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RangeAD: Fast On-Model Anomaly Detection

Luca Hinkamp, Simon Klüttermann, Emmanuel Müller · Mar 18, 2026 · Citations: 0

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Coverage: Stale

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Best use

Background context only

Metadata: Stale

Trust level

Provisional

Signals: Stale

What still needs checking

Structured extraction is still processing; current fields are metadata-first.

Signal confidence unavailable

Abstract

In practice, machine learning methods commonly require anomaly detection (AD) to filter inputs or detect distributional shifts. Typically, this is implemented by running a separate AD model alongside the primary model. However, this separation ignores the fact that the primary model already encodes substantial information about the target distribution. In this paper, we introduce On-Model AD, a setting for anomaly detection that explicitly leverages access to a related machine learning model. Within this setting, we propose RangeAD, an algorithm that utilizes neuron-wise output ranges derived from the primary model. RangeAD achieves superior performance even on high-dimensional tasks while incurring substantially lower inference costs. Our results demonstrate the potential of the On-Model AD setting as a practical framework for efficient anomaly detection.

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Structured extraction is still processing; current fields are metadata-first.

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HFEPX Relevance Assessment

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Best use

Background context only

Use if you need

A provisional background reference while structured extraction finishes.

Main weakness

Structured extraction is still processing; current fields are metadata-first.

Trust level

Provisional

Eval-Fit Score

Unavailable

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Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Weak / implicit signal

HFEPX Fit

Provisional (processing)

Extraction confidence: Provisional

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What This Page Found In The Paper

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Human Feedback Types

provisional

None explicit

Confidence: Provisional Best-effort inference

No explicit feedback protocol extracted.

Evidence snippet: In practice, machine learning methods commonly require anomaly detection (AD) to filter inputs or detect distributional shifts.

Evaluation Modes

provisional

None explicit

Confidence: Provisional Best-effort inference

Validate eval design from full paper text.

Evidence snippet: In practice, machine learning methods commonly require anomaly detection (AD) to filter inputs or detect distributional shifts.

Quality Controls

provisional

Not reported

Confidence: Provisional Best-effort inference

No explicit QC controls found.

Evidence snippet: In practice, machine learning methods commonly require anomaly detection (AD) to filter inputs or detect distributional shifts.

Benchmarks / Datasets

provisional

Not extracted

Confidence: Provisional Best-effort inference

No benchmark anchors detected.

Evidence snippet: In practice, machine learning methods commonly require anomaly detection (AD) to filter inputs or detect distributional shifts.

Reported Metrics

provisional

Not extracted

Confidence: Provisional Best-effort inference

No metric anchors detected.

Evidence snippet: In practice, machine learning methods commonly require anomaly detection (AD) to filter inputs or detect distributional shifts.

Rater Population

provisional

Unknown

Confidence: Provisional Best-effort inference

Rater source not explicitly reported.

Evidence snippet: In practice, machine learning methods commonly require anomaly detection (AD) to filter inputs or detect distributional shifts.

Human Data Lens

This page is using abstract-level cues only right now. Treat the signals below as provisional.

Potential human-data signal: No explicit human-data keywords detected.
Potential benchmark anchors: No benchmark names detected in abstract.
Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Lens

Evaluation fields are inferred from the abstract only.

Potential evaluation modes: No explicit eval keywords detected.
Potential metric signals: No metric keywords detected.
Confidence: Provisional (metadata-only fallback).

Research Brief

Metadata summary

In practice, machine learning methods commonly require anomaly detection (AD) to filter inputs or detect distributional shifts.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

In practice, machine learning methods commonly require anomaly detection (AD) to filter inputs or detect distributional shifts.
Typically, this is implemented by running a separate AD model alongside the primary model.
However, this separation ignores the fact that the primary model already encodes substantial information about the target distribution.

Researcher Actions

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Caveats

Generated from abstract + metadata only; no PDF parsing.
Signals below are heuristic and may miss details reported outside the abstract.

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