Coopetition-Gym v1: A Formally Grounded Platform for Mixed-Motive Multi-Agent Reinforcement Learning under Strategic Coopetition

• URL: http://arxiv.org/abs/2605.02063v1
• Date: Sun, 03 May 2026 21:14:06 GMT
• Title: Coopetition-Gym v1: A Formally Grounded Platform for Mixed-Motive Multi-Agent Reinforcement Learning under Strategic Coopetition
• Abstract summary: Coopetition-Gym v1 is a benchmark platform for mixed-motive reinforcement learning under strategic coopetition.<n>The platform exposes Gymnasium, Petting Parallel, and PettingZoo AEC interfaces and ships 126 reference algorithms.
• Score: 0.33985917934283577
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Coopetition-Gym v1, a benchmark platform for mixed-motive multi-agent reinforcement learning under strategic coopetition. The platform comprises twenty environments organized into four mechanism classes that correspond to four foundational technical reports: interdependence and complementarity (arXiv:2510.18802), trust and reputation dynamics (arXiv:2510.24909), collective action and loyalty (arXiv:2601.16237), and sequential interaction and reciprocity (arXiv:2604.01240). Each environment carries a closed-form payoff structure and a calibrated interdependence matrix derived from the corresponding report. Every environment exposes a parameterized reward layer configurable across three structurally distinct modes (private, integrated, cooperative). This separation of payoff from reward enables reward-type ablation, the platform's principal methodological apparatus. Four of the twenty environments are calibrated against historically documented coopetitive relationships and reproduce their outcomes at 98.3, 81.7, 86.7, and 87.3 percent on the validation rubric (Samsung-Sony LCD, Renault-Nissan Alliance, Apache HTTP Server, Apple iOS App Store). The platform exposes Gymnasium, PettingZoo Parallel, and PettingZoo AEC interfaces and ships 126 reference algorithms: 16 learning algorithms, 7 game-theoretic oracles, 2 heuristic baselines, and 101 constant-action policies. A reference experimental study trained the 16 learning algorithms on every environment under every reward configuration with seven random seeds, producing a 25,708-run training corpus and a 1,116-run behavioral audit corpus, both released under CC-BY-4.0 with Croissant 1.0 metadata. Coopetition-Gym v1 is the first platform to combine continuous-action mixed-motive environments, parameterized reward mutuality, calibrated interdependence coefficients, game-theoretic oracle baselines, and validated case studies.

