CONDESION-BENCH: Conditional Decision-Making of Large Language Models in Compositional Action Space

Dongha Lee; Jinyoung Yeo; Minju Kim; Sungyong Park; Yeonjun Hwang

arxiv: 2604.09029 · v1 · submitted 2026-04-10 · 💻 cs.CL · cs.AI

CONDESION-BENCH: Conditional Decision-Making of Large Language Models in Compositional Action Space

Yeonjun Hwang , Sungyong Park , Minju Kim , Dongha Lee , Jinyoung Yeo This is my paper

Pith reviewed 2026-05-10 17:22 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords conditional decision-makinglarge language modelsbenchmarkcompositional action spaceoracle evaluationcondition adherencedecision support

0 comments

The pith

A new benchmark tests LLMs on decisions where actions are variable allocations restricted by explicit conditions at three levels.

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

Existing benchmarks for LLM decision-making assume actions come from a fixed list and ignore conditions that limit which actions are valid. This misses how real decisions involve composing actions under variable, contextual, and allocation constraints. CONDESION-BENCH defines actions as allocations to decision variables and imposes conditions at those three levels. It evaluates both the quality of decisions and how well models stick to the conditions using oracles, offering a stricter test of LLMs as decision aids.

Core claim

The paper establishes CONDESION-BENCH as a benchmark for conditional decision-making of large language models by representing actions as allocations to decision variables subject to explicit conditions at the variable, contextual, and allocation levels, with oracle-based metrics for both decision quality and adherence to those conditions.

What carries the argument

The CONDESION-BENCH benchmark, which structures decision problems around allocations restricted by three tiers of conditions and uses oracle evaluation to measure performance and adherence.

If this is right

LLM assessments will now need to account for adherence to multi-level feasibility conditions rather than just action selection.
Development of decision-support systems will focus on handling compositional constraints in high-stakes domains.
Prior benchmarks may have overstated LLM readiness for real constrained decision tasks.
Model training and prompting techniques will target better condition following in allocation problems.

Where Pith is reading between the lines

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

This benchmark could guide training data creation for LLMs in planning and resource allocation tasks with real constraints.
The three-level condition structure might extend to other domains like scheduling or policy decisions where validity depends on context.
Performance gaps across condition types could pinpoint specific reasoning weaknesses in current LLMs.

Load-bearing premise

The introduced condition levels and oracle evaluation capture the compositional structure of real-world actions and lead to a meaningfully more rigorous assessment than prior benchmarks.

What would settle it

A comparison showing that LLM performance rankings and reliability judgments stay the same between CONDESION-BENCH and earlier benchmarks, or that oracle scores fail to align with human expert ratings of decision quality.

Figures

Figures reproduced from arXiv: 2604.09029 by Dongha Lee, Jinyoung Yeo, Minju Kim, Sungyong Park, Yeonjun Hwang.

**Figure 2.** Figure 2: The overview of the dataset construction pipeline and the evaluation protocol of C [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Decision Satisfaction Rate (DSR) over all gen [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 5.** Figure 5: Decision Satisfaction Rate (DSR) over all [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Sectors and following stock (decision variable) lists used for C [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗

**Figure 7.** Figure 7: Mapping of stock tickers to company names used for C [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗

**Figure 8.** Figure 8: Qualitative example of scenario S, condition C, action a (feasible action) and evaluation on CONDESIONBENCH. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗

**Figure 9.** Figure 9: Qualitative example of scenario S, condition C, action a (infeasible action) and evaluation on CONDESIONBENCH. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗

**Figure 10.** Figure 10: Prompt for summarizing overall market situation. [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗

**Figure 11.** Figure 11: Prompt for generating final action. Input data are given in Figure [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗

**Figure 12.** Figure 12: Prompt for sampling action candidates. Input data are given in Figure [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗

read the original abstract

Large language models have been widely explored as decision-support tools in high-stakes domains due to their contextual understanding and reasoning capabilities. However, existing decision-making benchmarks rely on two simplifying assumptions: actions are selected from a finite set of pre-defined candidates, and explicit conditions restricting action feasibility are not incorporated into the decision-making process. These assumptions fail to capture the compositional structure of real-world actions and the explicit conditions that constrain their validity. To address these limitations, we introduce CONDESION-BENCH, a benchmark designed to evaluate conditional decision-making in compositional action space. In CONDESION-BENCH, actions are defined as allocations to decision variables and are restricted by explicit conditions at the variable, contextual, and allocation levels. By employing oracle-based evaluation of both decision quality and condition adherence, we provide a more rigorous assessment of LLMs as decision-support tools.

