Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 20 inbound Pith citation observations for arXiv:2408.15971.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T10:21:49.681539Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T22:57:26.233385Z
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 e535dccd-c87d-439e-bf4c-2d038c92299b · inbound
Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Game cites this paper.
Multi-agent KTO: Reinforcing Strategic Interactions of Large Language Model in Language Game BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 2016
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T15:20:07.197962Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bdeb7504-dd83-401b-9027-98444f02d6a4 · inbound
Why Do Multi-Agent LLM Systems Fail? cites this paper.
Why Do Multi-Agent LLM Systems Fail? BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 31
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T05:42:57.912351Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 54ca6850-b338-4c94-b88e-acb57778a5e9 · inbound
Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant cites this paper.
Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T10:21:49.681539Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ea404cf2-4c3f-4944-91f5-ceac3ea54560 · inbound
MAEBE: Multi-Agent Emergent Behavior Framework cites this paper.
MAEBE: Multi-Agent Emergent Behavior Framework BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:14:47.229043Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9583adb3-51b2-41e2-bde5-666e154bd097 · inbound
TextAtari: 100K Frames Game Playing with Language Agents cites this paper.
TextAtari: 100K Frames Game Playing with Language Agents BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 78
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:51:58.050301Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bdc7057b-65d2-454b-a014-bacf7035e2d2 · inbound
G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems cites this paper.
G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:39:58.873471Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b26edf21-e77e-4e9b-ba80-c3c40b23c3bc · inbound
Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey cites this paper.
Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 152
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T06:00:19.709367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5ddcaa60-af84-408e-804c-f494c3de74ea · inbound
The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind cites this paper.
The Decrypto Benchmark for Multi-Agent Reasoning and Theory of Mind BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:49:39.475102Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5818bfc3-d269-4ac5-a028-690f96bb1ba7 · inbound
Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review cites this paper.
Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 150
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T17:42:58.980429Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fea5e3e3-1c7c-4e94-8f15-652267ca4ce0 · inbound
Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory cites this paper.
Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 93
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-14T23:13:15.347355Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 8810323f-30ad-4717-974d-a0aca85c4436 · inbound
Agentic Reasoning for Large Language Models cites this paper.
Agentic Reasoning for Large Language Models BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T15:14:25.817356Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 6c817dfd-babd-4460-a9de-0eb0b64aecfe · inbound
CivBench: Progress-Based Evaluation for LLMs' Strategic Decision-Making in Civilization V cites this paper.
CivBench: Progress-Based Evaluation for LLMs' Strategic Decision-Making in Civilization V BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 39
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T05:41:01.876999Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 1939d673-476e-47e9-9aff-740c74e61895 · inbound
Cooperate to Compete: Strategic Coordination in Multi-Agent Conquest cites this paper.
Cooperate to Compete: Strategic Coordination in Multi-Agent Conquest BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T23:36:36.074202Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 878cc54b-7d8c-4338-a0f8-f425dfa61bde · inbound
When Does Hierarchy Help? Benchmarking Agent Coordination in Event-Driven Industrial Scheduling cites this paper.
When Does Hierarchy Help? Benchmarking Agent Coordination in Event-Driven Industrial Scheduling BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T02:23:37.908608Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 6f036a5a-a79e-4408-ba1e-50cc7b7eb24f · inbound
Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems cites this paper.
Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 295
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-15T03:08:58.183231Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation a0f51e4a-ddc6-42f5-bf98-e83312e239df · inbound
Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems cites this paper.
Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 296
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-19T16:52:39.862066Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation e417114f-0aa8-484c-b6db-7c7270a90df1 · inbound
Interactive Evaluation Requires a Design Science cites this paper.
Interactive Evaluation Requires a Design Science BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 57
- Resolution
- verified exact
- arxiv_id, observed 2026-05-20T10:58:14.125624Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation f55197ef-28be-4ac5-9d2f-ff2cc0a035a1 · inbound
CollabSim: A CSCW-Grounded Methodology for Investigating Collaborative Competence of LLM Agents through Controlled Multi-Agent Experiments cites this paper.
CollabSim: A CSCW-Grounded Methodology for Investigating Collaborative Competence of LLM Agents through Controlled Multi-Agent Experiments BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 102
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T13:16:58.884053Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 54db5e46-b1ba-41bb-82a2-6991ed0d43bc · inbound
Benchmarking Open-Ended Multi-Agent Coordination in Language Agents cites this paper.
Benchmarking Open-Ended Multi-Agent Coordination in Language Agents BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 63
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T21:57:26.483468Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 034bce39-cc41-46be-96ed-0315da7095d4 · inbound
RAILS: Verification-Native Clearing For Agentic Commerce cites this paper.
RAILS: Verification-Native Clearing For Agentic Commerce BattleAgentBench: A Benchmark for Evaluating Cooperation and Competition Capabilities of Language Models in Multi-Agent Systems
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T22:57:26.234866Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.