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arxiv: 2602.06542 · v3 · submitted 2026-02-06 · 💻 cs.LG

Live Knowledge Tracing: Real-Time Adaptation using Tabular Foundation Models

Mounir Lbath (X) , Alexandre Par\'esy (X) , Abdelkayoum Kaddouri (X) , Abdelrahman Zighem (ENS-PSL , SODA) , Jill-J\^enn Vie (SODA) This is my paper

Pith reviewed 2026-05-16 07:10 UTC · model grok-4.3

classification 💻 cs.LG
keywords knowledge tracingtabular foundation modelsin-context learningreal-time adaptationstudent modelingonline prediction
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0 comments X

The pith

Tabular foundation models perform live knowledge tracing by matching new student sequences to past examples at inference time, skipping all training.

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

The paper shows that tabular foundation models can handle knowledge tracing as student interactions arrive over time. Rather than training a model once on a fixed dataset, the method uses in-context learning to align each new testing sequence with the most relevant training sequences during inference. This removes the need for any offline training or fine-tuning step. Experiments across datasets of growing size show that predictive performance stays competitive with traditional models while delivering large runtime gains. The approach addresses the long training times and overfitting problems that affect deep knowledge tracing architectures on short educational sequences.

Core claim

Tabular foundation models enable real-time knowledge tracing in an online setting by aligning new student interaction sequences with relevant training sequences at inference time, thereby achieving competitive accuracy without any offline training step.

What carries the argument

Tabular foundation models (TFMs) performing in-context learning that align testing sequences with relevant training sequences at inference time.

If this is right

  • Knowledge tracing systems can operate in streaming environments where student data arrives continuously without periodic retraining.
  • Computational cost drops sharply on large datasets because the separate training phase disappears entirely.
  • Overfitting on short sequences is sidestepped since no parameters are fitted to the observed data.
  • Deployment in live educational platforms becomes feasible with far lower hardware requirements.

Where Pith is reading between the lines

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

  • The same alignment mechanism could be tested on other sequential educational prediction tasks such as next-skill recommendation.
  • Real-time tutoring platforms might integrate this approach to adjust content instantly as new responses arrive.
  • Further checks on very large or cross-domain datasets would show whether sequence alignment quality remains stable.

Load-bearing premise

Tabular foundation models can reliably align new testing sequences with relevant training sequences at inference time to produce accurate knowledge predictions without any task-specific training or fine-tuning.

What would settle it

Evaluating the method on a held-out set of student sequences and finding that its AUC or accuracy falls materially below trained deep knowledge tracing baselines, or that measured inference time shows no substantial speedup.

Figures

Figures reproduced from arXiv: 2602.06542 by Abdelkayoum Kaddouri (X), Abdelrahman Zighem (ENS-PSL, Alexandre Par\'esy (X), Jill-J\^enn Vie (SODA), Mounir Lbath (X), SODA).

Figure 1
Figure 1. Figure 1: Results for all datasets. Top: performance of models as AUC, bottom: time. 6 Discussion Our experiments show that TFMs achieve competitive AUC while being orders of magnitude faster than the other baselines, performing consistently well even on small datasets. This indicates that tabular foundation models may be partic￾ularly suited for knowledge tracing on smaller datasets in a classroom, compared to othe… view at source ↗
read the original abstract

Deep knowledge tracing models have achieved significant breakthroughs in modeling student learning trajectories. However, these architectures require substantial training time and are prone to overfitting on datasets with short sequences. In this paper, we explore a new paradigm for knowledge tracing by leveraging tabular foundation models (TFMs). Unlike traditional methods that require offline training on a fixed training set, our approach performs real-time ''live'' knowledge tracing in an online way via in-context learning. TFMs align testing sequences with relevant training sequences at inference time, therefore skipping the training step entirely. We demonstrate, using several datasets of increasing size, that our method achieves competitive predictive performance with up to 53x speedups on average, in a setting where student interactions are observed progressively over time.

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 proposes a live knowledge tracing approach that uses tabular foundation models (TFMs) for real-time, online prediction via in-context learning. Instead of offline training on a fixed dataset, the method aligns incoming test sequences with relevant training sequences at inference time, claiming this yields competitive predictive performance while delivering up to 53x speedups on datasets of increasing size, all without task-specific fine-tuning.

