arxiv: 2604.13151 · v1 · submitted 2026-04-14 · 💻 cs.AI

Recognition: unknown

Exploration and Exploitation Errors Are Measurable for Language Model Agents

Jaden Park , Jungtaek Kim , Jongwon Jeong , Robert D. Nowak , Kangwook Lee , Yong Jae Lee

Authors on Pith no claims yet

Pith reviewed 2026-05-10 14:55 UTC · model grok-4.3

classification 💻 cs.AI

keywords language model agentsexploration and exploitationevaluation metricgrid environmentsDAG taskspolicy-agnostic evaluationfrontier modelsembodied AI

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The pith

A policy-agnostic metric quantifies exploration and exploitation errors in language model agents from observed actions alone.

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

The paper creates controllable environments with partially observable 2D grids and unknown task DAGs that mimic embodied AI scenarios. It introduces a metric to separate and score exploration errors from exploitation errors using only the sequence of actions the agent takes. Testing on frontier models shows that even top systems fail at the balance, yet each model type displays its own characteristic mistakes. Reasoning models handle the tasks better overall, and small changes to how the agent is prompted or harnessed yield clear gains in both exploration and exploitation. These results matter because LM agents are now deployed in open tasks where failing to explore enough or to exploit what is known leads to incomplete or inefficient solutions.

Core claim

The paper claims that exploration and exploitation errors are measurable for LM agents by designing adjustable grid-and-DAG environments and a metric that extracts the two error types directly from action sequences without any access to the agent's internal policy or additional ground truth.

What carries the argument

The policy-agnostic metric that scores exploration and exploitation errors from the agent's observed actions inside programmatically adjustable grid maps paired with unknown task DAGs.

If this is right

Frontier LM agents exhibit distinct failure modes when forced to balance exploration and exploitation.
Reasoning models achieve higher success rates on the designed tasks than non-reasoning models.
Minimal harness engineering can produce large gains in both exploration and exploitation performance.
The environments can be tuned to make either exploration or exploitation the dominant difficulty.

Where Pith is reading between the lines

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

The metric offers a practical way to diagnose why a deployed agent is failing in real tasks without inspecting its weights or prompts.
Similar action-only error measures could be adapted to non-LM agents such as reinforcement-learning policies or robotic controllers.
The observed gains from harness engineering suggest that interface design may be as important as model scale for reliable agent behavior.

Load-bearing premise

The metric can correctly separate exploration mistakes from exploitation mistakes using nothing but the sequence of actions the agent produces in these grid-and-DAG setups.

What would settle it

Run an agent that systematically visits every new grid cell before returning versus one that repeatedly revisits the same cells; check whether the metric assigns markedly higher exploration error to the second agent and higher exploitation error to the first, even when both reach the same final task outcome.

Figures

Figures reproduced from arXiv: 2604.13151 by Jaden Park, Jongwon Jeong, Jungtaek Kim, Kangwook Lee, Robert D. Nowak, Yong Jae Lee.

**Figure 2.** Figure 2: Illustration of the LM agent traversing the 2D grid map to achieve the goal given [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Three illustrative examples demonstrating edge cases. In all three maps, node [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Different exploration behavior between Claude Opus 4.6 and Gemini 3.1 Pro, both [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Effect of prompts on exploration and exploitation error, and success rate for GPT [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Harness Design Comparison. (a) In the baseline, the agent harness passes obser [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗

**Figure 7.** Figure 7: An oracle view of the 2D grid map and the corresponding task DAG for experi [PITH_FULL_IMAGE:figures/full_fig_p023_7.png] view at source ↗

**Figure 8.** Figure 8: Results of action trajecotries and metric values over each timestep for Claude [PITH_FULL_IMAGE:figures/full_fig_p024_8.png] view at source ↗

**Figure 9.** Figure 9: Results of action trajecotries and metric values over iterations using Claude Haiku [PITH_FULL_IMAGE:figures/full_fig_p025_9.png] view at source ↗

**Figure 10.** Figure 10: Results of action trajecotries and metric values over iterations using Claude [PITH_FULL_IMAGE:figures/full_fig_p026_10.png] view at source ↗

**Figure 11.** Figure 11: Results of action trajecotries and metric values over each timestep for Gemini 3.1 [PITH_FULL_IMAGE:figures/full_fig_p027_11.png] view at source ↗

**Figure 12.** Figure 12: Results of action trajecotries and metric values over each timestep for Gemini 3.1 [PITH_FULL_IMAGE:figures/full_fig_p028_12.png] view at source ↗

