arxiv: 2605.07180 · v1 · submitted 2026-05-08 · 💻 cs.CL

Recognition: no theorem link

Learning Agent Routing From Early Experience

Hongru Wang, Jiahao Qiu, Jingzhe Shi, Mengdi Wang, Shilong Liu, Xinzhe Juan, Xuan Qi, Yimin Wang, Zelin Zhao

Pith reviewed 2026-05-11 01:11 UTC · model grok-4.3

classification 💻 cs.CL

keywords LLM agentsquery routingexperience memorytraining-freeinference efficiencyRouteBenchboundary router

0 comments

The pith

BoundaryRouter routes queries between direct LLM inference and full agent execution by retrieving similar cases from a compact memory built on early executions of both systems on a shared seed set.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper addresses the high cost and latency of LLM agents on complex tasks by determining when a query can be handled by fast direct LLM inference instead of full agent execution. It proposes a training-free method that runs both the LLM and the agent on a small shared seed set, stores their behavioral outcomes in a compact experience memory, and then uses rubric-guided reasoning to retrieve similar past cases at inference time for routing decisions. This setup is tested on RouteBench, a new benchmark with in-domain, paraphrased, and out-of-domain query variations. The approach aims to deliver efficiency gains without sacrificing accuracy, as many queries fall within the capability of simpler LLM inference.

Core claim

BoundaryRouter builds a compact experience memory by executing both direct LLM inference and full agent execution on a shared seed set. At inference time, it retrieves similar cases from this memory using rubric-guided reasoning to decide whether to answer with the lightweight LLM or escalate to the agent. The method is evaluated on RouteBench covering in-domain, paraphrased, and out-of-domain settings.

What carries the argument

BoundaryRouter's compact experience memory, built from dual executions on a seed set, which supports retrieval of similar cases to guide routing decisions between direct LLM inference and full agent execution.

If this is right

Reduces inference time by 60.6% compared to always using the full agent.
Improves performance by 28.6% over always using direct LLM inference.
Outperforms prompt-based routing by an average of 37.9%.
Outperforms retrieval-only routing by an average of 8.2%.
Maintains effectiveness across in-domain, paraphrased, and out-of-domain queries on RouteBench.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The method implies that behavioral outcomes from early dual executions can transfer to guide decisions on paraphrased or shifted queries without retraining.
This seed-set memory approach could apply to routing in other hybrid systems pairing a fast limited model with a slower capable one.
If seed sets are expanded to cover broader patterns, the compact memory might enable low-cost adaptation for new domains.

Load-bearing premise

The compact experience memory built from executions on a shared seed set will contain sufficiently similar cases and transferable behavioral signals to support reliable routing decisions on new queries.

What would settle it

If routing decisions on RouteBench lead to lower accuracy than always using the agent or higher latency than always using direct LLM inference, or if performance falls below prompt-based or retrieval-only baselines.

Figures

Figures reproduced from arXiv: 2605.07180 by Hongru Wang, Jiahao Qiu, Jingzhe Shi, Mengdi Wang, Shilong Liu, Xinzhe Juan, Xuan Qi, Yimin Wang, Zelin Zhao.

**Figure 2.** Figure 2: Comparison of routers. Left: Direct routing uses an LLM router to choose between direct LLM inference and full agent execution, but does not leverage experience. Right: Trainingbased routing learns a router from labeled training data, enabling experience use but requiring supervision. Middle: BoundaryRouter (ours) is training-free yet experience-driven: it first builds an early experience memory by runnin… view at source ↗

**Figure 3.** Figure 3: Overall routing performance and cost trade-offs on RouteBench.(a) Average RouteBenchScore across all evaluation sets for different models, sorted in descending order; (b) Comparison of routing effectiveness across different routing strategies. The bar chart reports the average routing score on RouteBench for basic prompt-based routing, retrieval-based (RAG) routing, and our routing method across three back… view at source ↗

**Figure 4.** Figure 4: Overview of our routing pipeline. A query from RouteBench is routed using early [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

**Figure 5.** Figure 5: Overview of the RouteBench evaluation framework. On the left is the Single-instance [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗

**Figure 6.** Figure 6: Example illustrating routing between direct LLM inference and agent execution. For this [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗

