LLM Efficiency // Semantic Prompt Saccading

Cut LLM Token Consumption by 70%

Large Language Models do not need grammatical fluff to reason. By using self-attention voting and information entropy, Saccade prunes 60–75% of prompt tokens ("breaking the grammar") while preserving 100% of the core semantic facts. * Note: Benchmark demonstrations are for research and token-efficiency analysis only; clinical/medical usage is not recommended.

⚡ Semantic Budget Controller
Allowed Tokens: 35 / 124 (28.2% kept)
Max Compression (15%) Original (100%)
Token Reduction
71.8%
Tokens Saved
89 Tokens
TTFT Prefill Speedup
3.5×
Cost / 1M Calls ($10/M)
$350 vs $1,240
1. Prompt Token Pruning (Live Attention Filter) Struck = Discarded
2. What the LLM Receives ("Broken Grammar") 35 Tokens
Why this works: Modern transformers (GPT-4, Claude, LLaMA) project text tokens into high-dimensional semantic vector spaces. Syntactic stop-words ("the", "please carefully inform whether there is") produce negligible attention mass. Removing them breaks formal grammar, but retains 100% of the entity hubs, numbers, and technical terms necessary to answer the prompt correctly.