The paper introduces RIKA, a method for reliable knowledge acquisition in continual learning agents. RIKA hierarchically extracts factual, conceptual, procedural, and metacognitive knowledge and uses evidence-based admission to decide which claims are incorporated into the agent’s context, avoiding the reuse of unreliable information. Experiments on six task streams and four model backbones show that RIKA outperforms existing memory and experience-learning systems on aggregate performance.
Autonomous agents are increasingly deployed across sequences of related tasks, yet they often fail to retain and improve their knowledge over time. Existing approaches convert interaction trajectories...
arXiv:2610.34043v1 Announce Type: cross Abstract: Autonomous agents are increasingly deployed across sequences of related tasks, yet they often fail to retain and improve their knowledge over time. E...
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. 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:2610.02150v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly rely on persistent external sources to solve sequences of knowledge-intensive tasks. Existing methods...
The paper introduces the Continual Learning Exam (CLE), a benchmark designed to evaluate how large language model (LLM) agents improve over time by learning from experience. CLE consists of 120 human-authored tasks organized into five-level ladders, totaling 600 levels across 89 professional roles in 12 domains, where agents must pass each level to unlock the next. Experiments with 17 frontier LLM configurations show that providing diagnostic feedback and allowing one revision improves median completion by 5.3 percentage points, yet the best system only clears 47.7% of levels and completes all five levels in 23.3% of tasks, underscoring the challenge of sustained learning from experience.
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:2609.23989v1 Announce Type: new Abstract: Building general-purpose agents for industrial deployment requires integrating multiple capabilities, each typically acquired at a distinct stage of tr...
Learn2Play Bench is a new benchmark that tests how well large language model agents learn from experience in unfamiliar, text‑based games with novel or counterintuitive rules. The benchmark provides reproducible feedback, automatic scoring, and varied game instances to evaluate learning across repeated attempts and transfer to new situations. Findings show that retaining full action records aids learning, human players outperform agents, and the choice of harness significantly impacts performance and inference cost.
The paper introduces AMBER, an append‑only memory framework for language‑model agents that interact over long horizons. AMBER lets agents jointly learn to reason, act, and write free‑form memory, guaranteeing retention by construction and enabling end‑to‑end reinforcement learning without extensive curated data. Experiments on WebArena Lite show AMBER outperforms overwrite‑based memory by 4.09 percentage points in average success and improves task completion rates in repeated runs.
arXiv:2610.10379v1 Announce Type: new Abstract: Continual learning requires models to adapt to new domains and new classes while retaining prior knowledge. Many existing methods rely on continued opt...