arXiv AI
Aug 5
ContinualSkillBench: Can LLM Agents Truly Evolve Their Capabilities?
arXiv:2608. 03874v1 Announce Type: new Abstract: Modern agent frameworks equip large language models with external skill libraries to solve complex tasks.
Skill Neologisms: Towards Skill-based Continual Learning
arXiv:2605. 04970v3 Announce Type: replace-cross Abstract: Modern LLMs show mastery over an ever-growing range of skills, as well as the ability to compose them flexibly.
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Hugging Face Trending Papers
Aug 4
ContinualSkillBench: Can LLM Agents Truly Evolve Their Capabilities?
Modern agent frameworks equip large language models with external skill libraries to solve complex tasks. However, it remains unclear whether these systems can effectively evolve their skills and whether the resulting skills improve task-solving capabilities.
arXiv AI
4d ago
Semantic Projection for Continual Self-Evolution of Language Agents
arXiv:2609.36626v1 Announce Type: new Abstract: Language-model agents increasingly rely on persistent natural-language skills to adapt beyond their frozen model parameters. When a shared skill is rep...
arXiv AI
Aug 11
Beyond Static Models: An Evolving Framework for Continual Learning in Large Language Models across Training Stages
arXiv:2603. 12658v2 Announce Type: replace-cross Abstract: Continual learning (CL) has emerged as a pivotal paradigm to enable large language models (LLMs) to dynamically adapt to evolving knowledge and sequential tasks while mitigating catastrophic forgetting, a critical limitation of the static pre-training paradigm inherent to modern LLMs.
arXiv Machine Learning
Sep 15
Realistic Continual Learning Approach using Pre-trained Models
arXiv:2404.07729v2 Announce Type: replace Abstract: Continual learning (CL) evaluates adaptability in learning solutions to retain knowledge. Our research addresses the challenge of catastrophic forg...
arXiv Machine Learning
Jul 10
When Does Continual Learning Require Learning
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?
arXiv AI
Jun 24
Evolving Programmatic Skill Networks
arXiv:2601. 03509v2 Announce Type: replace Abstract: We study continual skill acquisition in open-ended embodied environments where an agent must construct, refine, and reuse an expanding library of executable skills.
arXiv AI
Aug 18
SkillCommit: Evolving Agent Skills through Behaviorally Validated Scope Expansion
arXiv:2608. 15165v1 Announce Type: new Abstract: Large language model (LLM) agents can continually improve without parameter updates by converting historical experience into reusable procedural knowledge.
arXiv Machine Learning
Jun 4
Rethinking Continual Experience Internalization for Self-Evolving LLM Agents
arXiv:2606. 04703v1 Announce Type: cross Abstract: Experience internalization converts contextual experience from past interactions into reusable parametric capability, offering a promising path toward continual learning in large language models (LLMs).
arXiv AI
6d ago
Estimating and Orthogonalizing Unknown Pre-training Gradients for Continual Fine-tuning of Large Language Models
The paper introduces EoupCT, a framework that estimates and orthogonalizes unknown pre‑training gradients to mitigate catastrophic forgetting during continual fine‑tuning of large language models. It generates pseudo data most susceptible to forgetting using a learnable soft prompt with Gumbel‑Softmax, then jointly optimizes model parameters and the prompt via a first‑order Pareto optimizer to enforce orthogonality between new task updates and the estimated gradients. Experiments on multiple LLMs show that EoupCT preserves both task‑specific performance and the models’ inherent general‑purpose knowledge.
arXiv AI
Aug 18
Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents
arXiv:2608. 15071v1 Announce Type: new Abstract: Learning from experience is critical for developing capable, self-improving large language model (LLM) agents.
arXiv Machine Learning
Jul 30
Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting
arXiv:2607. 26643v1 Announce Type: cross Abstract: Enabling large language model (LLM) agents to accumulate and reuse experience from past interactions remains a central challenge in real-world applications.