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Paper Citation Record · LEDGER

Exploration by Running Away from the Past

As of 14 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2411.14085.

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

pith.paper-citation-record.v1
2411.14085 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:40:22.993933Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

43 of 43 outbound references displayed

  • verified exact2
  • verified fuzzy9
  • unresolved32
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bda0ac68-1155-4998-9d0d-b046979b36b5 · outbound

This paper cites Understanding the impact of entropy on policy optimization.

Exploration by Running Away from the Past Understanding the impact of entropy on policy optimization

Reference 1

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source=arxiv_source observed=2026-08-12T15:40:22.671319Z digest=sha256:1a0235e2fad7e3a22ea5052b334e00c8c5a20c6d3128f9ad7c7983f89e0a02d4

Observation 2aaebc90-2d1e-4992-afb0-189339e19f25 · outbound

This paper cites Logarithmic online regret bounds for undiscounted reinforcement learning.

Exploration by Running Away from the Past Logarithmic online regret bounds for undiscounted reinforcement learning

Reference 2

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source=arxiv_source observed=2026-08-12T15:40:22.681679Z digest=sha256:e6f5e2e26001ebf57aba4127ffe03fa3dfc8dc993a3f02970aa8b5cd4caf69cc

Observation 156b852c-f9c9-4aa6-b4c0-53c03ad72b68 · outbound

This paper cites Never Give Up: Learning Directed Exploration Strategies.

Exploration by Running Away from the Past Never Give Up: Learning Directed Exploration Strategies

Reference 3

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source=arxiv_source observed=2026-08-12T15:40:22.692471Z digest=sha256:4fc3c896e853de436761b20ed9c5651178a52d44e48903cfe7ba0611f748488e

Observation 97adbe7e-59de-4aba-b7e1-b88de64e1a3e · outbound

This paper cites Unifying count-based exploration and intrinsic motivation.

Exploration by Running Away from the Past Unifying count-based exploration and intrinsic motivation

Reference 4

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source=arxiv_source observed=2026-08-12T15:40:22.699322Z digest=sha256:d2876509abebf8fdb304ef05d411505ba89804362e3771ebf68c4f7228ff7576

Observation bfbb4694-67b4-48d2-bbbe-435260879b5b · outbound

This paper cites Tightening exploration in upper confidence reinforcement learning.

Exploration by Running Away from the Past Tightening exploration in upper confidence reinforcement learning

Reference 5

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raw_fallback, observed 2026-08-12T15:40:24.217540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T15:40:22.705292Z digest=sha256:d6fc02c05dfe1651f2f5049d0f155e77be1561d551cdd5454f0612462c79d28b

Observation 352e47e8-aa12-4e3a-a089-c0924e23141f · outbound

This paper cites R-max-a general polynomial time algorithm for near-optimal reinforcement learning.

Exploration by Running Away from the Past R-max-a general polynomial time algorithm for near-optimal reinforcement learning

Reference 6

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T15:40:22.712526Z digest=sha256:05317c38ab6234264317fcb32a8773f9aa9d7aaa76b9464f06c7281b3b039075

Observation cf5f0624-03ce-4d9e-8057-347c88ce7fcb · outbound

This paper cites Openai gym, 2016.

Exploration by Running Away from the Past Openai gym, 2016

Reference 7

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source=arxiv_source observed=2026-08-12T15:40:22.719361Z digest=sha256:e6ff191e3b42ac8c539ec7c32125629d70162ca62690cb7d2c081df7889eb7b6

Observation e9264c41-c1b6-4e18-8f7b-556c108554e6 · outbound

This paper cites Exploration by Random Network Distillation.

Exploration by Running Away from the Past Exploration by Random Network Distillation

Reference 8

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source=arxiv_source observed=2026-08-12T15:40:22.725940Z digest=sha256:ee6dcc490d56609be41dfae5257b66cf928087a5087f8fe145876e400f6e47f1

Observation ebeed464-1bb4-4ce0-ba2b-6cf43477017b · outbound

This paper cites Explore, discover and learn: Unsupervised discovery of state-covering skills.

Exploration by Running Away from the Past Explore, discover and learn: Unsupervised discovery of state-covering skills

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T15:40:22.732004Z digest=sha256:da7d2b70878f90297e99dc8c3dfa9b3b8f4263770f069bb56a27f39ba741b0c0

Observation e07adfe4-06e6-4667-a28c-3f7ae357af6a · outbound

This paper cites Gymnasium robotics, 2023.

Exploration by Running Away from the Past Gymnasium robotics, 2023

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-12T15:40:24.118146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T15:40:22.738746Z digest=sha256:6fd47734d97bf7f92885b5b7305c6b2e56a8a6fc769fe3ba9245cc7480121501

Observation 48bb4e06-1fa2-4b2b-855e-895f0cbb78f1 · outbound

This paper cites Adversarial intrinsic motivation for reinforcement learning.

