Paper Citation Record · LEDGER

Factorized Spectral Representations for Reinforcement Learning

As of 23 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2607.13498.

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

pith.paper-citation-record.v1
2607.13498 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T05:04:24.671215Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

18 of 18 outbound references displayed

verified exact0
verified fuzzy0
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3c1be75d-2566-4912-8fae-401a688ebf98 · outbound

This paper cites If η≤c 0(1−γ)ϵ for a sufficiently small numerical constantc 0, then LSVI-UCB returns anϵ-optimal policy after eO poly d, W, Bψ,(1−γ) −1, ϵ−1 episodes.

Factorized Spectral Representations for Reinforcement Learning If η≤c 0(1−γ)ϵ for a sufficiently small numerical constantc 0, then LSVI-UCB returns anϵ-optimal policy after eO poly d, W, Bψ,(1−γ) −1, ϵ−1 episodes

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:24.603332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ff222c8-4f0c-47a9-bec6-dacd521ec250 · outbound

This paper cites an unresolved cited work.

Factorized Spectral Representations for Reinforcement Learning Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:24.358928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0a31a84-985c-4c3b-a7b7-dd5bdfeee988 · outbound

This paper cites Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency.

Factorized Spectral Representations for Reinforcement Learning Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:23.737254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d12ae90-279b-40c4-b53d-bc5c68f9b986 · outbound

This paper cites On the method of bounded differences.

Factorized Spectral Representations for Reinforcement Learning On the method of bounded differences

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:23.800653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e866fa3d-e7fd-4032-be52-b542cae3b3ee · outbound

This paper cites Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning.

Factorized Spectral Representations for Reinforcement Learning Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:23.868813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 849bfe80-9148-40ba-ac08-7c74cf68513b · outbound

This paper cites DeepMind Control Suite.

Factorized Spectral Representations for Reinforcement Learning DeepMind Control Suite

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:23.962710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ca3b134-bbf3-4ba5-a548-468330e40602 · outbound

This paper cites The CP form ϕs(s)⊙ϕ a(a) treats the state and the action as two atomic axes.

Factorized Spectral Representations for Reinforcement Learning The CP form ϕs(s)⊙ϕ a(a) treats the state and the action as two atomic axes

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:24.283925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6681f0e9-ac4a-4777-9a5d-5eb9bf5bb9c3 · outbound

This paper cites an unresolved cited work.

Factorized Spectral Representations for Reinforcement Learning Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:24.395239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12c16a2c-e124-42b2-beb6-e80f7b3c9302 · outbound

This paper cites ∞X t=0 γtr(st, at) s0 =s # . Since|r(s, a)| ≤Rmax for all(s, a)and0≤γ <1, we have, for everys, |V π(s)|= Eπ.

Factorized Spectral Representations for Reinforcement Learning ∞X t=0 γtr(st, at) s0 =s # . Since|r(s, a)| ≤Rmax for all(s, a)and0≤γ <1, we have, for everys, |V π(s)|= Eπ

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:24.536724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cff9a04b-c3b2-4de8-ade5-bddb15787d4e · outbound

This paper cites Masatoshi Uehara, Xuezhou Zhang, and Wen Sun.

Factorized Spectral Representations for Reinforcement Learning Masatoshi Uehara, Xuezhou Zhang, and Wen Sun

Reference 1966

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:24.100769Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57094b61-0e68-4918-ad34-cf312f186574 · outbound

This paper cites Scott Fujimoto, Herke van Hoof, and David Meger.

Factorized Spectral Representations for Reinforcement Learning Scott Fujimoto, Herke van Hoof, and David Meger

Reference 1967

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:23.557787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e64c9883-091f-4649-a2b5-ad0dfe37400a · outbound

This paper cites an unresolved cited work.

Factorized Spectral Representations for Reinforcement Learning Unresolved cited work

Reference 1970

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:23.267058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab750e76-c47e-418a-a807-a9dfefce0044 · outbound

This paper cites Haohong Lin, Wenhao Ding, Jian Chen, Laixi Shi, Jiacheng Zhu, Bo Li, and Ding Zhao.

Factorized Spectral Representations for Reinforcement Learning Haohong Lin, Wenhao Ding, Jian Chen, Laixi Shi, Jiacheng Zhu, Bo Li, and Ding Zhao

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:23.671770Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d52be4bb-debd-4595-a7f1-73507c0bc471 · outbound

This paper cites Shift before you learn: Enabling low-rank representations in reinforcement learning.arXiv preprint arXiv:2509.05193,.

Factorized Spectral Representations for Reinforcement Learning Shift before you learn: Enabling low-rank representations in reinforcement learning.arXiv preprint arXiv:2509.05193,

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:23.407070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60b847c0-9736-4036-a189-6ef3a063fea5 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Factorized Spectral Representations for Reinforcement Learning Representation Learning with Contrastive Predictive Coding

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:24.207046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cb87e57-c46d-46aa-8f55-ea10020f932a · outbound

This paper cites Spectral representation-based reinforcement learning.arXiv preprint arXiv:2512.15036,.

Factorized Spectral Representations for Reinforcement Learning Spectral representation-based reinforcement learning.arXiv preprint arXiv:2512.15036,

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:23.611362Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6f7e3f6-0de1-43cf-a27c-5cc2b642283f · outbound

This paper cites B.5 SAC SAC [Haarnoja et al., 2018] uses a stochastic tanh-Gaussian policy and a twin Q critic with ELU activations and LayerNorm.

Factorized Spectral Representations for Reinforcement Learning B.5 SAC SAC [Haarnoja et al., 2018] uses a stochastic tanh-Gaussian policy and a twin Q critic with ELU activations and LayerNorm

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:24.460563Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 802fb8aa-35a7-4e6a-90b5-8c7c135e8867 · outbound

This paper cites The dimension ds=da=64 keeps total parameters and per-step floating-point operations (FLOPs) within 10% of FaStR’s.

Factorized Spectral Representations for Reinforcement Learning The dimension ds=da=64 keeps total parameters and per-step floating-point operations (FLOPs) within 10% of FaStR’s

Reference 4096

Resolution: unresolved; no resolver link, observed 2026-08-02T05:04:24.671215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.