Paper Citation Record · LEDGER

Never Give Up: Learning Directed Exploration Strategies

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2002.06038.

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

pith.paper-citation-record.v1
2002.06038 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:37:01.760052Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T18:40:02.690126Z

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 7e99525c-3df7-406f-896d-d4c77bf09dc6 · inbound

From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning cites this paper.

From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning Never Give Up: Learning Directed Exploration Strategies

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T04:37:01.760052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26a78126-7459-4a64-adf7-f6558001cd03 · inbound

Uncertainty Prioritized Experience Replay cites this paper.

Uncertainty Prioritized Experience Replay Never Give Up: Learning Directed Exploration Strategies

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T04:59:04.925034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57244e40-7330-4372-87c8-f93f8b3b48f2 · inbound

Student-Centered Distillation Narrows the Agentic Gap Between Small and Large LLMs cites this paper.

Student-Centered Distillation Narrows the Agentic Gap Between Small and Large LLMs Never Give Up: Learning Directed Exploration Strategies

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T17:57:52.319352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a17297b8-b27d-490b-a4ab-dedb2d119d7f · inbound

Beyond Noisy-TVs: Noise-Robust Exploration Via Learning Progress Monitoring cites this paper.

Beyond Noisy-TVs: Noise-Robust Exploration Via Learning Progress Monitoring Never Give Up: Learning Directed Exploration Strategies

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-18T11:51:20.199561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d0929453-0725-4f45-97af-0e5499f6c402 · inbound

GrowthHacker: Automated Off-Policy Evaluation Optimization Using Code-Modifying LLM Agents cites this paper.

GrowthHacker: Automated Off-Policy Evaluation Optimization Using Code-Modifying LLM Agents Never Give Up: Learning Directed Exploration Strategies

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-04T00:30:38.113645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82ea8c2c-85e5-479f-97c2-133cae128a88 · inbound

Baba in Wonderland: Online Self-Supervised Dynamics Discovery for Executable World Models cites this paper.

Baba in Wonderland: Online Self-Supervised Dynamics Discovery for Executable World Models Never Give Up: Learning Directed Exploration Strategies

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-19T21:47:48.320974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation eebdfe6e-ea07-4155-8678-9a1f7db78252 · inbound

Heuresis: Search Strategies for Autonomous AI Research Agents Across Quality, Diversity and Novelty cites this paper.

Heuresis: Search Strategies for Autonomous AI Research Agents Across Quality, Diversity and Novelty Never Give Up: Learning Directed Exploration Strategies

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-04T18:40:02.691736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3a36944c-e7f2-4d96-93f2-3f3f70d06463 · inbound

Heuresis: Search Strategies for Autonomous AI Research Agents Across Quality, Diversity and Novelty cites this paper.

Heuresis: Search Strategies for Autonomous AI Research Agents Across Quality, Diversity and Novelty Never Give Up: Learning Directed Exploration Strategies

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:17:23.795265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e294e5f7-8778-474d-9d8a-49704e2ca071 · inbound

Information-Based Exploration via Random Features for Reinforcement Learning cites this paper.

Information-Based Exploration via Random Features for Reinforcement Learning Never Give Up: Learning Directed Exploration Strategies

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T16:35:00.586734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa0be051-4a90-4866-83bd-5efdcb9afd4f · inbound

Cooperative Multi-UAV Navigation in Complex Environments via Systematic Multi-Agent Deep Reinforcement Learning cites this paper.

Cooperative Multi-UAV Navigation in Complex Environments via Systematic Multi-Agent Deep Reinforcement Learning Never Give Up: Learning Directed Exploration Strategies

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T01:39:36.532933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.