AI Intelligence Archive

If your workflow is "paste code, ask nicely, copy the answer back, repeat," you're not using an AI...

Three measured ways an agent run reported success while the work never happened — and the smallest check that catches each one.

n8n has introduced n8n Agents, a preview feature that expands the automation platform beyond fixed...

A synthetic experiment for Symptomato shows how an omitted source can hide a conflict, even when the final accepted value is correct.

I've spent the last few months building Galactic Idle, a space-empire incremental game, using Vue 3,...

This is a submission for the Sanity Challenge, Path Two: Vibe-Code Something Strange What I...

My coding agent is a lot like a very eager intern who's been told they'll get a gold star when the...

This is a submission for the Kaggle Benchmarking Challenge What I Benchmarked Most...

Why I Built This I use Claude, Cursor, and ChatGPT daily. I wanted them to control my...

This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content ...

This is a submission for the Sanity Challenge, Path One: Ship an Agent That Queries Real Content ...

Ditch brittle OCR regex pipelines and expensive SaaS fees. Build a deterministic multi-modal invoice extractor in n8n with Gemini Flash.

You ask your AI assistant how to do something. It gives you clean, confident code, with an install...

I've been building CORTEX, an AI agent that runs entirely on my own laptop. It plans a task, runs...

I’ve been experimenting with an AI-Driven Development workflow at Relipa, and one thing became clear:...

Time series anomaly detection (TSAD) is increasingly deployed in streaming settings, where data arrive sequentially and may exhibit non-stationarity. As a result, several works from the recent literature propose streaming anomaly detection…

The scariest security failure isn't the guardrail that's down, or the one that's weak. It's the one that's running, passing every health check, and quietly configured to catch nothing — green by construction. I found one in a benchmark of …

We study additive regression under a potentially non-product random design on $[0,1]^d$, allowing the dimension $d$ to grow with the sample size $n$. We introduce coupled smoothness classes that separately control the regularity of the mar…

Prologue: Like Jev — A Harness for Cheap Brains Source version of CodeSmith: v0.5.0...

Large Audio Language Models (LALMs) rely on effective audio encoders for multi-task performance. We introduce UniAE-MoE, a unified audio encoder designed to model cross-domain audio representations and achieve outstanding downstream unders…

VRAM for LLMs is a bandwidth problem: every token streams the whole model from memory. Bandwidth per tier, the 20x offload cliff, and what fits in 16, 24 or 48 GB.

Wireless split learning (SL) reduces on-device computation by offloading upper layers to a server, yet transmitting high-dimensional intermediate features at each iteration remains a major communication bottleneck. Existing methods select …

A deep classifier is defined not only by the decision it produces, but also by the sequence of transformations through which that decision is formed. Treating this evolution as a dynamical system across layers provides a natural framework …

Trustworthy Legal AI requires systems that can answer legal questions while grounding their responses in authoritative sources. However, existing Vietnamese legal benchmarks provide limited coverage of real-world legal consultations. We in…

Developmental psychology holds that certain priors are given to infants prior to experience rather than induced from data, and that the influence of such priors is suppressed under strong, well-constrained conditions but reasserts itself u…

Mamba-style and hybrid language models compress their past into a fixed-size recurrent state that is rewritten at every generated token. Storing this state in low precision saves memory bandwidth, but every rounding error is fed back into …

Fiber orientation and compartmental microstructure are central to the characterization of white matter tissue in diffusion MRI, yet existing methods either resolve fiber orientations without quantifying microstructure, or quantify microstr…

World Models are appearing as the next major frontier in computer vision. However, their robustness is currently largely unexplored. We identify the phenomenon of hallucination in latent World Models: given a state and an action, the predi…

Deep neural networks tend to rely on simple features that may be spurious and thus fail to generalize. We study this problem in the setting of linear probes, where a (generalized) linear model is fitted on the representations of a (pretrai…

Building a 45k-line app with AI agents: what worked, and what broke “Now that we have AI, why can’t...

In this paper, we consider asymptotic properties of the support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings under a spiked model. The existing theory of the SVM in the HDLSS context relies on the geometric repr…

Fingerprint recognition is a widely deployed biometric, but supervised training requires large labeled enrollment sets. Self-supervised learning (SSL) removes this requirement, and hybrid quantum-classical models have been proposed to enri…

Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet existing frameworks rely on coarse, sequence-level reward signals that lack the fine-grained sup…

Persistent spatial memory enables embodied agents to navigate familiar environments across repeated visits. However, targets may move while unobserved, including during navigation, making remembered locations unreliable by the time an agen…

Score-based variational inference (VI) provides an alternative to Kullback--Leibler (KL)-based VI by minimizing the Fisher divergence between the variational distribution and the target. A prior score-VI approach formulates this optimizati…

Speaker diarization systems based on speaker embeddings and neural diarization exploit complementary forms of speaker information, but their intermediate representations are not directly compatible. We introduce Training-Free Affinity Fusi…

Deep research tasks require agents to navigate large knowledge spaces, synthesize evidence across many sources, and adapt their plans as findings emerge. Directed acyclic graph (DAG)-based multi-agent systems suit this setting because they…

Does memory-dependent control need nonlinear recurrent dynamics? We study simulated air-hockey defence under temporary loss of puck tracking. A DreamerV3 teacher outperforms a memoryless policy under tracking loss, while resetting the teac…

Textual skills enable large language model (LLM) based agents to accumulate reusable procedural knowledge without updating model parameters. Yet existing skill evolution remains largely confined to the text space: an optimizer must diagnos…

Before transformers, before word embeddings, before anyone said "attention is all you need",...