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Paper Citation Record · LEDGER

Evolving in the Agent Jungle via History-Informed Opponent Awareness

As of 7 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2608.02005.

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

pith.paper-citation-record.v1
2608.02005 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T16:51:38.793007Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

24 of 24 outbound references displayed

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  • unresolved24
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  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a1a2f81f-f628-46d4-a7ee-2a7cc82d3a90 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Evolving in the Agent Jungle via History-Informed Opponent Awareness ReAct: Synergizing Reasoning and Acting in Language Models

Reference 1

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source=pdf_text observed=2026-08-04T16:51:38.704247Z digest=sha256:2d1a1d78fd68ca0020c8618d15c8084389cae726e3e082c9fadbab47a231e97e

Observation dd2f13dd-d913-48ed-ade1-4e1caa1fb1cc · outbound

This paper cites Generative agents: Interactive simulacra of human behavior.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Generative agents: Interactive simulacra of human behavior

Reference 2

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source=pdf_text observed=2026-08-04T16:51:38.709555Z digest=sha256:d9e2b684a4fa22792b75d4ba0fe20d3b7c70b2cb2fa6c41358cdc416faad83ff

Observation cc1fa47c-3a64-4849-b955-77c399488133 · outbound

This paper cites Reflexion: language agents with verbal reinforcement learning.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Reflexion: language agents with verbal reinforcement learning

Reference 3

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no resolver link, observed 2026-08-04T16:51:38.714081Z

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source=pdf_text observed=2026-08-04T16:51:38.714081Z digest=sha256:aab0b662ea34a55d5884e2035ee1886178807db08a4ec67de3e96e36353dacef

Observation 427f0cc0-25bc-4d02-97d7-9c04dc54780e · outbound

This paper cites SELF-REFINE: iterative refinement with self-feedback.

Evolving in the Agent Jungle via History-Informed Opponent Awareness SELF-REFINE: iterative refinement with self-feedback

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T16:51:38.718140Z digest=sha256:be8e1779f24b4abc40f01fb902b8d6357020a2550a3e2a9ab0c03ce32851219d

Observation e101fe91-0b9d-4e17-9192-3ac6d8388aaf · outbound

This paper cites Voyager: An Open-Ended Embodied Agent with Large Language Models.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Voyager: An Open-Ended Embodied Agent with Large Language Models

Reference 5

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T16:51:38.722869Z digest=sha256:9edf7b2dec500ff02cdf4125469cf0ad491d42d44ddffc0026f5feb45841ed23

Observation 0de9bd0f-9c0d-4bd3-9885-8a25faa11827 · outbound

This paper cites Connecting large language models with evolutionary algorithms yields powerful prompt optimizers.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Connecting large language models with evolutionary algorithms yields powerful prompt optimizers

Reference 6

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no resolver link, observed 2026-08-04T16:51:38.727042Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T16:51:38.727042Z digest=sha256:c0afd655dd8bb3568e70645dd79a73280de3c728164a4a11bb92b2a77c0eaf6a

Observation a23cb9ac-c38f-4a2b-9bdc-4dc9b0ecf5c4 · outbound

This paper cites Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution

Reference 7

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no resolver link, observed 2026-08-04T16:51:38.731564Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T16:51:38.731564Z digest=sha256:76b0b32a844939a515866d7ee264a0e69df0727bb06ccea68567b74b044f9d83

Observation 218c467b-f6f5-48b4-a5d1-a3e796a5e4c6 · outbound

This paper cites Eureka: Human-level reward design via coding large language models.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Eureka: Human-level reward design via coding large language models

Reference 8

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no resolver link, observed 2026-08-04T16:51:38.735520Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T16:51:38.735520Z digest=sha256:5e437d3c23e9f7c591e0b8a1b70ec22355815d5b0602a03839c2a9a54211d439

Observation 68395b8a-2245-4802-9197-042d96260d25 · outbound

This paper cites Lifelong robot library learning: Bootstrapping composable and generalizable skills for embodied control with language models.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Lifelong robot library learning: Bootstrapping composable and generalizable skills for embodied control with language models

Reference 9

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no resolver link, observed 2026-08-04T16:51:38.739307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T16:51:38.739307Z digest=sha256:2d71be820d834015b4ed9c1ed607b32eae5fde8b26e5c3d30681a708c1c752fc

Observation 2f8d2f47-d8ea-4877-9ffe-4085349cc40e · outbound

This paper cites A unified game-theoretic approach to multiagent reinforcement learning.

Evolving in the Agent Jungle via History-Informed Opponent Awareness A unified game-theoretic approach to multiagent reinforcement learning

Reference 10

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source=pdf_text observed=2026-08-04T16:51:38.743020Z digest=sha256:22e5e8f56dd0a4c6eac41d4df1154c6d66e0f62ca9494ffa3159527c00424397

Observation e8f6d01b-be63-4d7d-8b3b-5a8650833b0b · outbound

This paper cites Open-ended learning in symmetric zero-sum games.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Open-ended learning in symmetric zero-sum games

Reference 11

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source=pdf_text observed=2026-08-04T16:51:38.746821Z digest=sha256:80e644813c4b6eb791ce3b8a76de2f9c8fe993f2768500a52ee04a007df48584

Observation af07c8a1-be22-4df0-bdc5-b5d26c4394ad · outbound

This paper cites Steps toward artificial intelligence.Proceedings of the IRE, 49(1):8–30, 1961.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Steps toward artificial intelligence.Proceedings of the IRE, 49(1):8–30, 1961

