Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2406.15131.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-01T16:10:18.681378Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T22:12:11.798052Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 4ed4c4aa-4cef-46dd-a4ec-d0fcac987f05 · inbound
Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba cites this paper.
Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty
Reference 63
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T22:12:11.800357Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d62718c6-1f64-412a-b6d5-efa6781f6b9a · inbound
Belief-State RWKV for Reinforcement Learning under Partial Observability cites this paper.
Belief-State RWKV for Reinforcement Learning under Partial Observability KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T21:58:19.929937Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6f0403c1-ceca-4fc0-89c2-2c904cefa538 · inbound
NMKFR: A Robust Framework for Time-Aware Cold-Start Recommendation cites this paper.
NMKFR: A Robust Framework for Time-Aware Cold-Start Recommendation KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T16:10:18.681378Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.