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arxiv: 2605.28465 · v1 · pith:SRG3IOMYnew · submitted 2026-05-27 · 💻 cs.CL

Beyond One Path: Evaluating and Enhancing Divergent Thinking in Interactive LLM Agents

Jihyeong Park , Ingeol Baek , Jeonghyun Park , Hwanhee Lee This is my paper

Pith reviewed 2026-06-29 12:20 UTC · model grok-4.3

classification 💻 cs.CL
keywords divergent thinkingLLM agentsinteractive benchmarkcreativity evaluationMUTATEReDNApath-level divergenceaction-level divergence
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The pith

ReDNA enables LLM agents to improve divergent thinking by separating unconstrained candidate generation from convergent selection.

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

The paper introduces the MUTATE benchmark to evaluate how LLM agents reason divergently through iterative interactions, scoring both multiple paths to a goal and non-typical actions within steps. It shows that frontier models tend to fixate on immediate actions when facing convergence pressure, limiting their ability to explore alternatives. ReDNA addresses this by first generating diverse candidates without constraints and then selecting among them using task constraints. Experiments indicate this yields higher performance on both path-level and action-level divergence measures than prior methods, with gains tied to improved reasoning resilience rather than broader search.

Core claim

ReDNA significantly outperforms prior methods across both divergence levels and generalizes effectively to an external creativity environment. Its success stems from a qualitative enhancement of resilient divergent reasoning rather than simple environmental exploration.

What carries the argument

ReDNA, which separates unconstrained divergent candidate generation from convergent constraint selection.

If this is right

  • LLM agents using ReDNA discover more alternative paths to the same goal on interactive tasks.
  • Agents generate more non-typical, mechanism-shifting actions at the individual step level.
  • Gains persist even when models face immediate pressure to converge on a solution.
  • The approach transfers to creativity environments outside the MUTATE benchmark.
  • Improvements trace to better quality of divergent reasoning steps rather than increased volume of attempts.

Where Pith is reading between the lines

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

  • The separation of generation and selection phases could be tested in other interactive agent domains such as scientific hypothesis formation.
  • Re-running the experiments across a broader set of base models would clarify whether the reported isolation of reasoning quality holds beyond the tested frontier LLMs.
  • Future benchmarks could add metrics that distinguish reasoning resilience from simple increases in exploration budget.

Load-bearing premise

The MUTATE benchmark and the reported experiments accurately isolate the effect of ReDNA on divergent reasoning quality independent of task design choices or the specific frontier LLMs tested.

What would settle it

Showing that ReDNA produces no performance gain over baselines when the same tasks are run with altered design choices or different LLMs would indicate the gains do not stem from resilient divergent reasoning.

Figures

Figures reproduced from arXiv: 2605.28465 by Hwanhee Lee, Ingeol Baek, Jeonghyun Park, Jihyeong Park.

