LLMSlim Engineering Papers
Deep technical explorations into graph centrality, token economics, attention saliency, and sub-30ms prompt compression gateways written for senior AI engineers.
Graph Centrality & TF-IDF Vectorization for In-Context Redundancy Reduction
Mathematical Derivation of LexRank Stationary Distributions and Priority Tier Filtering — A formal mathematical and algorithmic breakdown of how graph centrality over TF-IDF term matrices ranks and prunes redundant sentences in long context prompts while safeguarding imperative instructions.
Algorithms Track (2 Papers)
Graph Centrality & TF-IDF Vectorization for In-Context Redundancy Reduction
Mathematical Derivation of LexRank Stationary Distributions and Priority Tier Filtering
Quadratic Attention Scaling O(N^2) & In-Context Token Reduction Economics
Deriving Computation Savings in Transformer Self-Attention Layers
Systems & Architecture Track (3 Papers)
Deterministic Safety Shields: Priority Tier Protection Mechanisms
Formal Syntactic Safeguards for Directives, Code Blocks, and Structured Data
AST Syntax-Aware Normalization for JSON, XML, and YAML Prompts
Compressing Structural Data Payloads Without Invocation Failures
Reverse Proxy Gateway Integration: Context Compression in Production Python Gateways
Deploying Transparent Pre-Dispatch Context Optimization in Enterprise Services