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arxiv: 2605.18859 · v2 · pith:DHYKZGZWnew · submitted 2026-05-14 · 💻 cs.LG · cs.AI

TwinRouterBench: Fast Static and Live Dynamic Evaluation for Realistic Agentic LLM Routing

Pei Yang , Wanyi Chen , Tongyun Yang , Pengbin Feng , Jiarong Xing , Wentao Guo , Yuhang Yao , Yuhang Han
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Hanchen Li Xu Wang Zeyu Wang Jie Xiao Anjie Yang Liang Tian Lynn Ai Eric Yang Tianyu Shi
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Pith reviewed 2026-05-25 05:56 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords LLM routingagentic evaluationbenchmarkmodel tieringdynamic harnesscost measurementSWE-benchstatic prefixes
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The pith

TwinRouterBench supplies step-level prefixes with execution-verified target tiers and a live dynamic harness for evaluating agentic LLM routers.

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

The paper contends that one-shot prompt benchmarks are insufficient for routing in long-horizon agentic applications like coding agents where multiple model calls occur. It presents TwinRouterBench with a static track offering 970 router-visible prefixes from 520 instances across several datasets, each labeled with target tiers via a downgrade-and-cascade protocol, scored deterministically without LLM judges. The dynamic track provides a harness for running routers on full SWE-bench Verified trajectories, measuring both task resolution and actual API spend. This dual approach enables rapid offline development of routers followed by realistic live validation. Readers would care because effective routing can lower costs in complex agent systems without losing performance on downstream tasks.

Core claim

TwinRouterBench establishes a step-level routing benchmark consisting of two tracks: the static track pairs 970 prefixes with execution-verified target tiers derived from a downgrade-and-cascade protocol across multiple benchmarks, allowing deterministic arithmetic scoring based on tier labels, trajectory membership, and token costs; the dynamic track runs routers in a live harness on the SWE-bench Verified suite, selecting models at each call and evaluating official task success alongside realized spend, with a 100-case held-out set reported.

What carries the argument

The downgrade-and-cascade protocol for assigning target tiers to prefixes by sequentially testing cheaper models to find the minimal sufficient tier that preserves execution success.

Load-bearing premise

The downgrade-and-cascade protocol assigns target tiers that remain stable and generalize beyond the specific 520 instances and model pool used to create the labels.

What would settle it

If re-running the downgrade-and-cascade protocol on the same prefixes with a different model pool or additional instances produces inconsistent tier assignments, the benchmark labels would not reliably indicate the cheapest sufficient model.

Figures

Figures reproduced from arXiv: 2605.18859 by Anjie Yang, Eric Yang, Hanchen Li, Jiarong Xing, Jie Xiao, Liang Tian, Lynn Ai, Pei Yang, Pengbin Feng, Tianyu Shi, Tongyun Yang, Wanyi Chen, Wentao Guo, Xu Wang, Yuhang Han, Yuhang Yao, Zeyu Wang.

Figure 1
Figure 1. Figure 1: Overview of TwinRouterBench. The benchmark provides a fast static track for offline router development and a live dynamic track for end-to-end validation. The static track covers 970 step-level rows from 520 instances across five workloads, each with an execution-verified target tier; the dynamic track runs routers on SWE-bench Verified with realized API cost. workloads, ground labels in execution outcomes… view at source ↗
Figure 2
Figure 2. Figure 2: TwinRouterBench construction pipeline. For each multi-turn case, the pipeline starts from a successful strong￾model trajectory and progressively downgrades individual steps to cheaper tiers via execution-verified search, produc￾ing the verified tier label for every LLM call in the trace. Search lower tiers under causal prefixes. For each surviving trajectory, Claude Opus 4.6 provides a search hint: whether… view at source ↗
read the original abstract

LLM routing matters most in long-horizon applications such as coding agents, deep research systems, and computer-use agents, where a single user request triggers many model calls. Routing each call to the cheapest sufficient model can cut costs without sacrificing quality, yet existing router benchmarks evaluate routers only on one-shot prompts. They never expose the router-visible prefix at an intermediate agent step, never test whether a cheaper replacement preserves downstream task success, and often rely on online LLM judges at evaluation time. We introduce TwinRouterBench, a step-level routing benchmark with two tracks. The static track provides 970 router-visible prefixes from 520 instances across SWE-bench, BFCL, mtRAG, QMSum, and PinchBench, each paired with an execution-verified target tier estimated under a released downgrade-and-cascade protocol; scoring is deterministic arithmetic over tier labels, trajectory membership, and token costs, with no online evaluator-side LLM judge. The dynamic track supplies a harness that runs routers on the full 500-case SWE-bench Verified suite; in this paper we report a 100-case held-out evaluation disjoint from the static SWE supervision split. At each LLM call the router selects a concrete model from a locked pool, and success is measured by official task resolution and realized API spend. The two tracks support fast offline iteration followed by end-to-end validation under live agent execution. Code and data are available at https://github.com/CommonstackAI/TwinRouterBench.

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.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces TwinRouterBench, a step-level benchmark for LLM routing in agentic workflows. The static track supplies 970 router-visible prefixes drawn from 520 instances across SWE-bench, BFCL, mtRAG, QMSum, and PinchBench; each prefix is paired with an execution-verified target tier produced by a released downgrade-and-cascade protocol. Scoring is performed by deterministic arithmetic over tier labels, trajectory membership, and token costs with no online LLM judge. The dynamic track supplies a harness that executes routers on the full 500-case SWE-bench Verified suite (reporting results on a 100-case held-out set disjoint from the static SWE supervision split), measuring official task resolution and realized API spend under live execution with a locked model pool.

Significance. If the tier labels are stable, the benchmark supplies a useful advance by enabling fast offline router iteration on execution-verified step-level targets followed by end-to-end live validation. Strengths include the released downgrade protocol, deterministic arithmetic scoring, absence of evaluator-side LLM judges, and open code/data release. These features directly address documented shortcomings of one-shot routing benchmarks and could accelerate work on cost-efficient routing for long-horizon agents.

major comments (2)
  1. [Abstract] Abstract: The central claim that each prefix carries an 'execution-verified target tier' rests on the downgrade-and-cascade protocol applied to the 520-instance construction set. No held-out validation set, inter-run reproducibility statistics, or sensitivity analysis to cascade ordering or model pool is reported. This is load-bearing for both tracks, because label noise or overfitting would inflate static-track router scores and undermine transfer to the dynamic track or new tasks.
  2. [Abstract] Abstract (dynamic track paragraph): The 100-case held-out evaluation is described as disjoint from the static SWE supervision split, yet the manuscript does not state whether the tier labels constructed on the static set are used to score dynamic runs or whether success is measured solely by official task resolution. Clarification is required to confirm that the two tracks provide independent validation of the routing protocol.
minor comments (1)
  1. [Abstract] Abstract: The size and identity of the locked model pool used for both tracks and for the downgrade protocol are not stated; adding these details would improve immediate usability of the benchmark description.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on TwinRouterBench. We address each major comment below with clarifications from the manuscript and indicate revisions where the presentation can be strengthened.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that each prefix carries an 'execution-verified target tier' rests on the downgrade-and-cascade protocol applied to the 520-instance construction set. No held-out validation set, inter-run reproducibility statistics, or sensitivity analysis to cascade ordering or model pool is reported. This is load-bearing for both tracks, because label noise or overfitting would inflate static-track router scores and undermine transfer to the dynamic track or new tasks.

    Authors: The downgrade-and-cascade protocol produces execution-verified labels by construction: each candidate tier is tested by actually executing the prefix with the downgraded model and confirming whether the downstream trajectory succeeds or requires a cascade. The full protocol, model pool, and ordering are released with the benchmark to enable external verification. We agree that the manuscript would be strengthened by reporting inter-run reproducibility (e.g., label stability across repeated protocol runs) and sensitivity to cascade ordering and model pool. We will add these analyses to the revised version, using the released code to compute them on the construction set. revision: yes

  2. Referee: [Abstract] Abstract (dynamic track paragraph): The 100-case held-out evaluation is described as disjoint from the static SWE supervision split, yet the manuscript does not state whether the tier labels constructed on the static set are used to score dynamic runs or whether success is measured solely by official task resolution. Clarification is required to confirm that the two tracks provide independent validation of the routing protocol.

    Authors: The dynamic track evaluates routers under live execution on the 100-case held-out SWE-bench Verified subset. Success is measured exclusively by official task resolution rate and realized API spend; the static tier labels are not used to score or supervise the dynamic runs. The held-out set is disjoint from the static SWE supervision split by design, ensuring the dynamic track supplies independent end-to-end validation. We will revise the abstract and dynamic-track section to state this explicitly. revision: yes

Circularity Check

0 steps flagged

No significant circularity; labels and scoring rest on external execution metrics

full rationale

The paper constructs target tiers for the static track by applying an explicit, released downgrade-and-cascade protocol to public datasets (SWE-bench, BFCL, mtRAG, etc.) and measures success via official task resolution plus deterministic arithmetic on tier labels and costs. No parameters are fitted to a subset and then repurposed as predictions; no self-citations supply load-bearing uniqueness theorems or ansatzes; the dynamic track uses held-out cases and live execution. The derivation chain is self-contained against external benchmarks and does not reduce to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Benchmark construction relies on existing public datasets and execution oracles; no new free parameters, axioms, or invented entities are introduced.

pith-pipeline@v0.9.0 · 5852 in / 1076 out tokens · 28611 ms · 2026-05-25T05:56:18.802748+00:00 · methodology

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