AI Intelligence Archive

What I Built Your Prince Is Not Real (YPINR) — a system that catches scams during the interaction,...

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

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

Editor's Note: Originally published on the g factor engineering blog. All benchmarks and telemetry...

Hello, I'm Rijul, and I'm building LiveReview — a blast-radius aware AI code review built for your...

Learn how to shift from syntax validation to intent-level testing to prevent LLMs from generating code that passes tests but violates business logic. Includes practical patterns and examples.

Modern video games provide a measurable testbed for AI models, combining abilities of visual understanding, instruction decomposition, goal planning, and precise action control over multiple temporal horizons. Existing datasets and benchma…

Multi-turn tool-use failures can hinge on a single model call, yet reward variation alone does not reveal which call would benefit from training. When rewards depend on later interactions, their variation can reflect downstream randomness …

Video world models enable interactive exploration of dynamic environments, yet struggle to respect prior observations over long horizons and across viewpoints. We present WorldCrafter, a video world model that learns a camera-queryable imp…

We present onPanda, an interactive tool for efficiently annotating LLM alignment data and agent trajectories. onPanda adopts token-level correction as its core interaction: while reading a model response, the annotator locates the first in…

On-device large language models (`LLMs'), e.g. running on mobile phones, are ripe for improvement via personalization. The limited compute resources of mobile devices impose limits on model scale and thus model quality, making any realizab…

Dexterous manipulation depends on contact dynamics that are often only partially observable from vision. Recent World-Action Models (WAMs) couple predictive video world modeling with action generation, but remain largely vision-centric and…

Agent harnesses, the external systems that mediate model-environment interaction, can substantially improve agent performance, but their gains remain tied to the harness at deployment. Because the best harness varies across domains, instan…

An LLM agent's capability is largely magnified by its harness, namely the prompts, control flow, tooling, memory, and context management surrounding the frozen backbone model. Recent methods increasingly automate this process by iterativel…

Agents today often take real-world actions that depend on long-term memory and context recall over time. However, most current memory benchmarks are built for a conversational question-answer format, where the question itself signals that …

As agents are deployed with increased autonomy, even extremely rare events along their stochastic output trajectories can occur and prove catastrophic. Safe deployment therefore does not depend on whether these events can occur, but on how…

LLM agents are increasingly deployed in collaborative settings, yet long-term interaction may give rise to undesirable coordination. We study the emergence of collusion in a long-horizon multi-agent environment: two agents repeatedly compl…

Scientific workflows often require choosing among known relations before a deterministic calculation can proceed. Whether observations share a culture, treatment or reference standard can change the scientific meaning of the resulting coun…

Visual instructions for physical tasks are typically authored in one context and followed in another, requiring users to translate demonstrated tools, materials, and spatial relationships into their own environment. We introduce Generative…

Pharmaceutical process characterization is central to Quality by Design because it defines how variations in process parameters affect the ability to meet product quality specifications, thereby supporting proven acceptable ranges and robu…

Neural operators evaluate parametric partial differential equations cheaply but degrade sharply outside their training distribution. Physics-informed neural networks avoid dependence on labeled data, yet their optimization can be basin-fra…

Let me be blunt: I've spent the last three years watching teams build multi-agent systems and then...

A model generalizes outside its training distribution only when it computes a representation structurally equivalent to the generating mechanism, not an approximation fitted to it. Such equivalence is necessary for exactness in and out of …

Despite the success of neural models in natural language processing, their black-box nature limits interpretability and conceals the linguistic phenomena underlying their predictions. We present SLITE, an explainable hybrid model for Recog…

In this paper, we study nonasymptotic $L^p$ error bounds for interval length and conditional coverage in split conformalized quantile regression (CQR). Our bounds rely on local regularity conditions and accuracy guarantees for the estimate…

Personal AI agents make recommendations and take actions on people's behalf in high-stakes economic contexts, e.g., buying a flight, choosing health insurance, or selecting a graduate program. The agent is given access to the user's person…

Scientific weak signals are early, low-visibility research directions that later become central to mature scientific topics, yet existing resources such as trend tracking, citation forecasting, and foresight reports rarely provide validate…

An agent evaluation fails in CI but passes locally. Before blaming the model, ask whether both runs...

This is part 6 and the last. Parts 0 through 5 walked the diagram in badge order: the whole reference...

Connecting one MCP server to one agent is a five minute job. Connecting fifteen of them across four...

What happens when enterprise requirements - human approval gates, audit trails, structured output -...

An agent I dispatched exited with status zero, did nothing at all to the code, and wrote a report...

Last week we wrote about the classification problem hiding in your LLM bill, and about how to read a...

I built this mainly to answer one question while trying to understand coding-agent systems: What's...

TL;DR My autonomous coding agent kept shipping code against library APIs that didn't...

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

For us, modular code is simply easier because of our limited working memory and reading speed. If you...

"Done. All 48 tests pass and the auth refactor is complete." I have read that sentence hundreds of...

Principal Component Analysis (PCA) minimises aggregate reconstruction error, which can inadvertently represent majority subgroups with substantially higher fidelity than minority subgroups. Fairness-aware extensions of PCA correct this dis…

We introduce the Endless Exam, a benchmark for measuring mathematical progress from today's models toward artificial superintelligence through fourteen parameterised construction families. Each submitted object is checked automatically for…