Figure 1
Figure 1. Figure 1: Example of a MUTATE scenario. Agents pur [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Evaluation structure of MUTATE. MUTATE separates action-level divergence, measured from Thought￾Action attempts, from problem-level divergence, measured by distinct solution paths discovered for the same goal. an object of measurement. We bridge this gap with a benchmark that simultaneously formalizes and quantifies both step-level behavioral and path-level diversity within a unified framework. 3 MUTATE We… view at source ↗
Figure 3
Figure 3. Figure 3: Architecture of ReDNA. Reflect accumulates object-level failure feedback. DN module generates candi [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Case study of ReDNA on Zombie Lab. Claude benefits from Narrowing, while GPT benefits from Diverge. Condition Overall Step Orig. Elab. Ground. Base 27 / 56 (48.2%) 25.3 2.67 2.73 3.28 Re only 33 / 56 (58.9%) 39.2 2.70 2.73 3.52 ReDNA 38 / 56 (67.9%) 36.0 2.77 3.26 3.72 [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Human evaluation UI. 22 [PITH_FULL_IMAGE:figures/full_fig_p022_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Prompt 1. Shared System Prompt (Base / EscapeAgent / Self-Refine). 23 [PITH_FULL_IMAGE:figures/full_fig_p023_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Prompt 2. Base User Prompt Template (Base). DIVERSITY CONSTRAINT (VERY IMPORTANT): - You are running multiple trials. You MUST discover a DIFFERENT solution each trial. - You MUST NOT finish the game using any of the following forbidden finish actions: * <FORBIDDEN_FINISH_ACTION> - If you think one of them would solve it, you must intentionally pursue an alternative approach [PITH_FULL_IMAGE:figures/full_… view at source ↗
Figure 8
Figure 8. Figure 8: Prompt 3. Diversity Constraint (All methods). You are currently exploring the scene freely. You should try explore new scenes, interact with the items through click, input or apply actions, and try crafting new tools: - If there's still <interactable items> you haven't tried any action to interact with, you should try 'click' them first. - Otherwise, explore other new <interactable scene> you haven't been … view at source ↗
Figure 9
Figure 9. Figure 9: Prompt 4. Free Exploration (EscapeAgent). 24 [PITH_FULL_IMAGE:figures/full_fig_p024_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Prompt 5. Reflection System (EscapeAgent). Your current position: <POSITION> <SCENE_DESCRIPTION> <POSSIBLE_ACTIONS> Your action: <ACTION> Response from the environment: <ENVIRONMENT_RESPONSE> Now please make an action call to maintain the task list in one line. Follow the system instruction to extract hint and fill in the parameter for the function call. Your Response [PITH_FULL_IMAGE:figures/full_fig_p0… view at source ↗
Figure 11
Figure 11. Figure 11: Prompt 6. Reflection User (EscapeAgent). 25 [PITH_FULL_IMAGE:figures/full_fig_p025_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Prompt 7. Forethought for New Tool (EscapeAgent). You have to use your creativity to figure out if you could use any tools you have now to solve a new task you have just discovered. There are generally three ways to solve a task: 1. Click the target item. 2. Apply a bag tool to the target item. 3. Input a string to the target item. Hints: 1. Pay attention to what the task needs. Always first try simple cl… view at source ↗
Figure 13
Figure 13. Figure 13: Prompt 8. Forethought for New Task (EscapeAgent). 26 [PITH_FULL_IMAGE:figures/full_fig_p026_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Prompt 9. Self-Refine Suffix (Self-Refine). You are a one-shot feedback-guided creative reasoning module for a text environment. The normal action policy has been temporarily replaced because the agent has accumulated feedback from failed interactions with the same target item. Your job is to use a Divergent-Convergent process to choose the NEXT action. Game action grammar: - apply(<tool in your bag>, <in… view at source ↗
Figure 15
Figure 15. Figure 15: Prompt 10. ReDNA System Prompt (ReDNA). 27 [PITH_FULL_IMAGE:figures/full_fig_p027_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Prompt 11. ReDNA User Prompt Template (ReDNA). 28 [PITH_FULL_IMAGE:figures/full_fig_p028_16.png] view at source ↗
read the original abstract

Divergent thinking is a core dimension of creativity, yet existing evaluations of Large Language Models (LLMs) treat them as single-turn text generations, failing to capture how an agent reasons through iterative interaction. To address this, we introduce MUTATE, an interactive benchmark designed to evaluate agentic divergent thinking at two levels: path-level, where an agent discovers multiple alternative paths to the same goal, and action-level, where individual actions require non-typical, mechanism-shifting object uses. Unlike success-only evaluations, MUTATE scores both completed paths and off-path attempts, capturing divergent reasoning that conventional success rates discard. Our experiments with frontier LLMs reveal a structural blind spot in existing frameworks: when exposed to immediate convergence pressure, they tend to fall into immediate action fixation, failing to improve action-level divergence. To overcome this, we propose ReDNA, which separates unconstrained divergent candidate generation from convergent constraint selection. ReDNA significantly outperforms prior methods across both divergence levels and generalizes effectively to an external creativity environment. We also confirm its success stems from a qualitative enhancement of resilient divergent reasoning rather than simple environmental exploration.

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

3 major / 2 minor

Summary. The paper introduces MUTATE, an interactive benchmark evaluating LLM agents on divergent thinking at path-level (multiple alternative paths to a goal) and action-level (non-typical object uses), scoring both successful and off-path attempts. It identifies a structural blind spot where frontier LLMs exhibit action fixation under convergence pressure, and proposes ReDNA, which decouples unconstrained divergent candidate generation from convergent constraint selection. Experiments claim ReDNA significantly outperforms prior methods on both divergence levels, generalizes to an external creativity environment, and achieves this via qualitative enhancement of resilient divergent reasoning rather than mere increases in exploration volume.

Significance. If substantiated with appropriate controls, the work supplies a needed interactive benchmark for agentic creativity and a method that could improve evaluation and training of divergent reasoning in LLMs, addressing limitations of single-turn success-only metrics.

major comments (3)
  1. [Experiments section (results on MUTATE and external environment)] The central claim that ReDNA's gains reflect qualitative enhancement of resilient divergent reasoning (rather than exploration volume) is load-bearing yet unsecured: the experiments section provides no ablations that apply equivalent numbers of attempts or path-diversity pressure to baselines, nor path-diversity metrics that would isolate the effect.
  2. [§5 (generalization experiments)] The generalization result to the external creativity environment is presented without evidence that task design choices or LLM-specific factors were controlled equivalently across methods; this leaves open whether outperformance is benchmark- or model-specific rather than a general property of ReDNA.
  3. [Table 2 (action-level results)] Table reporting action-level divergence scores does not include error bars or statistical tests across multiple frontier LLMs, making it impossible to assess whether the reported improvement over baselines is robust or driven by particular model behaviors.
minor comments (2)
  1. [§3 (MUTATE benchmark)] Define the precise scoring formula for off-path attempts in MUTATE more explicitly, including how partial credit or mechanism-shifting is quantified.
  2. [§4 (ReDNA method)] Clarify the exact prompting templates used for the 'unconstrained divergent candidate generation' step in ReDNA versus baselines.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments on our work. We address each major point below, clarifying our experimental design where possible and committing to revisions that strengthen the evidence.

