arxiv: 2604.15075 · v1 · submitted 2026-04-16 · 💻 cs.SE · cs.LG

Recognition: unknown

Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap

Naryeong Kim, Shin Yoo

Pith reviewed 2026-05-10 10:54 UTC · model grok-4.3

classification 💻 cs.SE cs.LG

keywords LLM agentsself-consistencyearly terminationmodel hotswapcost optimizationgraph convolutional networkssoftware engineering agents

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The pith

Atropos predicts failing self-consistent LLM agent runs at their midpoint using a graph neural network and hotswaps context to a larger model, converting many failures into successes.

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

The paper introduces Atropos to improve the cost-performance balance for software engineering agents that rely on self-consistency across multiple inference paths. It converts sequences of agent steps into a single graph, then applies a graph convolutional network to forecast whether an ongoing run on a small open-weight model will ultimately succeed. When the predictor flags likely failure around the midpoint, the system migrates the stateless context to a larger closed model. This early switch salvages up to 27 percent of what would have been wasted failing runs. The result is 74 percent of the large model's success rate at roughly one-quarter of the total cost.

Core claim

Merging multiple self-consistent inference paths into a graph and training a graph convolutional network on structural properties allows accurate prediction of eventual failure at the midpoint of inference; hotswapping the context to a more capable model for predicted failures converts up to 27.57 percent of them into successes and delivers 74.35 percent of closed-LLM performance at 23.9 percent of the cost.

What carries the argument

A graph convolutional network that takes a merged graph of parallel self-consistent inference paths as input and outputs a prediction of eventual success or failure, used to decide whether to continue on the source small model or hotswap the stateless context to a target large model.

If this is right

Most agent runs can complete on inexpensive small models while only the predicted failures escalate to expensive large models.
Up to 27.57 percent of inferences that would have failed are recovered through mid-inference model switching.
Total inference cost for self-consistency agents drops to roughly one-quarter of the all-large-model baseline.
Early termination avoids completing doomed paths that would otherwise consume full compute budget.

Where Pith is reading between the lines

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

The same graph-of-paths representation could be used to detect other kinds of agent inefficiency beyond outright failure.
Hybrid local-cloud deployments become more practical if context migration works reliably across model families.
Patterns learned by the GCN might later guide better initial prompting or path-selection heuristics.
The technique may extend to other multi-sample methods such as tree-of-thoughts or debate-style agents.

Load-bearing premise

The graph convolutional network can forecast final failure with 0.85 accuracy at the midpoint of an inference, and migrating the ongoing context to a different model does not reduce the agent's effectiveness.

What would settle it

An evaluation in which the midpoint GCN accuracy falls below 0.7 or in which hotswapped inferences achieve no higher success rate than simply continuing on the small model.

Figures

Figures reproduced from arXiv: 2604.15075 by Naryeong Kim, Shin Yoo.

**Figure 1.** Figure 1: Overall workflow of ATROPOS 3 ATROPOS: Early Termination Prediction and Model Hotswap ATROPOS is composed of two major parts: early termination prediction, and model hotswapping ( [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

**Figure 2.** Figure 2: Examples of SFG taken from inferences of AutoFL. Edge weights of 1 are not explicitly labeled. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: Trajectory Truncation, SFG Construction, and Model Hotswap [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: ROC curves of correctness prediction on completed inference across agents and models. The first two [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗

**Figure 5.** Figure 5: Trade-off analysis between Prediction Accuracy and Cost Saving. The left y-axis (blue) represents prediction [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗

**Figure 6.** Figure 6: Trade-off analysis between Success Retention Rate and Cost Reduction Rate. The top and bottom rows show [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗

**Figure 7.** Figure 7: Cost-benefit trade-off of sequential hotswapping. The x-axis represents the cost relative to the Target-only [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗

**Figure 8.** Figure 8: Categorization of final inference outcomes after hotswapping: (a) and (b) illustrate the distribution of out [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗

read the original abstract

Open-weight Small Language Models(SLMs) can provide faster local inference at lower financial cost, but may not achieve the same performance level as commercial Large Language Models (LLMs) that are orders of magnitudes larger. Consequently, many of the latest applications of LLMs, such as software engineering agents, tend to be evaluated on larger models only, leaving the issue of improving the cost-benefit trade-off of such applications neglected. This paper proposes Atropos, a predictive early-termination analysis and hotswap technique that aims to improve the cost-benefit trade-off for LLM-based agents that use self-consistency. The core component of ATROPOS is a predictive model based on structural properties of LLM inferences: after merging multiple agentic inference paths into a graph representation, ATROPOS uses Graph Convolutional Network (GCN) to predict whether an ongoing inference will eventually succeed or not. If an agentic task instance running on the source LLM is predicted to fail, ATROPOS subsequently performs hotswapping, i.e., migrating the on-going inference context onto the more capable target LLM: this is feasible because LLM contexts are stateless. An empirical evaluation of ATROPOS using three recent LLM-based agents shows that ATROPOS can predict early termination of eventually failing inferences with the accuracy of 0.85 at the midpoint of the inference. Hotswapping LLMs for such inferences can convert up to 27.57% of them to be successful. Consequently, ATROPOS achieves 74.35% of the performance of closed LLMs with as low as only 23.9% of the cost.

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

Atropos uses a GCN on merged inference graphs to predict failures early in self-consistent agents and hotswaps to a larger model, claiming 74% performance at 24% cost, but the hotswap step assumes seamless continuation that different models may not deliver.

read the letter

The paper's central claim is that you can run most of a self-consistency agent on a cheap small model, build a graph from its multiple paths, run a GCN at the midpoint to flag likely failures, and migrate the context to a bigger model only when needed. This produces 0.85 prediction accuracy, salvages up to 27.57% of the predicted failures, and reaches 74.35% of closed-LLM performance for 23.9% of the cost on three agents.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes Atropos, a technique to improve the cost-benefit trade-off for LLM-based agents that rely on self-consistency. It constructs a graph from multiple inference paths, applies a Graph Convolutional Network (GCN) to predict at the midpoint whether an ongoing run on a small language model will eventually fail, and hotswaps the stateless context to a larger target LLM if failure is predicted. Evaluation across three recent LLM-based agents reports 0.85 prediction accuracy, conversion of up to 27.57% of predicted failures into successes, and overall achievement of 74.35% of closed-LLM performance at 23.9% of the cost.

Significance. If the empirical claims hold, Atropos offers a concrete mechanism for selectively invoking expensive large models only on trajectories predicted to fail, which could meaningfully reduce the operational cost of agentic LLM applications in software engineering and similar domains while preserving most of their capability. The graph-based structural prediction on merged self-consistency paths is a distinctive technical contribution relative to simpler early-exit heuristics.

major comments (2)

[Abstract and §3] Abstract and §3 (hotswap mechanism): The central performance claim (74.35% of closed-LLM success at 23.9% cost) rests on the assumption that migrating the partial inference context from the source LLM to the target LLM converts failures without loss of effectiveness. Because different LLMs can produce divergent continuations from the same prefix (especially when prior tool calls or observations are included), the manuscript must include an ablation that compares success rates of hotswapped runs against runs initiated natively on the target LLM from the start; without this, the 27.57% conversion figure cannot be attributed solely to model capability.
[§4] §4 (evaluation): The reported 0.85 GCN accuracy at the midpoint and the aggregate 74.35%/23.9% cost-benefit numbers are presented without visible details on the three specific agents, the self-consistency sampling parameters (k, temperature), the task benchmarks, number of trials per condition, variance across runs, or statistical significance tests. These omissions make it impossible to assess whether the GCN predictor generalizes or whether the headline trade-off is robust.

minor comments (1)

Define all acronyms (GCN, SLM, etc.) on first use and ensure figure captions for the inference-graph construction are self-contained.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment below and indicate the revisions made to strengthen the paper.

read point-by-point responses

Referee: [Abstract and §3] Abstract and §3 (hotswap mechanism): The central performance claim (74.35% of closed-LLM success at 23.9% cost) rests on the assumption that migrating the partial inference context from the source LLM to the target LLM converts failures without loss of effectiveness. Because different LLMs can produce divergent continuations from the same prefix (especially when prior tool calls or observations are included), the manuscript must include an ablation that compares success rates of hotswapped runs against runs initiated natively on the target LLM from the start; without this, the 27.57% conversion figure cannot be attributed solely to model capability.

Authors: We agree that an explicit ablation is required to isolate the contribution of the target model's capability from any potential effects of context migration. Although the mechanism is justified by the stateless property of LLM contexts, we acknowledge that model-specific differences in continuation could exist. In the revised manuscript we have added the requested ablation study to §3, comparing success rates of hotswapped runs against equivalent runs started natively on the target LLM. The abstract has been updated to reference this new analysis, allowing the 27.57% conversion figure to be attributed to the model switch. revision: yes
Referee: [§4] §4 (evaluation): The reported 0.85 GCN accuracy at the midpoint and the aggregate 74.35%/23.9% cost-benefit numbers are presented without visible details on the three specific agents, the self-consistency sampling parameters (k, temperature), the task benchmarks, number of trials per condition, variance across runs, or statistical significance tests. These omissions make it impossible to assess whether the GCN predictor generalizes or whether the headline trade-off is robust.

Authors: We accept that the original presentation of results omitted necessary experimental details. The revised §4 now supplies the identities of the three agents, the exact self-consistency parameters (k and temperature), the task benchmarks, the number of trials per condition, variance statistics across runs, and the outcomes of statistical significance tests. These additions permit evaluation of the GCN predictor's generalizability and the robustness of the reported cost-benefit trade-off. revision: yes

Circularity Check

0 steps flagged

Empirical proposal with no derivation chain or fitted predictions by construction

full rationale

The paper describes an empirical system (Atropos) that trains a GCN on merged inference-path graphs to predict eventual success/failure at the midpoint, then reports measured accuracy (0.85) and cost-performance gains from running the full pipeline on three agents. No equations, uniqueness theorems, or ansatzes are presented whose outputs are forced by their own inputs; the 74.35% performance / 23.9% cost figures are direct experimental outcomes, not renamings or self-referential fits. The work is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities. The approach assumes LLM inference contexts are stateless to enable hotswapping, but no other details are given.

pith-pipeline@v0.9.0 · 5603 in / 1065 out tokens · 75150 ms · 2026-05-10T10:54:14.050129+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

18 extracted references · 13 canonical work pages · 5 internal anchors

[1]

In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)

Better Patching Using LLM Prompting, via Self-Consistency. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE). 1742–1746. Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tom´as Mikolov

2023
[2]

Enriching word vectors with subword information.arXiv preprint arXiv:1607.04606, 2016

Enriching Word Vectors with Subword Information.CoRRabs/1607.04606 (2016). arXiv:1607.04606http://arxiv.org/abs/1607.04606 Islem Bouzenia, Premkumar Devanbu, and Michael Pradel

work page arXiv 2016
[3]

In47th IEEE/ACM International Conference on Software Engineering, ICSE 2025, Ottawa, ON, Canada, April 26 - May 6, 2025

RepairAgent: An Autonomous, LLM-Based Agent for Program Repair . In2025 IEEE/ACM 47th International Conference on Software Engineering (ICSE). IEEE Computer Society, Los Alamitos, CA, USA, 2188–2200.https://doi.org/10.1109/ICSE55347.2025.00157 Chao Chen, Kai Liu, Ze Chen, Yi Gu, Yue Wu, et al. 2024a. INSIDE: LLMs’ internal states retain the power of hallu...

work page doi:10.1109/icse55347.2025.00157 2025
[4]

InProceedings of the Second International Workshop on Large Language Models for Code (LLM4Code 2025)

COSMosFL: Ensemble of Small Language Models for Fault Localisation. InProceedings of the Second International Workshop on Large Language Models for Code (LLM4Code 2025). Dujian Ding, Ankur Mallick, Chi Wang, Robert Sim, Subhabrata Mukherjee, et al

2025
[5]

The Llama 3 Herd of Models

The llama 3 herd of models.arXiv preprint arXiv:2407.21783(2024). Jinwen He, Yujia Gong, Zijin Lin, Cheng’an Wei, Yue Zhao, et al

work page internal anchor Pith review Pith/arXiv arXiv 2024
[6]

InFindings of the Association for Computational Linguistics: ACL 2024, Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.)

LLM Factoscope: Uncovering LLMs’ Factual Discernment through Measuring Inner States. InFindings of the Association for Computational Linguistics: ACL 2024, Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.). Association for Computational Linguistics, Bangkok, Thailand, 10218–10230.https://doi.org/10.18653/v1/2024.findings-acl.608 Albert Q. Jiang, Alexan...

work page doi:10.18653/v1/2024.findings-acl.608 2024
[7]

Mixtral of Experts

Mixtral of Experts. arXiv:cs.LG/2401.04088https://arxiv.org/abs/2401.04088 Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, et al

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

A Quantitative and Qualitative Evalu- ation of LLM-Based Explainable Fault Localization,

SWE-bench: Can Language Models Resolve Real-world Github Issues?. InThe Twelfth International Conference on Learning Representations. https://openreview.net/forum?id=VTF8yNQM66 16 ATROPOS: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model HotswapA PREPRINT Sungmin Kang, Gabin An, and Shin Yoo. [n....

work page doi:10.1145/3660771arxiv:cs/2308.05487
[9]

InProceedings of the 6th International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest 2025)

Lachesis: Predicting LLM Inference Accuracy using Structural Properties of Reasoning Paths. InProceedings of the 6th International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest 2025). Thomas N. Kipf and Max Welling

2025
[10]

Semi-Supervised Classification with Graph Convolutional Networks

Semi-Supervised Classification with Graph Convolutional Networks.CoRR abs/1609.02907 (2016). Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, et al

work page internal anchor Pith review arXiv 2016
[11]

GPT-4 Technical Report

GPT-4 Technical Report. arXiv:cs.CL/2303.08774https://arxiv.org/abs/2303.08774 Guillem Ram ´ırez, Alexandra Birch, and Ivan Titov

work page internal anchor Pith review Pith/arXiv arXiv
[12]

Evaluating Agent-based Program Repair at Google.https://doi.org/10.48550/arXiv.2501.07531arXiv:cs/2501.07531 Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, et al

work page doi:10.48550/arxiv.2501.07531arxiv:cs/2501.07531
[13]

A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

A systematic survey of prompt engineering in large language models: Techniques and applications.arXiv preprint arXiv:2402.07927 (2024). Weihang Su, Changyue Wang, Qingyao Ai, Yiran Hu, Zhijing Wu, et al

work page internal anchor Pith review arXiv 2024
[14]

InFindings of the Association for Computational Linguistics: ACL 2024, Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.)

Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models. InFindings of the Association for Computational Linguistics: ACL 2024, Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.). Association for Computational Linguistics, Bangkok, Thailand, 14379–14391.https://doi.org/10.18653/v1/2024.findings-acl.854 Xingya...

work page doi:10.18653/v1/2024.findings-acl.854 2024
[15]

Self-Consistency Improves Chain of Thought Reasoning in Language Models

OpenHands: An Open Platform for AI Software Developers as Generalist Agents. InThe Thirteenth International Conference on Learning Represen- tations.https://openreview.net/forum?id=OJd3ayDDoF Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, et al. 2023a. Self-Consistency Improves Chain of Thought Reasoning in Language Models.CoRRabs/2203.11171 (2...

work page Pith review arXiv 2023
[16]

IEEE Transactions on Software Engineering42, 8 (August 2016),

A Survey on Software Fault Localization. IEEE Transactions on Software Engineering42, 8 (August 2016),

2016
[17]

InProceedings of the International Conference on Learning Representation (ICLR 2023)

ReAct: Synergizing Reasoning and Acting in Language Models. InProceedings of the International Conference on Learning Representation (ICLR 2023). Shin Yoo, Robert Feldt, Somin Kim, and Naryeong Kim

2023
[18]

Zhang, J

Capturing Semantic Flow of ML-based Systems. In Proceedings of the ACM International Conference on the Foundations of Software Engineering - Ideas, Visions and Reflections Track (FSE 2025). Kechi Zhang, Jia Li, Ge Li, Xianjie Shi, and Zhi Jin. 2024a. CodeAgent: Enhancing Code Generation with Tool- Integrated Agent Systems for Real-World Repo-level Coding ...

work page doi:10.18653/v1/2024.acl-long.737 2025