AI Intelligence Archive

AI coding tools can dramatically speed up development, but only when you give them enough context and...

Empirical evaluation is central to recommender-systems (RecSys) research, but turning experimental designs into executable code remains a manual and error-prone task. We present AutoRecLab, a Python-based autonomous RecSys lab that automat…

Nearly 90% of drugs entering clinical development ultimately fail, despite billions of dollars in investment. Pharmaceutical companies therefore rely on clinical development planning (CDP) and probability of technical and regulatory succes…

Large language models (LLMs) have achieved state-of-the-art performance across a wide range of tasks, motivating two important aspects of deployment: inference efficiency and output provenance, which can be tackled by speculative sampling …

LLMs are increasingly used in social simulations for socially interactive agents and robots, offering more flexibility than rule-based systems. However, even though they mimic human behaviour very well, there is a persistent alienness to t…

A North Star Metric is one number that represents value delivered. That is the whole...

On-device inference is booming, but the momentum is almost all in language models. Diffusion pipelines are memory hungry, latency-sensitive, and require orchestrating an embedder, a transformer, a decoder, and often further postprocessing …

I've been using AI coding agents heavily, and I kept running into the same problem: Useful...

Long transcripts are costly inputs for downstream NLP systems and often contain irrelevant context. We study query-conditioned topic localization: predicting the sentence span in a transcript that best addresses a topic-title query. To imp…

Frontier language models now produce professional deliverables that expert graders judge to match human work on a substantial share of economically valuable tasks, yet most enterprise GenAI initiatives fail to show a measurable business ef…

Clustering high-dimensional data is a fundamental task in unsupervised machine learning with applications to a variety of domains. In the centralized data scenario, this task is commonly solved using deep clustering methods that utilize de…

Blind and low-vision users often face challenges when locating and physically acquiring objects in unfamiliar indoor environments. Existing vision-language-model-based assistants can provide semantic descriptions but may introduce latency,…

Speculative decoding accelerates LLM inference by drafting multiple tokens in parallel, with tree-based methods further improving efficiency through hierarchical structures. Dynamic-tree methods such as EAGLE-3 perform well under greedy de…

This paper presents a novel framework integrating Bayesian physiological modeling with optimal control strategies to achieve uncertainty-aware, personalized hemodynamic regulation during fluid resuscitation. An uncertainty-aware variationa…

Adaptive optimization methods such as AdaGrad and Adam are widely used in modern neural-network training, but their adaptive scaling is primarily designed for vector-valued parameters and does not explicitly exploit matrix structure. Recen…

Multimodal survival models can combine complementary prognostic information from whole-slide images and genomic profiles, but effective fusion remains challenging amid external cohort shift and computational complexity. To address these ch…

Background: Just-in-time adaptive interventions (JITAIs) can use behavioral data to adapt support to changing contexts, but many rely on predefined rules and manual configuration. Objective: We developed a proof-of-concept sleep JITAI usin…

We study how far a simple statistical pipeline can go on univariate time series anomaly detection under a strict selection protocol. The method extracts a small pool of statistics over sliding windows, scores each window with a transductiv…

Defenses against jailbreak attacks on Large Language Models (LLMs) operate at different pipeline stages, such as input modification or output guard, but it remains unclear which defenses to deploy at each stage and how to combine them. Pri…

As deep learning models continue to advance, knowledge tracing models have achieved higher accuracy. However, these gains come at the cost of reduced interpretability, which is crucial for practitioners in educational settings to adopt new…

Most multilingual dysarthria-severity systems either train on a single aetiology-language pair or pool heterogeneous aetiologies into one label space. We test that pooling assumption with four matched HuBERT-base contrastive embedding mode…

A pretrained robot foundation policy may execute most of a long-horizon task yet repeatedly fail at a few critical subtasks. Collecting additional full-task demonstrations for supervised fine-tuning (SFT) requires operators to repeat behav…

Quick disclosure up front, because it matters for how much weight to put on anything below: this was...

Ten parts ago, ClinicLive worked but looked like the template it was born from. This series made the...

Introduction You build a dispatch command centre with ToolJet MCP for a regional broadband...

A US automotive SaaS product had an AI assistant that missed intent and took up to 30 seconds to answer over 120 GB of documents. What we rebuilt, wha

A medical model's benchmark score does not establish that the same conclusion holds under a different evaluation. This study tests whether claims about model ranking, score reliability and screening performance survive changes in cohort, p…

Lovable is nice. You describe a site, an agent writes it, you click publish. It also costs $25–50 a...

Métricas de efetividade de uso de IA — Spark Monitor Resumo da pesquisa sobre como medir...

How to create any sort of pipelines using DSCI and LLM

If you're rolling out Claude Code across a company, at some point you’ll eventually hit the same...

In 2024, AI coding agents (Claude, Cursor, Copilot, etc.) became capable enough to do real work....

How to embed your app in Claude, ChatGPT, Cursor, Copilot, and Gemini via MCP. Strategy, embedding depth, auth, build-vs-buy, and discovery for 2026.

MCP authentication: OAuth 2.1 + PKCE, Dynamic Client Registration, scope design per-resource per-verb per-sensitivity, audit logs, and SOC 2 mapping.

MCP client comparison: Claude, ChatGPT, Cursor, Microsoft Copilot, Gemini, Perplexity. Distribution, auth, audience, and discovery side-by-side.

MCP server cost in 2026: $100K–$300K read-only, $300K–$700K with actions, $1M+ agent-resident, plus $5K–$25K/month to maintain. Why using one is

This is a condensed version. The full write-up — the module decision table, the customized...

Every week, someone tells me our company shouldn't exist. The line is always some version of this:...

Your agent's permissions drift silently. A PR adds an MCP server with write scope, or allow: Bash(*)...

I did not need Jev to beat Claude or Kimi on a benchmark. I needed to know whether I could trust it...