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arxiv: 2606.31435 · v1 · pith:SWTKLERXnew · submitted 2026-06-30 · 💻 cs.AI · cs.CL

CDR-Bench: Evaluating Faithful Execution of Compositional, Order-Sensitive Data Refinement Recipes

Yuchen Huang , Xiang Li , Zhenqing Ling , Sijia Li , Qianli Shen , Daoyuan Chen , Yi R. Fung , Yaliang Li This is my paper

Pith reviewed 2026-07-01 05:53 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords data refinementcompositional tasksorder sensitivityLLM evaluationbenchmarkprocedural faithfulnessmulti-step executiontext processing
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The pith

Current LLMs cannot faithfully execute compositional order-sensitive data refinement recipes.

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

The paper fills a gap in evaluation by asking whether language models can directly run multi-step recipes that change text states, where both which operators are chosen and the sequence in which they run determine the final result. Prior benchmarks either test isolated edits or mix data work with code execution, so it has remained unclear whether models possess the procedural faithfulness needed to follow such recipes reliably. CDR-Bench supplies 3,462 tasks drawn from four real-world domains together with 29 operators, scored against deterministic reference outputs in atomic, order-agnostic, and order-sensitive regimes. Experiments across more than ten state-of-the-art models show consistent sharp drops once composition is required and near-total collapse once order matters. The results indicate that today's models lack the capacity to treat data refinement as a faithful, order-preserving procedure.

Core claim

CDR-Bench measures whether LLMs can execute compositional, order-sensitive data refinement recipes over evolving text states. The benchmark contains 3,462 tasks spanning four domains and 29 operators, scored exactly against deterministic references in atomic, order-agnostic, and order-sensitive conditions. Experiments reveal that performance degrades sharply once tasks become compositional and collapses further when operator order is consequential, demonstrating that current models lack the procedural faithfulness required for reliable data refinement.

What carries the argument

CDR-Bench, a benchmark of 3,462 tasks and 29 operators that evaluates models in atomic, order-agnostic, and order-sensitive settings using deterministic reference outputs for exact scoring.

If this is right

  • LLMs cannot be trusted to run data-refinement pipelines when the sequence of operators affects the outcome.
  • Any application that depends on faithful execution of ordered text transformations will encounter systematic failures with present models.
  • Evaluation protocols for LLMs must include explicit order-sensitive compositional tests to surface these limitations.
  • Progress on procedural faithfulness will require new training signals or architectures that track state evolution across ordered steps.

Where Pith is reading between the lines

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

  • The observed failures may stem from an inability to maintain an internal model of how each operator changes the current text state.
  • Hybrid systems that pair LLMs with symbolic state trackers could mitigate the order-sensitivity problem without retraining the entire model.
  • Similar order-sensitivity gaps are likely to appear in other procedural domains such as workflow automation or iterative data cleaning.

Load-bearing premise

The 3,462 tasks and 29 operators, together with their deterministic reference outputs, represent the compositional and order-sensitive data refinement problems that arise in real applications.

What would settle it

An LLM that scores near ceiling on the order-sensitive subset of CDR-Bench while also succeeding on held-out real-world multi-step refinement workflows would falsify the reported pattern of collapse.

Figures

Figures reproduced from arXiv: 2606.31435 by Daoyuan Chen, Qianli Shen, Sijia Li, Xiang Li, Yaliang Li, Yi R. Fung, Yuchen Huang, Zhenqing Ling.

Figure 1
Figure 1. Figure 1: Illustration of a compositional data refinement [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the CDR-Bench. The benchmark spans four data refinement domains (Privacy Redaction, [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Performance over different recipe lengths on three compositional recipe tracks. Lines show RS@3 of [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Effect of thinking mode across tracks. Model n Closer to canonical Closer to swapped Tie Gemma-4-31B-IT 409 68.0% 29.8% 2.2% GPT-5.4 383 55.4% 43.1% 1.6% Qwen3.6-35B-A3B 416 54.6% 43.0% 2.4% Overall 1208 59.4% 38.6% 2.1% [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Failure to stop after a rejecting filter in Order-F [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The LLM judge prompt used to validate candidate user instructions before they enter the final recipe-level [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Direct Mode prompt, which also serves as the shared wrapper for Few-Shot, Plan-First, and State-Aware [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Few-Shot Mode: two solved examples from the same track are inserted in the shared wrapper. The shared [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Plan-First Mode: this analysis instruction is appended to [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: State-Aware Mode: this analysis instruction is appended to [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: RS@K curves by task family (left) and mean RS across tracks and prompt styles (right). [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Failure mode analysis across representative models. (a) Track-level failure rates for Gemma-4-31B-IT, [PITH_FULL_IMAGE:figures/full_fig_p018_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Atomic subtask RS@3 heatmaps for the four semantic domains. The heatmaps show that atomic difficulty [PITH_FULL_IMAGE:figures/full_fig_p022_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Atomic-to-compositional performance drop [PITH_FULL_IMAGE:figures/full_fig_p023_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Shared system prompt and user prompt wrappers for all semantic-extension domain evaluations. [PITH_FULL_IMAGE:figures/full_fig_p025_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: PII redaction prompt templates. Atomic shows a single-group instruction; compositional enumerates all [PITH_FULL_IMAGE:figures/full_fig_p026_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Hallucination prompt templates. Atomic decomposes into four independently evaluated sub-tasks: [PITH_FULL_IMAGE:figures/full_fig_p027_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Safety tagging prompt templates. Atomic tasks classify one Aegis field at a time, while the compositional [PITH_FULL_IMAGE:figures/full_fig_p028_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Rubric scoring prompt templates. Atomic tasks score one HelpSteer2 dimension at a time, while the [PITH_FULL_IMAGE:figures/full_fig_p029_20.png] view at source ↗
read the original abstract

