arXiv:2607. 23842v1 Announce Type: cross Abstract: Artificial life systems are typically defined by a set of dynamical rules over an environment, an agent, or both, from which lifelike patterns may emerge.
arXiv:2608. 07537v1 Announce Type: cross Abstract: In this study, we propose a framework that incorporates subjective evaluations provided by a Vision-Language Model (VLM) into the fitness evaluation and selection processes of a genetic algorithm.
The paper introduces Morphogene, a compact latent blueprint that links an agent’s body and control policy, enabling coordinated changes at the limb level through AdaConcat. Building on this, GeCode treats co-design as exploration within Morphogene space, using local refinement and global exploration to efficiently navigate design regions. Experiments on various 2D and 3D tasks show that GeCode outperforms state‑of‑the‑art methods, achieving faster convergence and higher performance.
arXiv:2604. 08780v2 Announce Type: replace-cross Abstract: World models promise a paradigm shift in robotics, where an agent learns the physics of its environment once and then acquires behaviors efficiently.
arXiv:2606. 02832v1 Announce Type: new Abstract: Despite a great deal of prior research into Procedural Content Generation (PCG), relatively little prior work has explored generating enemies for video games.
arXiv:2610.02196v1 Announce Type: cross Abstract: We study test-time evolution for humanoid loco-manipulation: solving tasks that a controller was never trained for by repurposing its existing skills...
arXiv:2601. 16806v4 Announce Type: replace Abstract: Insect neuroethology provides a compelling biological template for efficient autonomous navigation.
arXiv:2608. 08311v1 Announce Type: cross Abstract: We present Ouroboros, a self-developing agent harness whose tools, prompts, context assembly, and core implementation improve through reviewed commits that become the runtime for later work.
arXiv:2608. 07645v1 Announce Type: new Abstract: Self-improving coding agents that iteratively rewrite their own source code have demonstrated impressive performance on coding tasks.
The paper investigates whether artificial evolution can replicate biological neuromodulation and diverse neuron types in indirectly encoded substrates. Experiments show that neuromodulation alone cannot overcome a 75% performance ceiling on parity tasks, but combining neuromodulation with per‑task activation function selection allows a single evolving genotype to achieve 100% success across five tasks. This demonstrates that both neuromodulation and evolvable computational primitives are necessary for multi‑behavioral open‑ended evolution.
arXiv:2608. 05158v1 Announce Type: cross Abstract: In biological evolution, unconstrained mutation can lead to catastrophic outcomes: organisms may evolve enhanced capabilities while losing essential functions for survival.
arXiv:2507.11482v5 Announce Type: replace Abstract: Artificial learning systems are graduating from passive learners to increasingly autonomous agents, lending pragmatic urgency to the question of wh...
The paper introduces Autopoietic Game Theory, a computational model where social interactions, replication mechanisms, and computational costs co-evolve within a substrate of randomly initialized Z80 machine code programs. By embedding a social dilemma directly into the physics of computation, the authors demonstrate that scarcity of resources can make defection self-limiting, leading to the emergence of self-replicating, cooperative strategies. Empirical results show evolved programs suppress stealing, and spatial assortment enhances structural complexity and task performance, while the framework can also incorporate exogenous pressures such as math tasks tied to computation budgets.