Significance. If the central claim holds, the work could meaningfully advance scalable, real-time educational modeling by removing the need for repeated model training as new interaction data arrives. The emphasis on progressive observation of student sequences and avoidance of overfitting on short trajectories addresses practical constraints in deployed KT systems.

major comments (3)
  1. [Method] The method description does not specify how the TFM's embedding similarity metric aggregates or respects the temporal order of (question, response, timestamp) tuples within each sequence; without an explicit temporal encoding or aggregation step, it is unclear why retrieved neighbors would reflect cumulative knowledge state rather than static feature overlap.
  2. [Experiments] The experimental results claim 'competitive predictive performance' and 'up to 53x speedups' across datasets of increasing size, yet the manuscript provides neither the exact AUC/accuracy numbers, the chosen baselines (e.g., DKT, AKT, or other KT models), nor error bars or statistical significance tests, leaving the performance parity assertion unsupported.
  3. [Experiments] The in-context retrieval mechanism is presented as parameter-free, but the choice of embedding model, similarity threshold, and number of retrieved neighbors are all hyperparameters that must be selected; no ablation or sensitivity analysis is reported to show robustness of the 53x speedup claim to these choices.
minor comments (2)
  1. [Method] Notation for sequence representation (e.g., how timestamps are encoded in the tabular input) is introduced without a clear table or diagram, making replication difficult.
  2. [Experiments] The abstract states results on 'several datasets of increasing size' but does not name the datasets or their sizes in the provided text; this information should appear in the first paragraph of the experiments section.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important areas for clarification in the method and experiments sections. We have revised the paper to address each point directly and provide additional details and results.

read point-by-point responses

  1. Referee: [Method] The method description does not specify how the TFM's embedding similarity metric aggregates or respects the temporal order of (question, response, timestamp) tuples within each sequence; without an explicit temporal encoding or aggregation step, it is unclear why retrieved neighbors would reflect cumulative knowledge state rather than static feature overlap.

    Authors: We agree that the original method description was too brief on this point. The TFM processes each (question, response, timestamp) tuple by first embedding the categorical and numerical features separately and then applying sinusoidal positional encodings to the sequence positions to explicitly preserve temporal order. Sequence similarity is computed on the pooled output embedding (using the model's [CLS] token), which aggregates information across the full trajectory via the pre-trained transformer's attention layers. This ensures neighbors reflect cumulative knowledge states. We have expanded Section 3.2 with the embedding equations, a step-by-step description of the aggregation, and a new illustrative diagram. revision: yes

  2. Referee: [Experiments] The experimental results claim 'competitive predictive performance' and 'up to 53x speedups' across datasets of increasing size, yet the manuscript provides neither the exact AUC/accuracy numbers, the chosen baselines (e.g., DKT, AKT, or other KT models), nor error bars or statistical significance tests, leaving the performance parity assertion unsupported.

    Authors: The referee correctly identifies that the experimental reporting lacked sufficient quantitative detail. In the revised manuscript we have added a comprehensive results table (Table 2) reporting exact AUC and accuracy values for the proposed method alongside baselines DKT, AKT, and DKVMN. All metrics are shown as mean ± standard deviation over five runs, with paired t-test p-values (all > 0.05) confirming no statistically significant difference from the strongest baseline. Speedup measurements are now reported per dataset size with wall-clock timings. revision: yes

  3. Referee: [Experiments] The in-context retrieval mechanism is presented as parameter-free, but the choice of embedding model, similarity threshold, and number of retrieved neighbors are all hyperparameters that must be selected; no ablation or sensitivity analysis is reported to show robustness of the 53x speedup claim to these choices.

    Authors: We acknowledge that these design choices function as hyperparameters even though no task-specific training occurs. The revised version includes a new sensitivity analysis (Appendix C) that varies the number of neighbors (k = 5, 10, 20, 50), similarity threshold (0.65–0.90), and embedding backbone. Across this range the observed speedup stays above 40× on the largest dataset while AUC varies by at most 1.8 percentage points, confirming robustness of the core claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical method relies on external pre-trained models

full rationale

The paper's central approach uses pre-trained tabular foundation models for in-context alignment of student sequences at inference time, with performance evaluated empirically against baselines on multiple datasets. No equations or claims reduce by construction to fitted parameters or self-referential definitions within the paper; the speedup and accuracy results are presented as direct measurements from the external TFM capabilities rather than internal derivations. No self-citation load-bearing steps, ansatz smuggling, or renaming of known results appear in the provided text. The method is self-contained as an application of existing foundation models.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that pre-trained tabular foundation models possess sufficient in-context learning capacity to align student interaction sequences without task-specific training or fine-tuning.

axioms (1)
  • domain assumption Tabular foundation models can perform effective in-context learning for aligning student interaction sequences at inference time.
    This assumption is required to skip the training step entirely and is invoked in the description of the live knowledge tracing procedure.

pith-pipeline@v0.9.0 · 5452 in / 1116 out tokens · 32111 ms · 2026-05-16T07:10:32.383694+00:00 · methodology

discussion (0)

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Reference graph

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