The paper introduces R$^2$A, an adversarial attack that uses suffix optimization to mislead black-box LLM routers into consistently selecting expensive, high-capability models.

The paper introduces 'Routing Hijacking,' a severe attack where malicious clients forge semantic profiles in Federated RAG systems to misroute target queries, and proposes a trust-aware post-routing f…

Misrouter introduces an input-only adversarial framework to exploit the routing mechanisms of Mixture-of-Experts (MoE) LLMs, enabling unsafe behavior induction against remotely hosted, black-box servi…

The paper introduces TRACE-Router, a task-level routing framework for large language models that aligns routing with the unit of supervision using delayed task feedback.

The paper introduces ProbMoE, a probabilistic routing framework that tackles the non-differentiability of top-$k$ routing in Mixture-of-Experts (MoE) models, achieving strong performance with improved…

The paper presents an end-to-end system that translates high-level operator intents into low-level, safe routing constraints for LEO mega-constellations, achieving high accuracy and safety guarantees.

The paper proposes Predictive Routing Replay (PR2) to stabilize reinforcement learning on Mixture of Experts (MoE) LLMs by predicting and incorporating short-horizon router evolution during training a…

This paper introduces a benchmark for multi-agent routing from natural-language prompts, evaluating various methods including supervised routers, linear multilabel models, and dependency-aware baselin…

This paper proposes a carbon-aware routing policy for geo-distributed cloud deployments, achieving up to 46.8% carbon reduction while maintaining zero SLA violations.

The paper proposes RoRo, a rubric-guided process reward framework that improves stepwise model routing by evaluating the quality of intermediate reasoning steps, leading to better performance and cost…

SS-ZKR is a novel, three-mechanism protocol that enables privacy-preserving, content-based semantic routing of agent payloads across organizational trust boundaries without requiring the intermediary…

SS-ZKR is a novel, three-mechanism protocol that enables privacy-preserving, content-based semantic routing of agent payloads across organizational trust boundaries without requiring the intermediary…

The paper proposes PPRoute, a privacy-preserving LLM routing framework that significantly speeds up secure model selection while maintaining high performance comparable to non-private methods.

This paper compares the efficiency of different routing algorithms for collectively routed fleets of autonomous vehicles and proposes a market design to encourage social welfare oriented cooperation.

The paper addresses the challenge of routing across rapidly expanding model hubs by proposing CARvE, a contrastive embedding approach that significantly improves continual model selection accuracy.

The paper introduces RA-MoE, a novel fine-tuning framework that leverages the internal routing structure of Mixture-of-Experts (MoE) models to improve performance on multilingual downstream tasks by a…

This paper presents AlphaRoute, a multi-objective adaptive search framework for VLSI global routing using Large Language Models as semantic policy optimizers.

This paper proposes a reliability-aware framework to solve the fuzzy shortest path problem in directed graphs, optimizing routes based not only on cost but also on the reliability of the associated fu…

This paper introduces the $k$-Step-Central Shortest Path problem to maximize reachability in transportation networks and provides a polynomial-time algorithm for unweighted graphs.