Articles & Guides

VideoRAE explores whether frozen video foundation model representations can become compact, reconstructable, and generation-friendly video latents. The work targets a key limitation of conventional 3D-VAEs: latents optimized mainly for pixel reconstruction.

A new arXiv paper introduces MOJO, a joint training framework that combines masked autoencoding with supervised decoding for spike-tokenizing neural models. The approach aims to make better use of unlabelled neural recordings, especially when behavioural labels are scarce.

A new arXiv paper proposes measuring non-Gaussianity with the squared Wasserstein distance to a standard Gaussian. The resulting OT-ICA algorithm aims to replace common proxy contrasts used in classical ICA.

A new arXiv paper reframes the race toward generative game engines around a classic loop: action, state, and observation. Its central message is that playable worlds need persistent rules and evolving states, not just convincing video prediction.

Files obtained in a hacking incident reportedly show Suno scraping large amounts of music and lyrics from platforms including YouTube Music, Deezer, and Genius. The report sharpens the debate over whether AI music training is fair use or unauthorized extraction.

MetaPerch explores a simple but underused idea: bioacoustic models should learn not only from animal sounds, but also from the metadata attached to those recordings. The work uses signals such as location and time as auxiliary supervision for more robust species identification.

A new arXiv paper tests whether frozen Evo 2 representations contain linearly accessible biosecurity-relevant information. Lightweight probes show strong performance on antimicrobial resistance detection, while virulence and generated-sequence labels remain more limited.

Ai2’s Shippy shows that production agents in high-stakes domains are not just model wrappers. Reliability comes from tool design, sandboxing, explicit limits, and workflow-level evaluation.

IBM Research argues that model routing in agentic systems is less a classification problem than a systems optimization problem. Real-world performance depends on cost, latency, caching, infrastructure, and governance constraints.

The Hindcast paper proposes a time-aware evaluation setup for LLM forecasters, replaying resolved Polymarket questions as if models were standing at an earlier date. Its goal is to separate genuine forecasting from answer leakage through retrieval or training data.

A new arXiv paper introduces Deep Interaction, a human-in-the-loop method that lets users edit faulty reasoning steps in an LLM’s original answer and reuse the corrected chain to steer future output.

A new arXiv paper introduces Earthquaker-AI, a hybrid framework that combines Lego WeDo2 robotics, a retrieval-augmented conversational assistant, and rubric-based assessment for earthquake preparedness education.

A new arXiv paper proposes an AI-accelerated framework for professional upskilling that spans knowledge acquisition, content creation, review, teaching, and assessment. Its main contribution is treating workforce learning as an integrated production-and-learning pipeline rather than a set of isolated AI tools.

A new arXiv paper studies a multi-expert OCR pipeline for historical Manchu documents with sharply different visual styles. The system routes each page to a specialist model using a lightweight page-level image classifier.

A new arXiv paper introduces PAT, a RAG-based system designed to push LLM translation beyond sentence-by-sentence rendering. The study finds that specifications and corpus-informed context can encourage broader reformulation, though the benefits remain inconsistent.

A new arXiv study of 2,361 popular GitHub repositories finds that agentic coding tools are entering open-source workflows, but intensive use remains concentrated in a small number of projects.

A new arXiv paper examines feature selection in renewable power prediction and proposes Cluster-based Sequential Feature Selection. CSFS matches the predictive performance of standard sequential feature selection while cutting computational cost by 21% on average.

A new arXiv paper reframes familiar Transformer feedforward-block choices as mechanisms for preserving gradient rank across depth. Skip connections, normalization placement, and width expansion are interpreted as part of a shared tradeoff among rank collapse, composition, and parameter cost.

A new arXiv paper introduces Lighthouse RL, a reinforcement learning approach that restarts episodes from high-performing circuit configurations found during training. The goal is to reduce wasted exploration in sample-expensive analog circuit sizing.

A new arXiv paper argues that penetration testing for AI-enabled systems should evaluate whether adversaries can induce harmful behavior, not only whether they can compromise resources. The proposed framework reframes testing around operational objectives and behavioral failure.

A new arXiv paper introduces M⁴World, a driving world model that generates surround-view video and synchronized LiDAR while supporting object-level manipulation. The work focuses on controllability, long-horizon stability, and rare-case customization for autonomous-driving simulation.