AI Intelligence Archive

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…

As language-model compute continues to scale, high-quality training data is becoming an increasingly important bottleneck. Conventional next-token prediction supervises what follows a context but leaves the intermediate reasoning behind th…

I wanted to find a free LLM API for small tools and experiments. There are many lists online, but...

Collecting natural-language referring expressions along with region annotations, such as masks or boxes, is a major bottleneck in visual grounding (VG), as annotators must write descriptions that distinguish target regions from visually si…

Synthetic data is increasingly used to train large language models (LLMs), yet its security implications remain poorly understood. Prior work on subliminal learning suggests that models can inherit behavioral traits from seemingly unrelate…

Organizing unstructured feedback text into hierarchical taxonomy is a fundamental challenge in NLP, particularly in domains where feedback arrives at massive scale in varied forms such as reviews, transcripts, and surveys. Existing approac…

Model Context Protocol servers make tools easier to discover and integrate, but discovery is only the...

Large language models are increasingly capable in general, but their utility can remain modest in niche or understudied areas. One approach to address this limitation is to specialise existing models through additional training on target-d…

With the development of large language models (LLMs), generative retrieval is becoming increasingly important in e-commerce scenarios. Current mainstream approaches typically use a two-stage training strategy: first train a product embeddi…

Robust estimation is a core computer vision task frequently tackled using sample consensus. However, traditional methods suffer from inefficient sampling as they struggle to identify effective minimum sets before hypothesis evaluation. To …

I rewrote the system prompt fourteen times. Version 3 was "be thorough." Version 7 was a 900-word...

Valuation Guide There are only two ways anyone prices software. What it earns, or what it cost to...

Short answer: choose ChatGPT Plus if you mainly use ChatGPT itself, consider ChatGPT Pro only when...

Short answer: choose ChatGPT Plus if you mainly use ChatGPT itself, consider ChatGPT Pro only when...

For the last couple of months I've been handing an AI agent (Claude Code) real DevOps work — not toy...

For the last couple of months I've been handing an AI agent (Claude Code) real DevOps work — not toy...

How my agent actually finds answers — and what happens when it doesn't Part 5 findings of an...

How my agent actually finds answers — and what happens when it doesn't Part 5 findings of an...

The README for the thing I'm building puts the limits above the features. Not as a humility move....

The README for the thing I'm building puts the limits above the features. Not as a humility move....