**Figure 13.** Figure 13: Results of action trajecotries and metric values over each timestep for Gemini 3.1 [PITH_FULL_IMAGE:figures/full_fig_p029_13.png] view at source ↗

**Figure 14.** Figure 14: Results of action trajecotries and metric values over each timestep for GPT-4.1. [PITH_FULL_IMAGE:figures/full_fig_p030_14.png] view at source ↗

**Figure 15.** Figure 15: Results of action trajecotries and metric values over each timestep for GPT-4.1. [PITH_FULL_IMAGE:figures/full_fig_p031_15.png] view at source ↗

**Figure 16.** Figure 16: Results of action trajecotries and metric values over each timestep for GPT-4.1. [PITH_FULL_IMAGE:figures/full_fig_p032_16.png] view at source ↗

**Figure 17.** Figure 17: Results of action trajecotries and metric values over each timestep for Claude [PITH_FULL_IMAGE:figures/full_fig_p033_17.png] view at source ↗

**Figure 18.** Figure 18: Results of action trajecotries and metric values over iterations using Claude Opus [PITH_FULL_IMAGE:figures/full_fig_p034_18.png] view at source ↗

**Figure 19.** Figure 19: Results of action trajecotries and metric values over iterations using Claude Opus [PITH_FULL_IMAGE:figures/full_fig_p035_19.png] view at source ↗

**Figure 20.** Figure 20: Error analysis of GPT-4.1 with varying exploration demand by varying the map [PITH_FULL_IMAGE:figures/full_fig_p036_20.png] view at source ↗

read the original abstract

Language Model (LM) agents are increasingly used in complex open-ended decision-making tasks, from AI coding to physical AI. A core requirement in these settings is the ability to both explore the problem space and exploit acquired knowledge effectively. However, systematically distinguishing and quantifying exploration and exploitation from observed actions without access to the agent's internal policy remains challenging. To address this, we design controllable environments inspired by practical embodied AI scenarios. Each environment consists of a partially observable 2D grid map and an unknown task Directed Acyclic Graph (DAG). The map generation can be programmatically adjusted to emphasize exploration or exploitation difficulty. To enable policy-agnostic evaluation, we design a metric to quantify exploration and exploitation errors from agent's actions. We evaluate a variety of frontier LM agents and find that even state-of-the-art models struggle on our task, with different models exhibiting distinct failure modes. We further observe that reasoning models solve the task more effectively and show both exploration and exploitation can be significantly improved through minimal harness engineering. We release our code \href{https://github.com/jjj-madison/measurable-explore-exploit}{here}.

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

The paper gives LM agent researchers a controllable grid-plus-DAG setup and an action-only metric to score exploration versus exploitation failures, but the metric's separation looks fragile once partial observability is taken seriously.

read the letter

The core contribution is a pair of adjustable 2D environments that combine a partially observable grid map with an unknown task DAG, plus a metric that tries to label each action as an exploration error or an exploitation error without needing the agent's policy. They run frontier models on versions tuned for each kind of difficulty, report that even strong models fail in distinct ways, note that reasoning models do better, and show that light harness changes can improve both behaviors. Releasing the code is a clear plus for anyone who wants to reproduce or extend the testbeds.

Referee Report

2 major / 2 minor

Summary. The paper claims to introduce controllable, partially observable environments consisting of 2D grid maps and unknown task DAGs that can be programmatically tuned for exploration or exploitation difficulty. It proposes a policy-agnostic metric that attributes exploration and exploitation errors directly from an agent's observed actions (without access to its internal policy), evaluates a range of frontier LM agents on these environments, and reports that even state-of-the-art models exhibit distinct failure modes, that reasoning models perform better, and that both error types can be reduced via minimal harness engineering.

Significance. If the metric can be shown to cleanly separate the two error types using only observable trajectories, the work supplies a reproducible benchmark and diagnostic tool for LM-agent decision making in open-ended settings. The code release and programmatic environment generation are concrete strengths that would allow targeted follow-up experiments.

major comments (2)

[§3] §3 (Environment and metric definition): The central claim that the metric is policy-agnostic and isolates exploration errors (failure to visit relevant states) from exploitation errors (failure to use known information) using only observed actions is load-bearing. In the partially observable grid+DAG setting, an action that appears non-exploratory can be a rational response to the agent's current belief over the hidden DAG; the metric must therefore embed an implicit model of what the agent 'should have known.' If that model is derived from the evaluator's full ground-truth map rather than the agent's observable history alone, the reported error rates become sensitive to the precise observability assumptions chosen in the environment definition.
[§5] §5 (Evaluation): The reported finding that frontier models 'struggle' and exhibit 'distinct failure modes' is presented without quantitative validation details (error bars, statistical significance, baseline comparisons against non-LM agents or random policies, or explicit controls for prompt/harness variation). These omissions make it impossible to assess whether the observed differences are robust or confounded by implementation choices.

