Agentic Interpretability in LLMs

• Agentic interpretability is an interactive paradigm that uses multi-turn dialogue to help users build accurate mental models of LLM reasoning.
• It leverages proactive teaching, adaptive explanations, and mutual mental modeling to translate complex, machine-internal concepts into human-understandable insights.
• This approach enhances human learning and concept transfer while posing challenges in auditability and reproducibility compared to static interpretability methods.

Agentic interpretability is a paradigm of model analysis and explanation that centers on interactive, multi-turn dialog between humans and LLMs, in which the LLM proactively assists users in building accurate, individualized mental models of its reasoning and internal concepts. Distinct from traditional “inspective” interpretability—which relies primarily on static inspection of internal structures such as attention weights, circuit motifs, or feature importance maps—agentic interpretability leverages the LLM’s dialogic, adaptive, and teaching capacities to foster human understanding in a conversational setting. This new approach arises directly from the linguistic and meta-cognitive capabilities that modern LLMs possess and is framed as enabling humans not only to audit but to learn from models, including acquiring “superhuman” concepts internal to the LLM.

1. Core Definition and Distinction from Traditional Approaches

Agentic interpretability is defined as a multi-turn conversation in which the LLM actively seeks to improve human understanding by leveraging a dynamically inferred mental model of the user. This includes tracking the user’s background knowledge, confusions, and learning preferences, and iteratively adapting explanations as the dialog unfolds. The approach departs fundamentally from traditional interpretability methods that are “inspective” (i.e., aim to open the black box for static inspection by exposing internals, as with saliency maps or mechanistic circuit dissection).

Table: Contrasting Agentic and Traditional Interpretability

Aspect	Traditional (Inspective)	Agentic Interpretability
Explanation Mode	Post-hoc, static	Interactive, adaptive conversation
LLM’s Role	Passive object of inspection	Active teacher and conversational agent
Human’s Role	Inspector, evaluator	Co-learner, dialog partner
Mutual Modeling	Absent	Present: LLM models user, vice versa
Artifact	Explanation object (maps, etc)	Dialog transcript or summary
Addressed Concepts	Human-interpretable, observable	Machine-internal, “superhuman”
Evaluation	Completeness, reproducibility	Human learning, comprehension grooming

This paradigm shift is motivated by the explosion of LLM capabilities and their capacity to sustain not just single-point responses, but extended modeled dialog and pedagogical behavior (2506.12152).

2. Mechanisms: Interaction, Mental Modeling, and Teaching

Agentic interpretability centers on three interactive mechanisms:

1. Proactive Assistance: The LLM takes initiative based on inferences about the user’s state (e.g., “I notice you haven’t encountered X before. Shall I explain?”) rather than waiting for explicit queries.
2. Multi-Turn Dialog: Knowledge and explanations unfold gradually, allowing for clarification, probing, and scaffolding akin to effective tutoring. The process is deeply tailored to the user’s evolving comprehension.
3. Mutual Mental Models: The LLM maintains (implicitly or explicitly) a representation of the user’s understanding and confusion. This mutual modeling allows it to situate explanations within the user’s “zone of proximal development,” maximizing pedagogical effectiveness.

Formally, if $x$ is the prompt/context, $y$ the LLM’s output, and $f: (x, y) \mapsto c$ a mapping to a (possibly “superhuman”) concept, the agentic paradigm aims for the user, after dialog, to better learn or predict $c$ or for the LLM to better align $f(x, y)$ with human expectations (through joint dialog refinement).

Example Scenario:

• LLM observes, from the dialog, the user is unclear about “gravitational waves.”
• It offers a progressive explanation, checks for confirmation, then extends the conversation to more advanced topics, adapting as it senses the user’s growing expertise.

3. Benefits and Trade-Offs

Benefits

• Enhanced Human Learning: By modeling the user’s mental state and proactively teaching, agentic interpretability supports the adoption of machine-internal “superhuman” concepts that would be difficult to extract and communicate using static tools (e.g., AlphaZero’s novel chess strategies).
• Adaptivity and Contextualization: Explanations are adjusted to the learner’s knowledge, confusion, and curiosity, improving understanding and retention relative to one-shot post-hoc explanations.
• Co-discovery: The dialogic, collaborative mode allows discovery of new knowledge or unanticipated concepts on both sides.

