Memory-Centric Continual Learning for AI Agents proposes a framework that redefines agent memory as a driver of future actions rather than a passive retrieval cache. It introduces a three-axis taxonomy—substrates, assets, and policies—to structure memory, a two-loop lifecycle for updating memory state and refining management policies, and an evaluation protocol that isolates genuine behavioral gains from simple context expansion. The paper also presents a memory scaling framework to model how performance scales with experience, storage, and compute across non-parametric and parametric memory.
arXiv:2602. 06052v4 Announce Type: replace-cross Abstract: Research in artificial intelligence is shifting from model innovations and benchmark scores towards problem definition and rigorous real-world evaluation.
The paper introduces Harness Continual Learning (HCL), a paradigm where an agent’s state evolves through prompts, memories, tools, skills, and routing rules while keeping the underlying foundation model frozen. HCL defines harness-level forgetting and proposes a guarded evolution process involving a Continual Optimizer and Evaluator to ensure improvements without losing prior behavior. Experiments across textual reasoning, multimodal perception, and open‑world interaction show over 10% performance gains and demonstrate how the stability–plasticity trade‑off can be explicitly tuned.
The paper introduces Harness Continual Learning (HCL), a paradigm where an agent’s state evolves through prompts, memories, tools, skills, and routing rules while keeping the foundation model frozen. HCL defines harness-level forgetting and proposes guarded harness evolution with a Continual Optimizer and Evaluator to balance improvement, retention, and validity. Experiments across textual reasoning, multimodal perception, and open‑world interaction show over 10% performance gains and demonstrate explicit control over the stability–plasticity trade‑off.
arXiv:2607. 07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn?
Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks. Yet nearly all existing approaches, from graph-structured memories to reflective insight stores, access memory through fixed, hand-designed heuristics.
arXiv:2603. 01761v2 Announce Type: replace-cross Abstract: Foundation models have transformed machine learning through large-scale pretraining and increased test-time compute.
arXiv:2606. 02461v2 Announce Type: replace Abstract: Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future episodes.
arXiv:2606. 02461v1 Announce Type: new Abstract: Language agents spend substantial inference time solving individual tasks, yet the experience acquired in one episode is often underutilized in future episodes.
arXiv:2606. 05661v1 Announce Type: new Abstract: Continual learning, the ability of AI systems to improve through sequential experience, has attracted substantial interest, but no high-quality benchmark exists to evaluate it.
The paper introduces HaMo, a Harness-Model collaborative framework designed for long-horizon continual learning. HaMo combines fast non‑parametric experience accumulation—distilling trajectories into strategy‑level experience cards for immediate retrieval—with slow parametric adaptation, training the model to interpret, ground, and execute these strategies across changing contexts. A retrieval‑coverage‑based selection mechanism identifies compact, complementary trajectories for parameter updates, and experiments demonstrate that HaMo outperforms both harness‑only and model‑only approaches in within‑domain and cross‑domain settings, improving retention and adaptation.
arXiv:2607. 13591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.
The paper introduces TokenWorth, a lightweight framework that learns the value of past experiences for self-improving agents operating under strict resource constraints. By using sparse execution feedback, TokenWorth guides both retrieval and memory management without updating the core model, enabling agents to adaptively reuse only the most beneficial experiences. Experiments on LifelongAgentBench show that TokenWorth outperforms existing lifelong-memory methods in task success while minimizing output tokens within the given budgets.