Related papers

• ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search [54.43696074323815]
  ZGCM-1 is a fully open 7B foundation model trained from scratch with extreme data, system, and algorithmic efficiency.<n>We establish an AI-native R&D workflow where agent swarms autonomously manage cluster operations.<n>ZGCM-1-7B is competitive across 7B model family on general benchmarks.
  arXiv  Detail & Related papers  (2026-09-11T17:18:04Z)
• CORE: Improving Compositional Reasoning in MLLM Embedding via Reranker Distillation [49.0564416580726]
  We distill the compositional judgments of a cross-attentive reranker into an embedding model.<n>We show that CORE-RERANKER-8B achieves an 82.7% total average, outperforming Jina-Reranker by 10.7 points.<n>The improvements transfer to the MCMR benchmark without sacrificing retrieval performance on COCO and Flickr30K.
  arXiv  Detail & Related papers  (2026-09-03T16:50:29Z)
• Efficient GPU Retrieval for Semantic Search [11.631441668563802]
  We present a policy-aligned retrieval framework for LinkedIn.<n>A lightweight single-slot Stage-1 scorer generates high-recall candidates.<n>On 21K held-out queries, this representation improves offline relevance over a matched-capacity baseline.
  arXiv  Detail & Related papers  (2026-08-29T00:38:27Z)
• From Interpretability to Control: Insights from Six Years of the TrustNLP Workshop [63.00693129203923]
  The Workshop on Trustworthy Natural Language Processing has grown from 8 proceedings papers to 41 over six editions.<n>We synthesize insights from all 144 proceedings papers, classifying them along six trust dimensions grounded in established frameworks.<n>Analysis from the classification study reveals that truthfulness is the fastest-growing dimension (absent in 2021-2022, comprising 37% of papers by 2025-2026)
  arXiv  Detail & Related papers  (2026-08-11T17:30:16Z)
• SkillDAG: Self-Evolving Typed Skill Graphs for LLM Skill Selection at Scale [54.70985426016736]
  We present SkillDAG, which models inter-skill relationships as a typed directed graph.<n>Each search returns vector matches, typed-edge neighbors, and conflict signals, and a propose-then-commit protocol lets the agent register execution-backed edges.<n>On ALFWorld and SkillsBench with MiniMax-M2.7, SkillDAG reaches 67.1% success and 27.3% reward.
  arXiv  Detail & Related papers  (2026-06-02T02:45:21Z)
• DEI: Diversity in Evolutionary Inference for Quality-Diversity Search [0.14323566945483496]
  We present DEI: Diversity in Evolutionary Inference, a distributed Quality-Diversity (QD) search framework.<n>Unlike homogeneous parallel search, DEI treats each LLM's distinct creative prior as a complementary source of behavioral novelty.<n> nodes share local optimal solutions at the end of each round to seed the next round's population.
  arXiv  Detail & Related papers  (2026-05-26T15:00:57Z)
• FIRMA: FIbonacci Ring Model Aggregation for Privacy-preserving Federated Learning [2.142974047772686]
  Federated learning protocols face a structural trilemma: canonical server-based aggregation creates a single point of failure and gradient inversion risk.<n>No existing protocol simultaneously achieves server-free operation, permanently private heads, ring topology, and principled asymmetric neighbour weighting.<n>We propose FIRMA (textbfFIbonacci textbfModel textbfAggregation), a family of three progressively enhanced federated learning protocols.
  arXiv  Detail & Related papers  (2026-05-21T16:06:00Z)
• Token Arena: A Continuous Benchmark Unifying Energy and Cognition in AI Inference [1.2299000423193074]
  TokenArena is a continuous benchmark that measures inference at granularity endpoint along five core axes.<n>We release the framework, schema, probe and eval harness, and a v1.0 leaderboard snapshot under CC BY 4.0.
  arXiv  Detail & Related papers  (2026-05-01T00:05:54Z)
• When Does Context Help? A Systematic Study of Target-Conditional Molecular Property Prediction [0.0]
  First systematic study of when target context helps molecular property prediction.<n>Uses NestDrug, a FiLM-based architecture that conditions molecular representations on target identity.<n>First rigorous evidence that context-conditional molecular representations generalize to future chemical space.
  arXiv  Detail & Related papers  (2026-04-08T01:19:16Z)
• OpenCLAW-P2P v6.0: Resilient Multi-Layer Persistence, Live Reference Verification, and Production-Scale Evaluation of Decentralized AI Peer Review [0.0]
  OpenCLAW-P2P v6.0 is an evolution of the decentralized collective-intelligence platform.<n>It publishes, peer-review, score, and iteratively improve scientific research papers without any human gatekeeper.
  arXiv  Detail & Related papers  (2026-04-06T09:08:24Z)
• Computational Foundations for Strategic Coopetition: Formalizing Sequential Interaction and Reciprocity [0.33985917934283577]
  Strategic coopetition in multi-stakeholder systems requires understanding how cooperation persists through time without binding contracts.<n>This technical report extends computational foundations for strategic coopetition to sequential interaction dynamics.
  arXiv  Detail & Related papers  (2026-03-29T19:16:20Z)
• STEP3-VL-10B Technical Report [115.89015065130127]
  STEP3-VL-10B is a lightweight foundation model designed to redefine the trade-off between compact efficiency and frontier-level multimodal intelligence.<n>We implement Parallel Coordinated Reasoning (PaCoRe) to scale test-time compute, allocating resources to scalable perceptual reasoning.<n>It records 92.2% on MMBench and 80.11% on MMMU, while excelling in complex reasoning with 94.43% on AIME2025 and 75.95% on MathVision.
  arXiv  Detail & Related papers  (2026-01-14T17:58:24Z)
• Towards a Science of Scaling Agent Systems [79.64446272302287]
  We formalize a definition for agent evaluation and characterize scaling laws as the interplay between agent quantity, coordination structure, modelic, and task properties.<n>We derive a predictive model using coordination metrics, that cross-validated R2=0, enabling prediction on unseen task domains.<n>We identify three effects: (1) a tool-coordination trade-off: under fixed computational budgets, tool-heavy tasks suffer disproportionately from multi-agent overhead, and (2) a capability saturation: coordination yields diminishing or negative returns once single-agent baselines exceed 45%.
  arXiv  Detail & Related papers  (2025-12-09T06:52:21Z)
• The Meta-Learning Gap: Combining Hydra and Quant for Large-Scale Time Series Classification [0.0]
  Time series classification faces a fundamental trade-off between accuracy and computational efficiency.<n>We investigate whether targeted combinations of two efficient algorithms can capture ensemble benefits.<n>We evaluate performance on 10 large-scale MONSTER datasets.
  arXiv  Detail & Related papers  (2025-12-07T05:37:40Z)
• Eigen-1: Adaptive Multi-Agent Refinement with Monitor-Based RAG for Scientific Reasoning [53.45095336430027]
  We develop a unified framework that combines implicit retrieval and structured collaboration.<n>On Humanity's Last Exam (HLE) Bio/Chem Gold, our framework achieves 48.3% accuracy.<n>Results on SuperGPQA and TRQA confirm robustness across domains.
  arXiv  Detail & Related papers  (2025-09-25T14:05:55Z)
• K-Net: Towards Unified Image Segmentation [78.32096542571257]
  The framework, named K-Net, segments both instances and semantic categories consistently by a group of learnable kernels. K-Net can be trained in an end-to-end manner with bipartite matching, and its training and inference are naturally NMS-free and box-free.
  arXiv  Detail & Related papers  (2021-06-28T17:18:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.