Paper Citation Record · LEDGER

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals

As of 20 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2507.01470.

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

pith.paper-citation-record.v1
2507.01470 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T20:59:16.541167Z

measured 33 of 33 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

33 of 33 outbound references displayed

verified exact4
verified fuzzy13
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7d4da9db-1c46-43ae-a885-322a86a7f863 · outbound

This paper cites Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:13.632344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99c0bd26-131a-419b-b279-4e8d9470bc95 · outbound

This paper cites Concrete Problems in AI Safety.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Concrete Problems in AI Safety

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:13.697707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8836cee5-a8d6-4999-a299-a65dc61cdfee · outbound

This paper cites Hindsight experience replay.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Hindsight experience replay

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:59:19.581118Z

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 14ad7b67-bf52-4977-83fa-a00466b041a1 · outbound

This paper cites Dynamic programming.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Dynamic programming

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:59:19.452897Z

Source-reported events for the cited work

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Observation e4fb16a7-b28d-410a-9931-4d125ab8aa59 · outbound

This paper cites Exploration by Random Network Distillation.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Exploration by Random Network Distillation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:13.902535Z

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Unavailable: canonical work link unavailable.

Observation bc377857-9684-4efc-b592-66816d3ead45 · outbound

This paper cites an unresolved cited work.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Unresolved cited work

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:13.972079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5bfdeb87-e46b-4126-ac23-e2f7d48435d9 · outbound

This paper cites HiSOMA : A hierarchical multi-agent model integrating self-organizing neural networks with multi-agent deep reinforcement learning.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals HiSOMA : A hierarchical multi-agent model integrating self-organizing neural networks with multi-agent deep reinforcement learning

Reference 7

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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 dde65c51-d02b-4875-bbc3-adef23066d2a · outbound

This paper cites MASER : Multi-agent reinforcement learning with subgoals generated from experience replay buffer.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals MASER : Multi-agent reinforcement learning with subgoals generated from experience replay buffer

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:59:19.333627Z

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 d311e481-1434-40ba-9ab9-c61e70c41aed · outbound

This paper cites Automatic discovery of subgoals in reinforcement learning using strongly connected components.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Automatic discovery of subgoals in reinforcement learning using strongly connected components

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:59:19.194182Z

Source-reported events for the cited work

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Observation 467cdcee-7649-43a1-8f56-49382aafe968 · outbound

This paper cites Exploration in deep reinforcement learning: A survey.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Exploration in deep reinforcement learning: A survey

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:14.308450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c6a3b94-79d5-418b-9334-34cc91bef9c4 · outbound

This paper cites Lecun, L.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Lecun, L

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:14.397566Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9ce9363-1912-4b43-b219-5e02f2885fd2 · outbound

This paper cites Automatic discovery of subgoals in reinforcement learning using diverse density.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Automatic discovery of subgoals in reinforcement learning using diverse density

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:59:19.082703Z

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 5d82be80-cdee-46ff-818b-67cb0c575fd3 · outbound

This paper cites Research on Multi -agent Sparse Reward Problem.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Research on Multi -agent Sparse Reward Problem

Reference 13

Resolution: verified exact; doi, observed 2026-08-06T20:59:17.239155Z

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 5a082f66-e373-4a5f-a84a-68e4806cc08a · outbound

This paper cites Laser learning environment: A new environment for coordination-critical multi-agent tasks.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Laser learning environment: A new environment for coordination-critical multi-agent tasks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:59:18.975027Z

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 2c60f28c-eda0-4176-a4fa-434b66a51ee5 · outbound

This paper cites An overview of environmental features that impact deep reinforcement learning in sparse-reward domains.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals An overview of environmental features that impact deep reinforcement learning in sparse-reward domains

Reference 15

Resolution: verified exact; doi, observed 2026-08-06T20:59:17.060987Z

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 ddab0dbb-b9fd-4f0d-800b-2f05df26db66 · outbound

This paper cites Efros, and Trevor Darrell.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Efros, and Trevor Darrell

