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REVIEW 2 major objections 3 minor 25 references

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Embedding-based shortlisting recovers 10 to 11 percentage points of routing F1 when scaling from 10 to 110 agents.

2026-06-27 01:18 UTC pith:GQR4WE3N

load-bearing objection The paper quantifies a 16-23pp F1 drop in routing as the catalog scales to 110 agents and shows embedding shortlisting recovers 10-11pp, but the drop may partly reflect unmatched request distributions rather than scale alone. the 2 major comments →

arxiv 2606.17519 v1 pith:GQR4WE3N submitted 2026-06-16 cs.CL cs.AI

Scaling Enterprise Agent Routing: Degradation, Diagnosis, and Recovery

Kellen Gillespie , Robyn Perry This is my paper
classification cs.CL cs.AI
keywords LLM routingagent scalingtool selectionembedding retrievalenterprise assistantsrouting degradationshortlisting
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper measures how single-step routing accuracy falls as an enterprise assistant's catalog grows from 10 agents to 110 agents and 584 tools. F1 on under-specified requests declines 16 to 23 points across three frontier models. Oracle analysis splits the loss into a retrieval gap, where the model fails to surface the correct tool, and a confusion gap, where even perfect retrieval yields a 10-point lower ceiling. Embedding shortlisting narrows the candidate pool before the final decision and restores 10 to 11 points at full scale; the same gain appears on 1,435 real utterances labeled by three annotators.

Core claim

Routing F1 on under-specified requests drops 16--23 percentage points across models when the catalog expands from 10 to 110 agents. The drop decomposes into a retrieval gap and a confusion gap that lowers the oracle ceiling by 10 points. Embedding-based shortlisting recovers +10--11pp F1 at full scale across three models and two providers; a production annotation study on real traffic confirms +10--17pp recovery despite 10--15pp lower absolute scores.

What carries the argument

Embedding-based shortlisting, which retrieves a reduced candidate set of agents via vector similarity before the LLM performs final routing.

Load-bearing premise

The observed drop in routing accuracy is driven primarily by catalog scale rather than shifts in request distribution or differences between the 10-agent and 110-agent evaluation regimes.

What would settle it

Re-running the 110-agent evaluation on the exact request distribution used for the 10-agent regime and finding no F1 degradation would falsify the scale attribution.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Catalogs of 110 agents become feasible for production use without proportional accuracy loss when shortlisting precedes routing.
  • Even perfect retrieval leaves a 10-point confusion ceiling, indicating single-step routing has inherent limits at large scale.
  • The recovery holds across three different frontier models and two providers.
  • Gains observed in controlled tests translate to real production traffic at comparable magnitude.

Where Pith is reading between the lines

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

  • The same shortlisting step may reduce error rates in multi-turn or hierarchical routing setups that the paper does not test.
  • Combining shortlisting with model fine-tuning on routing examples could close part of the remaining confusion gap.
  • Scaling the catalog beyond 110 agents would test whether the recovered performance plateaus or continues to degrade.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 3 minor

Summary. The paper studies single-step routing degradation in a production enterprise assistant as the agent catalog scales from 10 to 110 agents (584 tools). It reports 16-23pp F1 drops on under-specified requests across three frontier models, decomposes the drop via oracle analysis into retrieval and confusion gaps, shows embedding-based shortlisting recovers +10-11pp F1 at full scale (across models and providers), and confirms +10-17pp recovery on 1,435 real-traffic utterances via a three-annotator production study.

Significance. If the observed degradation is attributable to catalog scale rather than distributional confounds, the work supplies concrete empirical measurements, a diagnostic decomposition, and a practical mitigation (embedding shortlisting) for scaling agent routing. The multi-model evaluation and human-validated production study are strengths that would make the findings directly useful to deployed systems.

major comments (2)
  1. [Evaluation methodology and results (abstract and § on scaling experiments)] The central attribution of the 16-23pp F1 drop to catalog growth (rather than request-distribution shifts between the 10-agent and 110-agent regimes) is load-bearing for interpreting the embedding-shortlisting recovery as a scale remedy. The manuscript provides no evidence that the test sets are matched (identical utterances, same distribution, or controlled sampling) across regimes; both the oracle gap decomposition and the production annotation results inherit this issue.
  2. [Production annotation study] The production annotation study (1,435 utterances) is presented as confirmation on real traffic, but without details on how the 10-agent vs. 110-agent subsets were sampled or whether request distributions were balanced, the +10-17pp recovery cannot be cleanly attributed to the shortlisting intervention versus other factors.
minor comments (3)
  1. [Methods] Dataset construction, exact prompt templates, and request-sampling procedures for the 10-agent and 110-agent regimes are not fully specified, limiting reproducibility.
  2. [Results tables/figures] F1 scores and recovery deltas are reported without error bars, confidence intervals, or statistical tests; adding these would strengthen the claims.
  3. [Shortlisting experiments] Clarify whether the embedding shortlisting uses the same embedding model across all three evaluated LLMs and both providers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful reading and for highlighting the need to strengthen the attribution of performance changes to catalog scale. We address each major comment below and will revise the manuscript accordingly to provide the requested details on test-set construction and sampling.