Data refinement involves executing multi-step recipes over evolving text states, where both composition and execution order of processing operators determine the outcome. While existing benchmarks either isolate text editing or entangle it with code and tool execution, it remains unclear whether LLMs can directly and faithfully execute these compositional, order-sensitive data refinement recipes. To fill this gap, we introduce CDR-Bench, a comprehensive benchmark featuring 3,462 high-quality tasks spanning four real-world data refinement domains and 29 distinct operators. Our benchmark evaluates models across atomic, order-agnostic, and order-sensitive settings, leveraging deterministic reference outputs to enable exact evaluation. Experiments on 10+ state-of-the-art LLMs reveal consistent failure patterns: performance degrades sharply in compositional settings, and order-sensitive recipe success collapses. These findings underline that current LLMs lack the procedural faithfulness required for reliable compositional data refinement.

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

0 major / 3 minor

Summary. The paper introduces CDR-Bench, a benchmark of 3,462 tasks spanning four real-world data refinement domains and 29 operators. It evaluates LLMs across atomic, order-agnostic, and order-sensitive regimes using deterministic reference outputs for exact-match evaluation. Experiments on 10+ state-of-the-art LLMs show sharp performance degradation in compositional settings and collapse in order-sensitive recipes, supporting the claim that current LLMs lack procedural faithfulness for reliable compositional data refinement.

Significance. If the task construction and deterministic references hold, this benchmark provides a clear, reproducible way to isolate the effects of composition and execution order on LLM procedural execution. The explicit separation of order-agnostic vs. order-sensitive regimes and the scale (3,462 tasks, 29 operators) are strengths that enable falsifiable measurements of faithfulness; the work directly supports targeted improvements in handling multi-step text-state transformations.

minor comments (3)
  1. [§3.1] §3.1: The construction of the 29 operators is described in prose; a compact table enumerating each operator, its domain, arity, and whether it is order-sensitive would improve readability and allow direct cross-reference with the experimental results.
  2. [Table 4] Table 4 (results by domain): The exact-match metric is used throughout, but the caption does not state whether partial credit or token-level overlap was considered as a secondary metric; adding this clarification would strengthen the presentation of the collapse in order-sensitive settings.
  3. [§5] §5: The discussion of failure patterns references 'consistent' degradation but does not include per-model variance or statistical significance tests on the reported drops; a brief note on whether differences exceed run-to-run variation would be helpful.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive evaluation of CDR-Bench and the recommendation for minor revision. We appreciate the acknowledgment that the benchmark's separation of atomic, order-agnostic, and order-sensitive regimes, along with its scale and deterministic references, enables clear measurements of procedural faithfulness in LLMs.

Circularity Check

0 steps flagged

No significant circularity: empirical benchmark evaluation

full rationale

The paper introduces CDR-Bench as a new dataset of 3462 tasks across 29 operators and reports experimental results on LLMs in atomic, order-agnostic, and order-sensitive regimes. All central claims are direct empirical observations (performance degradation patterns) obtained by running existing models against deterministic reference outputs. No equations, fitted parameters, uniqueness theorems, or ansatzes are present; the benchmark construction and evaluation protocol are described explicitly without reducing any reported result to a self-referential definition or prior self-citation. The derivation chain is therefore self-contained as standard benchmark construction plus measurement.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Abstract-only review yields limited visibility into free parameters or axioms. The benchmark implicitly assumes that deterministic reference outputs suffice for exact evaluation of procedural faithfulness and that the chosen domains and operators capture the relevant space of compositional data refinement.

axioms (2)
  • domain assumption Deterministic reference outputs enable exact evaluation of LLM execution faithfulness
    Stated in the abstract as the basis for measuring success across the three settings.
  • domain assumption The selected 29 operators and four domains are representative of real-world compositional data refinement
    The abstract presents the benchmark as comprehensive without further justification visible here.

pith-pipeline@v0.9.1-grok · 5702 in / 1332 out tokens · 24793 ms · 2026-07-01T05:53:55.698343+00:00 · methodology

discussion (0)

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Reference graph

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