Paper Citation Record · LEDGER

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning

As of 21 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2607.26059.

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

pith.paper-citation-record.v1
2607.26059 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T13:59:09.094315Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

46 of 46 outbound references displayed

verified exact0
verified fuzzy0
unresolved46
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8f6acdc3-f647-4df5-9419-6e12040f2f94 · outbound

This paper cites Loss of plasticity in continual deep reinforcement learning.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Loss of plasticity in continual deep reinforcement learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:02.852614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d819f2f7-3da8-41a1-a10e-eed5581eeb87 · outbound

This paper cites Database-friendly random projections: J ohnson-- L indenstrauss with binary coins.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Database-friendly random projections: J ohnson-- L indenstrauss with binary coins

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:02.999994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b31f9d26-ec54-4dfe-8363-e3cea7a3fc57 · outbound

This paper cites Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:03.104886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac239ff2-0078-4e0b-a5aa-f98518eb0e7b · outbound

This paper cites Unsupervised state representation learning in A tari.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Unsupervised state representation learning in A tari

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:03.239734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 882cf573-3468-4b80-b1a0-64036cad5a39 · outbound

This paper cites SGD with large step sizes learns sparse features.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning SGD with large step sizes learns sparse features

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:03.372467Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a838838b-1b89-4bf7-83cd-95ab7806898b · outbound

This paper cites DiffuserCam : lensless single-exposure 3D imaging.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning DiffuserCam : lensless single-exposure 3D imaging

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:03.494383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81c4b804-91e4-422b-b46e-43d1c802ba06 · outbound

This paper cites Random projection in dimensionality reduction: applications to image and text data.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Random projection in dimensionality reduction: applications to image and text data

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:03.670215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35b39ab9-b586-41be-b676-bddbc51ba49c · outbound

This paper cites Robust uncertainty principles.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Robust uncertainty principles

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:03.799841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f57858f-8466-4663-911a-3162eca65cd9 · outbound

This paper cites Reinforcement learning with convolutional reservoir computing.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Reinforcement learning with convolutional reservoir computing

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:03.902646Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 027dbc31-accd-4ebb-ba98-823d1ba3d605 · outbound

This paper cites What makes freezing layers effective? arXiv preprint, 2025.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning What makes freezing layers effective? arXiv preprint, 2025

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:04.062196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7054120-c05a-47c5-94ea-f8f170e2c51b · outbound

This paper cites Playing A tari with six neurons.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Playing A tari with six neurons

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:04.185856Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 020fbfe3-3cff-468f-87e8-a7e7c496abea · outbound

This paper cites An elementary proof of a theorem of J ohnson and L indenstrauss.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning An elementary proof of a theorem of J ohnson and L indenstrauss

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:04.330386Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd7efaa2-3a2e-4d36-b472-33bf2d43c484 · outbound

This paper cites The interplay between sparsity and training in deep reinforcement learning.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning The interplay between sparsity and training in deep reinforcement learning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:04.388503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb479d54-cca2-48f5-87a0-6eba45f29353 · outbound

This paper cites Loss of plasticity in deep continual learning.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Loss of plasticity in deep continual learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:04.553125Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59c5a0be-eaf0-4040-bba4-f3aa29437020 · outbound

This paper cites Compressed sensing.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Compressed sensing

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:04.671635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae776114-7f85-41bd-bea3-941f1abb0a48 · outbound

This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning The lottery ticket hypothesis: Finding sparse, trainable neural networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:04.764764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8dd0b217-1576-499d-8780-e247ae5326b1 · outbound

This paper cites Why random pruning is all we need to start sparse.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Why random pruning is all we need to start sparse

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:04.887703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7e1ac90-f830-4e24-9805-179ad3778421 · outbound

This paper cites Weight agnostic neural networks.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Weight agnostic neural networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:05.012960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dad5906b-aa9f-42d5-8753-c07da7aa4653 · outbound

This paper cites Implicit regularization in matrix factorization.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Implicit regularization in matrix factorization

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:05.127044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab98531e-0cd4-48a9-abe8-b0e6dd9c2249 · outbound

This paper cites Learning both weights and connections for efficient neural networks.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Learning both weights and connections for efficient neural networks

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:05.254279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9aca7d9-e2a7-4769-b138-ee97173186e5 · outbound

This paper cites Delving deep into rectifiers.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Delving deep into rectifiers

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:05.373285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58185850-d8da-4a16-9aaa-edff84709e1c · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning LoRA: Low-Rank Adaptation of Large Language Models

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:05.498028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf2484ed-9851-4a92-8866-934095cb117c · outbound

This paper cites The ``echo state'' approach to analysing and training recurrent neural networks.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning The ``echo state'' approach to analysing and training recurrent neural networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:05.620817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc858e3c-7bf3-4d21-b032-722a3fe6d11a · outbound

This paper cites Extensions of L ipschitz mappings into a H ilbert space.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Extensions of L ipschitz mappings into a H ilbert space

