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arxiv: 2604.05345 · v1 · submitted 2026-04-07 · 💻 cs.AI

Dynamic Agentic AI Expert Profiler System Architecture for Multidomain Intelligence Modeling

Aisvarya Adeseye , Jouni Isoaho , Seppo Virtanen , Mohammad Tahir This is my paper

Pith reviewed 2026-05-10 20:01 UTC · model grok-4.3

classification 💻 cs.AI
keywords AI profilerexpertise classificationagentic AInatural language responsesdynamic assessmentmultidomain intelligenceLLaMAuser modeling
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The pith

An agentic AI profiler classifies natural-language responses into novice, basic, advanced, or expert levels with 83-97% agreement to self-ratings.

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

The paper presents a layered AI system that takes user answers in any domain and outputs one of four expertise tiers. It processes text through preprocessing, scoring, aggregation, and final classification steps built on LLaMA v3.1. Tests on 82 static transcripts and 402 live agent-conducted interviews show the output matches participants' own ratings most of the time. A reader would care because many AI interactions fail when the system cannot gauge how much the user already knows. The architecture supports updating the assessment after every single reply rather than waiting until an entire conversation ends.

Core claim

The proposed modular architecture on LLaMA v3.1 (8B) classifies responses into four expertise levels and reaches 83% to 97% agreement with participant self-assessments across domains, with the dynamic version updating its judgment after each individual answer during 402 live interviews.

What carries the argument

Modular layered architecture with separate stages for text preprocessing, scoring, aggregation, and classification running on LLaMA v3.1 (8B).

If this is right

  • Expertise can be reassessed after every single response instead of only at the end of an interview.
  • Agreement rates between 83% and 97% hold across multiple domains in both static and live settings.
  • Most mismatches trace to self-rating bias, unclear answers, or occasional model misreads of subtle knowledge.
  • The same pipeline can support real-time context awareness in human-machine conversations.

Where Pith is reading between the lines

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

  • Systems using this profiler could adjust question difficulty or explanation depth on the fly.
  • The per-response update pattern may shorten the length of assessment interviews needed for reliable results.
  • Similar modular scoring stages could be adapted to measure other user attributes such as confidence or learning style.
  • Deployment in tutoring or onboarding tools would let the AI start at an appropriate level without separate calibration questions.

Load-bearing premise

Participant self-ratings accurately reflect their true expertise and the language model can reliably distinguish nuanced differences in knowledge from short answers.

What would settle it

Collect independent expert ratings of the same set of responses and check whether the AI profiler agrees with those ratings more or less often than it agrees with the original self-ratings.

Figures

Figures reproduced from arXiv: 2604.05345 by Aisvarya Adeseye, Jouni Isoaho, Mohammad Tahir, Seppo Virtanen.

Figure 1
Figure 1. Figure 1: Expert Profiler System Architecture: A Layered Framework for Expertise Classification from Textual Responses [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
read the original abstract

In today's artificial intelligence driven world, modern systems communicate with people from diverse backgrounds and skill levels. For human-machine interaction to be meaningful, systems must be aware of context and user expertise. This study proposes an agentic AI profiler that classifies natural language responses into four levels: Novice, Basic, Advanced, and Expert. The system uses a modular layered architecture built on LLaMA v3.1 (8B), with components for text preprocessing, scoring, aggregation, and classification. Evaluation was conducted in two phases: a static phase using pre-recorded transcripts from 82 participants, and a dynamic phase with 402 live interviews conducted by an agentic AI interviewer. In both phases, participant self-ratings were compared with profiler predictions. In the dynamic phase, expertise was assessed after each response rather than at the end of the interview. Across domains, 83% to 97% of profiler evaluations matched participant self-assessments. Remaining differences were due to self-rating bias, unclear responses, and occasional misinterpretation of nuanced expertise by the language model.

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 / 1 minor

Summary. The manuscript proposes a modular agentic AI profiler built on LLaMA v3.1 (8B) that classifies natural-language responses into a four-level expertise taxonomy (Novice/Basic/Advanced/Expert) via preprocessing, scoring, aggregation, and classification stages. It reports results from a static phase (82 pre-recorded transcripts) and a dynamic phase (402 live agentic interviews) in which profiler outputs are compared directly to participants' self-assessed expertise levels, claiming 83–97 % agreement across domains; residual mismatches are attributed to self-rating bias, unclear responses, and occasional model misinterpretation.