Exploration by Running Away from the Past Adversarial intrinsic motivation for reinforcement learning

Reference 11

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source=arxiv_source observed=2026-08-12T15:40:22.746176Z digest=sha256:37cbf2d165322c5afd4c81817fc0451cacf6d64cdcf34911b080b88b99887a04

Observation b7076def-4eb5-4022-95f7-01dc1bd63f7b · outbound

This paper cites Diversity is All You Need: Learning Skills without a Reward Function.

Exploration by Running Away from the Past Diversity is All You Need: Learning Skills without a Reward Function

Reference 12

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source=arxiv_source observed=2026-08-12T15:40:22.755343Z digest=sha256:301bf7865bbca8bbc58acc06b06130b3e246bcf061f822a1956f2c992eeb4e29

Observation bf860004-ea3f-4702-b52b-9dd515496c77 · outbound

This paper cites C-Learning: Learning to Achieve Goals via Recursive Classification.

Exploration by Running Away from the Past C-Learning: Learning to Achieve Goals via Recursive Classification

Reference 13

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source=arxiv_source observed=2026-08-12T15:40:22.764035Z digest=sha256:e7a96f34da878d38433774998e1f619387af551470a8ebe604a4c8ed9859b71b

Observation 398a2689-1d68-4d2d-b4f1-b6ed540f17ad · outbound

This paper cites A theory of regularized markov decision processes.

Exploration by Running Away from the Past A theory of regularized markov decision processes

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T15:40:22.772157Z digest=sha256:d00864db5b31a73bbe660fde7bf337a958d5a7ffa298d6c2676e60b515f563ad

Observation 28eebf9b-7734-4f51-81ab-073146859ec2 · outbound

This paper cites Variational Intrinsic Control.

Exploration by Running Away from the Past Variational Intrinsic Control

Reference 15

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source=arxiv_source observed=2026-08-12T15:40:22.778062Z digest=sha256:7b5a03e06cdb4ceedd2e4f40292ac79029ce5126a85440f978cfdea4132a334c

Observation a9ab1715-c75d-41f8-a8da-63e0d6fd77c7 · outbound

This paper cites Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor.

Exploration by Running Away from the Past Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 16

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source=arxiv_source observed=2026-08-12T15:40:22.784544Z digest=sha256:14a297f6346c221685f012355849e5bcb48e89f1fc2b0f12313a41c7aedcd354

Observation 3a0182ee-2e04-487b-8761-76456bef3f97 · outbound

This paper cites Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery.

Exploration by Running Away from the Past Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery

Reference 17

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source=arxiv_source observed=2026-08-12T15:40:22.791638Z digest=sha256:22f424495719d4f12b9839af36a62a98ba43ab5e0d0fb3280313a307dba47a2e

Observation cce507e4-c2be-4db1-9446-2cf17a23dbbf · outbound

This paper cites Provably efficient maximum entropy exploration.

Exploration by Running Away from the Past Provably efficient maximum entropy exploration

Reference 18

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source=arxiv_source observed=2026-08-12T15:40:22.799039Z digest=sha256:3973bd00afda525e6e055968d791babc07b42cd524299a0b4d94309870bb783a

Observation 5a961092-b5b2-4dd1-99f5-a9b585ea6427 · outbound

This paper cites Reinforcement Learning with Unsupervised Auxiliary Tasks.

Exploration by Running Away from the Past Reinforcement Learning with Unsupervised Auxiliary Tasks

Reference 19

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source=arxiv_source observed=2026-08-12T15:40:22.806820Z digest=sha256:8ea1bcedf868c80ed0da2f2e1960ecf7ac3816a06837fa93cae6109bc4324e90

Observation c37f81e9-a6bd-4726-97d1-4e1ff5b61807 · outbound

This paper cites Near-optimal regret bounds for reinforcement learning.

Exploration by Running Away from the Past Near-optimal regret bounds for reinforcement learning

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:40:23.978194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T15:40:22.813752Z digest=sha256:40b0c75a9aad13a2118fa83604f1da41431d3a5056b16d820b66c9f9d0c28aa7

Observation cc3446e3-6e52-4083-90d6-4a328068dd0e · outbound

This paper cites Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments.

Exploration by Running Away from the Past Curiosity in Hindsight: Intrinsic Exploration in Stochastic Environments

Reference 21

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verified exact
local_arxiv, observed 2026-08-12T15:40:23.441585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T15:40:22.821313Z digest=sha256:13be1e9b4c5315131fa71a8a991a670f317c3ae27653e6555ad0868606bbbf30

Observation 59b4327b-47b6-4cda-9521-2d49a4febf03 · outbound

This paper cites Learning to achieve goals.