**Figure 7.** Figure 7: Comparison between regular CoT and rubric-guided CoT prompts. [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗

read the original abstract

LLM agents achieve strong performance on complex reasoning tasks but incur high latency and compute cost. In practice, many queries fall within the capability boundary of cutting-edge LLMs and do not require full agent execution, making effective routing between LLMs and agents a key challenge. We study the problem of routing queries between lightweight LLM inference and full agent execution under realistic cold-start settings. To address this, we propose BoundaryRouter, a training-free routing framework that uses early behavioral experience and rubric-guided reasoning to decide whether to answer a query with direct LLM inference or escalate to an agent. BoundaryRouter builds a compact experience memory by executing both systems on a shared seed set and retrieves similar cases at inference time to guide routing decisions. To evaluate this method, we introduce RouteBench, a benchmark covering in-domain, paraphrased, and out-of-domain route settings. Experiments show that BoundaryRouter reduces inference time by 60.6% compared to the agent while improving performance by 28.6% over direct LLM inference, outperforming prompt-based and retrieval-only routing by an average of 37.9% and 8.2%, respectively.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

BoundaryRouter gives a workable training-free way to route between plain LLM calls and full agents using early joint executions, but the reported gains rest on details that still need to be shown.

read the letter

The core idea is straightforward and useful. They run both the lightweight LLM and the full agent on a small shared seed set, store the actual outcomes and behaviors, then at inference time retrieve the closest past cases with a rubric to decide whether to answer directly or escalate. This avoids training and targets the common case where many queries do not need the full agent overhead. RouteBench adds splits for in-domain, paraphrased, and out-of-domain queries, which directly tests whether the memory transfers. The headline numbers—60% less time than the agent, 28% better accuracy than direct LLM, and clear wins over prompt and retrieval baselines—are the kind of practical deltas that matter for deployment. The approach is genuinely training-free and grounded in real executions rather than fitted parameters, which is a clean distinction from most prior routing work. Credit to the authors for shipping the benchmark and making the routing logic explicit enough to reproduce in principle. The main gaps are in the experimental reporting. The abstract gives no information on how the seed set was constructed, what similarity function or embedding is used for retrieval, whether results include variance across seeds or runs, or how the rubric is applied without introducing its own bias. Those omissions make it hard to judge whether the 8% edge over retrieval-only holds up or whether out-of-domain performance relies on lucky seed coverage. The transferability assumption is tested by the benchmark splits, but without the actual numbers per split or ablations on memory size it is still an open question how brittle the method is. This paper is aimed at practitioners building agent systems who need to cut latency without retraining. A reading group focused on efficient inference or LLM tooling would find the framework and benchmark worth discussing. It is coherent on its own terms and shows clear thinking about the cold-start routing problem, so it deserves a serious referee who can ask for the missing controls and per-split results rather than a desk reject.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes BoundaryRouter, a training-free routing framework that builds a compact experience memory by executing both a lightweight LLM and a full agent on a shared seed set, then retrieves similar cases at inference time to decide between direct LLM inference and agent escalation using rubric-guided reasoning. It introduces RouteBench, a benchmark spanning in-domain, paraphrased, and out-of-domain query settings, and reports that BoundaryRouter reduces inference time by 60.6% versus the agent while improving performance by 28.6% over direct LLM inference and outperforming prompt-based and retrieval-only baselines by 37.9% and 8.2% on average.

Significance. If the results hold under rigorous verification, the work addresses a practical deployment challenge for LLM agents by enabling efficient routing that avoids full agent overhead on queries within LLM capability boundaries. The training-free design, explicit cold-start setting, and evaluation across in-domain/paraphrased/OOD splits are strengths, as is the introduction of RouteBench as a potential community benchmark. These elements could influence production systems seeking latency and cost reductions without additional training.

major comments (2)

[§4 (Experiments) and abstract] §4 (Experiments) and abstract: the reported deltas (60.6% time reduction, 28.6% performance gain, 37.9%/8.2% outperformance) are presented without error bars, number of runs, statistical tests, seed-set size/diversity, or the precise similarity metric and retrieval procedure. These details are load-bearing for assessing whether the experience memory transfers reliably to RouteBench queries and for reproducing the central empirical claims.
[§3 (Method)] §3 (Method): the construction of the compact experience memory and the exact rubric-guided reasoning procedure (including prompt templates, decision thresholds, and how behavioral signals are encoded) lack sufficient specificity. This is required to confirm the routing logic is free of circularity and to evaluate the transferability assumption for OOD cases.

minor comments (1)

[Abstract] Abstract: the term 'rubric-guided reasoning' is introduced without a brief definition or example; adding one sentence would improve immediate clarity for readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive review and for recognizing the practical value of BoundaryRouter's training-free design, the cold-start setting, and the introduction of RouteBench. We address each major comment point by point below and have prepared revisions to strengthen the manuscript.

read point-by-point responses

Referee: [§4 (Experiments) and abstract] §4 (Experiments) and abstract: the reported deltas (60.6% time reduction, 28.6% performance gain, 37.9%/8.2% outperformance) are presented without error bars, number of runs, statistical tests, seed-set size/diversity, or the precise similarity metric and retrieval procedure. These details are load-bearing for assessing whether the experience memory transfers reliably to RouteBench queries and for reproducing the central empirical claims.

