AI Intelligence Archive

Language generation in the limit asks for valid unseen elements from every exhaustive positive presentation of an unknown infinite language. We characterize this task for arbitrary families over a countable universe. Generation is possible…

Foundation vision-language models (VLMs) exhibit broad intelligence about the world, yet translating this intelligence into robot control remains challenging. We present Show-Harness, an Embodied Harness that enables VLMs to "play" robots …

The first time an n8n AI workflow fails, it often does not fail cleanly. It returns a 200 status...

We determine the optimal sample complexity of low-rank quantum state tomography when each measurement may act jointly on at most $t$ samples. For sufficiently small $\varepsilon$, estimating an unknown state on $\mathbb{C}^d$ of rank at mo…

The dangerous AI workflow is not the one that says, “I don’t know.” It is the one that gives a...

An n8n workflow can receive a webhook, call an LLM, query a database, enrich a CRM record, store a...

Credit default prediction is a tabular classification problem in which modest gains in F1 translate directly into reduced financial exposure. We ask whether Instantaneous Quantum Polynomial-time (IQP) circuits can produce features that imp…

Open an n8n workflow with 40 nodes and two thoughts usually appear at the same time. First: This is...

Perhaps you've heard the term Loop Engineering: instead of solving a problem by hand, you build a...

An agentic RAG system can do something a simple retrieval pipeline cannot: notice missing evidence,...

In real-time colonoscopy, ground-truth annotations are unavailable at inference, so polyp segmentation models can fail silently. We propose Referee-Based Quality Estimation (RBQE), a reference-free framework measuring agreement between a p…

Enterprises deploy systems, not checkpoints. Usable capability depends jointly on weights, serving route, precision, output contract, and harness, yet all 18 audited benchmarks score advertised model identifiers. We treat this as measureme…

Most RAG failures are not model failures. The model did not forget how to read. The prompt is not...

This feature article provides an overview of the theoretical foundations for coVariance neural networks (VNNs), i.e., graph neural networks (GNNs) operating on covariance matrices as graphs. Covariance matrices are ubiquitous across domain…

The first time a RAG system gets a large knowledge base, it often does not fail loudly. It gets...

We construct counterexamples to Rockafellar's sum conjecture in which two maximally monotone operators satisfy the interior-domain condition but their sum is not maximally monotone. We give one counterexample on $c_0$ and another on $\ell^…

Your RAG system can retrieve the “right” document and still give the wrong answer. The document may...

Your RAG system did not fail because the model hallucinated. It failed because the only “facts” the...

More Electric Aircraft require fast and reliable monitoring of high-frequency electrical networks, yet most power quality disturbance and fault diagnosis methods are developed for conventional 50 or 60 Hz grids. This work presents a hardwa…

You check the logs. The vector search returned the exact policy document the user asked about. The...

An AI agent that works can complete a task. An AI agent you can trust can do something harder: it...

Joint-Embedding Predictive Architecture (JEPA) world models learn a compact latent representation of the world that supports prediction and planning, but their capability to learn physics and generate physically realistic dynamics remains …

Most agent failures are not model failures. They are loop failures. The model may be smart enough....

Real-world GUI usage frequently involves workflows that span multiple devices and platforms, requiring the transfer of intermediate results, maintenance of shared state, and coordination across heterogeneous environments. However, existing…

Reasoning language models have made substantial advances on a variety of complex tasks, yet their capabilities remain overwhelmingly English-centric: models primarily reason in English regardless of the language they are prompted in. This …

While Large Language Models (LLMs) have demonstrated impressive capabilities, they often struggle with extremely long contexts due to fixed context limits. To address this, sequential approaches like MemAgent extend the effective context b…

Large Language Models (LLMs) can memorize and reproduce sensitive, copyrighted, or otherwise undesirable training content, creating privacy, safety, and regulatory concerns. Machine unlearning offers a practical alternative to full retrain…

Almost every RAG evaluation metric on offer needs a language model to produce it. Faithfulness,...

Self-supervised speech foundation models are now used in a wide array of downstream applications, including traditional speech recognition and as the basis for tokens in speech-aware language models. Attempts to understand their usefulness…

This study develops a deep reinforcement learning framework for training Unmanned Aerial Vehicle (UAV) agents to navigate and monitor simulated wildfire environments. Results show that agents learn increasingly stable and effective behavio…

Algorithmic stability refers to the property of an algorithm being insensitive to perturbations of the input data, where the type of perturbation may vary depending on the setting. In this work, we develop a general framework to quantify t…

Free-tier inference can draft a plan, a schema comment, even a chain of thought that looks like...

Two posts ago msgboard.dev was a toy. One post ago it was a prompt-injection honeypot. This post is...

METR found spoofed tool calls in 7% of agent transcripts from the Hugging Face incident. A transcript monitor reads them as clean. Diff against a...

Simultaneous assessment of medical imaging and patient records is often required in clinical diagnosis. However, standard machine learning algorithms cannot analyze these data types together. Meanwhile, compliance with HIPAA and GDPR can c…

On a Tuesday incident call, a backend engineer pasted an agent transcript and declared the webhook...

For a while now I have used LLMs to automate parts of my work. One of those parts is debugging hard...

Time-series foundation models are evaluated almost exclusively on public archives that predate them, so a strong score cannot be separated from having seen the test set during pretraining. The obvious remedy is a hold-out that postdates th…

Video understanding has rapidly evolved toward video large language models (VideoLLMs): systems that couple video representations with pretrained large language models and condition generation on a textual prompt. Their strong performance …

The banking system now depends on a small set of shared artificial intelligence vendors for fraud screening, credit decisioning, anti-money-laundering triage, customer analytics, and internal decision support. This paper studies how a comp…