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

This benchmark paper defines actions as multi-level constrained allocations and uses an oracle for dual scoring of quality and adherence, but the synthetic setup leaves open whether the added rigor actually tracks real decision utility.

read the letter

The paper's core move is to replace finite-action choice with allocations over decision variables that must satisfy conditions at three explicit layers: variable, contextual, and allocation. An oracle then scores both the decision quality and how cleanly the allocation respects those conditions. That framing is the main thing that is new relative to the usual LLM decision benchmarks, and it does a clean job of naming the two simplifications the authors want to drop. The three-layer taxonomy is concrete enough that someone could implement it without too much ambiguity, which is useful for a benchmark proposal. The oracle approach also removes some of the subjectivity that creeps into human or LLM-as-judge evaluations. Those are the parts that feel like genuine progress on the evaluation side. The soft spot is that the whole thing stays inside a hand-crafted synthetic world. Because the conditions are fully enumerated and the oracle has perfect access to them, high scores can be achieved by satisfying the stated rules even if the underlying allocation would be poor once implicit trade-offs, stakeholder preferences, or dynamic feasibility enter the picture. The abstract gives no numbers on how the benchmark behaves with current models or whether oracle scores predict anything outside the constructed tasks, so it is hard to tell how much the added structure buys in practice. If the full paper only reports results on these synthetic instances, the claim of a more rigorous assessment rests on an untested transfer assumption. This is the kind of work that belongs in a reading group focused on LLM evaluation or agent benchmarks; people building constrained decision systems might borrow the condition layering. It is worth sending to referees because the design is explicit and the gap it targets is real, even though the experiments will probably need more external validation before the benchmark becomes a standard.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces CONDESION-BENCH, a benchmark for evaluating LLMs as decision-support tools in compositional action spaces. It identifies two limitations in prior benchmarks—reliance on finite pre-defined action sets and omission of explicit feasibility conditions—and proposes actions as allocations to decision variables constrained at variable, contextual, and allocation levels. The core contribution is an oracle-based evaluation protocol that jointly scores decision quality and condition adherence, with the claim that this yields a more rigorous assessment than existing finite-action benchmarks.

Significance. If validated, the benchmark could advance evaluation of LLMs in high-stakes domains by explicitly testing condition adherence alongside decision quality. The oracle approach offers an objective, reproducible scoring mechanism that prior work lacks. However, significance is tempered by the absence of empirical results demonstrating that the three-level taxonomy produces meaningfully stricter or more predictive assessments than prior benchmarks, and by the synthetic nature of the conditions.

major comments (3)

[Abstract and §3] Abstract and §3 (Benchmark Design): The claim that the benchmark 'captures the compositional structure of real-world actions' rests on the three-level condition taxonomy, yet the manuscript provides no formal composition operator or interaction rules between levels. Without this, it is unclear whether conditions are truly compositional (e.g., context-dependent allocation constraints) or merely additive, weakening the contrast with prior finite-action benchmarks.
[§4] §4 (Evaluation): The assertion that oracle-based evaluation of decision quality plus condition adherence is 'more rigorous' is not supported by any comparative results, ablation on condition levels, or transfer experiments to real domains. The oracle can only score against the hand-crafted constraints; the manuscript does not address how this transfers when real decisions involve unstated trade-offs or dynamic feasibility.
[§5] §5 (Experiments): No tables or figures report LLM performance deltas attributable to the new condition levels versus a no-condition baseline. Without such controls, the central claim that the benchmark provides a stricter assessment cannot be evaluated.

minor comments (2)

[§3] Notation for decision variables and allocations is introduced without a compact mathematical definition; a single equation summarizing the action space would improve clarity.
[§2] The abstract references 'high-stakes domains' but the related-work section cites few concrete decision-making papers from those domains; adding 2–3 targeted references would strengthen motivation.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on CONDESION-BENCH. We respond to each major comment below and describe the planned revisions to the manuscript.

read point-by-point responses

Referee: [Abstract and §3] Abstract and §3 (Benchmark Design): The claim that the benchmark 'captures the compositional structure of real-world actions' rests on the three-level condition taxonomy, yet the manuscript provides no formal composition operator or interaction rules between levels. Without this, it is unclear whether conditions are truly compositional (e.g., context-dependent allocation constraints) or merely additive, weakening the contrast with prior finite-action benchmarks.