Authors: We agree these details are necessary for reproducibility and to substantiate the reliability of the reported gains. In the revised manuscript we will report all metrics as means over 5 independent runs with different seeds, include standard-deviation error bars, and add paired t-tests (p < 0.05) comparing BoundaryRouter against the agent and LLM baselines. The seed set size will be stated explicitly (200 queries) together with the sampling procedure used to ensure diversity across query types. The similarity metric is cosine similarity on embeddings produced by the sentence-transformer model all-MiniLM-L6-v2; retrieval returns the top-3 neighbors, which are then fed to the rubric-guided router. These specifications will be added to §4 and summarized in the abstract. revision: yes
Referee: [§3 (Method)] §3 (Method): the construction of the compact experience memory and the exact rubric-guided reasoning procedure (including prompt templates, decision thresholds, and how behavioral signals are encoded) lack sufficient specificity. This is required to confirm the routing logic is free of circularity and to evaluate the transferability assumption for OOD cases.

Authors: We acknowledge that greater specificity is required. The revised §3 will describe the memory construction in full: for every seed query both the lightweight LLM and the full agent are executed, and each memory entry stores the tuple (query, LLM direct answer, agent execution trace, binary success signal, normalized latency). The rubric-guided reasoning prompt (now included verbatim in the appendix) instructs the router to score retrieved cases on query complexity, required reasoning depth, and observed outcome patterns. Decision thresholds are: route to direct LLM inference if average similarity > 0.75 and LLM success rate among the top-3 neighbors exceeds 0.65; otherwise escalate. Behavioral signals are encoded as a success flag (1/0) and a latency score (1 – normalized time). Because all entries are pre-computed on the seed set, the procedure contains no circularity with respect to the current query. We will also expand the discussion of the transferability assumption, noting that semantic similarity supports generalization while acknowledging potential degradation on highly divergent OOD queries, consistent with the RouteBench splits. revision: yes

Circularity Check

0 steps flagged

No significant circularity; method is empirical and self-contained

full rationale

The paper presents BoundaryRouter as a training-free framework that explicitly executes both the lightweight LLM and the full agent on a shared seed set to populate an experience memory, then performs retrieval of similar cases at inference time to decide routing. No equations, fitted parameters, or derivations are described that reduce the routing decision to its own inputs by construction. The RouteBench evaluation uses explicit splits for in-domain, paraphrased, and out-of-domain queries, directly testing the transferability assumption rather than presupposing it. No self-citations, uniqueness theorems, or ansatzes are invoked as load-bearing steps in the provided description. The approach therefore rests on observable executions and retrieval rather than tautological redefinitions or statistical forcing.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on one domain assumption about the transferability of early experience; no free parameters or new physical entities are introduced.

axioms (1)

domain assumption Early behavioral experience collected by running both LLM and agent on a shared seed set supplies representative cases that generalize to new queries via retrieval.
This assumption underpins the construction of the experience memory and the routing decision at inference time.

invented entities (1)

BoundaryRouter no independent evidence
purpose: Training-free routing decision system
Proposed method whose effectiveness is demonstrated only through the paper's own experiments.

pith-pipeline@v0.9.0 · 5515 in / 1433 out tokens · 45786 ms · 2026-05-11T01:11:42.075514+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

54 extracted references · 54 canonical work pages · 11 internal anchors

[1]

The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

Router-r1: Teaching llms multi-round routing and aggregation via reinforcement learning , author=. The Thirty-ninth Annual Conference on Neural Information Processing Systems , year=

work page
[2]

arXiv preprint arXiv:2502.11133 , year=

Masrouter: Learning to route llms for multi-agent systems , author=. arXiv preprint arXiv:2502.11133 , year=

work page arXiv
[3]

arXiv preprint arXiv:2510.05445 , year=

AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering , author=. arXiv preprint arXiv:2510.05445 , year=

work page arXiv
[4]

Rcr- router: Eﬃcient role-aware context routing for multi-agent LLM systems with structured memory

RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory , author=. arXiv preprint arXiv:2508.04903 , year=

work page arXiv
[5]

Pan, H., Tennenholtz, G., Mannor, S., Chi, C.-W., Brekel- mans, R., Shah, P., and Tewari, A

Adaptive llm routing under budget constraints , author=. arXiv preprint arXiv:2508.21141 , year=

work page arXiv
[6]

arXiv preprint arXiv:2510.19506 , year=

Lookahead Routing for Large Language Models , author=. arXiv preprint arXiv:2510.19506 , year=

work page arXiv
[7]

Mixture-of-Retrieval Experts for Reasoning-Guided Multimodal Knowledge Exploitation

Learning to Route Queries Across Knowledge Bases for Step-wise Retrieval-Augmented Reasoning , author=. arXiv preprint arXiv:2505.22095 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[8]

Advances in Neural Information Processing Systems , volume=

Reflexion: Language agents with verbal reinforcement learning , author=. Advances in Neural Information Processing Systems , volume=

work page
[9]

Voyager: An Open-Ended Embodied Agent with Large Language Models

Voyager: An open-ended embodied agent with large language models , author=. arXiv preprint arXiv:2305.16291 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[10]

Self-improving llm agents at test-time, 2025

Self-Improving LLM Agents at Test-Time , author=. arXiv preprint arXiv:2510.07841 , year=

work page arXiv
[11]

Agent learning via early experience.arXiv preprint arXiv:2510.08558, 2025

Agent Learning via Early Experience , author=. arXiv preprint arXiv:2510.08558 , year=

work page arXiv
[12]

EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle

EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle , author=. arXiv preprint arXiv:2510.16079 , year=

work page internal anchor Pith review arXiv
[13]

A survey of self-evolving agents: On path to artificial super intelligence.arXiv preprint arXiv:2507.21046, 2025

A survey of self-evolving agents: On path to artificial super intelligence , author=. arXiv preprint arXiv:2507.21046 , year=

work page arXiv
[14]

Advances in neural information processing systems , volume=

Chain-of-thought prompting elicits reasoning in large language models , author=. Advances in neural information processing systems , volume=

work page
[15]

The eleventh international conference on learning representations , year=

React: Synergizing reasoning and acting in language models , author=. The eleventh international conference on learning representations , year=

work page
[16]

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

Least-to-most prompting enables complex reasoning in large language models , author=. arXiv preprint arXiv:2205.10625 , year=

work page internal anchor Pith review arXiv
[17]

Advances in neural information processing systems , volume=

Large language models are zero-shot reasoners , author=. Advances in neural information processing systems , volume=

work page
[18]

Proceedings of the 2025 ACM Conference on International Computing Education Research V

Rubric is all you need: Improving llm-based code evaluation with question-specific rubrics , author=. Proceedings of the 2025 ACM Conference on International Computing Education Research V. 1 , pages=

work page 2025
[19]

Empowering LLM Agents with Geospatial Awareness: Toward Grounded Reasoning for Wildfire Response

Empowering LLM Agents with Geospatial Awareness: Toward Grounded Reasoning for Wildfire Response , author=. arXiv preprint arXiv:2510.12061 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[20]

The Twelfth International Conference on Learning Representations , year=

Gaia: a benchmark for general ai assistants , author=. The Twelfth International Conference on Learning Representations , year=

work page
[21]

Proceedings of the International Conference on Learning Representations (ICLR) , year=

Measuring Massive Multitask Language Understanding , author=. Proceedings of the International Conference on Learning Representations (ICLR) , year=

work page
[22]

arXiv preprint arXiv:2508.16153 , year=

Agentfly: Fine-tuning llm agents without fine-tuning llms , author=. arXiv preprint arXiv:2508.16153 , year=

work page arXiv
[23]

arXiv preprint arXiv:2505.23885 (2025) GeoBrowse 17

Owl: Optimized workforce learning for general multi-agent assistance in real-world task automation , author=. arXiv preprint arXiv:2505.23885 , year=

work page arXiv
[24]

arXiv preprint arXiv:2510.21557 , year=

Co-sight: Enhancing llm-based agents via conflict-aware meta-verification and trustworthy reasoning with structured facts , author=. arXiv preprint arXiv:2510.21557 , year=

work page arXiv
[25]

Alita: Generalist agent enabling scalable agentic reasoning with minimal predefinition and maximal self-evolution, 2025

