Modern applications process massive data volumes that overwhelm the storage and retrieval capabilities of memory systems, making memory the primary performance and energy-efficiency bottleneck of computing systems. Although many microarchitectural techniques attempt to hide or tolerate long memory access latency, rapidly growing data footprints continue to outpace technology scaling, requiring more effective solutions. This dissertation shows that modern processors observe large amounts of application and system data during execution, yet many microarchitectural mechanisms make decisions largely independent of […]

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arXiv:2601.20909v1 Announce Type: new Abstract: Domain-Driven Design (DDD) is a key framework for developing customer-oriented software, focusing on the precise modeling of an application’s domain. Traditionally, metamodels that describe these domains are created manually by system designers, forming the basis for iterative software development. This paper explores the partial automation of metamodel generation using generative AI, particularly for producing domain-specific JSON objects. By training a model on real-world DDD project data, we demonstrate that generative AI can produce […]

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Debugging an AI agent that runs for dozens of steps: reading files, calling APIs, writing code, and revising its own output, is not like debugging a regular function. There is no single stack trace to read. Instead, developers are left staring at hundreds of lines of raw JSON, trying to reconstruct what the model was actually thinking and doing at each step. OpenAI team is taking a direct swing at that problem with the release of Euphony, a […]

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arXiv:2601.19900v1 Announce Type: new Abstract: Bit truncation has demonstrated great potential to enable run-time quality-power adaptive data storage, thereby optimizing the power/energy efficiency of approximate applications and supporting their deployment in edge environments. However, existing bit-truncation memories require custom designs for a specific application. In this paper, we present a novel bit-truncation memory with full adaptation flexibility, which can truncate any number of data bits at run time to meet different quality and power trade-off requirements for various […]

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Production query plans without production data Radim Marek describes the new pg_restore_relation_stats() and pg_restore_attribute_stats() functions that were introduced in PostgreSQL 18 in September 2025. The PostgreSQL query planner makes use of internal statistics to help it decide how to best execute a query. These statistics often differ between production data and development environments, which means the query plans used in production may not be replicable in development. PostgreSQL’s new features now let you copy those statistics down to […]

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arXiv:2602.11235v1 Announce Type: new Abstract: Industrial recommendation systems typically involve multiple scenarios, yet existing cross-domain (CDR) and multi-scenario (MSR) methods often require prohibitive resources and strict input alignment, limiting their extensibility. We propose MTFM (Meituan Foundation Model for Recommendation), a transformer-based framework that addresses these challenges. Instead of pre-aligning inputs, MTFM transforms cross-domain data into heterogeneous tokens, capturing multi-scenario knowledge in an alignment-free manner. To enhance efficiency, we first introduce a multi-scenario user-level sample aggregation that significantly enhances […]

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Deep clustering (DC) is often quoted to have a key advantage over $k$-means clustering. Yet, this advantage is often demonstrated using image datasets only, and it is unclear whether it addresses the fundamental limitations of $k$-means clustering. Deep Embedded Clustering (DEC) learns a latent representation via an autoencoder and performs clustering based on a $k$-means-like procedure, while the optimization is conducted in an end-to-end manner. This paper investigates whether the deep-learned representation has enabled DEC to overcome the […]

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