feat(router): submit Krusch Cascade Router to RouterArena leaderboard

[kruschdev]

Router Submission: Krusch Cascade Router

📌 Overview

Krusch Cascade Router is an open-source, framework-agnostic LLM router designed for high-efficiency agentic workflows. It eliminates the TTFT (Time-To-First-Token) latency penalty of extra router LLM calls by combining a sub-50ms predictive prompt classifier (evaluating prompt length, syntax/code blocks, mathematical density, and cognitive task keywords) with speculative logprob confidence thresholding.

• Router Name: Krusch Cascade Router
• Affiliation: @kruschdev
• Repository: https://github.com/kruschdev/krusch-cascade-router
• Model Pool: gpt-4o-mini (Fast Edge Model) & gemini-2.0-flash-001 (Heavy Cloud Model)

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📊 Benchmark Evaluation Results

Evaluated on RouterArena dataset (sub_10 split, 1,618 total entries):

• 🚀 Acc-Cost Arena Score: 65.98
• 📈 Average Accuracy: 65.23%
• 💰 Cost per 1K Queries: $0.0675 ($0.0000675 / query)
• ⚖️ Workload Balance: 50.0% Fast (gpt-4o-mini) / 50.0% Heavy (gemini-2.0-flash-001)

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💡 Empirical Domain Routing Strategy

• STEM & Complex Reasoning Escalation: Multi-step math problems (AIME, GSM8K, MATH), code generation (LiveCodeBench), MMLU-Pro reasoning (72.88%), and medical/scientific QA (MedMCQA, PubMedQA) are routed to gemini-2.0-flash-001.
• Fast Edge Model Routing: Geography (GeoBench 83.64%), Social QA (SocialiQA 78.69%), and Multilingual Translation (WMT19) are directed to gpt-4o-mini for maximum accuracy and cost efficiency.

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📁 Submitted Files

• router_inference/config/krusch-cascade-router.json
• router_inference/router/krusch_cascade_adapter.py
• router_inference/predictions/krusch-cascade-router.json
• Registered in leaderboard_manifest.yaml

[gemini-code-assist]

Caution

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