Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T05:04:30.772800Z
Paper Citation Record · LEDGER
No Press Diplomacy: Modeling Multi-Agent Gameplay
As of 14 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 1 inbound Pith citation observation for arXiv:1909.02128.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T02:36:39.663614Z
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
37 of 37 outbound references displayed
- verified exact2
- verified fuzzy24
- unresolved10
- parse uncertain1
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 312fd4f9-3ea6-4d29-9f3b-4294315d9104 · outbound
This paper cites Multi-agent reinforcement learning in sequential social dilemmas.
No Press Diplomacy: Modeling Multi-Agent Gameplay Multi-agent reinforcement learning in sequential social dilemmas
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:31.213800Z
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.
Observation a60fd523-34f4-4172-b292-d663ad8bd594 · outbound
This paper cites The Hanabi Challenge: A New Frontier for AI Research.
No Press Diplomacy: Modeling Multi-Agent Gameplay The Hanabi Challenge: A New Frontier for AI Research
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:04:30.649262Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8db80bad-5d91-44d8-9ee3-8b8cc1b2e26d · outbound
This paper cites Superhuman ai for heads-up no-limit poker: Libratus beats top professionals.
No Press Diplomacy: Modeling Multi-Agent Gameplay Superhuman ai for heads-up no-limit poker: Libratus beats top professionals
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:31.201405Z
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.
Observation b271008c-e417-41a2-bcb3-495d84e90b72 · outbound
This paper cites Deepstack: Expert-level artificial intelligence in heads-up no-limit poker.
No Press Diplomacy: Modeling Multi-Agent Gameplay Deepstack: Expert-level artificial intelligence in heads-up no-limit poker
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:31.188783Z
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.
Observation cefd9eeb-e8be-437f-aef4-b9f4b4585e1f · outbound
This paper cites Openai five.
No Press Diplomacy: Modeling Multi-Agent Gameplay Openai five
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:04:30.660691Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a10e306b-8cc3-456a-9d69-d47f80a3d70f · outbound
This paper cites an unresolved cited work.
No Press Diplomacy: Modeling Multi-Agent Gameplay Unresolved cited work
Reference 6
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T05:04:31.166134Z
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.
Observation 8d6d1480-baac-4cde-8215-45dd45cbf6d5 · outbound
This paper cites Dp w1995a: Communication in no-press diplomacy.
No Press Diplomacy: Modeling Multi-Agent Gameplay Dp w1995a: Communication in no-press diplomacy
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:31.154755Z
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.
Observation d57ff683-5873-48ef-8874-7525b4dbb830 · outbound
This paper cites Daide - diplomacy artificial intelligence development environment.
No Press Diplomacy: Modeling Multi-Agent Gameplay Daide - diplomacy artificial intelligence development environment
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:31.143171Z
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.
Observation fe4c44a9-b55a-41a6-999a-96fe3498235b · outbound
This paper cites Diplomacy ai - albert.
No Press Diplomacy: Modeling Multi-Agent Gameplay Diplomacy ai - albert
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:31.131190Z
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.
Observation e11671d1-c003-417a-8a40-a7585e23168d · outbound
This paper cites TrueskillTM: a bayesian skill rating system.
No Press Diplomacy: Modeling Multi-Agent Gameplay TrueskillTM: a bayesian skill rating system
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:31.105368Z
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.
Observation 8b38f28d-aebc-4ca7-933e-4cdcb02b62f3 · outbound
This paper cites A player rating system for diplomacy.
No Press Diplomacy: Modeling Multi-Agent Gameplay A player rating system for diplomacy
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:31.092453Z
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.
Observation af57052a-a0f6-43e4-b772-ad7669225978 · outbound
This paper cites Ghost-ratings explained.
No Press Diplomacy: Modeling Multi-Agent Gameplay Ghost-ratings explained
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:31.079611Z
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.
Observation 0f434df6-25f9-4d62-9780-b4d5ee7133ba · outbound
This paper cites Site scoring system.
No Press Diplomacy: Modeling Multi-Agent Gameplay Site scoring system
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:31.067180Z
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.
Observation 0d01a86f-3fb2-4221-b963-2214be739221 · outbound
This paper cites Asynchronous methods for deep reinforcement learning.
No Press Diplomacy: Modeling Multi-Agent Gameplay Asynchronous methods for deep reinforcement learning
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:04:30.696544Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2190fe4a-97d3-4ece-bcdf-41fb4760654f · outbound
This paper cites Mastering the game of go with deep neural networks and tree search.
No Press Diplomacy: Modeling Multi-Agent Gameplay Mastering the game of go with deep neural networks and tree search
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:31.047775Z
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.
Observation ba5260c5-b241-4711-8b36-9c5cabeb0fb0 · outbound
This paper cites Mastering the game of go without human knowledge.
No Press Diplomacy: Modeling Multi-Agent Gameplay Mastering the game of go without human knowledge
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:04:30.703161Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 48bf3784-ec99-4e5a-9dcc-c7b5f3b8298d · outbound
This paper cites Human-level performance in first-person multiplayer games with population-based deep reinforcement learning.
No Press Diplomacy: Modeling Multi-Agent Gameplay Human-level performance in first-person multiplayer games with population-based deep reinforcement learning
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:04:30.706463Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dc2e4a3a-1092-4127-b6c9-ba96a99cb05d · outbound
This paper cites Dipblue: A diplomacy agent with strategic and trust reasoning.
No Press Diplomacy: Modeling Multi-Agent Gameplay Dipblue: A diplomacy agent with strategic and trust reasoning
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:31.027777Z
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.
Observation e5abc4c3-d115-4e1e-b91b-4e233fa237b8 · outbound
This paper cites Dipgame: A testbed for multiagent systems.