read point-by-point responses

  1. Referee: [Evaluation methodology and results (abstract and § on scaling experiments)] The central attribution of the 16-23pp F1 drop to catalog growth (rather than request-distribution shifts between the 10-agent and 110-agent regimes) is load-bearing for interpreting the embedding-shortlisting recovery as a scale remedy. The manuscript provides no evidence that the test sets are matched (identical utterances, same distribution, or controlled sampling) across regimes; both the oracle gap decomposition and the production annotation results inherit this issue.

    Authors: The requests used for the scaling experiments were drawn once from the same enterprise query log and then held fixed while the agent catalog was expanded from 10 to 110 agents; the 10-agent regime simply omits the additional agents. This design isolates catalog size as the variable. We agree that the manuscript does not state this procedure explicitly and will add a dedicated paragraph in the scaling-experiments section describing the fixed-request sampling, confirming identical utterances across regimes, and noting the controlled addition of agents. The oracle decomposition and shortlisting results will be re-stated with this clarification. revision: yes

  2. Referee: [Production annotation study] The production annotation study (1,435 utterances) is presented as confirmation on real traffic, but without details on how the 10-agent vs. 110-agent subsets were sampled or whether request distributions were balanced, the +10-17pp recovery cannot be cleanly attributed to the shortlisting intervention versus other factors.

    Authors: The 1,435 utterances were sampled from live traffic on the deployed 110-agent system. The 10-agent comparison was obtained by restricting the catalog available to the model during annotation while using the identical utterances. We acknowledge that the current text does not describe this restriction or any balancing steps. We will expand the production-study subsection to specify the sampling frame, the catalog-restriction method used for the 10-agent baseline, and any stratification applied to maintain request-type balance. This will make the attribution of the observed recovery to shortlisting explicit. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical measurements with no derivations or self-referential reductions

full rationale

The paper presents direct empirical measurements of F1 degradation from 10 to 110 agents, oracle decomposition into retrieval vs. confusion gaps, recovery via embedding shortlisting, and confirmation on 1,435 human-labeled utterances. No equations, fitted parameters renamed as predictions, ansatzes, uniqueness theorems, or self-citations appear in the provided text. All reported quantities are observations on held-out or annotated data rather than quantities that reduce to their inputs by construction. The attribution of degradation to scale (vs. possible distribution shifts) is a validity concern, not a circularity issue per the evaluation rules.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical measurement study; no mathematical model, free parameters, or new entities are introduced.

pith-pipeline@v0.9.1-grok · 5679 in / 1120 out tokens · 35857 ms · 2026-06-27T01:18:06.411067+00:00 · methodology

0 comments
read the original abstract

Production LLM assistants route user requests to growing libraries of specialized tools, but how does routing accuracy degrade as the catalog scales? We study single-step routing on a 110-agent, 584-tool catalog from a deployed enterprise productivity assistant, evaluating three frontier models from 10 to 110 agents. Routing F1 on under-specified requests drops 16--23 percentage points across models. An oracle analysis decomposes the degradation into a \emph{retrieval} gap (the model cannot surface the right tool) and a \emph{confusion} gap (even with perfect retrieval, the oracle ceiling drops 10pp). Embedding-based shortlisting recovers +10--11pp F1 at full scale across all three models and two providers. A production annotation study (1,435 human-labeled utterances, three annotators) confirms the recovery on real traffic at +10--17pp despite 10--15pp lower absolute performance.

Figures

Figures reproduced from arXiv: 2606.17519 by Kellen Gillespie, Robyn Perry.

Figure 1
Figure 1. Figure 1: Routing F1 (implicit queries) across catalog scale (GPT-5.4). Shaded bands: fold standard [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Error analysis across catalog scale (GPT-5.4). (a) Query-level outcomes: shortlisting converts [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Precision (left) and recall (right) on implicit queries across scale. Precision is relatively stable [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Retriever comparison across scale (GPT-5.4). (a) Tool-level retrievers ranked by end-to-end [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗

discussion (0)

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Reference graph

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