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arxiv: 2512.04111 · v3 · pith:WBZXHBKGnew · submitted 2025-11-30 · 💻 cs.SE · cs.AI· cs.HC

CentaurEval: Benchmarking Human-in-the-Loop Value in Agentic Coding

Hanjun Luo , Chiming Ni , Jiaheng Wen , Zhimu Huang , Yiran Wang , Bingduo Liao , Sylvia Chung , Yingbin Jin
show 4 more authors
Xinfeng Li Wenyuan Xu XiaoFeng Wang Hanan Salam
This is my paper

Pith reviewed 2026-05-22 11:55 UTC · model grok-4.3

classification 💻 cs.SE cs.AIcs.HC
keywords human-AI collaborationagentic codingcoding benchmarksLLM evaluationhuman-in-the-loopco-reasoningsoftware engineering
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The pith

Human-AI collaboration reaches 31.11 percent success on coding problems that neither humans nor LLMs can solve alone.

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

The paper presents CentaurEval as a benchmark designed to capture the value added when humans and AI coding agents work together on tasks. It defines Collaboration-Necessary templates that stand-alone humans or LLMs fail to complete but that become solvable once both contribute. Experiments with 45 participants and five LLMs across four intervention levels show solo pass rates of 18.89 percent for humans and 0.67 percent for LLMs, rising to 31.11 percent in collaboration. The results also indicate that strategic insights arise from either partner, pointing to a two-way co-reasoning dynamic rather than a one-directional tool relationship.

Core claim

CentaurEval supplies 45 Collaboration-Necessary problem templates that are intractable for standalone LLMs or humans yet solvable through effective collaboration. Dynamic instantiation produces a 450-task toolkit paired with a standardized IDE. Benchmarking yields 0.67 percent success for LLMs alone, 18.89 percent for humans alone, and 31.11 percent under human-AI collaboration, accompanied by evidence that breakthroughs can originate from either side and thereby challenge the conventional human-over-tool hierarchy.

What carries the argument

Collaboration-Necessary problem templates that isolate tasks requiring both human strategic reasoning and AI implementation efficiency.

If this is right

  • Human-AI teams can address a distinct class of coding problems beyond solo capabilities.
  • Strategic breakthroughs in agentic coding need not originate only from the human partner.
  • Evaluation of coding agents should incorporate controlled human-in-the-loop conditions.
  • Dynamic template instantiation offers a reproducible method for testing collaborative performance.

Where Pith is reading between the lines

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

  • Interfaces for coding agents may need to support bidirectional idea exchange instead of one-way instruction.
  • The same template approach could be adapted to measure hybrid value in domains such as debugging or software design.
  • Training objectives for LLMs might shift toward recognizing and building on partial human contributions.

Load-bearing premise

The 45 templates are genuinely intractable for either humans or LLMs working alone.

What would settle it

Showing that current LLMs or skilled humans can solve a substantial share of the templates at high rates without the other party would remove the measured collaborative gain.

Figures

Figures reproduced from arXiv: 2512.04111 by Bingduo Liao, Chiming Ni, Hanan Salam, Hanjun Luo, Jiaheng Wen, Sylvia Chung, Wenyuan Xu, Xiaofeng Wang, Xinfeng Li, Yingbin Jin, Yiran Wang, Zhimu Huang.

Figure 1
Figure 1. Figure 1: HAI-Eval provides two interfaces for evaluation, underscoring its two contributions to the [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The overall architecture of HAI-Eval. 4 HAI-EVAL FRAMEWORK [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The design-validation pipeline for transforming algorithmic cores into templates. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Visualization of key participant feedback. De [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Visual representations of the participants’ demographic data. [PITH_FULL_IMAGE:figures/full_fig_p032_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The standard workspace interface after initialization. The file explorer displays the core [PITH_FULL_IMAGE:figures/full_fig_p039_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: An example of a typical README.md. Step 3: Implementation. Participants are required to write their code in the designated solution.py file to complete each task. For every task, we provide a starter code template that includes a basic framework and helper functions, allowing participants to focus on implementing the core logic. The total time limit for completing all tasks is two hours [PITH_FULL_IMAGE:f… view at source ↗
Figure 8
Figure 8. Figure 8: An example of a typical solution.py. Step 4: Solution Submission. After completing their coding and local testing, participants are required to submit their solution by executing a shell script in the integrated terminal. They must first navigate to the corresponding problem directory and then run the submission command. This script automatically packages all necessary files and sends them to the backend e… view at source ↗
Figure 9
Figure 9. Figure 9: An example of the submission process. K DETAILED EXPERIMENTAL RESULTS K.1 DETAILED LLM BENCHMARKING RESULTS [PITH_FULL_IMAGE:figures/full_fig_p040_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Averaged Ratings on overall user experience and usability [PITH_FULL_IMAGE:figures/full_fig_p042_10.png] view at source ↗
read the original abstract