Authors: We agree that a formal specification would strengthen the presentation. The levels are meant to compose hierarchically, with allocation-level conditions depending on the outcomes of variable- and contextual-level constraints. In the revision, we will add a subsection in §3 defining the composition rules explicitly, including an operator for combining constraints across levels to determine feasible allocations. This will better support the claim of capturing compositional structure. revision: yes
Referee: [§4] §4 (Evaluation): The assertion that oracle-based evaluation of decision quality plus condition adherence is 'more rigorous' is not supported by any comparative results, ablation on condition levels, or transfer experiments to real domains. The oracle can only score against the hand-crafted constraints; the manuscript does not address how this transfers when real decisions involve unstated trade-offs or dynamic feasibility.

Authors: The oracle enables objective joint scoring of quality and adherence, addressing a gap in prior work. We will include ablations in the revised §5 to quantify the effect of each condition level. For real-domain transfer, we acknowledge the synthetic nature limits direct applicability to unstated trade-offs; we will add a discussion of this limitation and potential adaptations, but comprehensive transfer experiments are outside the current scope. revision: partial
Referee: [§5] §5 (Experiments): No tables or figures report LLM performance deltas attributable to the new condition levels versus a no-condition baseline. Without such controls, the central claim that the benchmark provides a stricter assessment cannot be evaluated.

Authors: We will add experimental results in §5, including a table comparing LLM decision quality and adherence scores under the full condition set versus no-condition and partial-condition baselines. This will provide the necessary deltas to evaluate the stricter assessment. revision: yes

Circularity Check

0 steps flagged

No circularity; benchmark definition is self-contained

full rationale

The paper defines CONDESION-BENCH as a new evaluation framework with compositional actions (allocations to decision variables) and three-level conditions, evaluated via oracle for decision quality and adherence. No equations, fitted parameters, predictions, or self-citations are used to derive the central claim of 'more rigorous assessment.' The contribution is a benchmark specification rather than a reduction of results to prior inputs or self-referential fits. This matches the default expectation of no significant circularity for definitional work.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The paper introduces a new benchmark without mathematical derivations, fitted parameters, or new physical entities; it rests on domain assumptions about how real-world decisions are structured.

axioms (2)

domain assumption Existing decision-making benchmarks rely on finite pre-defined action sets and omit explicit feasibility conditions.
Stated directly in the abstract as the motivation for the new benchmark.
domain assumption Oracle-based evaluation of decision quality and condition adherence provides a rigorous assessment.
Claimed in the abstract without further justification visible here.

invented entities (1)

CONDESION-BENCH no independent evidence
purpose: Benchmark for conditional decision-making in compositional action space
New artifact introduced by the authors.

pith-pipeline@v0.9.0 · 5461 in / 1293 out tokens · 35033 ms · 2026-05-10T17:22:07.783514+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

10 extracted references · 10 canonical work pages

[1]

Language models are alignable decision- makers: Dataset and application to the medical triage domain. InProceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies (Volume 6: Industry Track), pages 213–227, Mexico City, Mexico. Association for Computational Linguistics. Y...

work page arXiv 2024
[2]

Rossi, Handong Zhao, Ruiyi Zhang, Puneet Mathur, Nedim Lipka, Yu Wang, Trung Bui, Franck Dernoncourt, and Tianyi Zhou

Dynasaur: Large language agents beyond pre- defined actions.arXiv preprint arXiv:2411.01747. Allen Nie, Ching-An Cheng, Andrey Kolobov, and Adith Swaminathan. 2023. Importance of directional feedback for llm-based optimizers. InNeurIPS 2023 Foundation Models for Decision Making Workshop. James Obi and Edwin Agwu. 2017. Effective decision- making and organ...

work page arXiv 2023
[3]

arXiv preprint arXiv:2509.18180 (2025)

Learning to obey traffic rules using constrained policy optimization. In2022 IEEE 25th Interna- tional Conference on Intelligent Transportation Sys- tems (ITSC), pages 2415–2421. IEEE. Yang Wang and Kai Li. 2025. Large language models in operations research: Methods, applications, and challenges.Preprint, arXiv:2509.18180. xAI. 2025a. Grok 4 fast model ca...

work page arXiv 2025
[4]

I should buy stocks except for{stock}

V ariable Conditions Individual I should buy stocks including{stock}. I should buy stocks except for{stock}. Number I should buy more than{num}kinds of stocks. I should buy less than{num}kinds of stocks. Sector I should only buy the stock of the company regarding{sector}. I should not buy the stock of the company regarding{sector}

work page
[5]

I should buy the stock with lower current price than the last price

Contextual Conditions Price I should buy the stock with higher current price than the last price. I should buy the stock with lower current price than the last price. I should buy the stock that has achieved price increase more than{num}days for 2 weeks. I should buy the stock that has achieved price decrease more than{num}days for 2 weeks. I should buy t...

