BEAGLE achieves higher behavioral fidelity to real student cognitive-behavior distributions than LLM prompting and agent baselines on a Particle Simulator task (DKL = 0.31 vs. baselines ≥ 0.53)¶
Claim · All claims
Evidence · 1 study · designr2·q2
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
Subclaims¶
Reading the evidence codes
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q2 i? On the Particle Simulator task, BEAGLE reaches DKL = 0.31 and Ddebug = 0.02 against real student trajectories, while all baselines score DKL ≥ 0.53 and Ddebug 0.06–0.52. → Hanchen David Wang 2026
Evidence¶
Hanchen David Wang 2026¶
Hanchen David Wang, Clayton Cohn, Zifan Xu, Siyuan Guo, Gautam Biswas, Meiyi Ma. (2026). BEAGLE: Behavior-Enforced Agent for Grounded Learner Emulation. Preprint. https://arxiv.org/abs/2602.13280 doi:10.48550/arxiv.2602.13280
q2 · i? · design · r2
Main evaluation on the Particle Simulator (N=50 simulations, T=30 steps, Gemini 2.0 Flash) comparing BEAGLE against nine baselines including Vanilla, CoT, Few-Shot, SimStudent, LLM-SS, and CoderAgent. The paper reports "DKL = 0.31 (vs. baselines≥0.53 )" for behavioral divergence vs. the combined Real evaluation set.
"BEAGLEachieves DKL = 0.31 (vs. baselines≥0.53 ) and Ddebug = 0.02 (vs. 0.06–0.52)."
Discussion¶
Related Claims¶
- Ablations show the semi-Markov controller is the primary driver of behavioral fidelity (removal pushes DKL to 6.81) and Strategist/Executor separation is essential for epistemic fidelity and realism — related
- BEAGLE generalizes across tasks and LLM backbones: on the out-of-distribution Gradient Descent task it still shrinks DKL by ≥3×, raises error recurrence to ≥79%, and improves all three perceptual scores — related
- BEAGLE's error recurrence rate (86.2%) falls within the realistic novice envelope, far above vanilla LLMs (7.8%) and below rule-based SimStudent's mechanical saturation (92.0%) — related
- In a human Turing test, participants could not reliably distinguish BEAGLE traces from real student data (52.8% accuracy; d′ = 0.15 within the ±0.3 equivalence bound, pTOST = 0.038) — related
- BEAGLE's HIGH and LOW performer profiles differentiate by SRL strategy: HIGH performers allocate 72% of steps to PLANNING and MONITORING while LOW performers spend 50.8% trapped in ENACTING — related
- LLM-based simulated students surpass rule-based systems in natural language generation and situational flexibility, but concerns about bias and evaluation reliability persist — reports the opposite
- Across chess, L2 writing, and math, trained StudentSim simulators outperform every reference baseline on behavioral fidelity (F) — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
beagle-behavioral-fidelity-beats-baselines |
title |
BEAGLE achieves higher behavioral fidelity to real student cognitive-behavior distributions than LLM prompting and agent baselines on a Particle Simulator task (DKL = 0.31 vs. baselines ≥ 0.53) |
description |
BEAGLE achieves higher behavioral fidelity to real student cognitive-behavior distributions than LLM prompting and agent baselines on a Particle Simulator task (DKL = 0.31 vs. |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
source_checked |
2026-10-20 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — hanchen-david-wang-2026 |
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