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:14.850478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c7f1459-afb7-4eca-8ca3-3382338a08f2 · outbound

This paper cites Learning to Drive a Bicycle using Reinforcement Learning and Shaping.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Learning to Drive a Bicycle using Reinforcement Learning and Shaping

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:59:18.795445Z

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 cbeecba4-df41-4b81-83a4-5ee1fa5b4c80 · outbound

This paper cites QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:15.058602Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3724e73d-6be2-4af4-8957-7a144c55dbcc · outbound

This paper cites The StarCraft Multi-Agent Challenge.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals The StarCraft Multi-Agent Challenge

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:15.116702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a62b7a4-3661-41f7-885b-ab7abe8a9ad7 · outbound

This paper cites Normalized cuts and image segmentation.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Normalized cuts and image segmentation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:59:18.586251Z

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 f0da5f00-7721-4c86-8dcd-a8d99e8b560c · outbound

This paper cites Wolfe, and Andrew G.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Wolfe, and Andrew G

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:15.274544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 082ccee4-2d28-4ccb-ab72-b3b636db67bd · outbound

This paper cites Leibo, Karl Tuyls, and Thore Graepel.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Leibo, Karl Tuyls, and Thore Graepel

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:59:18.304632Z

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 6775740b-eb04-4378-a2ee-978b43fb2dca · outbound

This paper cites Faster MIL -based subgoal identification for reinforcement learning by tuning fewer hyperparameters.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Faster MIL -based subgoal identification for reinforcement learning by tuning fewer hyperparameters

Reference 23

Resolution: verified exact; doi, observed 2026-08-06T20:59:16.854696Z

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 25b4aadd-313e-4b70-a586-c290970713a2 · outbound

This paper cites Sutton and Andrew G.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Sutton and Andrew G

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:59:18.088589Z

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 308c9bdf-4d87-4580-a124-1fbafbe0520f · outbound

This paper cites Sutton, Doina Precup, and Satinder Singh.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Sutton, Doina Precup, and Satinder Singh

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:15.511885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c91e146-c36f-40b5-a796-6d9299277dbe · outbound

This paper cites \#exploration: A study of count-based exploration for deep reinforcement learning.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals \#exploration: A study of count-based exploration for deep reinforcement learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:59:17.954977Z

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 8a892e58-5ae4-4b7c-bf72-9a0072e396e2 · outbound

This paper cites Keeping your distance: Solving sparse reward tasks using self-balancing shaped rewards.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Keeping your distance: Solving sparse reward tasks using self-balancing shaped rewards

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:59:17.854308Z

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 26d0f6c0-c6e6-46bb-b059-8b1e0516bb2b · outbound

This paper cites Deep Reinforcement Learning with Double Q-learning.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Deep Reinforcement Learning with Double Q-learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:15.925516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e0992bd-c580-4fd7-870d-383f6305ef15 · outbound

This paper cites QPLEX: Duplex Dueling Multi-Agent Q-Learning.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals QPLEX: Duplex Dueling Multi-Agent Q-Learning

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:16.045425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2df939bb-a422-41e5-a98d-c16d404ec35e · outbound

This paper cites an unresolved cited work.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:59:17.742184Z

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 5f4be7ec-1b16-4267-b6c5-7c5eba4fd65b · outbound

This paper cites HAVEN : Hierarchical cooperative multi-agent reinforcement learning with dual coordination mechanism.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals HAVEN : Hierarchical cooperative multi-agent reinforcement learning with dual coordination mechanism

Reference 31

Resolution: verified exact; doi, observed 2026-08-06T20:59:16.693495Z

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 23accf97-5d55-4607-9472-1b9d15576a60 · outbound

This paper cites Ng, Daishi Harada, and Stuart Russell.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals Ng, Daishi Harada, and Stuart Russell

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:59:17.668579Z

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 ff4fd410-ad6b-4afd-aa4c-6643d5dac742 · outbound

This paper cites write newline.

Zero-Incentive Dynamics: a look at reward sparsity through the lens of unrewarded subgoals write newline

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T20:59:16.541167Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.