The study investigates what knowledge a student model inherits from its teachers beyond accuracy when using knowledge distillation for encrypted‑traffic classification. By distilling a 101k‑parameter student from two teachers of equal accuracy but different construction, the authors test ten hypotheses over a year of real TLS traffic, finding that unknown‑traffic detection and shortcut reliance can transfer depending on temperature settings and model size, while other abilities do not. The results show that distillation can propagate teacher habits, but some inherited capabilities can also be achieved without a teacher.
arXiv:2605. 31191v2 Announce Type: replace Abstract: We investigate how teacher-student capacity relationships modulate knowledge distillation (KD) effectiveness in ResNet-based image classification on CIFAR-10.
The study evaluates large language model (LLM) graders on two computer‑science exams, testing 171 configurations of closed‑ and open‑weights models. While the best LLM configuration achieved a mean absolute error of 1.64/35—better than the 2.61/35 error between two human graders—its performance was highly sensitive to the prompt. A short "strict grader" preamble caused most open‑weight models to exceed acceptable error thresholds or stop grading entirely, whereas fine‑tuning with a single LoRA adapter restored parity with human graders and reduced sensitivity to harsh prompts.
The paper evaluates the common assumption that combining flow statistics and TLS handshake fingerprints improves encrypted command-and-control detection. Using 17,577 TLS flows from 62 real Cobalt Strike captures, the authors show that data leakage and preprocessing choices inflate performance metrics, revealing that the true benefit of multi-view fusion is minimal (0.022 F1). They also uncover that many captures contain only benign traffic and that class imbalance is an artifact of analysis rather than a real feature of the task.
arXiv:2606. 12171v1 Announce Type: cross Abstract: Knowledge Distillation (KD) and mixup have proven effective at inducing smoothness in class boundaries; KD captures inherent class relationships in probability distributions, and mixup enforces them through convex combinations of inputs.
arXiv:2607. 15467v1 Announce Type: new Abstract: Knowledge distillation enables an adversary to replicate a proprietary classifier by querying its prediction interface and training a surrogate on the returned probability vectors.
arXiv:2609.36426v1 Announce Type: cross Abstract: A detector pretrained on a broad corpus is fine-tuned on a narrow domain, its in-domain accuracy improves, and it ships. We ask what happens meanwhil...
The paper investigates selective on‑policy distillation, where a student model is trained only on token positions chosen by a selector. It demonstrates that the commonly used shared learning rate is not neutral: performance varies significantly with the learning rate for different selectors, leading to inconsistent comparisons. The authors attribute this selector‑rate entanglement to the selection process itself and recommend reporting the full arm‑by‑rate matrix for fair evaluation.
arXiv:2607. 09692v1 Announce Type: new Abstract: Model distillation -- training on outputs from stronger third-party models -- is widely used to boost performance, but raises concerns about unfair advantages and policy violations.
TwinMark is a watermarking scheme that embeds a single SHAKE128 secret into a vision model using two complementary linear functionals of model-output summaries: a covariance projector (cov‑Feat) and a class‑conditional Fisher‑aligned linear carrier (cc‑FALC). These readouts cover both classifier APIs attacked by KL knowledge distillation and representation‑only hosts attacked by feature‑matching distillation, each providing a teacher‑measurable a posteriori certificate. Across 13 attacks on datasets such as CIFAR‑10, CIFAR‑100, and Mini‑ImageNet, TwinMark remains detectable on every post‑attack model that retains task utility, survives cross‑architecture distillation onto ResNet‑18/50, VGG‑16, and MobileNet‑V3, and can be ported to GNSS few‑shot, VOC detection, ISIC segmentation, and STL‑10 SimCLR.
arXiv:2609.01345v1 Announce Type: new Abstract: Inference cascades cut cost by answering most queries with a cheap model and escalating a hard tail to a frontier model that acts as verifier. A natura...
arXiv:2607. 07050v3 Announce Type: replace-cross Abstract: Top-K teacher logits make on-policy distillation tractable, but probability mass is not the same as decision support.
The study examines whether accuracy gaps between source and target tasks can certify the failure of scalar recalibration maps for large language model judges. Across thirteen judges, two generators, eight domains, and 1,176 transfers, the accuracy gap only provides a weak lower bound on target calibration error and can predict opposite outcomes. Even with a finite‑sample lower certificate, the method shows low power (0.13) and does not reliably indicate when recalibration will fail.