arXiv:2608. 16409v1 Announce Type: new Abstract: Today, a neural system is almost always used in two phases -- trained, then deployed -- and in that regime it freezes twice: training ends, and the topology itself was never a degree of freedom.
arXiv:2608. 01548v2 Announce Type: replace Abstract: Language-first intelligence is constrained by which distinctions enter its symbolic record, which mappings its language--interpreter--environment complex can execute, and which possibilities can be realized with finite resources.
arXiv:2604. 27031v2 Announce Type: replace-cross Abstract: In a continual learning setting, we require a model to be plastic enough to learn a new task and stable enough to not disturb previously learned capabilities.
arXiv:2608.23889v1 Announce Type: cross Abstract: Plasticity under changing environments is central to both evolutionary biology and continual learning. Motivated by recent work on genotype--phenotyp...
The paper proposes a hierarchical architecture for long-horizon language‑model agents that must operate over days or weeks without forgetting. It introduces three key components: time‑scale levels that store bounded summaries, a clocked tick as the basic action unit, and cascaded intelligence that escalates tasks to more capable models only after review failures. A ten‑day experiment demonstrated that the agent maintained continuity across context resets, adapted its behavior based on early knowledge, and identified where learned components could be integrated.
arXiv:2609.36375v1 Announce Type: new Abstract: Continual learning is usually studied through mechanisms that preserve old knowledge. We develop Successional Learning Theory (SLT), a mesoscopic accou...
arXiv:2608. 06216v1 Announce Type: cross Abstract: Classical continual learning (CL) has primarily focused on enabling models to update and retain knowledge through parameter-centric mechanisms, e.
arXiv:2608. 01548v3 Announce Type: replace Abstract: Test-time emergence in LLM systems has a deployment boundary: additional computation can realize decisions already supported by the deployed information--execution structure, while evidence, tools, memory, and executable semantics can change the class inherited by later computation.
arXiv:2604. 15414v2 Announce Type: replace-cross Abstract: Continual reinforcement learning must balance retention with adaptation, yet many methods still rely on \emph{single-model preservation}, committing to one evolving policy as the main reusable solution across tasks.
The paper introduces Generalized Residual Closure (GRC), a framework that models learning as a recursive closure of future‑relevant discrepancies, enabling a learner to acquire new capabilities while preserving prior responsibilities. GRC identifies two primitive internal learning modes—transformation within a representation and revision of the representation itself—and provides a local tangent decomposition, a criterion for when representation revision is needed, and a necessary‑and‑sufficient condition for stability‑plasticity compatibility in an affine model. The authors further demonstrate how GRC supports safe, constrained updates that reduce residuals, extend representation adequacy to future updates, and connect the framework to optimization, machine learning, and control theory. whyItMatters":"The framework offers a principled way to balance stability and plasticity, ensuring continued learning without regression of previously acquired capabilities."
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.
arXiv:2602. 03846v2 Announce Type: replace-cross Abstract: We develop a continual learning method for pretrained models that \emph{requires no access to old-task data}, addressing a practical barrier in foundation model adaptation where pretraining distributions are often unavailable.