work page
[6]

information technology

Allocation Conditions Resource I should buy more than{num}shares of any single stock. Table 4: Types and manually generated natural language formats of conditionsC. SECTOR_LIST = { "information technology": ["NVDA", "AAPL", "MSFT", "AVGO", "ORCL"], "communication service": ["GOOGL", "META"], "consumer discretionary": ["AMZN", "TSLA"], "consumer staples": ...

work page 2025
[7]

**Market Overview**: Mention the overall market performance, including the closing percentage change of major indices such as the S&P 500, Nasdaq, and Dow Jones

work page
[8]

**Key Driver**: Clearly explain the most significant reason for the market's movement on that day (e.g., a Fed announcement, CPI data, specific corporate earnings, geopolitical issues)

work page
[9]

**Leading Sectors/Stocks**: Mention the sectors or major individual stocks that led the market's movement or showed particularly notable performance

work page
[10]

it seems that,

**Other Key Indicators**: Briefly include changes in other important indicators that reflect the market's state (e.g., T reasury yields, oil prices, the V olatility Index (VIX)). * The summary must consist of exactly four sentences. * Use only objective facts and data, avoiding speculation or opinions (e.g., "it seems that," "is expected to"). **Filter an...

work page

[1] [1]

Language models are alignable decision- makers: Dataset and application to the medical triage domain. InProceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies (Volume 6: Industry Track), pages 213–227, Mexico City, Mexico. Association for Computational Linguistics. Y...

work page arXiv 2024

[2] [2]

Rossi, Handong Zhao, Ruiyi Zhang, Puneet Mathur, Nedim Lipka, Yu Wang, Trung Bui, Franck Dernoncourt, and Tianyi Zhou

Dynasaur: Large language agents beyond pre- defined actions.arXiv preprint arXiv:2411.01747. Allen Nie, Ching-An Cheng, Andrey Kolobov, and Adith Swaminathan. 2023. Importance of directional feedback for llm-based optimizers. InNeurIPS 2023 Foundation Models for Decision Making Workshop. James Obi and Edwin Agwu. 2017. Effective decision- making and organ...

work page arXiv 2023

[3] [3]

arXiv preprint arXiv:2509.18180 (2025)

Learning to obey traffic rules using constrained policy optimization. In2022 IEEE 25th Interna- tional Conference on Intelligent Transportation Sys- tems (ITSC), pages 2415–2421. IEEE. Yang Wang and Kai Li. 2025. Large language models in operations research: Methods, applications, and challenges.Preprint, arXiv:2509.18180. xAI. 2025a. Grok 4 fast model ca...

work page arXiv 2025

[4] [4]

I should buy stocks except for{stock}

V ariable Conditions Individual I should buy stocks including{stock}. I should buy stocks except for{stock}. Number I should buy more than{num}kinds of stocks. I should buy less than{num}kinds of stocks. Sector I should only buy the stock of the company regarding{sector}. I should not buy the stock of the company regarding{sector}

work page

[5] [5]

I should buy the stock with lower current price than the last price

Contextual Conditions Price I should buy the stock with higher current price than the last price. I should buy the stock with lower current price than the last price. I should buy the stock that has achieved price increase more than{num}days for 2 weeks. I should buy the stock that has achieved price decrease more than{num}days for 2 weeks. I should buy t...

work page

[6] [6]

information technology

Allocation Conditions Resource I should buy more than{num}shares of any single stock. Table 4: Types and manually generated natural language formats of conditionsC. SECTOR_LIST = { "information technology": ["NVDA", "AAPL", "MSFT", "AVGO", "ORCL"], "communication service": ["GOOGL", "META"], "consumer discretionary": ["AMZN", "TSLA"], "consumer staples": ...

work page 2025

[7] [7]

**Market Overview**: Mention the overall market performance, including the closing percentage change of major indices such as the S&P 500, Nasdaq, and Dow Jones

work page

[8] [8]

**Key Driver**: Clearly explain the most significant reason for the market's movement on that day (e.g., a Fed announcement, CPI data, specific corporate earnings, geopolitical issues)

work page

[9] [9]

**Leading Sectors/Stocks**: Mention the sectors or major individual stocks that led the market's movement or showed particularly notable performance

work page

[10] [10]

it seems that,

**Other Key Indicators**: Briefly include changes in other important indicators that reflect the market's state (e.g., T reasury yields, oil prices, the V olatility Index (VIX)). * The summary must consist of exactly four sentences. * Use only objective facts and data, avoiding speculation or opinions (e.g., "it seems that," "is expected to"). **Filter an...

work page