Reference 12

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source=pdf_text observed=2026-08-04T16:51:38.750345Z digest=sha256:b1c5a24c3f5bcdb6688502671cae64a30600836b06d03f9869b2c27362151c17

Observation 60a55ac4-68cf-43df-9ca8-3208b5a886f7 · outbound

This paper cites Counterfactual multi-agent policy gradients.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Counterfactual multi-agent policy gradients

Reference 13

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source=pdf_text observed=2026-08-04T16:51:38.754151Z digest=sha256:dcea4d9a13350675de7af6c91e0d9e4dc7ef0c6a5af5ee0f530e49489cc0d19e

Observation a69428f8-7e53-48e5-b673-5263d23cc401 · outbound

This paper cites Large language models as optimizers.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Large language models as optimizers

Reference 14

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source=pdf_text observed=2026-08-04T16:51:38.758174Z digest=sha256:60974dbda5e5ab139bd4f643efe2d4c24f82f499ce91fc9ff02f2a83fb8e267e

Observation d971265b-8eed-4c57-8d04-7604d1d3b800 · outbound

This paper cites The mechanics of n-player differentiable games.

Evolving in the Agent Jungle via History-Informed Opponent Awareness The mechanics of n-player differentiable games

Reference 15

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source=pdf_text observed=2026-08-04T16:51:38.761833Z digest=sha256:aa026dfbf33cfbf21327610143097aa1678fb8f00494dea5e393bc5b1b95caa4

Observation 40f997b4-0e8f-4623-8ba1-5eccb816cc0d · outbound

This paper cites Stable opponent shaping in differentiable games.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Stable opponent shaping in differentiable games

Reference 16

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source=pdf_text observed=2026-08-04T16:51:38.765157Z digest=sha256:6588e3d8f8268df277e1fa35a65dbb175a5eaac1ef6840b4567caf6510e85ff8

Observation bfb090ec-3656-47d8-9261-92068dd1061b · outbound

This paper cites Competitive gradient descent.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Competitive gradient descent

Reference 17

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source=pdf_text observed=2026-08-04T16:51:38.768725Z digest=sha256:385a2b73864245252a88c96b5201e8ab333769881e3aba577f7bd14c4019ae0b

Observation daf5b2cd-1a22-4c8d-abaf-bc909696cf7a · outbound

This paper cites An iterative method of solving a game.Annals of mathematics, 54(2):296–301, 1951.

Evolving in the Agent Jungle via History-Informed Opponent Awareness An iterative method of solving a game.Annals of mathematics, 54(2):296–301, 1951

Reference 18

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source=pdf_text observed=2026-08-04T16:51:38.772043Z digest=sha256:297be57511e013706308414f8cdd0febb0070904778a53ac60eaa31c9c4fef9c

Observation 02c024cb-ab79-4fe4-b76a-dcffe565c444 · outbound

This paper cites Pipeline PSRO: a scalable approach for finding approximate nash equilibria in large games.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Pipeline PSRO: a scalable approach for finding approximate nash equilibria in large games

Reference 19

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source=pdf_text observed=2026-08-04T16:51:38.775592Z digest=sha256:9d8a5b52a3588ab21a612b7536563a71f6c9e7cc6b1ad36671c366dc06777922

Observation e9ab45f0-ff49-46e6-b972-1b56b7236224 · outbound

This paper cites XDO: a double oracle algorithm for extensive-form games.

Evolving in the Agent Jungle via History-Informed Opponent Awareness XDO: a double oracle algorithm for extensive-form games

Reference 20

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source=pdf_text observed=2026-08-04T16:51:38.779071Z digest=sha256:4656b15f941de7937de54cd3c4147c0d1df23a3d34069c8009d5ecd4b7166b23

Observation f4e9d64c-b6e4-49a7-af69-68b39e598087 · outbound

This paper cites Policy Space Response Oracles: A Survey.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Policy Space Response Oracles: A Survey

Reference 21

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source=pdf_text observed=2026-08-04T16:51:38.782680Z digest=sha256:8faf6403222f927bf2202ff81bd23acf938ab67d011c7aa38f7a5640fd454f0c

Observation 7d2c1da8-74b9-45ec-88b3-69268f3bad6d · outbound

This paper cites Can large language models serve as rational players in game theory? a systematic analysis.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Can large language models serve as rational players in game theory? a systematic analysis

Reference 22

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source=pdf_text observed=2026-08-04T16:51:38.786242Z digest=sha256:72ff4cd91bd6f05eb3387e6dc87175d620d7f9b3944064067b8f2f8da103b2bd

Observation 34781a56-44eb-4afa-961a-ee27bdda458f · outbound

This paper cites Academic Press, 2 edition, 2009.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Academic Press, 2 edition, 2009

Reference 23

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source=pdf_text observed=2026-08-04T16:51:38.789758Z digest=sha256:8cf855a23bc83f7611f8265f82987471c5df570121be4e9893ed47ed7c1e3948

Observation c09ca4f1-c678-4d3b-82be-39461e0eb1a3 · outbound

This paper cites Bayesian nash equilibrium in “linear” cournot models with private information about costs.

Evolving in the Agent Jungle via History-Informed Opponent Awareness Bayesian nash equilibrium in “linear” cournot models with private information about costs

Reference 24

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source=pdf_text observed=2026-08-04T16:51:38.793007Z digest=sha256:7e65bf6167d18572face002deef305b4cda1c73d8984edca39acb0f3c64147b2

Pith citing papers

No inbound Pith citation observations are available.