The Archive

The AI buildout shows no signs of slowing. And with hundreds of billions of dollars a year going into data centers and GPUs, compute has become the single biggest cost for anyone building AI products. But for all that spending, there still isn’t a straightforward way to put a price on compute — or for firms to hedge their exposure when the price changes. Silicon Data […]

Anchoring neural and visual representations reduces brain-to-image retrieval latency by decoding from one or few trials instead of 80-trial averages.

Gradient-boosted ensembles enable exact contrastive explanations by treating leaf values as coordinates in R^M, making feature attribution traceable to tree splits.

LLM correction-handling requires operations discipline: versioning, provenance, recurrence monitoring, and stale-rule retirement—applying systems engineering to AI stack governance.

PGFS++ improves molecular property optimization under synthesis constraints via direct reaction template and reactant representation instead of embedding prediction.

MDTIM applies masked diffusion training for time series imputation by separating missing/observed values and predicting original signal instead of noise.

SRGAN applied to EBSD battery material microstructure analysis for throughput improvement.

With a looming IPO, intense competition from Anthropic, and Chinese and open-weight rivals nipping at its heels, OpenAI has plenty of reasons to move fast. Instead, it hit the brakes. On Tuesday, the company said it had slowed the pace of some AI development while it tightened security and safeguards. That included a two-week pause in reinforcement learning training on its "latest models intended for deployment," and an ongoing delay to its "largest planned frontier RL run." The decision is a very public test of an idea AI safety advocates have pushed for for years: that companies should be w...

SIMPLE: task-conditioned multi-view in-context learner for reusable view fusion across downstream tasks.

Meta AI can create content and make suggestions based on what it “sees” on your screen. | Image: Meta Meta is launching a new Mac app dedicated to its AI chatbot. In an announcement on Wednesday, Meta says you can share your window with its AI chatbot, which can provide suggestions, answer questions, or create content based on what's on your screen. Meta AI on the Mac also supports dictation across all apps. The launch comes as Meta makes its AI chatbot more like a productivity-focused assistant to better compete with its AI rivals - many of which already have desktop apps. You can share your...

Open-MOPD benchmark diagnoses capability imbalance in multi-teacher RL distillation on SmolLM3-3B.

DistScan detects backdoors in object detection via prediction distribution shift, generalizes to scene-level attacks.

DA-WAM aligns world model latents with planning decisions for decision-informative autonomous driving prediction.

S-JEPA ablation: tests whether GMM component mapping affects encoder representations beyond probability values.

User study on AI translation readability vs. fidelity: evaluability gap when source shown but quality judgment misaligns.

Learning Random Geometric Graphs via probabilistic metric spaces for generic multivariate dataset representation.

SPK decodes latent priors from object detectors to detect OoD hallucinations in real-time detection.

ReWEIGH calibrates visual-token evidence via ordinal ranks to mitigate hallucinations in vision-language models.

Wasserstein entropic value-at-risk extends robust optimization for agents under uncertainty, enabling hedging against model misspecification beyond entropy balls.

LLM agents can execute training loops but lack strategy-level adaptation; empirical analysis shows post-training strategies remain locked despite experimental feedback.

Diffusion models adapt to intrinsic dimensionality in clustered multimodal data via Bayesian classification on Gaussian mixtures.

Gaussian Splatting plug-in improves VLA robustness to camera viewpoint shifts without retraining, restoring LIBERO performance from 30% to 90%.

Robots need policies that can adapt to their sensors, environments, and tasks while running on onboard computing hardware. World models offer a foundation for... Robots need policies that can adapt to their sensors, environments, and tasks while running on onboard computing hardware. World models offer a foundation for learning physical interactions, but their size can make on-device deployment difficult. This changes with the new NVIDIA Cosmos 3 Edge. Cosmos 3 Edge is a 4B omni-model (with a 2B NVIDIA Nemotron-based reasoner) in the Cosmos 3 family. Source

AI agents are only as effective as the context they receive. Even with capable models and well-documented NVIDIA libraries, agents can spend extra steps finding... AI agents are only as effective as the context they receive. Even with capable models and well-documented NVIDIA libraries, agents can spend extra steps finding the right tools, burn tokens on dead ends, or struggle with specialized tasks. Skills package the instructions, examples, and tool guidance for agents to move faster from intent to solution. To measure whether these skills improve agent… Source

One ad featured a pornographic video with deepfake closely resembling a US politician.

TerraPower's nuclear power plant possesses a strategic advantage over competitors, especially when chasing after data center deals.

Deep Q-Network and multi-agent DRL algorithms benchmark mmWave base station placement on campus topology with max-min fairness objective.

Eureka meta-agent orchestrates long-horizon scientific discovery tasks via dynamic obligation graphs, receding-horizon planning, and cost-benefit-gated evolution.

Quantum machine learning via Bernstein-Vazirani Networks uses quantum interference in problem-adapted bases for supervised vision and representation tasks.

Classifier-free visual counterfactual explanations via Contrastive Analysis disentangle class representations without dependence on classifier biases.