Focused on frontier AI technologies, empowering AI researchers' progress.
The article argues that in fast‑moving AI markets, a firm’s organizational shape—not its product or technology—has become the most durable competitive advantage, illustrating this with examples from OpenAI, Anthropic and Palantir.
The article outlines a practical pipeline for creating agentic factual SFT and mid‑train datasets, covering how to define training goals, filter and classify queries, label processing tags, format trajectory samples, differentiate SFT from mid‑train data, and avoid common pitfalls when generating evidence‑driven AI training data.
The open‑source 35‑billion‑parameter Intern‑S2‑Preview model achieves scientific‑task performance comparable to trillion‑parameter models, thanks to full‑link “general‑specialized” training, reinforced‑learning scaling, and hardware‑aware optimizations, and it outperforms leading closed‑source models on benchmarks such as MolecularIQ and crystal‑structure generation.
The article examines the satirical “Rubbish” journal—an impact‑factor‑zero outlet that accepts failed experiments and quirky research, its rapid rise on social media, the wave of similar “bottom‑journal” imitators, and what this phenomenon reveals about pressure in modern academic publishing.
The article shares a six‑step workflow for leveraging Codex in research tasks—read the codebase first, define long‑term AGENTS.md rules, always plan complex changes, verify before coding, start a new session after each task, and never trust the final output without a detailed audit.
The article reviews CVPR 2026 medical imaging papers, highlighting a shift from pure image recognition toward efficient model adaptation, clinical semantic understanding, and cross‑modal reasoning, with examples ranging from simple AI agents optimizing workflows to multimodal foundation models for CT, ultrasound, spatial transcriptomics, IMU‑video alignment, and dual‑view X‑ray analysis.
The paper reveals a language‑agnostic "semantic bottleneck" layer inside large language models and introduces LASA, a three‑step framework that locates this layer, extracts safety signals with a lightweight interpreter, and injects them via KTO loss, dramatically improving multilingual safety without per‑language data collection.
A leaked OpenAI GPT‑5.6 model (iris‑alpha) promises a 1.5 million‑token context window, a breakthrough "de‑slop" UI generation that produces pixel‑perfect designs, dual standard/Pro variants for advanced reasoning and agent workflows, and a rapid June release that fuels an AI arms race with Anthropic, Google and others.
The paper presents Terminal-World, an automated pipeline that uses Agent Skills to generate diverse terminal‑agent training data, builds over 5,700 environments, and trains models that outperform existing baselines on multiple benchmarks despite using far less data.
The ICLR 2026 paper introduces Next‑ToBE, a training‑objective modification that replaces the one‑hot next‑token label with a soft distribution over a future token window, unlocking latent foresight in LLMs, improving future‑token hit rate, downstream reasoning performance, and reducing training memory and time.
