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Decision Shifts, Lost Label Functionality, and an Inconclusive Grounding Audit in Correctness-Gated Multi-Teacher Distillation

arxiv.org/abs/2609.09702

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Updated 5 h ago · first seen 11 Sept 2026

paper_01M294GK865PFFDT2MKYA2VR4V

Published
11 Sept 2026
T1 · 5 h ago
arXiv
2609.09702
T1 · 5 h ago
Category
cs.AI
T1 · 5 h ago

Abstract

Candidate decision correctness and rationale grounding are different objectives. We examine correctness-gated multi-teacher distillation in a fixed experiment. Eight arms share 4,330 sources, a 63.9M-parameter student, 12,990 optimization rows, 406 updates, evidence inputs, and a decoder; seven teacher-based arms use one fixed three-response pool. Three seeds are evaluated on 267 held-out examples. Relative to unfiltered distillation, the correctness-weighted arm differed in accuracy by +0.1660 (95% observed-matrix interval [0.0670, 0.2455]), five-label macro-F1 by +0.1323 ([0.0916, 0.1731]), and task-defined conditional unsafe-action rate by -0.4979 ([-0.5926, -0.3686]). These shifts do not imply uniformly better behavior. Source-label SFT had the highest mean macro-F1 (0.586). The weighted arm had zero Refuted recall in every seed, and two seeds assigned NotEnoughInfo to all 167 claim examples. In an availability-amended audit at one reference seed, weighted and unfiltered outputs had 0/20 versus 1/20 evidence-supported positives and 20/20 versus 19/20 positives containing unsupported material. Samples were non-paired, source overlap was not serialized, and the amendment followed automatic summarization but preceded annotation. The audit therefore cannot estimate a common-source grounding effect and is inconclusive about system-level improvement or harm. Hard filtering already achieved 0.660 accuracy, 0.530 macro-F1, and 0.135 conditional unsafe rate. The implemented weighted arm showed no demonstrated incremental decision benefit over hard filtering. This fixed-matrix failure analysis shows decision redistribution with lost label functionality; the available human audit does not establish a grounding gain.

Authors 1

Xiaofei Feng

Specification

Official page

Source:arXiv (Atom API + RSS)T1observed 5 h agohigh

Arxiv announce type
new

Source:arXiv (Atom API + RSS)T1observed 5 h agohigh

arXiv id
2609.09702

Source:arXiv (Atom API + RSS)T1observed 5 h agohigh

Categories
cs.AI

Source:arXiv (Atom API + RSS)T1observed 5 h agohigh

PDF

Source:arXiv (Atom API + RSS)T1observed 5 h agohigh

Primary category
cs.AI

Source:arXiv (Atom API + RSS)T1observed 5 h agohigh

Published
11 Sept 2026

Source:arXiv (Atom API + RSS)T1observed 5 h agohigh

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Provenance

Attributed facts

9

Source tiers

T19

Freshest observation

5 h ago

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None