CACM improves language-based drug discovery agents by 36.4% via protocol auditing, a grounded diagnostician, and compressed static/dynamic/corrective memory channels that localize failures and bias corrections.
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From Local Explanations to Global Understanding with Explainable AI for Trees
12 Pith papers cite this work. Polarity classification is still indexing.
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Using a corpus of 5542 fault-injected traces from 38 DL programs, the study finds a 0.19 balanced accuracy gap in fault diagnosis between within-program and cross-program evaluation caused by program-specific feature structures.
R3LM trains LLMs via two-stage reasoning-then-regression on a new dataset CRE-ReasonBench with mechanistic traces, achieving SOTA enhancer activity prediction across three cell types with interpretable outputs.
Teachers' views on AI benefits and risks vary widely across 55 countries, but LLMs compress these differences, overestimate both sides, and show little improvement from country prompting or better reasoning.
Pact is a choreographic language extended with game-theoretic operations that maps every protocol to a formal game for reasoning about agent decisions and solving for decision policies.
This paper reconstructs Toegye Yi Hwang's philosophy into a five-stage EEFS architecture with design principles, scenario classifications, and an evaluation instrument for ethical emotion regulation in agentic AI.
SAL is a spike-timing-based local learning rule that aligns feedback weights to forward weights in spiking networks by exploiting noise and Hebbian/anti-Hebbian plasticity to recover the true gradient.
Workshops with over 100 creative writers produced metaphors and four themes for language model governance that favor consent-driven, smaller open models encoding community values.
Insider action research in an AI startup identifies three patterns of how practitioners view regulatory requirements and proposes internal expert collaboration as a way to turn external governance rules into shared, practical ownership.
The UPDP pipeline filters privacy terms and generates de-identified radiology images that preserve diagnostic pathology information, enabling models with competitive disease detection accuracy but reduced identity leakage and improved cross-hospital performance.
ICA and VEIL enable privacy-preserving supervised ML by producing structurally non-invertible encodings aligned with downstream tasks while maintaining predictive utility.
The paper develops a transparent data-driven fault detection system for manufacturing that integrates supervised ML classification, SHAP explanations, and operator-focused visualizations, reporting 95.9% accuracy on univariate crimping time series data.
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Evaluation-Strategy Gap in Fault Diagnosis of Deep Learning Programs
Using a corpus of 5542 fault-injected traces from 38 DL programs, the study finds a 0.19 balanced accuracy gap in fault diagnosis between within-program and cross-program evaluation caused by program-specific feature structures.