Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision

A recent study has introduced Concept-Based Diversity (CBD), a highly efficient metric for image inputs that utilizes Vision-Language Models (VLMs) to enhance the performance of Deep Neural Networks (DNNs) through improved input selection. This approach addresses the computational intensity and scalability issues associated with traditional diversity-based selection methods.

A novel framework named ENACHI has been proposed for hierarchical online scheduling in energy-efficient split inference with Deep Neural Networks (DNNs), addressing the inefficiencies in current scheduling methods that fail to optimize both task-level decisions and packet-level dynamics. This framework integrates a two-tier Lyapunov-based approach and progressive transmission techniques to enhance adaptivity and resource utilization.

A recent study published on arXiv investigates the generalization capabilities of AI-generated text detectors, revealing that while these detectors perform well on in-domain benchmarks, they often fail to generalize across various generation conditions, such as unseen prompts and different model families. The research employs a comprehensive benchmark involving multiple prompting strategies and large language models to analyze performance variance through linguistic features.

A recent study titled 'Calibration Is Not Enough: Evaluating Confidence Estimation Under Language Variations' highlights the limitations of current confidence estimation methods for large language models (LLMs), emphasizing the need for evaluations that account for language variations and semantic differences. The research proposes a new framework that assesses confidence quality based on robustness, stability, and sensitivity to variations in prompts and answers.

A recent study titled 'BenchOverflow' investigates a failure mode in large language models (LLMs) where plain-text prompts lead to excessive outputs, termed Overflow. This phenomenon can increase operational costs, latency, and degrade performance across users, particularly in high-demand environments.

A recent study published on arXiv compares the effectiveness of neural models and large language models (LLMs) in predicting nationality and region from personal names. The research evaluates six neural models and six LLM prompting strategies across three levels of granularity, revealing that LLMs consistently outperform traditional models in accuracy.

A recent study published on arXiv investigates the phenomenon of negative constraints in large language models, revealing that such instructions often lead to unexpected failures. The research introduces the concept of semantic pressure, which quantitatively measures the likelihood of generating forbidden tokens, and establishes a logistic relationship between violation probability and semantic pressure.

The introduction of What If TSF (WIT) marks a significant advancement in time series forecasting by establishing a benchmark for scenario-guided multimodal forecasting. This new framework aims to evaluate the ability of models to condition forecasts on contextual text, particularly future scenarios, moving beyond traditional unimodal approaches that rely solely on historical data.