Rosetta Memory trains two profile-conditioned operators with a minimum-gain sampling curriculum and performance-gap reward to enable memory transfer between LLMs, showing gains on multi-hop QA benchmarks and robustness to unseen models.
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Levente Kocsis and Csaba Szepesvári
15 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Model routing is a simple technique for reducing the inference cost of large language models (LLMs), wherein one maintains a pool of candidate LLMs, and learns to route each prompt to the smallest feasible LLM. Existing works focus on learning a router for a fixed pool of LLMs. In this paper, we consider the problem of dynamic routing, where new, previously unobserved LLMs are available at test time. We propose UniRoute, a new approach to this problem that relies on representing each LLM as a feature vector, derived based on predictions on a set of representative prompts. Based on this, we detail two effective instantiations of UniRoute, relying on cluster-based routing and a learned cluster map respectively. We show that these are estimates of a theoretically optimal routing rule, and quantify their errors via an excess risk bound. Experiments on a range of public benchmarks show the effectiveness of UniRoute in routing amongst more than 30 unseen LLMs.
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LQM-ContextRoute routes LLM tool calls via latency-quality matching in a contextual bandit, improving F1 by 2.18 pp, accuracy by up to 18 pp, and NDCG by 2.91-3.22 pp over SW-UCB on web-search, StrategyQA, and retriever benchmarks.
A router that decomposes uncertainty to flexibly route queries between cheap models and oracles while providing regret bounds and supporting abstention in classification tasks with multiple annotations.
Switchcraft routes agentic tool-calling queries to the lowest-cost model that preserves correctness, reaching 82.9% accuracy and 84% cost reduction on five benchmarks.
SecureRouter accelerates secure transformer inference by 1.95x via an encrypted router that selects input-adaptive models from an MPC-optimized pool with negligible accuracy loss.
The paper defines prompting complexity as the length of the shortest plausible prompt that deterministically generates a target text with a fixed language model.
SWE-Router introduces trajectory-conditioned value-based routing for LLM agents on SWE tasks, with a Bayes-optimality theorem and empirical cost savings while retaining most strong-model performance.
The Capability Frontier shows standard LLM benchmarks underestimate performance by 82% by ignoring model specialization across questions and the benefits of multiple generations per query.
RouteBalance fuses routing and load balancing for heterogeneous LLM serving and traces the upper quality-cost-throughput frontier on a 13-instance 28-GPU cluster.
LLM routers across 21 methods on 5 benchmarks converge to similar accuracy below oracle due to learning global performance trends rather than fine-grained query signals.
BoundaryRouter routes queries to LLM or agent using early experience memory from a seed set, cutting inference time 60.6% versus always using agents and raising performance 28.6% versus always using direct LLM inference.
Develops COF algorithm for MAB-CS that intelligently checks cheap arm feasibility by pooling samples, with generalized instance-dependent lower bounds and matching upper bounds on cumulative cost and quality regret.
PPRoute achieves plaintext-level LLM routing quality with MPC-based privacy and a 20x speedup over naive encrypted implementations via MPC-friendly encoders, multi-step training, and O(1) communication Top-k search.
IR3DE is a ridge regression router for domain-expert LLMs that matches or exceeds baselines in language modeling and reasoning tasks while allowing dynamic expert addition or removal without retraining.
SMCS coordinates 15 open-source LLMs via retrieval-based prior selection and exploration-exploitation posterior enhancement, outperforming GPT-4.1 by 5.36% and GPT-o3-mini by 5.28% on eight benchmarks.
citing papers explorer
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Rosetta Memory: Adaptive Memory for Cross-LLM Agents
Rosetta Memory trains two profile-conditioned operators with a minimum-gain sampling curriculum and performance-gap reward to enable memory transfer between LLMs, showing gains on multi-hop QA benchmarks and robustness to unseen models.
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Latency-Quality Routing for Functionally Equivalent Tools in LLM Agents
LQM-ContextRoute routes LLM tool calls via latency-quality matching in a contextual bandit, improving F1 by 2.18 pp, accuracy by up to 18 pp, and NDCG by 2.91-3.22 pp over SW-UCB on web-search, StrategyQA, and retriever benchmarks.
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Flexible Routing via Uncertainty Decomposition
A router that decomposes uncertainty to flexibly route queries between cheap models and oracles while providing regret bounds and supporting abstention in classification tasks with multiple annotations.
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Switchcraft: AI Model Router for Agentic Tool Calling
Switchcraft routes agentic tool-calling queries to the lowest-cost model that preserves correctness, reaching 82.9% accuracy and 84% cost reduction on five benchmarks.
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SecureRouter: Encrypted Routing for Efficient Secure Inference
SecureRouter accelerates secure transformer inference by 1.95x via an encrypted router that selects input-adaptive models from an MPC-optimized pool with negligible accuracy loss.
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Prompting Complexity: Shortest Prompts for Texts and Behaviors in LLMs
The paper defines prompting complexity as the length of the shortest plausible prompt that deterministically generates a target text with a fixed language model.
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SWE-Router: Routing in Multi-turn Agentic Software Engineering Tasks
SWE-Router introduces trajectory-conditioned value-based routing for LLM agents on SWE tasks, with a Bayes-optimality theorem and empirical cost savings while retaining most strong-model performance.
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The Capability Frontier: Benchmarks Miss 82% of Model Performance
The Capability Frontier shows standard LLM benchmarks underestimate performance by 82% by ignoring model specialization across questions and the benefits of multiple generations per query.
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RouteBalance: Fused Model Routing and Load Balancing for Heterogeneous LLM Serving
RouteBalance fuses routing and load balancing for heterogeneous LLM serving and traces the upper quality-cost-throughput frontier on a 13-instance 28-GPU cluster.
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The Routing Plateau: Understanding and Breaking the Accuracy Limits of LLM Routers
LLM routers across 21 methods on 5 benchmarks converge to similar accuracy below oracle due to learning global performance trends rather than fine-grained query signals.
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Learning Agent Routing From Early Experience
BoundaryRouter routes queries to LLM or agent using early experience memory from a seed set, cutting inference time 60.6% versus always using agents and raising performance 28.6% versus always using direct LLM inference.
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Cost-Ordered Feasibility for Multi-Armed Bandits with Cost Subsidy
Develops COF algorithm for MAB-CS that intelligently checks cheap arm feasibility by pooling samples, with generalized instance-dependent lower bounds and matching upper bounds on cumulative cost and quality regret.
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Privacy-Preserving LLMs Routing
PPRoute achieves plaintext-level LLM routing quality with MPC-based privacy and a 20x speedup over naive encrypted implementations via MPC-friendly encoders, multi-step training, and O(1) communication Top-k search.
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IR3DE: A Linear Router for Large Language Models
IR3DE is a ridge regression router for domain-expert LLMs that matches or exceeds baselines in language modeling and reasoning tasks while allowing dynamic expert addition or removal without retraining.
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A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement
SMCS coordinates 15 open-source LLMs via retrieval-based prior selection and exploration-exploitation posterior enhancement, outperforming GPT-4.1 by 5.36% and GPT-o3-mini by 5.28% on eight benchmarks.