arXiv:2606.29961v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents can solve complex procedural tasks by interacting with environments over multiple turns, but this ability typically depends on large models, long contexts, and repeated inference calls. This makes advanced memory-augmented agents difficult to deploy on resource-constrained devices. We introduce DuoMem, a dual-space distillation framework that transfers procedural problem-solving ability from a large teacher model to compact student models. DuoMem distils in two complementary spaces: (1)context-space distill
The increasing computational demands of advanced AI models and the widespread availability of resource-constrained edge devices are driving innovation in efficient AI deployment.
This breakthrough addresses a critical bottleneck in deploying advanced AI agents on ubiquitous devices, expanding the reach and utility of sophisticated AI.
The ability to run powerful AI agents on-device opens new avenues for personalized, private, and real-time AI applications without continuous cloud dependence.
- · Edge device manufacturers
- · AI application developers
- · Consumers of AI services
- · Companies seeking on-device AI for privacy
- · Cloud-centric AI service providers (marginal impact)
- · Developers reliant solely on large, centralized AI models
More AI agents operating autonomously on a wider range of devices, from smartphones to IoT.
Increased demand for specialized edge AI hardware and a shift in AI model optimization strategies.
Enhanced personal autonomy and privacy as sensitive AI computations remain local to the user's device.
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Read at arXiv cs.LG