Trade-Offs

• Reduced Completeness: Agentic interpretability may yield explanations that are locally tailored but not globally complete or fully transparent. Key behaviors—especially those related to safety, deception, or edge cases—may be missed if not surfaced in dialog.
• Less Immediate Auditability: The approach does not, by default, produce single, transferable explanatory artifacts useful for compliance or systematic auditing.
• Evaluation Complexity: Because human-in-the-loop responses are integral to the process (“human-entangled-in-the-loop”), standard metrics of interpretability (e.g., completeness, alignment-to-truth) are difficult to apply.

A plausible implication is that agentic interpretability is best suited for scenarios where deep mutual understanding, context adaptation, or transfer of new concepts is the priority, but may be insufficient on its own in high-stakes, adversarial, or rigorous audit settings.

4. Human-Entangled-in-the-Loop: Design and Evaluation Challenges

The human response is a central component of the interpretability mechanism, introducing several challenges:

• Evaluation Difficulty: Since interpretability now depends on both LLM and human responses, metrics such as BLEU or static comprehensiveness are less meaningful. Traditional benchmarks for interpretability must be joined by user studies and assessments of comprehension gain.
• Reproducibility: Conversation trajectories are personalized and path-dependent, making direct comparison across sessions and users difficult.
• Variance: Both user backgrounds and LLM behaviors can cause high variance in learning and explanation outcomes.

Proxy Solutions Proposed:

• Use of simulated dialog partners (e.g., LLMs as “stand-in” users) for development-stage evaluation.
• End-task assessment (e.g., human ability to predict LLM responses or improve performance on domain tasks after interaction) as indirect proxies for learning.
• Automatic generation of summary artifacts post-dialog to support external review.

5. Societal and Epistemic Implications

As LLMs approach and surpass human parity in various tasks, agentic interpretability is positioned as an essential tool to bridge the epistemic gap between machines and humans. This paradigm allows people to access and internalize machine-originated, potentially superhuman concepts, maintaining alignment and avoiding growing societal divides in knowledge. The collaborative nature of agentic interpretability supports human flourishing in the face of increasingly autonomous and sophisticated AI systems.

However, for high-stakes scenarios—where completeness, transparency, and robustness against deception are paramount—traditional inspective methods remain indispensable. For most human–AI interactions, agentic interpretability offers a scalable, adaptive, and mutually educational approach to understanding complex AI behaviors.

6. Implications for the Design of Future Systems

The agentic interpretability paradigm introduces several design priorities:

• Personalized Dialog Frameworks: Systems need to support adaptive, multi-turn interactions with fine-grained memory of user state.
• Evaluation Infrastructure: Benchmarks and metrics must move beyond static explanation fidelity to measure knowledge transfer, mutual modeling, and learning outcomes.
• Hybrid Approaches: Combining agentic and inspective interpretability can mitigate each approach’s weaknesses: dialog-driven learning for concept transfer, with underpinning static analysis for safety and audit.
• Safeguarding Against Incompleteness or Deception: System design should incorporate mechanisms to surface, not just user-prompted, but also unexpected or safety-critical model behaviors, preserving auditability alongside interactive learning.

Summary Table: Agentic vs. Inspective Interpretability

Property	Agentic Interpretability	Traditional (Inspective) Interpretability
Interaction	Dialogic, user entangled	Static, user-initiated or passive
LLM Role	Teacher, collaborator	Passive object
Evaluation	Human learning/comprehension	Completeness, artifact coverage
Adaptivity	High (user-specific)	Low (one-size-fits-all)
Suitability	Societal integration, teaching	Safety, high-stakes auditing
Artifact	Conversation, summary report	Explanation object (saliency, circuits)

Agentic interpretability thus represents a crucial advance for the LLM era: leveraging interactive, pedagogical dialog to support mutual model-building and the transfer of novel concepts, while acknowledging fundamental trade-offs and evaluation challenges introduced by the approach.