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New Approach for Efficient Inference with Memory-Constrained AI Agents
## Compressed Query Delegation: A New Paradigm for Inference
Artificial intelligence research is increasingly focused on developing agents capable of handling complex tasks with limited resources. A new study introduces an innovative approach called "compressed query delegation" (CQD) to address the limitations of agents with restricted working memory.
## How CQD Works
CQD operates in three main stages:
1. **Compression:** The latent reasoning state, which can be high-dimensional, is compressed into a low-rank tensor query.
2. **Delegation:** The minimized query is delegated to an external oracle.
3. **Update:** The latent state is updated via Riemannian optimization on fixed-rank manifolds.
## Results and Implications
The researchers demonstrated that CQD is linked to classical rate-distortion and information bottleneck principles. They also derived convergence guarantees for Riemannian stochastic approximation under bounded oracle noise and smoothness assumptions. Empirical results show that CQD outperforms traditional baselines in a series of complex reasoning tasks and human cognitive benchmarks.
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