Paper Citation Record · LEDGER

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation

As of 19 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2505.08459.

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

pith.paper-citation-record.v1
2505.08459 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:59:53.994503Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

34 of 34 outbound references displayed

verified exact0
verified fuzzy13
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1761a9f2-3bf2-453b-a2eb-540aadb08004 · outbound

This paper cites StarCraft II: A New Challenge for Reinforcement Learning.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation StarCraft II: A New Challenge for Reinforcement Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.857981Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89ec4e44-3af6-4a5b-a23b-d89131346ceb · outbound

This paper cites Gym- µrts: Toward affordable full game real-time strategy games research with deep reinforcement learning,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Gym- µrts: Toward affordable full game real-time strategy games research with deep reinforcement learning,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.862457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bfb1f3f-079f-4677-a469-157e97028a76 · outbound

This paper cites The combinatorial multi-armed bandit problem and its application to real-time strategy games,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation The combinatorial multi-armed bandit problem and its application to real-time strategy games,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:59:54.419933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 858d8fe2-c163-41af-b2cc-aed27d4a054c · outbound

This paper cites Opponent modeling in deep reinforcement learning,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Opponent modeling in deep reinforcement learning,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:59:54.406922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f25f14b2-a10f-4d22-a52f-1335fa73e4a9 · outbound

This paper cites Bayes’ bluff: opponent modelling in poker,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Bayes’ bluff: opponent modelling in poker,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:59:54.395355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 32826ded-c559-4850-b1de-b9c579026215 · outbound

This paper cites Game theory-based opponent modeling in large imperfect-information games,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Game theory-based opponent modeling in large imperfect-information games,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:59:54.384775Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation feef75b3-a3f6-4040-971f-a4f33aced352 · outbound

This paper cites A survey of opponent modeling in adversarial domains,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation A survey of opponent modeling in adversarial domains,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:59:54.373343Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7f9904d9-3ec0-452d-be28-a6d31fd5f1c9 · outbound

This paper cites A robust and opponent-aware league training method for starcraft ii,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation A robust and opponent-aware league training method for starcraft ii,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:59:54.361703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fc9343b4-1ee9-4a2c-a9d1-c866a839d512 · outbound

This paper cites Language mod- els are few-shot learners,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Language mod- els are few-shot learners,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.893347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b49fdc04-4b55-474a-9e3f-4ee7a83ca868 · outbound

This paper cites Large lan- guage models are zero-shot reasoners,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Large lan- guage models are zero-shot reasoners,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.897952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91eb0e62-9f9b-44e1-a9f6-e5090be80755 · outbound

This paper cites Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.901867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d72ca18-692f-4d09-bf5a-7c5ddb887e1c · outbound

This paper cites Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.906880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40cfbb6b-c283-4430-90a1-4dc153a5edae · outbound

This paper cites Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Exploring Large Language Models for Communication Games: An Empirical Study on Werewolf

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.911826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8966785-e586-4474-ac80-460a58128123 · outbound

This paper cites A survey on large language model based autonomous agents,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation A survey on large language model based autonomous agents,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.916820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea4fddb5-fbbf-4d51-b20d-4eff485df090 · outbound

This paper cites Understanding the planning of LLM agents: A survey.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Understanding the planning of LLM agents: A survey

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.920933Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b993942a-5f98-418e-9271-52cb31a8d27e · outbound

This paper cites Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.924822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1af2a95b-0833-4ad0-9e58-52e421579d1a · outbound

This paper cites Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.928832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59fea17d-e84a-42d3-9ad1-35e9c957bf99 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Chain-of-thought prompting elicits reasoning in large language models,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.932887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66968a7f-21b0-488d-b2b5-fd05d792a4ca · outbound

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

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation ReAct: Synergizing Reasoning and Acting in Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.936312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc751a80-e7f8-41c1-a53f-b788bbc868b4 · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Tree of thoughts: Deliberate problem solving with large language models,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.939840Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82c74be7-e516-42a3-bd21-e0c51e0f547e · outbound

This paper cites Large language models as com- monsense knowledge for large-scale task planning,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Large language models as com- monsense knowledge for large-scale task planning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:59:54.308859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8560fdc7-fda0-4714-9570-fe405a1331ee · outbound

This paper cites Reasoning with Language Model is Planning with World Model.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Reasoning with Language Model is Planning with World Model

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.948813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 619306ef-38fc-497d-a90a-7baca8b18e6c · outbound

This paper cites LLM A: Human in the Loop Large Language Models Enabled A Search for Robotics.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation LLM A*: Human in the Loop Large Language Models Enabled A* Search for Robotics

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.952747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66141e0d-b21e-4918-85fd-e5a5a336aac6 · outbound

This paper cites Pddl| the planning domain definition language,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Pddl| the planning domain definition language,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:59:54.296515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8c6e4dcc-99e4-4c2e-ad2b-8a854d88dde6 · outbound

This paper cites LLM+P: Empowering Large Language Models with Optimal Planning Proficiency.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation LLM+P: Empowering Large Language Models with Optimal Planning Proficiency

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.960056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed3f0c71-9141-4ae6-9af5-4af23838d12e · outbound

This paper cites Dynamic Planning with a LLM.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Dynamic Planning with a LLM

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.963771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 332be458-3e7b-46b0-8c5d-02033bb886ca · outbound

This paper cites Lever- aging pre-trained large language models to construct and utilize world models for model-based task planning,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Lever- aging pre-trained large language models to construct and utilize world models for model-based task planning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:59:54.284322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 65abad5f-e528-4275-af5c-d43f9f354c86 · outbound

This paper cites Towards offline opponent modeling with in-context learning,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Towards offline opponent modeling with in-context learning,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:59:54.272275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 11cf7a7c-587a-4f68-8cbf-a2e27f23bc13 · outbound

This paper cites Limited information opponent modeling,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Limited information opponent modeling,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:59:54.261011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fb538cd9-a17a-4760-ba06-8927a430b2ed · outbound

This paper cites Modeling others using oneself in multi-agent reinforcement learning,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Modeling others using oneself in multi-agent reinforcement learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:59:54.247917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1bab39f4-5562-4e29-8807-32a44dd6f4ea · outbound

This paper cites Machine theory of mind,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Machine theory of mind,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.983353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eda1a259-d1b8-4300-85cf-bb54ef9b4a54 · outbound

This paper cites The minds of many: Opponent modeling in a stochastic game.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation The minds of many: Opponent modeling in a stochastic game

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.987360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65721d6b-6aec-4ff2-990d-38d11f86607e · outbound

This paper cites Enhancing Language Model Rationality with Bi-Directional Deliberation Reasoning.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Enhancing Language Model Rationality with Bi-Directional Deliberation Reasoning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T21:59:53.990773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e748689-5ba2-48c8-9f4f-3cd8d5ab8fa0 · outbound

This paper cites Transformers as policies for variable action envi- ronments,.

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation Transformers as policies for variable action envi- ronments,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T21:59:54.219993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.