper cites Variational Option Discovery Algorithms.
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching Variational Option Discovery Algorithms
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T17:19:02.982053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0330c2a1-e371-4af3-bd20-6cb4fb262cb0 · outbound
This paper cites Learning policy representations for steerable behavior synthesis.arXiv preprint arXiv:2601.22350,.
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching Learning policy representations for steerable behavior synthesis.arXiv preprint arXiv:2601.22350,
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T17:19:02.982053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f812df9f-ea7e-4330-9a72-71f35508d3b0 · outbound
This paper cites Mirco Mutti, Stefano Del Col, and Marcello Restelli.
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching Mirco Mutti, Stefano Del Col, and Marcello Restelli
Reference 3
- Resolution
- verified exact
- doi, observed 2026-07-13T17:19:46.580425Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ca27d3bb-591e-47ae-bfde-499053712d75 · outbound
This paper cites Learning to Learn with Generative Models of Neural Network Checkpoints.
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching Learning to Learn with Generative Models of Neural Network Checkpoints
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T17:19:02.982053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 92533859-3fc2-4755-b603-cfdd1cd6c4ad · outbound
This paper cites Proximal Policy Optimization Algorithms.
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching Proximal Policy Optimization Algorithms
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T17:19:02.982053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 88fa4cd6-1d89-48e4-b752-77dd7d5ad0b9 · outbound
This paper cites 11 Supplementary Materials The following content was not necessarily subject to peer review.
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching 11 Supplementary Materials The following content was not necessarily subject to peer review
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-07-13T17:19:46.588097Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation f2d34d48-87ea-4a6b-8c80-e8a6f2c7a393 · outbound
This paper cites In supervised learning, hyper-representations(Schürholt et al., 2021; 2022.
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching In supervised learning, hyper-representations(Schürholt et al., 2021; 2022
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T17:19:02.982053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ac37e8c2-3056-48c6-bdf2-8a9e20dda9d0 · outbound
This paper cites To circumvent this, recent works in supervised learning supplement standard parameter reconstruction losses with behavioral output matching (Meynent et al., 2025).
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching To circumvent this, recent works in supervised learning supplement standard parameter reconstruction losses with behavioral output matching (Meynent et al., 2025)
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T17:19:02.982053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1d6f86ff-f04d-42fa-9052-1d4c3b63b889 · outbound
This paper cites Similar architectures have been applied in Quality Diversity to improve the sample efficiency of diversity-based search (Rakicevic et al.,.
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching Similar architectures have been applied in Quality Diversity to improve the sample efficiency of diversity-based search (Rakicevic et al.,
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T17:19:02.982053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 39f8e67d-daa9-4265-8785-bacdee8dd9df · outbound
This paper cites Notably, these methods rely onparameter-reconstruction losses, which fundamentally restrict their compression ratios (e.g., up to 19 : 1 in Hegde et al.
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching Notably, these methods rely onparameter-reconstruction losses, which fundamentally restrict their compression ratios (e.g., up to 19 : 1 in Hegde et al
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T17:19:02.982053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ecaca591-6f3c-4726-ba4c-ce03679ce71d · outbound
This paper cites an unresolved cited work.
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching Unresolved cited work
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T17:19:02.982053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8018d7ca-3319-4b25-9668-26cebbd90ce7 · outbound
This paper cites Our contribution scales these concepts from single-expert matching to population-level alignment by developing a mixture-occupancy matching objective.
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching Our contribution scales these concepts from single-expert matching to population-level alignment by developing a mixture-occupancy matching objective
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T17:19:02.982053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a323aede-a36a-4f31-ac88-5b01ff0ced6a · outbound
This paper cites an unresolved cited work.
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching Unresolved cited work
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T17:19:02.982053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3fac1c43-d117-4862-b2a8-714a13a148db · outbound
This paper cites Proof.By the definition of the mixture, m(x) =w ipi(x)+P j̸=i wjpj(x).
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching Proof.By the definition of the mixture, m(x) =w ipi(x)+P j̸=i wjpj(x)
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T17:19:02.982053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 36a663ca-7f4d-488a-842d-7dced679c0e9 · outbound
This paper cites Reacher (RC).RC features a two-jointed robotic arm moving in a 2D plane.
Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching Reacher (RC).RC features a two-jointed robotic arm moving in a 2D plane
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T17:19:02.982053Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 068c1dd5-793c-4edf-aa67-363193e9e106 · inbound
Implicit Neural Representations of Individual Behavior cites this paper.
Implicit Neural Representations of Individual Behavior Unsupervised Behavioral Compression: Learning Low-Dimensional Policy Manifolds through State-Occupancy Matching
Reference 99
- Resolution
- verified exact
- arxiv_id, observed 2026-07-07T03:18:14.563422Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.