AI Intelligence Archive

Does eliciting five weighted estimates within a single response change how a language model responds to information? Across anchoring, hindsight, base-rate, conjunction, and status-quo/default tasks, we compare direct numerical answers wit…

Deploying generative world models for end-to-end closed-loop evaluation and policy learning requires precise scenario control, consistent observations, and sustained generation quality during extended interactions. This report presents \em…

A growing body of work reports benefits from orthogonality constraints in fine-tuning large language models (LLMs). These results raise an open question: do orthogonality constraints also help LLM pre-training? We conduct a systematic empi…

Finding the passage that answers a question is the first step of most practical systems built on documents, and for Tajik we found no openly described model for it. We present Saidzoda-Tajik-E5-Large, a Tajik text embedder built from multi…

Code generation has become cheap. Ask a model for a function and you get one that looks right in...

GnomeMan4201 · Study: composed-inference-ordering-v1.1 · Collected October 31, 2026 UTC What I ran:...

Google has confirmed a new Gemini Personal Intelligence flow that brings together Google Maps...

Google is rolling out Skills, a reusable-instructions feature for the Gemini app and Google...

Block Blast looks like a puzzle game. A decompile of the app suggests it's closer to a per-player ad...

Google has integrated its Antigravity coding environment into the Gemini app for macOS. The addition...

Google is expanding Gemini on several fronts at once: the global release of Gemini 4 Argon, a wider...

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine. ...

AI-referred visits to U.S. retail sites generated 83% more revenue per visit than non-AI traffic in...

Reddit has provided a notable first-party example of the pressure that changes in Google Search can...

Canada has formally joined the European High-Performance Computing Joint Undertaking, known as...

OpenAI has launched Intelligence Age, the blog of its new Strategic Futures team. The platform is...

AI search creates a measurement problem that conventional visibility reporting does not fully...

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine ...

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the...

Discord is preparing to make content from Discoverable Servers visible beyond its platform. From...

Shopify has introduced a Developer Preview of Promoted Placements for AI shopping experiences built...

The Binance theft totals from TRM and Elliptic, Cursor's notice on a modified Claude Code 2.1.313, Northflank's incident timeline and the Mistral Large 4 license clause.

TL;DR: I finished the Future AWS Agent Engineer nanodegree with three graded projects, each a...

Ahrefs' latest analysis suggests that AI Overviews have become a dominant feature of branded Google...

Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation tasks, yet they still struggle to effectively leverage language-based chain-of-thought reasoning for image generation. As a resu…

Decision models such as Jev have emerged as a highly promising paradigm for LLM agent systems, providing lightweight and reliable categorical reasoning for rapid System One execution. However, existing generation-oriented inference engines…

Agentic workflows are increasingly shifting towards long-horizon tasks, such as open-ended scientific work, where models benefit from long context windows. However, at ultra-long context (above 1M tokens), full attention is prohibitively e…

Planning with world models enables test-time adaptation for embodied agents, offering superior generalization over direct policy learning. However, long-horizon tasks remain hindered by cumulative errors and search complexity. Existing hie…

Recent work shows that a frozen robot policy performs far better inside an agent harness, where control code decides when to query the policy, consult vision tools, or command the arm directly. Yet today these harnesses are built by hand a…

In many practical deployments of Differentially Private Stochastic Gradient Descent (DP-SGD), the server processes data in batches. Randomly sampling these batches allows for better privacy bounds via privacy amplification results. Most sa…

Distribution Matching Distillation (DMD) trains a few-step student using the difference between a teacher's score and an online critic's estimate of the student distribution's score. We investigate what the critic needs, in capacity and in…

Instance-dependent limits for bandit regret, sequential tests, best-arm identification and Markov decision processes are proved by one chain. It runs from a divergence decomposition through data processing to a statistic, a testing step th…

Decision models target structured tasks by directly predicting choices or scores, avoiding autoregressive decoding latency. However, transferring a generative LLM's ranking knowledge to a decision model remains challenging: the teacher exp…

Objective: To distinguish the transferable predictive value of physiological trajectories from that of observation patterns in ICU mortality prediction, and to assess whether measurement timing affects performance independently of observat…

Understanding how gradient descent learns features remains a central challenge in neural network theory. We study this question in a minimal multi-index setting that already exhibits rich multi-phase gradient dynamics: a well-specified two…

In the random allocation sampling scheme each user's data is used in $k$ steps chosen randomly and uniformly from a sequence of $t$ steps. This scheme is known to improve on Poisson sampling in private optimization and in communication-eff…

In real-world deployments, large language model (LLM) systems are increasingly evolving from single-model invocation toward heterogeneous deployments in which different models are dynamically invoked for inference. When the same context is…

The quadratic scaling of attention is a fundamental bottleneck limiting the long-context capabilities of large language models (LLMs). A variety of approaches have attempted to circumvent this, but they either fail to match attention's exp…

Atmospheric fronts are associated with much of the high-impact weather in the mid-latitudes, yet their identification and classification remain largely manual and subjective. Existing deep learning approaches rely primarily on gridded rean…

Distributionally robust optimization (DRO) minimizes worst-case risk under distributional uncertainty, but its decisions can be suboptimal in terms of their excess risk, or regret. We study this gap for multivariate least-squares regressio…