When Classic Cache Policies Fail: Learning-Augmented Replacement for Semantic Retrieval Buffers

Innovation Summary

When Classic Cache Policies Fail: Learning-Augmented Replacement for Semantic Retrieval Buffers: We propose SOLAR, a learning-augmented framework that derives modification timing from regret accumulation (achieving sim17\% modification rate) and content selection from Bayesian online learning over implicit.

Executive Summary

When Classic Cache Policies Fail: Learning-Augmented Replacement for Semantic Retrieval Buffers: We propose SOLAR, a learning-augmented framework that derives modification timing from regret accumulation (achieving sim17\% modification rate) and content selection from Bayesian online learning over implicit. Why it matters: Overall signal 94/100 driven by novelty 100 and practical impact 100. Primary categories: Bayesian online learning, competitive ratio, embedding similarity, eviction regret, implicit retrieval feedback, online semantic cache replacement. Community signal includes 3 upvote(s) and 1 comment(s), which helps separate durable interest from title-only curiosity. Implementation angle: Implementation potential scores 100/100; prioritize adaptation paths for internal agent, evaluation, or platform workflows. No linked repository is present, so expect more translation work before the ideas are production-ready. Technical depth scores 100/100, so a quick skim should focus on architecture, data, and evaluation sections before full adoption work. Caveat: No linked implementation is available yet, which raises integration cost and lowers reproducibility confidence.

Why It Matters

• Overall signal 94/100 driven by novelty 100 and practical impact 100.
• Primary categories: Bayesian online learning, competitive ratio, embedding similarity, eviction regret, implicit retrieval feedback, online semantic cache replacement.
• Community signal includes 3 upvote(s) and 1 comment(s), which helps separate durable interest from title-only curiosity.

Implementation Angle

• Implementation potential scores 100/100; prioritize adaptation paths for internal agent, evaluation, or platform workflows.
• No linked repository is present, so expect more translation work before the ideas are production-ready.
• Technical depth scores 100/100, so a quick skim should focus on architecture, data, and evaluation sections before full adoption work.

Caveat

No linked implementation is available yet, which raises integration cost and lowers reproducibility confidence.

Estimated Reading Priority

High - 94/100 signal; read before acting on adjacent agent, evaluation, inference, or ML systems work.

Score Breakdown

Novelty

100

Practical Impact

100

Technical Depth

100

Implementation

100

Relevance

100

Community

38

Confidence

95