minor comments (2)

[§4] The abstract and §4 refer to 'minimal harness engineering' that improves both exploration and exploitation; the exact modifications (prompt changes, tool-use wrappers, etc.) should be listed explicitly with before/after numbers.
Notation for the metric (e.g., how an action is labeled 'exploration error' vs. 'exploitation error') should be formalized with a short pseudocode or equation block to aid reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We address each major point below and have revised the manuscript accordingly to improve clarity and rigor.

read point-by-point responses

Referee: §3 (Environment and metric definition): The central claim that the metric is policy-agnostic and isolates exploration errors (failure to visit relevant states) from exploitation errors (failure to use known information) using only observed actions is load-bearing. In the partially observable grid+DAG setting, an action that appears non-exploratory can be a rational response to the agent's current belief over the hidden DAG; the metric must therefore embed an implicit model of what the agent 'should have known.' If that model is derived from the evaluator's full ground-truth map rather than the agent's observable history alone, the reported error rates become sensitive to the precise observability assumptions chosen in the environment definition.

Authors: We thank the referee for this important clarification on POMDP challenges. Our metric is computed solely from the agent's observed action sequence and the revealed portions of the DAG (i.e., states and edges encountered during the trajectory). The evaluator uses ground truth only for post-hoc labeling: exploration error counts unvisited prerequisite states required for task completion, while exploitation error counts unused known edges after their observation. No internal policy or belief state is accessed. We have revised §3 to include a formal definition of the metric and added a limitations discussion noting that, without explicit belief modeling, some rational actions may be labeled as errors. This provides a consistent, policy-agnostic diagnostic while remaining reproducible. revision: partial
Referee: §5 (Evaluation): The reported finding that frontier models 'struggle' and exhibit 'distinct failure modes' is presented without quantitative validation details (error bars, statistical significance, baseline comparisons against non-LM agents or random policies, or explicit controls for prompt/harness variation). These omissions make it impossible to assess whether the observed differences are robust or confounded by implementation choices.

Authors: We agree that the original evaluation section lacked these details. In the revised manuscript we have added: error bars as standard deviation across 10 runs per condition; paired t-tests with p-values for model comparisons; baselines consisting of random agents, greedy exploitation heuristics, and BFS-based explorers; and ablation studies varying prompt phrasing and harness components (e.g., presence/absence of chain-of-thought). These results appear in §5 and Appendix C, enabling readers to evaluate robustness directly. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical metric and evaluation are self-contained

full rationale

The paper introduces new controllable environments (partially observable grid + unknown DAG) and defines a policy-agnostic metric directly from observed agent actions to quantify exploration vs. exploitation errors. No equations, derivations, or predictions are presented that reduce by construction to fitted inputs, self-definitions, or self-citation chains. Evaluation of frontier LM agents is purely observational, with results reported as empirical findings rather than forced outputs. The work is self-contained against external benchmarks and does not invoke uniqueness theorems or ansatzes from prior author work.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the environments serving as faithful proxies for embodied AI tasks and on the metric correctly attributing errors to exploration versus exploitation from actions alone.

axioms (1)

domain assumption The partially observable 2D grid maps and unknown task DAGs are suitable proxies for practical embodied AI decision-making scenarios.
Abstract states the environments are 'inspired by practical embodied AI scenarios' and consist of 'a partially observable 2D grid map and an unknown task Directed Acyclic Graph (DAG)'.

pith-pipeline@v0.9.0 · 5507 in / 1309 out tokens · 56472 ms · 2026-05-10T14:55:59.053344+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

12 extracted references · 10 canonical work pages · 4 internal anchors

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Under review

13 Preprint. Under review. Appendices Here, we provide additional details that were left out due to limited space. A Additional Related Work Exploration and Exploitation.The exploration–exploitation tradeoff has been extensively studied in reinforcement learning (RL) (Thompson, 1933; Auer et al., 2002; Bellemare et al., 2016; Pathak et al., 2017), and has...

1933
[12]

action":

Available directions: up, down, right System Prompt (Base) You are controlling an agent in a partially observed symbolic grid environment. Your objective is to activate the goal state. At each step, you are given your current position, the directions you can legally move, and any newly discovered symbolic states at your current cell. Newly discovered stat...

2023