33 articles tagged with #adaptive-systems. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce FluxMem, a memory framework for AI agents that treats memory as a continuously evolving graph rather than a static repository. The system dynamically refines memory connections through feedback and consolidation across three stages, achieving state-of-the-art results on multiple benchmarks.
Researchers present DelayMix, an online machine learning framework that models streaming time series as dynamic mixtures of time-delay systems, enabling rapid adaptation to regime shifts while maintaining memory efficiency. The method uses tensor decomposition to capture system dynamics and input delays, demonstrating superior forecasting accuracy on non-stationary data compared to existing approaches.
Researchers propose an adaptive framework for dynamically partitioning deep neural networks across edge-cloud infrastructure, addressing limitations of static approaches. Testing on real hardware demonstrates 27-35% energy reductions and 6-23% latency improvements compared to static baselines, validating the effectiveness of runtime-adaptive strategies for heterogeneous computing environments.
Researchers propose self-evolving software agents that combine Belief-Desire-Intention (BDI) reasoning with large language models to enable autonomous adaptation of goals, reasoning logic, and executable code beyond fixed design parameters. A prototype demonstrates that agents can discover new objectives and generate functional behaviors from minimal initial knowledge, though challenges remain in behavioral stability and inheritance.
Researchers introduce Dejavu, a post-deployment learning framework that enables frozen Vision-Language-Action policies to improve through experience retrieval and feedback networks. The system allows embodied AI agents to continuously learn from past trajectories without retraining, improving task performance across diverse robotic applications.
Researchers propose Streaming Continual Learning (SCL) as a unified paradigm that combines Continual Learning and Streaming Machine Learning approaches. SCL aims to enable AI systems to both rapidly adapt to new information and retain previously learned knowledge, addressing limitations of existing methods that excel at only one aspect.
Researchers developed a minimal AI architecture where a 'perspective latent' creates history-dependent perception in artificial agents. The system allows identical observations to be processed differently based on accumulated experience, demonstrating measurable plasticity that persists even after conditions return to normal.
Researchers propose CA-AFP, a new federated learning framework that combines client clustering with adaptive model pruning to address both statistical and system heterogeneity challenges. The approach achieves better accuracy and fairness while reducing communication costs compared to existing methods, as demonstrated on human activity recognition benchmarks.
