· ai
[Paper] Exploring Fine-Tuning for Tabular Foundation Models
Tabular Foundation Models (TFMs) have recently shown strong in-context learning capabilities on structured data, achieving zero-shot performance comparable to t...
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- · ai
[Paper] Identifying Models Behind Text-to-Image Leaderboards
Text-to-image (T2I) models are increasingly popular, producing a large share of AI-generated images online. To compare model quality, voting-based leaderboards ...
- · ai
[Paper] PersonalAlign: Hierarchical Implicit Intent Alignment for Personalized GUI Agent with Long-Term User-Centric Records
While GUI agents have shown strong performance under explicit and completion instructions, real-world deployment requires aligning with users' more complex impl...
- · ai
[Paper] LLM for Large-Scale Optimization Model Auto-Formulation: A Lightweight Few-Shot Learning Approach
Large-scale optimization is a key backbone of modern business decision-making. However, building these models is often labor-intensive and time-consuming. We ad...
- · ai
[Paper] From Prompt to Protocol: Fast Charging Batteries with Large Language Models
Efficiently optimizing battery charging protocols is challenging because each evaluation is slow, costly, and non-differentiable. Many existing approaches addre...
- · ai
Topic Modeling Techniques for 2026: Seeded Modeling, LLM Integration, and Data Summaries
Seeded topic modeling, integration with LLMs, and training on summarized data are the fresh parts of the NLP toolkit. The post Topic Modeling Techniques for 202...
- · ai
[Paper] High-Performance Serverless Computing: A Systematic Literature Review on Serverless for HPC, AI, and Big Data
The widespread deployment of large-scale, compute-intensive applications such as high-performance computing, artificial intelligence, and big data is leading to...
- · ai
LLMs are a 400-year-long confidence trick
In 1623 the German Wilhelm Schickard produced the first known designs for a mechanical calculator. Twenty years later Blaise Pascal produced a machine of an imp...
- · ai
[Paper] Cluster Workload Allocation: Semantic Soft Affinity Using Natural Language Processing
Cluster workload allocation often requires complex configurations, creating a usability gap. This paper introduces a semantic, intent-driven scheduling paradigm...
- · ai
How does a machine actually learn from data?
You’re probably wondering why you shouldn’t jump straight into scikit‑learn before you truly understand how a model learns. The key is to build a solid mental m...
- · ai
[Paper] LatencyPrism: Online Non-intrusive Latency Sculpting for SLO-Guaranteed LLM Inference
LLM inference latency critically determines user experience and operational costs, directly impacting throughput under SLO constraints. Even brief latency spike...
- · ai
[Paper] DP-FEDSOFIM: Differentially Private Federated Stochastic Optimization using Regularized Fisher Information Matrix
Differentially private federated learning (DP-FL) suffers from slow convergence under tight privacy budgets due to the overwhelming noise introduced to preserve...