arXiv AI
AgentWeave: Routing Before Reasoning for Efficient Function Calling in Tool-Rich Language Models
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arXiv AI
Jul 14
Agentic Routing: The Harness-Native Data Flywheel
arXiv:2607. 11399v1 Announce Type: cross Abstract: Large language model agents are increasingly executed not by a single model call, but by an execution harness that manages observation, context, control, action, state, and verification.
arXiv Machine Learning
Jul 27
TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI
arXiv:2607. 22465v1 Announce Type: cross Abstract: Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI.
arXiv AI
Oct 7
Do I Need the Cloud? Uncertainty-Aware Step-Level Handoff for Small Language Model Agents
The paper introduces STEPGATE, an uncertainty‑aware handoff framework that evaluates each step of a small language model (SLM) agent and selectively escalates difficult steps to a stronger model. On a 52‑task single‑step benchmark, the Qwen2.5‑1.5B/7B pair achieved 82.7% task success with only 30.8% escalation, outperforming local‑only and random escalation baselines. In multi‑turn tests, STEPGATE reached 69.0% trajectory success and 84.0% action success while using only 30.0% cloud actions, demonstrating that step‑level escalation can close much of the performance gap to a stronger backend with fewer remote tokens.
arXiv Machine Learning
Aug 4
Learning Compositional Meta-Routing for Agentic Workflows: An Executable Benchmark
arXiv:2608. 00106v1 Announce Type: new Abstract: Agentic systems must decide not only what answer to produce, but which reasoning and execution operations should precede it.
arXiv AI
Sep 15
The Router Within: Eliciting Native Skill Routing from a Frozen LLM
arXiv:2609.15982v1 Announce Type: cross Abstract: Skills extend an LLM agent beyond its parametric knowledge, and the gain they promise rests on picking the right one. Deployed harnesses route by pre...
arXiv Computation and Language
Oct 19
Who Pays for Efficient Inference? Confidence-Based Small-to-Large LLM Routing Across Tasks and Languages
The paper evaluates confidence‑based routing of small‑to‑large language models across multiple tasks, languages, and model families, covering 72 configurations and over 42,000 predictions. It finds that confidence routing improves performance on reading comprehension and topic classification datasets, but offers little benefit on a knowledge‑reasoning benchmark where small models remain confidently wrong. The study also shows that global confidence routing can create large language‑specific performance gaps, which equal‑quota routing mitigates with minimal overall accuracy loss.