Last week, a GitHub notification arrived while I was asleep: a bug had been reproduced in staging, documented with screenshots, and assigned to the right engineer. A bot I set up four days earlier did the work overnight. That is the gap Grok Bot is designed to close. Instead of suggesting what to do...
OpenBMB Releases MiniCPM5-2B: A 2.52B Dense Model Averaging 53.9 Across 34 Benchmarks and Built to Run On Device
OpenBMB has released MiniCPM5-2B, a dense causal language model with 2,516,756,480 parameters and a native 131,072 token context. It averages 53.9 across the 34 benchmarks in its model card, ahead of Qwen3.5-4B at 51.1, with its clearest leads in tool use, coding agents and long-context retrieval. P...
Axis Robotics Releases AXIS: A Browser-Based Data Engine With 207 Robot Manipulation Tasks and 50,129 Trajectories
Robot datasets have grown far slower than the models trained on them, mostly because collection stays locked to lab hardware. AXIS moves demonstration collection into a web browser and pushes everything expensive to backend GPUs. The result is 207 tasks and 50,129 verified Franka trajectories, and c...
AI Quantum Intelligence - Pic of the week (2026-09-04)
An evocative oil painting capturing solitude and reflection—an elderly woman sits at the edge of an unmade bed, gazing through a rain-streaked window. The interplay of warm candlelight and cool daylight conveys the quiet tension between memory and melancholy, inviting viewers to contemplate time, lo...
IFM Releases K2 Horizon: Six Apache 2.0 Models From 0.9B to 375B
Most open model launches release one checkpoint and a benchmark table. The Institute of Foundation Models (IFM) released something wider last week. IFM is the frontier lab launched by MBZUAI in May 2025. K2 Horizon is a fleet of six models: 375B-A23B, 36B-A4B, 32B, 7B, 3.7B and 0.9B. Shipping alongs...
Spectral-Target Physical Latent Structuring for JEPA-Style World Models
arXiv:2609.04264v1 Announce Type: new
Abstract: Latent world models have become increasingly popular as a method to predict and plan in latent space rather than pixel space. Recent architectures, such as LeWorldModel (LeWM), jointly train the encoder and predictor using regularization techniques li...
ProToMEx: Rapid, Interpretable Explanations via Structured Representations
arXiv:2609.04265v1 Announce Type: new
Abstract: Existing post-hoc explainers for machine learning classifiers primarily focus on feature attribution, assigning importance scores to individual features. While valuable, this approach struggles to articulate the complex, combinatorial patterns that of...
A Data Fusion Framework for Grounding Aerospace Surrogate Model via Experimental Wind-Tunnel Observations
arXiv:2609.04267v1 Announce Type: new
Abstract: Aerodynamic surrogate models trained on high-fidelity CFD data reproduce numerical predictions of both scalar outputs and entire fields accurately, yet their predictive fidelity is limited by systematic discrepancies between CFD and experimental obser...
Quantum-Assisted Memory-Efficient Training for Parameter-Intensive Wi-Fi-Based Human Activity Recognition
arXiv:2609.04271v1 Announce Type: new
Abstract: Wi-Fi-based human activity recognition (HAR) has become an important part of integrated sensing and communications, paving the way for a range of context-aware services. However, most existing Wi-Fi-based HAR systems rely on deep learning (DL) models ...
Evaluating Large Language Models for Forced Outage Risk Prediction: Benefits and Comparison to Machine Learning
arXiv:2609.04272v1 Announce Type: new
Abstract: This study examines the ability of large language models (LLMs) to predict the risk of weather-related forced outages in the distribution grid in a zero-shot framework, without labeled training data. The problem is formulated as a binary severity clas...
arXiv:2609.04239v1 Announce Type: new
Abstract: This technical report presents EXAONE Forecast for Finance (EXAONE Finance), a financial time series (TS) foundation model (TSFM) tailored to financial forecasting. Recent TSFMs achieve strong zero-shot performance through large-scale pretraining. How...
Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation
arXiv:2609.04298v1 Announce Type: new
Abstract: Evaluating agents on the growing number of agentic benchmarks is challenging because they often require complex environments and agent integrations. We introduce Harbor Adapters, a unified evaluation infrastructure for agentic benchmarks. Our work mak...
arXiv:2609.04304v1 Announce Type: new
Abstract: We present Iris-mini and Iris-pro, two search agents trained at the 35B-A3B and 397B-A17B scales, together with the data pipeline and training recipe behind them. Tasks are reverse-constructed from the hyperlink structure of a web corpus: we author mu...
Meta FAIR Introduces AI Research Preference Models (RPMs): Ranking ML Experiments Before Spending GPU Hours
AI research agents can propose far more experiments than they can afford to run. Meta FAIR, Oxford and UCL introduce AI Research Preference Models — frozen LLM judges that rank 15 unexecuted candidates and execute only one. On AIRS-Bench, the average normalized score rises from 0.684 to 0.729, and t...
Text Watermarking in Python: Catch Whoever Copies Your Writing
AI companies quietly watermark billions of words a day. Here’s how to apply the same three families of techniques to your own writing—and what real experiments reveal about which watermarks survive copy-paste, editing, and paraphrasing.
The post Text Watermarking in Python: Catch Whoever Copies Your...
Inside OpenAI, coding agents are reshaping AI research. Explore early data on agent usage, experiment velocity, task complexity, and research acceleration.
UC Berkeley Researchers Release CUA-Lite, an Open Platform Unifying Sandboxes, Data, Evaluation and RL for Computer-Use Agents
Training and benchmarking a computer-use agent needs four things — agents, environments, traces, and a framework to evaluate and train them — and all four ship in incompatible formats today. CUA-Lite, from a UC Berkeley led team, puts them behind one action space and one data schema, and replaces OS...
Perplexity Details Its GPU Embedding Stack: How Ivy, Tulip and ROSE Serve pplx-embed
Retrieval quality in an AI search product is bounded by two things: how good the embedding model is, and how cheaply you can run it across an index. This week, Perplexity Engineering team published Fast Embeddings on GPUs, an under-the-hood account of the second — the serving infrastructure behind p...
GitHub Introduces Project HydraFusion: Runtime Multi-Model Orchestration That Builds a Workflow Per Coding Task in Copilot CLI
We look at Project HydraFusion, GitHub's research preview that treats workflow selection as an optimization problem rather than a model picker. We break down the three execution patterns it routes between — Single, Cascade with a quality gate, and Critique with a read-only cross-family reviewer.
The...