Excited to share that TwinRouterBench has been accepted to the #RLEval# Workshop at #CAIS2026# 🎉 As LLM apps become long-horizon agents, one request can trigger many model calls across planning, tool use, retrieval, coding, and verification. That makes per-step LLM routing a core infrastructure problem: sending each call to the cheapest sufficient model without breaking downstream success. TwinRouterBench introduces: ⚡ Static track: 970 router-visible prefixes from 520 instances across SWE-bench, BFCL, mtRAG, QMSum, and PinchBench 🚀 Dynamic track: live SWE-bench Verified evaluation with official task resolution + realized API spend Key result: a router trained on static labels achieves comparable SWE-bench resolve rate while cutting API cost by ~53% vs. an unrouted Opus 4.6 baseline. Paper: Code: Dataset: Website: #LLM# #AgenticAI# #LLMRouting# #Benchmark# #SWEBench#

TL;DR: No single LLM is optimal for every query and budget. This work unifies the scattered field of LLM routing into "five building blocks" and ships an open-source infrastructure bundling 16+ routers with a dedicated benchmark. Title: LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers URL: Key points 🧩 Decomposes any router into 5 components: Context Encoder / Model Encoder / Scoring / Decision Rule / Learning Signal 📊 xRouteBench spans 5 tracks (Generic, Memory, Vision, TimeSeries, Personalized), 4,767 queries total 🛠️ A dense query-model matrix, built by dispatching every query to all 18 candidate models, serves as both training supervision and test bed 🚀 Learned routers beat the strongest fixed-model baseline (always pick the largest) by 14.6% relative ⚖️ No router dominates: RouterDC leads on accuracy but drops to 10th under tight cost constraints 🔁 Multi-turn routing adds cost without consistent gains (Router-R1 at just 22.3%) 👤 User conditioning helps, but the best design flips between simulated personas and real feedback A solid step that proves "the best router depends on task and budget" and lays a foundation for fair comparison. #LLM# #ModelRouting#

Omnigent smarter routing picks harness + model from your task. 🔀 Auto · smart routing harness option ⚙️ Activates automatically from llm:/routing: config (no OMNIGENT_SMART_ROUTING env var to set anymore) 🎛️ Composer gear modal: tune model, reasoning effort, and routing mid-chat Learn more: #Omnigent# #AIAgents#

Daily token throughput hit 1.51 trillion—a 71,500x+ surge in 5 months. is rapidly scaling as the next-gen AI infrastructure. From LLM routing and compute scheduling to Agent execution and automated settlement, sits "Above all models, beneath all Agents"—building the global intelligence settlement layer for the Agentic era. Powered by Web2 + Web3 payment rails, a full-stack Agent execution environment, and protocols like x402 and 8004, seamlessly connects compute, tasks, and capital. 📖 Read the deep dive: