pith. sign in

arxiv: 2605.26321 · v1 · pith:DCTL5LD4new · submitted 2026-05-25 · 💻 cs.AI

Anchor: Mitigating Artifact Drift in Agent Benchmark Generation

Maksim Ivanov , Abhijay Rana This is my paper

Pith reviewed 2026-06-29 21:27 UTC · model grok-4.3

classification 💻 cs.AI
keywords artifact drifttask generation pipelineconstraint optimizationERP benchmarkAI agent evaluationverifiable environmentslong-horizon tasksbusiness workflows
0
0 comments X

The pith

A single parametric specification generates consistent natural-language instructions, environments, optimal solutions, and verifiers for agent tasks.

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

The paper presents Anchor, a pipeline that formalizes expert business workflow specifications as constraint optimization programs. From this single source, it generates all necessary components for an evaluation environment without inconsistencies. This prevents artifact drift where instructions and verifiers disagree on task requirements. The approach is demonstrated on ERP-Bench, a set of 300 tasks in procurement and manufacturing, where model performance is measured against known optimal solutions. It enables scalable creation of auditable benchmarks for complex agent work.

Core claim

Anchor formalizes domain experts' specifications of business workflows into constraint optimization programs. From a single parametric specification, the pipeline jointly produces a natural-language instruction, environment configuration, solver-certified ground-truth solution, and state-based verifier. This produces harness-agnostic environments whose rewards depend solely on end-state business correctness, with generation parameters controlling difficulty and yielding known optimal solutions.

What carries the argument

The Anchor pipeline that encodes business workflows into constraint optimization programs to jointly generate all benchmark elements.

If this is right

  • Altering parameters produces new tasks with controlled difficulty and known optimal solutions.
  • Rewards in the environments depend only on end-state business correctness, independent of the harness.
  • Generation parameters predict the realized difficulty of tasks.
  • Frontier models satisfy explicit task constraints in 26.1% of trials but reach fully optimal solutions in only 17.4% of trials.

Where Pith is reading between the lines

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

  • This method could be applied to other enterprise domains if their workflows can be expressed as constraint programs.
  • Providing verified optimal solutions may enable more effective training of agents beyond evaluation.
  • State-based verifiers allow checking correctness without simulating full execution paths.

Load-bearing premise

Domain experts can accurately encode real business workflows as constraint optimization programs that stay consistent with the natural language instructions and generated environment states.

What would settle it

Finding cases where the solver-certified solution violates the natural-language instruction or where the state-based verifier accepts an incorrect end state.

Figures

Figures reproduced from arXiv: 2605.26321 by Abhijay Rana, Maksim Ivanov.

Figure 1
Figure 1. Figure 1: Artifact drift. Inconsistencies between a task’s four [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Anchor single-source task creation pipeline. A solved constraint satisfiability problem specification generates the [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: pass@5 by model, harness, and generated difficulty [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Task parameters correlate with realized difficulty. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Evaluation reveals a feasibility-optimality gap. [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: Resolution rate against dollar cost per task. The [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Per-harness cost drivers. Tokens per turn are com [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
read the original abstract

AI agents are beginning to complete valuable, long-horizon business operations tasks, but training and evaluation environments for enterprise work still struggle to balance realism, verifiability, and scale. Environment and task creation frequently suffers from a failure mode we call artifact drift: when instructions, environments, oracles, and verifiers are created by loosely coupled processes, they frequently disagree on what a task requires, producing environments that are unsolvable, reward-hackable, or inconsistent. We introduce Anchor, a task-generation pipeline that formalizes domain experts' specifications of business workflows into constraint optimization programs. From a single parametric specification, the pipeline jointly produces a natural-language instruction, environment configuration, solver-certified ground-truth solution, and state-based verifier. With Anchor, altering parameters yields new tasks with controlled difficulty and known optimal solutions, producing harness-agnostic environments whose rewards depend solely on end-state business correctness. We apply Anchor to produce ERP-Bench: a benchmark of 300 long-horizon tasks spanning procurement and manufacturing workflows in a production-grade ERP system. We find that generation parameters predict realized difficulty, and that frontier models satisfy explicit task constraints in 26.1% of trials but reach a fully optimal solution in only 17.4% of trials. Overall, we show that Anchor and ERP-Bench offer a concrete recipe for building auditable evaluation environments for economically valuable agent work. We release the task generator and ERP-Bench dataset at erpbench.ai

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 paper introduces the Anchor pipeline for generating benchmarks for AI agents performing business operations tasks. It formalizes domain experts' workflow specifications as constraint optimization programs (COPs), from which it jointly generates natural-language instructions, environment configurations, solver-certified ground-truth solutions, and state-based verifiers. The approach is applied to create ERP-Bench, consisting of 300 long-horizon tasks in procurement and manufacturing within a production-grade ERP system. The paper reports that generation parameters predict task difficulty, with frontier models satisfying explicit constraints in 26.1% of trials and reaching fully optimal solutions in 17.4% of trials.

Significance. If the consistency between the generated components holds, Anchor provides a scalable method for creating verifiable, auditable evaluation environments for agentic AI in economically valuable domains, addressing the artifact drift problem that plagues current benchmarks. The release of the task generator and dataset enhances reproducibility and allows for controlled difficulty variation.

major comments (2)
  1. [Abstract] The reported performance figures (26.1% constraint satisfaction, 17.4% optimal solutions) lack accompanying details on the number of models evaluated, experimental protocol, error bars, or baseline comparisons, making it difficult to assess the reliability of these claims about model capabilities.
  2. The central claim that the pipeline mitigates artifact drift rests on the assumption that natural-language instructions generated from the COP specification will be consistent with the formal constraints and solver solutions. The manuscript does not provide a concrete mechanism or validation for ensuring that the NL generation step preserves exact alignment, particularly for complex workflows involving sequential or conditional elements.
minor comments (1)
  1. The abstract could benefit from a brief mention of how the state-based verifier is constructed to depend solely on end-state correctness.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback on our manuscript. We address each major comment below, indicating where we will revise the paper to improve clarity and strengthen the presentation of our claims.

read point-by-point responses

  1. Referee: [Abstract] The reported performance figures (26.1% constraint satisfaction, 17.4% optimal solutions) lack accompanying details on the number of models evaluated, experimental protocol, error bars, or baseline comparisons, making it difficult to assess the reliability of these claims about model capabilities.

    Authors: We agree that the abstract would benefit from greater specificity to allow readers to better evaluate the reported figures. In the revised version, we will expand the abstract to note the number of frontier models evaluated, briefly describe the evaluation protocol (including trial counts and task sampling), and indicate that error bars along with baseline comparisons appear in the experimental results section. Given abstract length constraints, we will prioritize the most essential details while ensuring the claims are better contextualized. revision: yes

  2. Referee: [—] The central claim that the pipeline mitigates artifact drift rests on the assumption that natural-language instructions generated from the COP specification will be consistent with the formal constraints and solver solutions. The manuscript does not provide a concrete mechanism or validation for ensuring that the NL generation step preserves exact alignment, particularly for complex workflows involving sequential or conditional elements.

    Authors: The manuscript grounds the consistency claim in the joint generation process: all outputs (NL instruction, environment, solver solution, and verifier) are derived directly from a single parametric COP specification, with the NL component produced via a deterministic translation of the COP's constraints, objectives, and workflow structure into templated language. This design ensures that the NL instruction encodes the same constraints as the formal program by construction. However, we acknowledge that the current text provides limited explicit description of the translation rules and any post-generation validation (e.g., automated checks for constraint coverage in sequential or conditional cases). We will add a dedicated subsection detailing the NL generation mechanism, including examples of how sequential and conditional elements are rendered, and report results from an alignment validation procedure that compares extracted constraints from the NL text against the original COP. revision: yes

Circularity Check

0 steps flagged

No significant circularity; pipeline is an independent generation recipe

full rationale

The paper describes a task-generation pipeline that converts parametric specifications into COPs and then jointly emits NL instructions, configs, solver solutions, and verifiers. No equations, fitted parameters, or self-citations are presented that would make reported success rates (26.1% constraint satisfaction, 17.4% optimal) or difficulty predictions reduce to the input parameters by construction. The empirical findings on ERP-Bench are external observations rather than tautological outputs. The central claim rests on the engineering consistency of the pipeline, which is not shown to be self-definitional or imported via author prior work.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach rests on the premise that business workflows admit accurate formalization as constraint programs; no free parameters or invented entities are named in the abstract.

free parameters (1)
  • generation parameters
    Parameters altered to produce tasks of controlled difficulty; the abstract does not state whether they are fitted to data or chosen by hand.
axioms (1)
  • domain assumption Domain experts can encode business workflows as constraint optimization programs that remain consistent with generated natural-language instructions and environments.
    This premise is required for the joint production of the four artifacts to eliminate artifact drift.

pith-pipeline@v0.9.1-grok · 5788 in / 1262 out tokens · 36808 ms · 2026-06-29T21:27:57.185111+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

21 extracted references · 13 canonical work pages · 6 internal anchors

  1. [1]

    Dena Aleithan et al. 2025. SWE-Bench+: Enhanced Coding Benchmark for LLMs. arXiv:2410.06992 https://arxiv.org/abs/2410.06992

    work page arXiv 2025

  2. [2]

    AlphaProof and AlphaGeometry Teams. 2024. AI Achieves Silver-Medal Standard Solving International Mathematical Olympiad Problems. https://deepmind. google/discover/blog/ai-solves-imo-problems-at-silver-medal-level/

    2024

  3. [3]

    Victor Barres et al . 2025. 𝜏 2-Bench: Evaluating Conversational Agents in a Dual-Control Environment. arXiv:2506.07982 https://arxiv.org/abs/2506.07982 Anchor: Mitigating Artifact Drift in Agent Benchmark Generation RLEval ’26, May 26, 2026, San Jose, CA, USA

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  4. [4]

    Harbor Framework Team. 2026. Task Structure. https://www.harborframework. com/docs/tasks

    2026

  5. [5]

    Thomas Hubert et al. 2026. Olympiad-Level Formal Mathematical Reasoning with Reinforcement Learning.Nature651 (2026), 607–613. doi:10.1038/s41586- 025-09833-y

    work page doi:10.1038/s41586- 2026

  6. [6]

    SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

    Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. 2024. SWE-bench: Can Language Models Resolve Real- World GitHub Issues?. InInternational Conference on Learning Representations. arXiv:2310.06770 https://openreview.net/forum?id=8y2YPzvJaG

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  7. [7]

    Nikhil Mehta. 2025. Beyond Accuracy: A Multi-Dimensional Framework for Evaluating Enterprise Agentic AI Systems. arXiv:2511.14136 https://arxiv.org/ abs/2511.14136

    work page arXiv 2025

  8. [8]

    National Association of Manufacturers. 2025. Facts About Manufacturing. https: //nam.org/mfgdata/facts-about-manufacturing-expanded/

    2025

  9. [9]

    Odoo S.A. 2026. Odoo 19.0 Documentation. https://www.odoo.com/ documentation/19.0/

    2026

  10. [10]

    Alexander Pan et al. 2025. Measuring Agents in Production. arXiv:2512.04123 https://arxiv.org/abs/2512.04123

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  11. [11]

    Laurent Perron and Frederic Didier. 2025. OR-Tools CP-SAT v9.12. https: //developers.google.com/optimization/cp/cp_solver

    2025

  12. [12]

    Aarushi Saxena et al. 2025. Continuous Benchmark Generation for Evaluating Enterprise-scale LLM Agents. arXiv:2511.10049 https://arxiv.org/abs/2511.10049

    work page arXiv 2025

  13. [13]

    Bureau of Economic Analysis

    U.S. Bureau of Economic Analysis. 2025. Gross Domestic Product, 4th Quar- ter and Year 2024, Third Estimate, GDP by Industry, and Corporate Prof- its. https://www.bea.gov/news/2025/gross-domestic-product-4th-quarter-and- year-2024-third-estimate-gdp-industry-and

    2025

  14. [14]

    Bureau of Labor Statistics

    U.S. Bureau of Labor Statistics. 2025. Purchasing Managers, Buyers, and Pur- chasing Agents. https://www.bls.gov/ooh/business-and-financial/purchasing- managers-buyers-and-purchasing-agents.htm

    2025

  15. [15]

    Jian Xie et al . 2026. AgentSynth: Scalable Task Generation for Generalist Computer-Use Agents. arXiv:2506.14205 https://arxiv.org/abs/2506.14205

    work page arXiv 2026

  16. [16]

    TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks

    Frank F. Xu et al . 2025. TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks. arXiv:2412.14161 https://arxiv.org/abs/2412. 14161

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  17. [17]

    Shunyu Yao, Noah Shinn, Pedram Razavi, and Karthik Narasimhan. 2025. 𝜏- bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains. InInternational Conference on Learning Representations. arXiv:2406.12045 https: //openreview.net/forum?id=roNSXZpUDN

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  18. [18]

    Asaf Yehudai et al . 2025. Survey on Evaluation of LLM-Based Agents. arXiv:2503.16416 https://arxiv.org/abs/2503.16416

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  19. [19]

    Cunxi Yu et al. 2025. UTBoost: Rigorous Evaluation of Coding Agents on SWE- Bench. arXiv:2506.09289 https://arxiv.org/abs/2506.09289

    work page arXiv 2025

  20. [20]

    Mario Zechner. 2026. Pi Monorepo. https://github.com/badlogic/pi-mono

    2026

  21. [21]

    Mingchen Zhu et al . 2025. Establishing Best Practices for Building Rigorous Agentic Benchmarks. arXiv:2507.02825 https://arxiv.org/abs/2507.02825 RLEval ’26, May 26, 2026, San Jose, CA, USA M. Ivanov, A. Rana Table 1: Domain terms used throughout the paper. Defini- tions point at the meaning the term carries in Odoo and in ERP-Bench rather than the broad...

    work page arXiv 2025