Latent Space Control for Trustworthy LLM Behavior
From steering vectors to model calibrators, this paper frames latent-space intervention as a path to better LLM control and trust.
From steering vectors to model calibrators, this paper frames latent-space intervention as a path to better LLM control and trust.
A look at the main security risks in mobile on-device AI, focusing on attack surfaces across apps, models, and OS.
Examines distributed vs. concentrated public AI compute strategies and what they mean for sovereign AI capacity.
A look at high-risk AI oversight through the humans-as-handlers approach, focusing on intervention, accountability, and trust.
Examines whether AI safety remains consistent in long conversations and highlights gaps in session-level evaluation.
How Ising-based thermodynamic computing may scale training, with focus on sampling costs and hardware limits.
Examines how stale rollouts and learning rates affect stability in asynchronous RLHF, with practical signals like staleness and ESS.
Examines whether AI eliminates jobs or redesigns tasks, and why this shift matters for hiring, reskilling, and productivity.
A curated link roundup from recently collected official updates and tech news.
EM may depend on optimizers and batch settings, making finetuning recipes part of safety evaluation, not just data.
How a dual-agent LLM pipeline separates proposing tighter relaxations from verification in automated research.
Model value now depends on performance, quotas, throughput, and pricing, not benchmark scores alone.
Examines Google PAT's paper-checking results and limits, and where AI should fit in academic review workflows.
A curated link roundup from recently collected official updates and tech news.
Anthropic's Claude Science emphasizes integrating tools, data, compute, and review into one scientific workflow.
Rhythm game AI works best when API and local inference are split by function, balancing latency, limits, cost, and memory.
A compact fast-weight recurrent model reported lower pooled RMSE than a larger LSTM using only 22.4% of the parameters.
Office humanoid robots should be judged by learning pipelines, generalization, and public validation, not demos alone.
A curated link roundup from recently collected official updates and tech news.
Examines how class imbalance affects score learning in diffusion models and why frequency-guided noise schedules matter.
Compare cloud token-based LLM pricing with local deployment to assess cost, control, latency, and break-even conditions.
CoIn links 2D inpainting and 3DGS to reduce reliance on precise multiview masks in 3D scene editing workflows.
Why top satellite SR models on synthetic data may not lead on real cross-sensor imagery, and how to evaluate the gap.
How model distillation expands from efficiency to API cost, competitive training, and control over data and compute.