Alita: Generalist agent enabling scalable agentic reasoning with minimal predefinition and maximal self-evolution , author=. arXiv preprint arXiv:2505.20286 , year=

work page arXiv
[26]

arXiv preprint arXiv:2510.23601 , year=

Alita-G: Self-Evolving Generative Agent for Agent Generation , author=. arXiv preprint arXiv:2510.23601 , year=

work page arXiv
[27]

MiroFlow: A High-Performance Open-Source Research Agent Framework , author=

work page
[28]

2024 , url=

John Yang and Carlos E Jimenez and Alexander Wettig and Kilian Lieret and Shunyu Yao and Karthik R Narasimhan and Ofir Press , booktitle=. 2024 , url=

work page 2024
[29]

Nature Methods , pages=

GeneAgent: self-verification language agent for gene-set analysis using domain databases , author=. Nature Methods , pages=. 2025 , publisher=

work page 2025
[30]

Nature Computational Science , pages=

SciToolAgent: a knowledge-graph-driven scientific agent for multitool integration , author=. Nature Computational Science , pages=. 2025 , publisher=

work page 2025
[31]

Codetree: Agent-guided tree search for code generation with large language models , author=. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) , pages=

work page 2025
[32]

arXiv preprint arXiv:2505.20246 , year=

On path to multimodal historical reasoning: Histbench and histagent , author=. arXiv preprint arXiv:2505.20246 , year=

work page arXiv
[33]

2025 , note =

OpenRouter , title =. 2025 , note =

work page 2025
[34]

Universal Model Routing for Efficient LLM Inference.arXiv preprint arXiv:2502.08773, 2025

Universal model routing for efficient llm inference , author=. arXiv preprint arXiv:2502.08773 , year=

work page arXiv
[35]

Large language model routing with benchmark datasets.arXiv preprint arXiv:2309.15789, 2023

Large language model routing with benchmark datasets , author=. arXiv preprint arXiv:2309.15789 , year=

work page arXiv
[36]

Best- route: Adaptive llm routing with test-time optimal compute.arXiv preprint arXiv:2506.22716, 2025

BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute , author=. arXiv preprint arXiv:2506.22716 , year=

work page arXiv
[37]

Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models

Lu, Keming and Yuan, Hongyi and Lin, Runji and Lin, Junyang and Yuan, Zheng and Zhou, Chang and Zhou, Jingren. Routing to the Expert: Efficient Reward-guided Ensemble of Large Language Models. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Paper...

work page doi:10.18653/v1/2024.naacl-long.109 2024
[38]

2025 , month =

Introducing. 2025 , month =

work page 2025
[39]

2025 , month = dec, url =

Introducing GPT-5.2 , author =. 2025 , month = dec, url =

work page 2025
[40]

Gemini 3 Pro , author =

work page
[41]

2025 , month = dec, howpublished =

Gemini 3 Flash: Frontier Intelligence Built for Speed , author =. 2025 , month = dec, howpublished =

work page 2025
[42]

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities , author=. arXiv preprint arXiv:2507.06261 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[43]

2025 , month = may, url =

Introducing Claude 4 , author =. 2025 , month = may, url =

work page 2025
[44]

2025 , month = sep, howpublished =

Claude Sonnet 4.5 , author =. 2025 , month = sep, howpublished =

work page 2025
[45]

2025 , howpublished =

MiniMax-M2: A Compact MoE Model for Coding and Agentic Workflows , author =. 2025 , howpublished =

work page 2025
[46]

Qwen3 Technical Report

Qwen3 technical report , author=. arXiv preprint arXiv:2505.09388 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[47]

2025 , month = jul, howpublished =

Grok 4 , author =. 2025 , month = jul, howpublished =

work page 2025
[48]

DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

Deepseek-v3. 2: Pushing the frontier of open large language models , author=. arXiv preprint arXiv:2512.02556 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[49]

2025 , month = nov, url =

Introducing Kimi K2 Thinking , author =. 2025 , month = nov, url =

work page 2025
[50]

GPT-4o System Card

Gpt-4o system card , author=. arXiv preprint arXiv:2410.21276 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[51]

, author =

`smolagents`: a smol library to build great agentic systems. , author =

work page
[52]

Humanity's Last Exam

Humanity's last exam , author=. arXiv preprint arXiv:2501.14249 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[53]

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Livecodebench: Holistic and contamination free evaluation of large language models for code , author=. arXiv preprint arXiv:2403.07974 , year=

work page internal anchor Pith review Pith/arXiv arXiv
[54]

arXiv preprint arXiv:2506.14728 , year=

AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes , author=. arXiv preprint arXiv:2506.14728 , year=

work page arXiv