Significance. A reliable, domain-agnostic expertise profiler could support adaptive human–AI interfaces, but the present evaluation supplies no independent anchor for the claimed accuracy and therefore does not yet establish a usable advance over existing prompt-based or fine-tuned classifiers.

major comments (3)
  1. [Abstract] Abstract and Evaluation section: the central accuracy figures (83–97 % match) rest entirely on direct comparison to participant self-ratings; no objective knowledge tests, expert-panel ratings, or pre-existing performance records are used as external validation, so any systematic self-perception bias propagates directly into the reported percentages.
  2. [Abstract] Abstract: agreement rates are stated without statistical tests, confidence intervals, per-domain or per-phase breakdowns, or inter-rater reliability metrics, rendering it impossible to judge whether the headline claim is supported by the data.
  3. [Evaluation] Evaluation description: the four-level taxonomy is defined only by the model's internal scoring rules and the participants' own labels; the paper supplies no explicit decision criteria or threshold equations, making the classification procedure non-reproducible from the text alone.
minor comments (1)
  1. [Abstract] Abstract: the dynamic-phase protocol (expertise assessed after each response) is mentioned but not accompanied by details on how per-response scores are aggregated into a final profile.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments on our manuscript. We address each major point below and indicate the revisions we will make to improve clarity, statistical rigor, and reproducibility.

read point-by-point responses

  1. Referee: [Abstract] Abstract and Evaluation section: the central accuracy figures (83–97 % match) rest entirely on direct comparison to participant self-ratings; no objective knowledge tests, expert-panel ratings, or pre-existing performance records are used as external validation, so any systematic self-perception bias propagates directly into the reported percentages.

    Authors: We acknowledge that the reported agreement rates rely on participant self-assessments as the reference. This choice aligns with the profiler's goal of modeling perceived expertise in interactive settings, where self-perception influences user expectations and system adaptation. The manuscript already notes mismatches due to self-rating bias. To address the concern, we will expand the Evaluation and Discussion sections with a dedicated limitations subsection that explicitly discusses self-perception bias, its potential impact on the 83–97% figures, and directions for future work using objective tests or expert panels. This clarifies the scope of our claims while preserving the current evaluation design. revision: partial

  2. Referee: [Abstract] Abstract: agreement rates are stated without statistical tests, confidence intervals, per-domain or per-phase breakdowns, or inter-rater reliability metrics, rendering it impossible to judge whether the headline claim is supported by the data.

    Authors: We agree that the headline agreement rates require supporting statistical detail. The underlying data (82 static transcripts and 402 dynamic interviews) permit computation of confidence intervals, per-domain and per-phase breakdowns, and agreement metrics such as Cohen's kappa. In the revised manuscript we will add these analyses to the Evaluation section, update the Abstract with key statistical summaries, and include per-domain tables to allow readers to assess the robustness of the 83–97% range. revision: yes

  3. Referee: [Evaluation] Evaluation description: the four-level taxonomy is defined only by the model's internal scoring rules and the participants' own labels; the paper supplies no explicit decision criteria or threshold equations, making the classification procedure non-reproducible from the text alone.

    Authors: We appreciate the call for greater reproducibility. The four-level taxonomy is implemented via the scoring, aggregation, and classification stages described in the Methods. To make the procedure fully explicit, we will revise the Evaluation section to include the precise decision criteria, threshold equations for score aggregation, and pseudocode for the classification logic. These additions will enable independent reproduction from the text without requiring access to the original code. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical match rate uses external self-ratings with no derivation or self-referential reduction

full rationale

The paper presents a modular AI profiler architecture and reports an empirical evaluation result (83-97% agreement with participant self-ratings across domains) obtained by direct comparison of system outputs to those self-assessments. No equations, derivations, fitted parameters, or mathematical predictions appear in the provided text. The central claim is therefore an observational agreement metric rather than a quantity derived from the inputs by construction. No self-citations, uniqueness theorems, or ansatzes are invoked as load-bearing steps. The use of self-ratings as ground truth is a methodological limitation affecting validity, but it does not reduce any claimed result to the inputs via the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract contains no mathematical derivations, free parameters, axioms, or invented entities; all content is descriptive system design and empirical comparison.

pith-pipeline@v0.9.0 · 5495 in / 1000 out tokens · 49110 ms · 2026-05-10T20:01:26.470337+00:00 · methodology

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