Okay, here are some specific, focused research questions based on our outline. Looking into these externally will provide concrete details and evidence to strengthen your narrative: **I. Regarding the Hook (Deepseek vs. Giants):** 1. **Deepseek's Claimed Efficiency:** * "What specific algorithmic optimizations (related to matrix operations, model architecture, or inference techniques) have Deepseek developers publicly claimed or discussed regarding their models' efficiency?" * "Are there any independent analyses or benchmarks comparing Deepseek's *theoretical* FLOPs/inference speed per token generated versus its competitors, controlling for model size?" 2. **Deepseek's Scaling Issues:** * "Beyond personal experience, are there public reports, forum discussions (e.g., Reddit, Hugging Face forums), or social media trends documenting user difficulties with Deepseek API availability, latency, or rate limits?" 3. **Gemini/Qwen Scaling Approach:** * "Have Google (for Gemini) or Alibaba (for Qwen) published technical details suggesting novel *inference efficiency* mechanisms beyond simply using large amounts of hardware?" (Or is their performance primarily attributed to massive infrastructure investment?) * "Are there any credible public estimates or analyses regarding the likely operational costs or energy consumption associated with running flagship models like Gemini or Qwen at scale?" (Acknowledge this is often proprietary). **II. Regarding the Core Problem (Redundant Computation):** 4. **Quantifying Inference Cost:** * "What are typical computational costs (e.g., FLOPs per token, latency breakdowns per layer) for inference in large transformer models?" * "How effective is the standard KV cache in reducing computation, and what are its limitations, especially across *different but semantically similar* user queries?" **III. Regarding the Inspirations (Blockchain, CDNs):** 5. **Blockchain Efficiency Mechanisms:** * "Specifically, how do mechanisms like Proof-of-Stake checkpointing, Layer-2 optimistic/ZK rollups, or state channels reduce redundant computation compared to Bitcoin's Proof-of-Work full validation? What is the core principle of 'leveraging prior agreed state' in these systems?" 6. **CDN Caching Logic:** * "What caching strategies (e.g., LRU, LFU, tiered caching, dynamic content acceleration) are commonly used in CDNs, and could any underlying principles be conceptually relevant to caching *semantic results*?" **IV. Regarding Proposed Solutions (Reuse Mechanisms):** 7. **Semantic Caching for LLMs:** * "What existing research papers, projects, or keywords describe attempts at 'semantic caching' or 'query result caching' specifically for LLM outputs? What techniques are used for semantic similarity matching (e.g., embedding similarity)?" 8. **Intermediate Representation Reuse:** * "Are there published studies exploring the caching and reuse of 'intermediate representations' (like layer activations, attention weights, or hidden states) within transformer models to accelerate inference for similar inputs?" (Keywords: memoization, activation caching). 9. **Query Decomposition & Composition:** * "What research exists on decomposing complex natural language queries into sub-problems solvable by LLMs, and then composing the results? Does any work explore caching the results of common sub-problems?" **V. Regarding Challenges & Privacy:** 10. **Semantic Similarity Hurdles:** * "What are the documented challenges and limitations of using vector embeddings or other methods to reliably determine 'semantic equivalence' between nuanced user queries in practice?" 11. **Cache Invalidation in AI:** * "How do existing systems that use caching with machine learning models (if any) handle 'cache invalidation' when the underlying model is updated or fine-tuned?" 12. **ZKP/DP for Computation Reuse:** * "Are there any theoretical or practical explorations of using Zero-Knowledge Proofs or Differential Privacy techniques specifically to enable the *sharing or reuse of computational results* (not just model verification or training data privacy) in a privacy-preserving manner?" Answering these questions (even partially) through targeted searches (e.g., Google Scholar, arXiv, AI conference proceedings, reputable tech blogs/analyses) will give you the factual grounding needed to turn the outline into a compelling and well-supported detailed narrative. Good luck!