Exploration by Running Away from the Past Learning to achieve goals

Reference 22

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source=arxiv_source observed=2026-08-12T15:40:22.827828Z digest=sha256:9d9c03792b7e3bebdfd9d03e5683f22cef9b4ce1eb2bd83cc5ca76b1bc617580

Observation 2ea18cef-fc6a-4454-ae3b-7daedb5bd4f8 · outbound

This paper cites Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching.

Exploration by Running Away from the Past Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching

Reference 23

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verified exact
local_arxiv, observed 2026-08-12T15:40:23.397294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T15:40:22.835716Z digest=sha256:9635a6bf7b2cae86fc58238f53ddd82c9c4387bbdb68e49fd702b5719068f7cd

Observation 84a2c8df-3059-4f40-8a48-891b37448f6a · outbound

This paper cites Near-optimal reinforcement learning in polynomial time.

Exploration by Running Away from the Past Near-optimal reinforcement learning in polynomial time

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T15:40:22.842729Z digest=sha256:3b8c047e3ccd31e36e99adfbb72eb8e1fa552bf4d70cd5f71c31ee23884fb626

Observation 15a55ea9-11cf-4711-a47c-9a6a3559a711 · outbound

This paper cites Efficient Exploration via State Marginal Matching.

Exploration by Running Away from the Past Efficient Exploration via State Marginal Matching

Reference 25

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source=arxiv_source observed=2026-08-12T15:40:22.849543Z digest=sha256:752077d063f5f417af3ff2d0bedc79d47fb2197378dcff9f6db34a8a7af23344

Observation e173fc33-67ac-4aa5-bfdf-c62a8e28bc33 · outbound

This paper cites Behavior from the void: Unsupervised active pre-training.

Exploration by Running Away from the Past Behavior from the void: Unsupervised active pre-training

Reference 26

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source=arxiv_source observed=2026-08-12T15:40:22.857404Z digest=sha256:fc1aa3b76a124f000d762e6a569de07a7a081917ad907d98c7e2aba9f1d056c0

Observation 1f650188-b1c2-4f05-aea2-2cbd2c2edcb6 · outbound

This paper cites Playing Atari with Deep Reinforcement Learning.

Exploration by Running Away from the Past Playing Atari with Deep Reinforcement Learning

Reference 27

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source=arxiv_source observed=2026-08-12T15:40:22.866447Z digest=sha256:9ee758d5c8a3b536184d605f20a587476a04ff5cd9686fd852f579a8fdee0543

Observation c47e3dd3-11dd-4116-bbb9-587157d989d3 · outbound

This paper cites From bandits to monte-carlo tree search: The optimistic principle applied to optimization and planning.

Exploration by Running Away from the Past From bandits to monte-carlo tree search: The optimistic principle applied to optimization and planning

Reference 28

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raw_fallback, observed 2026-08-12T15:40:23.871085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T15:40:22.874049Z digest=sha256:983fd4f82044d7717495e8eb6e31508570e32c7258aa8ea8627f6fde79216b0f

Observation ce970b08-2dba-43e1-8ed7-d2da2ef7e48a · outbound

This paper cites Lipschitz-constrained unsupervised skill discovery.

Exploration by Running Away from the Past Lipschitz-constrained unsupervised skill discovery

Reference 29

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source=arxiv_source observed=2026-08-12T15:40:22.879764Z digest=sha256:fa54d6a086dd497772d5f888942450ce55989783ad6e072c8e8c4c79e8ef2986

Observation 97ebaf27-37cc-4660-8241-1614519a0804 · outbound

This paper cites Controllability-Aware Unsupervised Skill Discovery.

Exploration by Running Away from the Past Controllability-Aware Unsupervised Skill Discovery

Reference 30

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source=arxiv_source observed=2026-08-12T15:40:22.888083Z digest=sha256:f64efd4fd2a8895337d9d8b56fe76377aa88944b2cde04b68a13fd6d0137efe4

Observation a183e98b-a7ad-4878-bfcb-ffa433d719b7 · outbound

This paper cites METRA: Scalable Unsupervised RL with Metric-Aware Abstraction.

Exploration by Running Away from the Past METRA: Scalable Unsupervised RL with Metric-Aware Abstraction

Reference 31

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source=arxiv_source observed=2026-08-12T15:40:22.901439Z digest=sha256:2166404f559a65bdba33255012047910815a285d02ee5c2b4b1d55f6e48bf8dc

Observation 512b8519-aaed-4415-957c-468e426267b8 · outbound

This paper cites Curiosity-driven exploration by self-supervised prediction.

