arXiv:2607. 29577v1 Announce Type: new Abstract: Games and simulators make valuable benchmarks by turning decisions into measurable outcomes, but many current suites under-test rules-rich tactical reasoning: the ability to choose well when geometry, timing, resources, objectives, and rule interactions all matter at once.
Faynt is a family of Transformer policies (10M and 75M parameters) that control all 26 characters in Super Smash Bros. Melee from a single checkpoint. After reinforcement learning, the 10M model wins 98.4% of same‑character games against fourteen specialist and multi‑character releases, and defeats a zero‑delay Slippi‑AI model in all 68 evaluated games. The work details architecture, scaling, hyperparameter transfer, supervised pretraining on 840,000 human replays, post‑training curricula, distillation, and efficient inference, and it releases the weights, benchmark suites, and a platform for automated model tournaments.
arXiv:2606. 08340v1 Announce Type: new Abstract: As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks.
arXiv:2606. 09826v1 Announce Type: cross Abstract: Vision-language model (VLM) agents are increasingly deployed in interactive game environments.
The paper introduces Porimon, an LLM-based agent for Pokémon Battles that uses Long/Short-Term Knowledge Augmented Generation (LSTKAG) to incorporate past states of the current task and retrieve experience summaries from similar previous instances. Porimon also employs an external API for precise damage calculation and detailed game information. Experimental results from 15,000 simulated battles show that Porimon, with hyperparameter optimization, outperforms both the earlier PokéLLMon agent and a rule-based heuristic player, and ablation studies confirm the benefit of the game information retrieval extension, though the impact of Long-Term KAG remains inconclusive.
The paper introduces Adversarial Heuristic Learning (AHL), a framework that uses AI agents to refine game policies without changing model weights. It presents AAArena, a benchmark of 12 adversarial games and 1,920 human programs, where agents analyze replays, select opponents, and revise policies to maximize ranking within limited budgets. Experiments show that certain configurations achieve gold medals, while performance drops in games with complex rules, highlighting challenges in game understanding and long‑horizon policy development.
arXiv:2510.01264v2 Announce Type: replace Abstract: Progress in adversarial multi-agent reinforcement learning (MARL) for robotics has been hampered by a lack of shared, extensible infrastructure tha...
CHAMP is a cross‑domain matchmaking framework for Multiplayer Online Battle Arena games that tackles cold‑start, distribution shift, and data‑scarcity issues by using hybrid player profiles and a Domain‑Aware Win‑rate Network (DAWN). DAWN learns mode‑conditioned representations through a shared network, achieving 67.73% win‑rate prediction accuracy and improving match balance in large‑scale A/B tests. The system reduces imbalanced matches, notably cutting 5‑minute kill crushing rates by up to 20.73% for lower‑tier players.
ACEA (Adversarial Co‑Evolution Arena) is a platform that connects pluggable red‑team and blue‑team adapters to a shared target LLM, scoring attack and defense rates with an LLM judge. It introduces a minimal HTTP protocol (ASAP) for any language, an evaluation methodology that seeds the target with canonical secrets to separate leakage from hallucination, and a real‑time game‑style visualization with detailed end‑of‑battle reports. Additionally, ACEA offers an optional in‑context improvement loop that provides advisory hints to adapters for iterative refinement across rounds.
arXiv:2604. 09523v2 Announce Type: replace Abstract: Training reinforcement-learning agents for cyber defense requires an environment that reflects the operational setting: noisy, partial observations, several defenders coordinating across a network, and an adaptive adversary realized through self-play.
arXiv:2606. 10389v1 Announce Type: new Abstract: Recent advances in LLM-driven code evolution have enabled automated discovery by iteratively generating and improving programs.
arXiv:2610.30116v1 Announce Type: new Abstract: Through a tangle of real-world interactions, from synthetic training pipelines to data shared on the open web, frontier LLMs absorb what their predeces...