The DeepConf mechanism in Falcon-H1R can be repurposed as a dynamic 'uncertainty-aware gatekeeper' for Retrieval-Augmented Generation (RAG), initiating external information retrieval only when internal reasoning confidence falls below a learned threshold.

Motivation

Current RAG systems often retrieve context indiscriminately, increasing latency and noise. Falcon-H1R's ability to estimate internal confidence (DeepConf) suggests it 'knows what it knows,' offering a unique opportunity to create an adaptive system that relies on parametric memory for easy reasoning and switches to non-parametric retrieval only for high-uncertainty tasks.

Proposed Method

Fine-tune Falcon-H1R on a mixed dataset of 'closed-book' (logic/common sense) and 'open-book' (fact-heavy) questions. Modify the inference loop to monitor the DeepConf score at the initial reasoning steps; if the score is below a calibrated threshold $\tau$, trigger a search query and inject the retrieved context. Compare the accuracy-latency trade-off against standard 'always-retrieve' RAG and 'never-retrieve' baselines.

Expected Contribution

A protocol for 'Adaptive Reasoning-RAG' that significantly reduces computational overhead and latency by retrieving only when necessary, while maintaining high accuracy on complex tasks.

Required Resources

Access to Falcon-H1R weights, a retrieval corpus (e.g., Wikipedia dump), a vector database, and GPU compute for fine-tuning and inference.

Source Paper

Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling

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