AI Intelligence Archive

Large language models (LLMs) increasingly interact with external environments and accumulate substantial behavioral experience, yet existing agent benchmarks largely evaluate them as fixed policies. It therefore remains unclear whether an …

Accurate daily predictions of cold hardiness in woody plants are critical in regions where freezing temperatures can damage dormant buds and reduce seasonal yield. Existing biophysical, hybrid, and deep learning models have shown high pred…

Class-incremental learning (CIL) requires a model to incrementally learn tasks that contain new classes without accessing earlier training data while preserving the ability to recognize all seen classes. Recently, pretrained-model-based ap…

The fastest way to make an AI-agent test flaky is to assert the final sentence. You expect: I'll...

A speaker encoder turns a few seconds of speech into a vector, and the distance between two vectors...

AI model routing looks deceptively simple: Read the user's prompt. Ask an LLM which generation...

How to use dart-sdk-skills to empower your AI coding agent to seamlessly migrate Flutter projects to standalone material_ui and cupertino_ui packages.

Executive Summary After years building mission-critical systems for Citibank and...

In the previous article, we learned how to secure our APIs using JWT Authentication and protect...

The best enterprise MCP gateway is not the product with the longest feature list. It is the one whose...

Compare Meta Muse Spark 1.2 and xAI Grok 4.6: SWE-bench coding benchmarks, 256K context handling, tool calling schemas, and developer workflows.

Constraining a model's output with a JSON schema feels like the moment the LLM stops being a text...

I went looking for a memory MCP server the way anyone does. Sort by stars, check the registry, read...

I use browser-based AI tools like ChatGPT and Claude a lot while developing software. The models are...

Two years ago, I watched someone I love struggle through grief alone. Not because they didn't have...

I built a telemetry-aware music engine, then tried to disprove what I felt What driving...

Every beginner is quietly asking the same thing in 2026: why learn to code when the AI model can...

I’ve been building tooler, an open-source runtime written in Rust designed to give developers,...

The rivalry between Sam Altman and Elon Musk has burned through lawsuits, poached talent, and years...

Most case studies you read online are vague. They say things like "we improved performance" and "we...

Large Language Models are powerful. But there is one fundamental limitation: An LLM doesn't...

The design decisions behind the OpenClaw Control Plane's new Decision Runtime — bounded authority, attributable work, and a Decision Queue that's a projection, not a list.

RAG Evaluation: How to Know if Your RAG System Actually Works You built a RAG chatbot. It...

Retour d’experience sur l’automatisation de déploiements avec un agent LLM et sur les gardes-fous...

Three months ago, almost every AI coding tool had the same deal: pay a flat monthly fee, use it until...

This paper presents Murano, an open source framework for designing, running, and reproducing mechanistic interpretability studies of large language models, intended for researchers across disciplines. These studies often combine loading, r…

Large language model-based multi-agent systems are evolving from fixed interaction topologies toward dynamically orchestrated Agent Swarms. However, existing benchmarks are still largely based on single-agent or general-purpose agent tasks…

Production failed on Friday. My final transcript looked clean. The agent answered, cited sources, and...

I'd been checking my blog's search traffic by hand -- log into Search Console, click the property,...

Antarctic sea ice concentration (SIC) forecasting is an important yet challenging task due to the coexistence of complex spatial structure, long-range temporal dependencies, and strong seasonal variability. Conventional convolution-based m…

Multimodal Large Language Models (MLLMs) are increasingly deployed in multi-image scenarios requiring complex reasoning across visual contexts. However, current MLLMs remain fundamentally limited by object hallucination-generating plausibl…

Embedding methods have become popular due to their scalability on link prediction and/or triple classification tasks on Knowledge Graphs (KGs). Embedding models are trained relying on both positive and negative samples of triples. However,…

LLM agents need to sustain goal-consistent reasoning across long multi-turn interactions under strict resource constraints. However, as the multi-turn context accumulates, it can destabilize the underlying LLM's internal representation of …

Removing a specific individual's information from multimodal large language models (MLLMs) is often needed after deployment, but existing methods rely on a retain set, which is hardest to obtain at that point, and rebuilding it recreates t…

Language models can answer from precomputed memory, a model's saved reading of a body of material, reused across requests instead of read again at each. This paper maps where that practice preserves correctness and the conditions under whi…

Reliable uncertainty estimation is a crucial requirement for deploying large language models (LLMs) and vision-language models (VLMs) in safety-critical settings, especially when the model parameters are not accessible (black-box). We prop…

While self-supervised approaches to reinforcement learning have achieved strong results by learning representations of states and actions, a key open question is the time scale over which actions should be modeled. Departing from the stand…

Optimizing absorption, distribution, metabolism, and excretion (ADME) is an important part of small molecule drug discovery. Many machine learning models have been built to predict ADME properties to facilitate this optimization process, b…

Timely post-disaster building damage assessment from satellite imagery is a critical engineering decision support task, yet it remains constrained by class imbalance, ambiguous intermediate damage states, and limited cross-event transferab…

Reinforcement learning (RL), particularly RL with Verifiable Rewards (RLVR), has recently emerged as a central paradigm for enhancing large language models' (LLMs) reasoning abilities, demonstrating remarkable effectiveness across reasonin…