read point-by-point responses

  1. Referee: [Experiments section (results on MUTATE and external environment)] The central claim that ReDNA's gains reflect qualitative enhancement of resilient divergent reasoning (rather than exploration volume) is load-bearing yet unsecured: the experiments section provides no ablations that apply equivalent numbers of attempts or path-diversity pressure to baselines, nor path-diversity metrics that would isolate the effect.

    Authors: We agree that the current experiments do not include explicit ablations matching attempt volume or path-diversity pressure across methods. ReDNA's core design decouples generation to remove convergence pressure, which is the source of the claimed qualitative difference, but direct controls would better isolate this from volume effects. We will add such ablations (e.g., baselines given equivalent unconstrained generations) and report path-diversity metrics in the revised experiments section. revision: yes

  2. Referee: [§5 (generalization experiments)] The generalization result to the external creativity environment is presented without evidence that task design choices or LLM-specific factors were controlled equivalently across methods; this leaves open whether outperformance is benchmark- or model-specific rather than a general property of ReDNA.

    Authors: The external experiments used identical frontier LLMs and followed the published task protocols of the external environment as closely as possible. We will expand §5 with explicit documentation of these alignments and any minor adaptations made. If additional controls are feasible within compute limits, we will include them; otherwise the expanded description will clarify the scope of the generalization claim. revision: partial

  3. Referee: [Table 2 (action-level results)] Table reporting action-level divergence scores does not include error bars or statistical tests across multiple frontier LLMs, making it impossible to assess whether the reported improvement over baselines is robust or driven by particular model behaviors.

    Authors: We agree that error bars and statistical tests are needed for robustness assessment. We will re-run the action-level experiments over multiple seeds, add error bars to Table 2, and include statistical significance tests (e.g., paired t-tests) across the frontier LLMs in the revision. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical benchmark and method evaluation is self-contained

full rationale

The paper introduces the MUTATE benchmark for evaluating agentic divergent thinking at path and action levels, along with the ReDNA method that separates divergent candidate generation from convergent selection. Claims of outperformance and generalization rest on experimental results with frontier LLMs, scored on completed paths and off-path attempts. No equations, parameter fits renamed as predictions, self-citations as load-bearing premises, or ansatzes smuggled via prior work appear in the provided text. The central claims are independent empirical observations on a newly defined benchmark and are therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no information on free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5730 in / 1066 out tokens · 30095 ms · 2026-06-29T12:20:35.906116+00:00 · methodology

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Reference graph

Works this paper leans on

32 extracted references · 1 canonical work pages · 1 internal anchor

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    Section B provides per-model reasoning style analyses, including representative trajectory ex- cerpts for GPT, Claude, Llama, and Qwen fami- lies

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    Section C validates the evaluation metrics, in- cluding LLM-as-a-judge agreement with human annotations and the human solving protocol for Metric 1

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    Section D provides implementation details for the compared methods, including the Base agent, Self-Refine, EscapeAgent, and ReDNA

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    Section E describes the MUTATE bench- mark, including the YAML scenario structure, solution-path catalog, per-scenario path discov- ery, and the human-evaluation UI

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    the same level of agreement between humans

    Section F lists the full prompt templates used for the Base agent, EscapeAgent, Self-Refine, and ReDNA. B Per-Model Reasoning Style Analysis Section 5.1 groups the eight models into two action- level ideation profiles—high-ideation (Claude Son- net 4.6, Qwen3-235B) and narrow-ideation (GPT- 5.4, Llama-4-Maverick)—and shows that ReDNA acts through differen...

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    Use only creative interaction mechanisms:

    Convergent phase: apply the objective, valid actions, and accumulated failure evidence to choose exactly one valid next action. Use only creative interaction mechanisms:

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    Try an evidence-based input mechanism on the same target with input()

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    Thought:

    Craft tools to create a new capability that could address the target or objective. Thought: ... Action: ... Figure 15:Prompt 10. ReDNA System Prompt (ReDNA). 27 FEEDBACK-GUIDED R/DN MODULE Current step: <STEP> Current position: <POSITION> Feedback target item: <TARGET_ITEM> Failure count on this target: <FAILURE_COUNT> Non-click failure count on this targ...