arXiv:2606.16034v1 Announce Type: new Abstract: Temporal classification errors are often treated as representation failures, but they can also arise from how available evidence is converted into decisions. This paper proposes a representation--calibration decomposition for temporal classification. We keep a trained native classifier frozen and separate two inference-time interventions: a conservative residual multi-scale branch that adds auxiliary logits to the native prediction, and a post-hoc branch-aware calibrator that recombines native and residual evidence at decision time. This design d
This research addresses a fundamental issue in temporal classification, offering a novel approach to improve decision accuracy without altering core model representations, which is a continuous area of focus in AI development.
Improved decision calibration in temporal AI systems can lead to more reliable autonomous operations, better predictive analytics, and enhanced performance across various AI applications that rely on sequential data processing.
The proposed method introduces a decomposition of temporal classification that separates representation from decision-making, allowing for more targeted interventions to improve accuracy at inference time.
- · AI researchers and developers
- · Sectors using temporal classification (e.g., finance, healthcare, autonomous sys
- · Enterprises deploying real-time AI solutions
More precise and reliable AI model outputs in temporal classification tasks.
Reduced need for extensive model retraining or architectural overhauls when errors are decision-related rather than representation-related.
Accelerated adoption of AI in high-stakes temporal decision-making scenarios where calibration is critical.
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Read at arXiv cs.LG