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Artificial Intelligence Research Institute

Academic institutionasia · jp
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Research library17linked papers
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Selected work

Representative Papers

The More Popular, The Harder to Forget: Adaptive Popularity for LLM Unlearning

Aug 14, 2026

This study addresses the limitation of existing large language model unlearning methods that overlook fact popularity, rendering high-frequency knowledge difficult to remove. We propose AdaPop, a novel approach that models popularity as a learnable parameter by integrating token confidence with external proxy evaluations. Through a bi-ascent controller, AdaPop dynamically adjusts penalty intensity to achieve an adaptive balance between forgetting and retention. Experiments across three model families and two benchmarks demonstrate that AdaPop reduces content leakage under paraphrased queries by approximately fivefold and under adversarial reconstruction by 1.6 times. Furthermore, internal representation analysis confirms superior forgetting separation, effectively resolving the challenge of unlearning high-frequency facts while preserving general model utility.

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Data-Driven Forward and Inverse Modeling of V-Beam Thermal Sensors

Jul 04, 2026

This study addresses the ill-posed inverse design problem of V-beam thermal actuators, where multiple geometric and material configurations can yield the same target displacement at a given temperature. To overcome this non-uniqueness, the authors propose a data-driven two-stage optimization framework. First, a neural network forward model is trained to predict thermo-mechanical responses from geometric and material parameters. This model is then frozen and embedded within a gradient-based inverse optimization loop that simultaneously minimizes structural volume and mechanical stress. The approach effectively circumvents the failure of direct regression in ill-conditioned inverse problems and enables efficient multi-objective geometric optimization. Evaluated on a dataset of 3,000 samples, the forward model achieves a mean absolute percentage error (MAPE) of 4.76% in displacement prediction, with over 70% of samples exhibiting errors below 5%.

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Latest Papers

The More Popular, The Harder to Forget: Adaptive Popularity for LLM Unlearning

Aug 14, 2026

This study addresses the limitation of existing large language model unlearning methods that overlook fact popularity, rendering high-frequency knowledge difficult to remove. We propose AdaPop, a novel approach that models popularity as a learnable parameter by integrating token confidence with external proxy evaluations. Through a bi-ascent controller, AdaPop dynamically adjusts penalty intensity to achieve an adaptive balance between forgetting and retention. Experiments across three model families and two benchmarks demonstrate that AdaPop reduces content leakage under paraphrased queries by approximately fivefold and under adversarial reconstruction by 1.6 times. Furthermore, internal representation analysis confirms superior forgetting separation, effectively resolving the challenge of unlearning high-frequency facts while preserving general model utility.

0 citationsRead paper

Data-Driven Forward and Inverse Modeling of V-Beam Thermal Sensors

Jul 04, 2026

This study addresses the ill-posed inverse design problem of V-beam thermal actuators, where multiple geometric and material configurations can yield the same target displacement at a given temperature. To overcome this non-uniqueness, the authors propose a data-driven two-stage optimization framework. First, a neural network forward model is trained to predict thermo-mechanical responses from geometric and material parameters. This model is then frozen and embedded within a gradient-based inverse optimization loop that simultaneously minimizes structural volume and mechanical stress. The approach effectively circumvents the failure of direct regression in ill-conditioned inverse problems and enables efficient multi-objective geometric optimization. Evaluated on a dataset of 3,000 samples, the forward model achieves a mean absolute percentage error (MAPE) of 4.76% in displacement prediction, with over 70% of samples exhibiting errors below 5%.

0 citationsRead paper