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Representative Papers

Similarity-Distance-Magnitude Language Models

Oct 30, 2025

This work addresses the low statistical efficiency of large language models (LLMs) in instruction-following tasks, caused by frequent abstention—i.e., refusal to generate outputs despite being capable. To tackle this, we propose the Similarity–Distance–Magnitude (SDM) language model. Methodologically, we adapt a pretrained decoder-only Transformer via supervised fine-tuning into a sequence prediction model, introducing an SDM activation function at the output layer to explicitly partition high-probability regions into trustworthy generation intervals. We further enhance calibration and decision boundary sharpness through contrastive input encoding and online hard negative sampling, coupled with an SDM-based basis-transformed loss function. Our core contribution is the first integration of the SDM architecture into the final decoder layer, enabling explicit modeling of generation confidence. Experiments demonstrate substantial reductions in abstention rates, outperforming strong supervised baselines across multiple instruction-following benchmarks while improving both generation effectiveness and probabilistic calibration.

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Similarity-Distance-Magnitude Activations

Sep 16, 2025

Existing softmax lacks explicit modeling of sample similarity and distance to the training distribution, resulting in poor robustness under covariate shift and out-of-distribution (OOD) inputs, as well as limited interpretability. This paper proposes the Similarity–Distribution–Magnitude-aware (SDM) activation function, the first to jointly embed deep similarity matching, training-distribution distance estimation, and output magnitude control directly into the activation layer—enabling multi-dimensional disentangled modeling of prediction confidence. SDM supports instance-level interpretability, class-wise empirical cumulative distribution partitioning, and recall preservation, effectively mitigating low-recall issues in selective classification. Experiments demonstrate that SDM significantly outperforms softmax and state-of-the-art post-hoc calibration methods in robustness against OOD samples and covariate shift—particularly within high-confidence prediction regions—while maintaining strong discriminative performance.

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Similarity-Distance-Magnitude Universal Verification

Feb 27, 2025

This work addresses two fundamental challenges in deep learning: weak neural network robustness and the lack of human-interpretable uncertainty quantification. To this end, we propose the Similarity-Distance-Magnitude (SDM) activation function, introducing the first triple-aware mechanism that jointly perceives prediction similarity, distance to the training distribution, and output magnitude. Methodologically, we integrate class-conditional empirical cumulative distribution function (CDF) modeling, probabilistic region-wise calibration, and uncertainty-driven selective computation—including LLM routing and multi-model consensus verification—to enable conditional uncertainty estimation, class-specific accuracy assessment, and robust selective inference under distributional shift. Experiments demonstrate substantial improvements in out-of-distribution (OOD) detection and OOD robustness. The approach yields interpretable uncertainty summaries—e.g., calibrated confidence intervals and credibility quantiles—and supports selective classification, conditional generation, and model consensus validation. Overall, it establishes a new paradigm for trustworthy AI that balances theoretical rigor with practical deployability.

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Latest Papers

Similarity-Distance-Magnitude Language Models

Oct 30, 2025

This work addresses the low statistical efficiency of large language models (LLMs) in instruction-following tasks, caused by frequent abstention—i.e., refusal to generate outputs despite being capable. To tackle this, we propose the Similarity–Distance–Magnitude (SDM) language model. Methodologically, we adapt a pretrained decoder-only Transformer via supervised fine-tuning into a sequence prediction model, introducing an SDM activation function at the output layer to explicitly partition high-probability regions into trustworthy generation intervals. We further enhance calibration and decision boundary sharpness through contrastive input encoding and online hard negative sampling, coupled with an SDM-based basis-transformed loss function. Our core contribution is the first integration of the SDM architecture into the final decoder layer, enabling explicit modeling of generation confidence. Experiments demonstrate substantial reductions in abstention rates, outperforming strong supervised baselines across multiple instruction-following benchmarks while improving both generation effectiveness and probabilistic calibration.

0 citationsRead paper

Similarity-Distance-Magnitude Activations

Sep 16, 2025

Existing softmax lacks explicit modeling of sample similarity and distance to the training distribution, resulting in poor robustness under covariate shift and out-of-distribution (OOD) inputs, as well as limited interpretability. This paper proposes the Similarity–Distribution–Magnitude-aware (SDM) activation function, the first to jointly embed deep similarity matching, training-distribution distance estimation, and output magnitude control directly into the activation layer—enabling multi-dimensional disentangled modeling of prediction confidence. SDM supports instance-level interpretability, class-wise empirical cumulative distribution partitioning, and recall preservation, effectively mitigating low-recall issues in selective classification. Experiments demonstrate that SDM significantly outperforms softmax and state-of-the-art post-hoc calibration methods in robustness against OOD samples and covariate shift—particularly within high-confidence prediction regions—while maintaining strong discriminative performance.

0 citationsRead paper

Similarity-Distance-Magnitude Universal Verification

Feb 27, 2025

This work addresses two fundamental challenges in deep learning: weak neural network robustness and the lack of human-interpretable uncertainty quantification. To this end, we propose the Similarity-Distance-Magnitude (SDM) activation function, introducing the first triple-aware mechanism that jointly perceives prediction similarity, distance to the training distribution, and output magnitude. Methodologically, we integrate class-conditional empirical cumulative distribution function (CDF) modeling, probabilistic region-wise calibration, and uncertainty-driven selective computation—including LLM routing and multi-model consensus verification—to enable conditional uncertainty estimation, class-specific accuracy assessment, and robust selective inference under distributional shift. Experiments demonstrate substantial improvements in out-of-distribution (OOD) detection and OOD robustness. The approach yields interpretable uncertainty summaries—e.g., calibrated confidence intervals and credibility quantiles—and supports selective classification, conditional generation, and model consensus validation. Overall, it establishes a new paradigm for trustworthy AI that balances theoretical rigor with practical deployability.

0 citationsRead paper