OpenNovelty.org

Estimating the Empowerment of Language Model Agents

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
EmpowermentLanguage model agentsEvaluationInformation theory
Abstract:

As language model (LM) agents become more capable and gain broader access to real-world tools, there is a growing need for scalable evaluation frameworks of agentic capability. However, conventional benchmark-centric evaluations are costly to design and require human designers to come up with valid tasks that translate into insights about general model capabilities. In this work, we propose information-theoretic evaluation based on empowerment, the mutual information between an agent's actions and future states, as an open-ended method for evaluating LM agents. We introduce EELMA (Estimating Empowerment of Language Model Agents), an algorithm for approximating effective empowerment from multi-turn text interactions. We validate EELMA on both language games and scaled-up realistic web-browsing scenarios. We find that empowerment strongly correlates with average task performance, characterize the impact of environmental complexity and agentic factors such as chain-of-thought, model scale, and memory length on estimated empowerment, and that high empowerment states and actions are often pivotal moments for general capabilities. Together, these results demonstrate empowerment as an appealing general-purpose metric for evaluating and monitoring LM agents in complex, open-ended settings. Code available: https://anonymous.4open.science/r/EELMA-E227

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces EELMA, an algorithm for estimating empowerment in text-based language model agents through mutual information between actions and future states. It sits within the 'Information-Theoretic Empowerment Estimation for Language Model Agents' leaf, which contains only one sibling paper examining similar empowerment estimation approaches. This represents a relatively sparse research direction within the broader taxonomy of eleven papers across multiple branches, suggesting the work addresses an emerging rather than saturated area of investigation.

The taxonomy reveals neighboring work in 'Universal AI and Empowerment Theory' that develops broader theoretical frameworks, and 'Human Empowerment Maximization for Assistive Agents' that applies empowerment to training objectives rather than evaluation. The paper's focus on evaluation metrics distinguishes it from these adjacent directions. The scope boundaries indicate deliberate separation between empowerment as assessment tool versus training signal, positioning this work at the intersection of information theory and agent capability measurement rather than assistance paradigms or multi-agent coordination.

Among twenty-three candidates examined through limited semantic search, none clearly refute the three main contributions. The EELMA estimator examined nine candidates with zero refutations, the formalization as evaluation metric examined four candidates with zero refutations, and the agentic factors analysis examined ten candidates with zero refutations. This suggests that within the bounded search scope, the specific combination of text-based empowerment estimation, goal-agnostic evaluation framing, and systematic analysis of factors like chain-of-thought and memory length appears relatively unexplored in prior literature.

Based on top-twenty-three semantic matches, the work appears to occupy novel ground in applying information-theoretic empowerment specifically to language model agent evaluation. However, the limited search scope and sparse taxonomy leaf indicate this assessment reflects emerging research territory rather than exhaustive comparison against all possible prior work in reinforcement learning, information theory, or agent evaluation more broadly.

Taxonomy

Core-task Taxonomy Papers
11
3
Claimed Contributions
23
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Evaluating language model agent capability using empowerment estimation. The field structure reflects a multifaceted approach to understanding and enhancing agent autonomy through information-theoretic principles. The taxonomy organizes work into four main branches: theoretical frameworks that formalize empowerment-based evaluation metrics, training methodologies that leverage empowerment to improve agent assistance and learning, architectural innovations that enhance core agent capabilities, and domain-specific applications ranging from mobile interfaces to UAV reasoning. Empowerment-Based Evaluation and Theoretical Frameworks includes foundational work on measuring agent influence through mutual information, such as Estimating Agent Empowerment[0] and Universal AI Empowerment[9]. Empowerment-Driven Training and Assistance encompasses methods like Assistance via Empowerment[2] and Training Agents Empower Humans[1] that use empowerment to guide helpful behavior without explicit rewards. Agent Architecture branches explore general capability improvements including Step-Level Self-Critique[4] and Tool Learning Wild[5], while Domain-Specific Applications address specialized contexts like Mobile Agent RAG[10] and AirVista UAV Reasoning[8]. A particularly active line of work explores how empowerment can serve as an intrinsic evaluation signal without requiring hand-crafted reward functions, contrasting traditional supervised approaches with information-theoretic measures of agent influence over future states. Estimating Agent Empowerment[0] sits squarely within the theoretical evaluation branch, focusing on information-theoretic methods to quantify agent capability. This positions it closely to Assisting Without Rewards[3], which similarly explores empowerment-driven assistance without explicit objectives, though the latter emphasizes training dynamics rather than pure evaluation metrics. Compared to Universal AI Empowerment[9], which proposes broader frameworks applicable across AI systems, Estimating Agent Empowerment[0] appears more narrowly focused on language model agents specifically. The central tension across these branches involves balancing theoretical rigor in empowerment estimation against practical deployment in diverse real-world scenarios, with ongoing questions about how well information-theoretic measures correlate with human judgments of agent usefulness.

Claimed Contributions

EELMA: First empowerment estimator for text-based environments

The authors introduce EELMA, an algorithm that estimates empowerment from multi-turn language interactions by mapping textual observations and actions to embeddings and applying variational mutual information estimation. This is the first approach to quantify empowerment in language-based agent environments.

9 retrieved papers
Formalization of empowerment as goal-agnostic evaluation metric

The authors formalize and validate empowerment as a goal-agnostic metric for evaluating language model agent capability. They demonstrate both theoretically and empirically that empowerment correlates with average task performance without requiring explicit task specifications or reward functions.

4 retrieved papers
Comprehensive analysis of agentic factors affecting empowerment

The authors conduct a systematic analysis examining how different components of language model agents (chain-of-thought prompting, memory length, and model architecture) influence effective empowerment, providing insights into what drives agentic capability.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

EELMA: First empowerment estimator for text-based environments

The authors introduce EELMA, an algorithm that estimates empowerment from multi-turn language interactions by mapping textual observations and actions to embeddings and applying variational mutual information estimation. This is the first approach to quantify empowerment in language-based agent environments.

Contribution

Formalization of empowerment as goal-agnostic evaluation metric

The authors formalize and validate empowerment as a goal-agnostic metric for evaluating language model agent capability. They demonstrate both theoretically and empirically that empowerment correlates with average task performance without requiring explicit task specifications or reward functions.

Contribution

Comprehensive analysis of agentic factors affecting empowerment

The authors conduct a systematic analysis examining how different components of language model agents (chain-of-thought prompting, memory length, and model architecture) influence effective empowerment, providing insights into what drives agentic capability.

Table of Contents