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:05.748970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67b27c5e-a6e0-488e-9efd-9d56784b133d · outbound

This paper cites Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Implicit Under-Parameterization Inhibits Data-Efficient Deep Reinforcement Learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:05.867960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e93e0ba-8305-4523-98ee-c30ecbb2d383 · outbound

This paper cites Optimal brain damage.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Optimal brain damage

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:05.985951Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db9a9120-ea48-46da-8627-a8b6ea690895 · outbound

This paper cites Measuring the intrinsic dimension of objective landscapes.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Measuring the intrinsic dimension of objective landscapes

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:06.112917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d48f23b4-6510-4a54-b545-7b339e95cfcf · outbound

This paper cites Understanding plasticity in neural networks.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Understanding plasticity in neural networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:06.250526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07dc4c47-b6e7-4699-90bd-f7cdfafb005c · outbound

This paper cites Sparsity for free: Overcoming the limitations of dense scaling in deep reinforcement learning.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Sparsity for free: Overcoming the limitations of dense scaling in deep reinforcement learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:06.418912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94b63104-2126-4241-9be7-2800a494973f · outbound

This paper cites Real-time computing without stable states.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Real-time computing without stable states

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:06.550782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 425f15e3-1671-46fe-88f0-ec252986d16d · outbound

This paper cites Revisiting the A rcade L earning E nvironment: Evaluation protocols and open problems for general agents.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Revisiting the A rcade L earning E nvironment: Evaluation protocols and open problems for general agents

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:06.652193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e734dbe8-ed00-422f-8218-85f30f0a08b8 · outbound

This paper cites Proving the lottery ticket hypothesis: Pruning is all you need.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Proving the lottery ticket hypothesis: Pruning is all you need

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:06.820550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 373c4749-d161-4154-a9c7-82d32c8a9608 · outbound

This paper cites Human-level control through deep reinforcement learning.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Human-level control through deep reinforcement learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:07.012249Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da3e398f-dffc-48b2-811f-968f9ab2b6a2 · outbound

This paper cites No representation, no trust: Connecting representation, collapse, and trust issues in PPO.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning No representation, no trust: Connecting representation, collapse, and trust issues in PPO

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:07.197845Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 950efbcd-a9ef-453c-9f01-1e2d707b3cfa · outbound

This paper cites Pruning Convolutional Neural Networks for Resource Efficient Inference.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:07.412184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29f2342c-c47e-4a17-b673-275e7fd0aec1 · outbound

This paper cites The primacy bias in deep reinforcement learning.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning The primacy bias in deep reinforcement learning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:07.625394Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0176804d-3469-4281-83df-fba9ecca5131 · outbound

This paper cites In value-based deep reinforcement learning, a pruned network is a good network.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning In value-based deep reinforcement learning, a pruned network is a good network

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:07.801430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19071648-18b4-49e5-8eb3-d7441b61c55b · outbound

This paper cites Sample factory: Egocentric 3d control from pixels at 100000 FPS with asynchronous reinforcement learning.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Sample factory: Egocentric 3d control from pixels at 100000 FPS with asynchronous reinforcement learning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:07.955983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 959f82ab-dce5-4e6b-8fd3-cc306a9bf982 · outbound

This paper cites Stable- B aselines3: Reliable reinforcement learning implementations.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Stable- B aselines3: Reliable reinforcement learning implementations

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:08.114491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 762f5e7f-4e45-403e-87df-66c63223864b · outbound

This paper cites Random features for large-scale kernel machines.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Random features for large-scale kernel machines

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:08.287207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2169e80b-eccb-4289-8070-f120139b5db6 · outbound

This paper cites What's hidden in a randomly weighted neural network? In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning What's hidden in a randomly weighted neural network? In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:08.446510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5be55ddf-b303-432a-8856-49fdf9a61504 · outbound

This paper cites Proximal Policy Optimization Algorithms.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Proximal Policy Optimization Algorithms

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:08.592302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 057a986b-221c-4253-995f-b792f57829d3 · outbound

This paper cites Dormant neuron phenomenon in deep reinforcement learning.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Dormant neuron phenomenon in deep reinforcement learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:08.738862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 975deeea-f45a-4d5c-9e40-0be6ea10b5c7 · outbound

This paper cites Regression shrinkage and selection via the lasso.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Regression shrinkage and selection via the lasso

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:08.891131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f960f840-e1a0-4316-8a82-c52a34ea7e78 · outbound

This paper cites Kernel and rich regimes in overparameterized models.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Kernel and rich regimes in overparameterized models

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:09.009649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6fd306d4-8139-4aa1-9c93-928667ebd83b · outbound

This paper cites Pre-trained visual features for visual RL.

Emergent Sparsity in Frozen Random CNN Feature Extractors for Deep Reinforcement Learning Pre-trained visual features for visual RL

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-02T13:59:09.094315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.