Exploration by Running Away from the Past Curiosity-driven exploration by self-supervised prediction

Reference 32

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source=arxiv_source observed=2026-08-12T15:40:22.911757Z digest=sha256:4fa6dbfe071f6352b3e2aab8429005cf4b8782ad76d104b1e81a57d76ba27c07

Observation c526c8ad-3378-40e9-b29c-ba8211e66079 · outbound

This paper cites Quality diversity: A new frontier for evolutionary computation.

Exploration by Running Away from the Past Quality diversity: A new frontier for evolutionary computation

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:40:23.817369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-08-12T15:40:22.918538Z digest=sha256:f1a80a94dd7a97b67a64a54c18e85d6101f7940b05826cc85bea8520979e6f45

Observation c0969f63-0fcb-4107-9f41-8ab32ad40417 · outbound

This paper cites Markov decision processes: discrete stochastic dynamic programming.

Exploration by Running Away from the Past Markov decision processes: discrete stochastic dynamic programming

Reference 34

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source=arxiv_source observed=2026-08-12T15:40:22.924720Z digest=sha256:0939ce2650736f4a5870b0dd88f20f93f8338d9399af40229549050554269b08

Observation 05ec055b-250d-4ac9-b6f0-1459c4e9df7f · outbound

This paper cites Proximal Policy Optimization Algorithms.

Exploration by Running Away from the Past Proximal Policy Optimization Algorithms

Reference 35

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source=arxiv_source observed=2026-08-12T15:40:22.935545Z digest=sha256:fd864d884ce9bfc4bc0c3956e549b332e8c2958f8efed88eef4974c10e593cc5

Observation 27bc0bba-74c6-4ac1-892d-d0d54d2552a5 · outbound

This paper cites Dynamics-Aware Unsupervised Discovery of Skills.

Exploration by Running Away from the Past Dynamics-Aware Unsupervised Discovery of Skills

Reference 36

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source=arxiv_source observed=2026-08-12T15:40:22.941054Z digest=sha256:1a7dae7a239ddfb13bf59a1a37436287bac19596e6844afbd78fd1124964cdd8

Observation fbb7104e-b710-4690-a7e3-1a50ada25b5b · outbound

This paper cites Loss is its own Reward: Self-Supervision for Reinforcement Learning.

Exploration by Running Away from the Past Loss is its own Reward: Self-Supervision for Reinforcement Learning

Reference 37

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source=arxiv_source observed=2026-08-12T15:40:22.946923Z digest=sha256:08b8034f906b7e2f3fba4a3112b098b9cddb75a0272aa4cd27ced5ad97bd83bd

Observation 6a90249a-3c5f-4cbe-875f-f30070f4f9ee · outbound

This paper cites Mujoco: A physics engine for model-based control.

Exploration by Running Away from the Past Mujoco: A physics engine for model-based control

Reference 38

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source=arxiv_source observed=2026-08-12T15:40:22.953104Z digest=sha256:ce729f5fe63ac2257f011f1c1f40e5c80f2964eb23beeb130f1cc2dca9d82a70

Observation 90937fa2-3ba6-487c-bb54-2064e54cfd0c · outbound

This paper cites Optimal transport: old and new, volume 338.

Exploration by Running Away from the Past Optimal transport: old and new, volume 338

Reference 39

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T15:40:22.959323Z digest=sha256:e56583281ae00b75a65d1730a32b28df6b79befa924ce48b04958948a5cc3b31

Observation 5853646a-b8ee-4150-b821-b535e9233959 · outbound

This paper cites write newline.

Exploration by Running Away from the Past write newline

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-12T15:40:22.966704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T15:40:22.966704Z digest=sha256:5ccdf639b622b9413d97045bb9095300965fc471c47076656360e015f9d3bbde

Observation fc58cd2e-edc9-4505-a366-bb071d2ccd60 · outbound

This paper cites @esa (Ref.

Exploration by Running Away from the Past @esa (Ref

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-12T15:40:22.974577Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T15:40:22.974577Z digest=sha256:f5900921f3ee0c1d4b2dacd5198354d493763a644b0fe52a9cf3e8b95873aa8d

Observation ddc433b5-d970-429a-8340-1a47a354753a · outbound

This paper cites an unresolved cited work.

Exploration by Running Away from the Past Unresolved cited work

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-12T15:40:22.983934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T15:40:22.983934Z digest=sha256:5a109142c011cfbbb8f9c6ac30effd7514a23eb2fd34cb4c1a3fd436550e0bc6

Observation 5c83ec66-315c-4467-9424-6b9eadb33835 · outbound

This paper cites an unresolved cited work.

Exploration by Running Away from the Past Unresolved cited work

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-12T15:40:22.993933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T15:40:22.993933Z digest=sha256:73d8b30f507d4a4fc599db52df79e09aa7b132bffc234042708c192e289504db

Pith citing papers

No inbound Pith citation observations are available.