No Press Diplomacy: Modeling Multi-Agent Gameplay Dipgame: A testbed for multiagent systems
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:31.015852Z
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.
Observation 1d971de2-866d-4861-b0cc-f696b7171375 · outbound
This paper cites Negotiations over large agreement spaces, 2015.
No Press Diplomacy: Modeling Multi-Agent Gameplay Negotiations over large agreement spaces, 2015
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:31.003374Z
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.
Observation b7e28f74-dc3a-40a1-ae58-1199fe2e9740 · outbound
This paper cites Learning a game strategy using pattern-weights and self-play.
No Press Diplomacy: Modeling Multi-Agent Gameplay Learning a game strategy using pattern-weights and self-play
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:30.991125Z
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.
Observation 3c42ac02-1199-4e5c-ad76-b103b10baef7 · outbound
This paper cites The evolution of cooperation.
No Press Diplomacy: Modeling Multi-Agent Gameplay The evolution of cooperation
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:04:30.723862Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 53bd4373-5fdf-405a-a680-22abb44602fa · outbound
This paper cites Learning to communicate with deep multi-agent reinforcement learning.
No Press Diplomacy: Modeling Multi-Agent Gameplay Learning to communicate with deep multi-agent reinforcement learning
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:30.972557Z
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.
Observation 4aa618ef-edf7-4f1a-be95-434f4f167809 · outbound
This paper cites Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning.
No Press Diplomacy: Modeling Multi-Agent Gameplay Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:04:30.730802Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bbfdc330-08f6-4f3c-8ec8-cddb8166f8bb · outbound
This paper cites Inequity aversion improves cooperation in intertemporal social dilemmas.
No Press Diplomacy: Modeling Multi-Agent Gameplay Inequity aversion improves cooperation in intertemporal social dilemmas
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:30.960477Z
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.
Observation f5124e5f-3657-46f7-9870-cd51c5dce717 · outbound
This paper cites Consequentialist conditional cooperation in social dilemmas with imperfect information.
No Press Diplomacy: Modeling Multi-Agent Gameplay Consequentialist conditional cooperation in social dilemmas with imperfect information
Reference 26
- Resolution
- verified exact
- local_arxiv, observed 2026-08-14T05:04:30.835720Z
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.
Observation 0467d683-6a82-4a6e-8f55-bc48ff3c3e3d · outbound
This paper cites Behavioural game theory: thinking, learning and teaching.
No Press Diplomacy: Modeling Multi-Agent Gameplay Behavioural game theory: thinking, learning and teaching
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:30.949557Z
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.
Observation fda30ae8-2bec-4d83-8c39-5589facec195 · outbound
This paper cites Semi-Supervised Classification with Graph Convolutional Networks.
No Press Diplomacy: Modeling Multi-Agent Gameplay Semi-Supervised Classification with Graph Convolutional Networks
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:04:30.745091Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d25e74bc-05c2-4114-ad64-78e0546fdbce · outbound
This paper cites Film: Visual reasoning with a general conditioning layer.
No Press Diplomacy: Modeling Multi-Agent Gameplay Film: Visual reasoning with a general conditioning layer
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:30.937967Z
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.
Observation 16e9c278-9b64-437e-9b07-90643e0ad248 · outbound
This paper cites Weight normalization: A simple reparameterization to accelerate training of deep neural networks.
No Press Diplomacy: Modeling Multi-Agent Gameplay Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:30.926371Z
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.
Observation 550a7373-1b81-4f6e-8e8a-809f8361baca · outbound
This paper cites Feature-wise transformations.
No Press Diplomacy: Modeling Multi-Agent Gameplay Feature-wise transformations
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:30.914668Z
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.
Observation 738a52a5-2a16-4d29-a0b0-5ad4c71637c2 · outbound
This paper cites Deep residual learning for image recognition.
No Press Diplomacy: Modeling Multi-Agent Gameplay Deep residual learning for image recognition
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T05:04:30.759151Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 65c65e6e-9c99-431d-b7df-7a45fb9b75c2 · outbound
This paper cites Daide - clients.
No Press Diplomacy: Modeling Multi-Agent Gameplay Daide - clients
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:30.897842Z
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.
Observation 1330f5a4-aa91-402c-bdca-acf561a17682 · outbound
This paper cites Emergent Communication through Negotiation.
No Press Diplomacy: Modeling Multi-Agent Gameplay Emergent Communication through Negotiation
Reference 34
- Resolution
- verified exact
- local_arxiv, observed 2026-08-14T05:04:30.808517Z
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.
Observation b4f636b5-bc9f-4030-98c0-a0e16b4c7f9a · outbound
This paper cites Strategic information transmission.
No Press Diplomacy: Modeling Multi-Agent Gameplay Strategic information transmission
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:30.887382Z
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.
Observation ed89bb22-b0fe-49b5-aa0f-905b15fc51b4 · outbound
This paper cites Learning with opponent-learning awareness.
No Press Diplomacy: Modeling Multi-Agent Gameplay Learning with opponent-learning awareness
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T05:04:30.876207Z
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.
Observation fbb986b3-d048-430e-952d-3faa24f90e16 · outbound
This paper cites an unresolved cited work.
No Press Diplomacy: Modeling Multi-Agent Gameplay Unresolved cited work
Reference 2013
- Resolution
- parse uncertain
- raw_fallback, observed 2026-08-14T05:04:31.118007Z
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.
Pith citing papers
Observation b3e684f5-8a45-4222-83b0-3a78c8be14a2 · inbound
Cognitive Dark Matter: Measuring What AI Misses cites this paper.
Cognitive Dark Matter: Measuring What AI Misses No Press Diplomacy: Modeling Multi-Agent Gameplay
Reference 82
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T02:36:39.663614Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.