LLM-powered coding agents are reshaping the development paradigm. However, existing evaluation systems, neither traditional tests for humans nor benchmarks for LLMs, fail to capture this shift, excluding problems that require both human reasoning to guide solutions and AI efficiency for implementation. We introduce CentaurEval, a unified, ecologically valid benchmark for measuring human-in-the-loop value in coding. CentaurEval's core innovation is its "Collaboration-Necessary" problem templates, which are intractable for standalone LLMs or humans, but solvable through effective collaboration. CentaurEval dynamically instantiates tasks from 45 templates, providing a standardized IDE for humans and a reproducible 450-task toolkit for LLMs. We benchmark 45 participants against 5 LLMs under 4 levels of human intervention. Results show that while LLMs or humans alone achieve poor pass rates (0.67% and 18.89%), human-AI collaboration significantly improves to 31.11%. Our analysis reveals an emerging co-reasoning partnership, challenging the traditional human-tool hierarchy by showing that strategic breakthroughs can originate from either humans or 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

1 major / 2 minor

Summary. The paper introduces CentaurEval, a benchmark for human-in-the-loop value in agentic coding. Its central innovation is 45 'Collaboration-Necessary' problem templates claimed to be intractable for standalone LLMs or humans yet solvable through collaboration. The benchmark provides a standardized IDE and a reproducible 450-task toolkit. Experiments with 45 human participants and 5 LLMs across 4 intervention levels report pass rates of 0.67% for LLMs alone, 18.89% for humans alone, and 31.11% for collaboration, with analysis suggesting an emerging co-reasoning partnership that challenges the traditional human-tool hierarchy.

Significance. If the intractability claim is substantiated, the work supplies a valuable, ecologically valid benchmark that quantifies genuine collaborative gains in coding tasks and offers a reproducible toolkit for future studies. The emphasis on bidirectional strategic breakthroughs (from either humans or AI) could inform agent design and evaluation in software engineering.

major comments (1)
  1. Abstract and template description: The headline result (solo LLMs 0.67%, solo humans 18.89%, collaboration 31.11%) rests on the assertion that the 45 Collaboration-Necessary templates are genuinely intractable for either party alone. No evidence is supplied for how this property was established—e.g., no report of exhaustive solo-LLM trials with modern prompting techniques, no expert-human time limits or failure criteria, and no pre-registration or validation protocol for template difficulty. Without this, the 12-point lift cannot be confidently attributed to collaboration value rather than task-selection artifacts.
minor comments (2)
  1. The abstract and results sections report aggregate pass rates without error bars, confidence intervals, or statistical tests comparing conditions, which would clarify the reliability of the observed differences.
  2. Details on participant recruitment, expertise levels, task randomization, and selection criteria for the 45 templates are not provided, limiting assessment of generalizability and potential biases.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the careful review and for highlighting the need to better substantiate the intractability claim for the Collaboration-Necessary templates. We address this point directly below and commit to revisions that will strengthen the manuscript without altering its core claims.

read point-by-point responses

  1. Referee: Abstract and template description: The headline result (solo LLMs 0.67%, solo humans 18.89%, collaboration 31.11%) rests on the assertion that the 45 Collaboration-Necessary templates are genuinely intractable for either party alone. No evidence is supplied for how this property was established—e.g., no report of exhaustive solo-LLM trials with modern prompting techniques, no expert-human time limits or failure criteria, and no pre-registration or validation protocol for template difficulty. Without this, the 12-point lift cannot be confidently attributed to collaboration value rather than task-selection artifacts.

    Authors: We agree that the submitted manuscript provides insufficient detail on the validation process used to confirm the Collaboration-Necessary property. The templates were developed through iterative pilot testing in which both LLMs (across multiple prompting regimes) and human participants consistently failed to solve the problems within reasonable bounds; however, these steps were not fully documented. We will add a dedicated subsection to the Methods section that reports: (1) the specific LLM prompting techniques evaluated during validation (including chain-of-thought, few-shot, and self-consistency variants), (2) the human trial protocol (30-minute time limit per task, clear failure criteria based on inability to produce a passing solution), and (3) the iterative refinement criteria that led to the final 45 templates. While the study was not formally pre-registered, the validation followed a documented internal protocol. These additions will allow readers to assess whether the observed 12-point gain reflects genuine collaborative value rather than selection effects. We view this as a necessary clarification. revision: yes

Circularity Check

0 steps flagged

No circularity detected in empirical benchmark results

full rationale

The paper reports direct empirical measurements of pass rates for solo LLMs (0.67%), solo humans (18.89%), and human-AI collaboration (31.11%) on tasks dynamically instantiated from 45 templates. No equations, derivations, fitted parameters, or self-referential definitions appear in the provided text that would reduce the reported collaboration gains to inputs by construction. The core claims rest on observable experimental outcomes under controlled conditions with a reproducible toolkit, allowing independent verification and falsification outside any internal definitions.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that the chosen templates isolate collaboration value; no free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption The 45 Collaboration-Necessary problem templates are intractable for standalone LLMs or humans but solvable through effective collaboration
    This premise is invoked to justify the benchmark's ecological validity and is stated as the core innovation in the abstract.

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Scaling Human-AI Coding Collaboration Requires a Governable Consensus Layer

    cs.SE 2026-04 unverdicted novelty 5.0

    Agentic Consensus replaces code as the main artifact with a typed property graph world model that maintains commitments and evidence through synchronization operators, shifting evaluation to alignment fidelity and con...

Reference graph

Works this paper leans on

54 extracted references · 54 canonical work pages · cited by 1 Pith paper · 1 internal anchor

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