Chart pathways beyond static language models by integrating continual learning, hybrid architectures, and on-the-job adaptation.
1. **Modular experts:** Maintain a stable core model while spawning specialized adapters for new domains. Route inputs via gating networks that learn over time.
2. **Memory-augmented systems:** Combine base models with episodic memory stores or vector databases that update in real time, separating storage from parametric knowledge.
3. **Reinforcement-enhanced agents:** Pair language models with reinforcement learners that fine-tune action policies through interactions, guided by human feedback.
4. **Neuromorphic inspirations:** Explore architectures that mimic synaptic consolidation, allowing selective plasticity across layers.