Humanoids, autonomy, and embodied AI.
Hub content is updated incrementally.
Overview of an LLM framework that automates superconducting qubit control and measurement via schema-less tool generation, plus safety and logging needs.
Guardian turns messy case docs into schema-aligned spatiotemporal states, builds Markov risk surfaces, plans with RL, then validates via LLM QA.
Because citations can be non-deterministic, treat visibility as a sampled distribution and compare it statistically over time.
Guardian proposes a multi-LLM pipeline with a consensus engine for early missing-child searches, emphasizing auditable TEVV operations.
arXiv:2603.09356 discusses dataset condensation for medical data, extending to trees and Cox via DP and zero-order optimization.
As AI-driven R&D loops accelerate, alignment-faking signals (12%) raise operational risk. Lock in TEVV, independent review, and monitoring.
Clinical LLM recommendations can shift with intersecting SDoH (gender, insurance, housing). Test cross-profiles and measure over-refusal before deployment.
Using executable per-instance checkers to provide verifiable rewards for multi-turn tool agents, reducing labeling while surfacing risks.
As prompts shrink, video work shifts from generating to operating: lock identity with references, storyboard panel prompts, set multimodal priority rules, and track rights risk.
A curated link roundup from recently collected official updates and tech news.
Why pathology AI lags after strong benchmarks: external validation, drift/OOD monitoring, workflow fit, and auditable logging.
Explains why token logprobs differ from natural-language confidence, and how to test multi-candidate prompts with seeds and evals.
RAG-Driver grounds driving explanations with retrieved expert demonstrations via RA-ICL, but evaluation still relies on BLEU, METEOR, and CIDEr.
Discusses whether LIM learning-energy lower bounds should be design KPIs or only benchmarks, given ADC/DAC and calibration overheads.
Move beyond context/output limits: evaluate LLM code integration with task decomposition, tool parity, and reproducible build/test rubrics.
RM-R1 proposes reward models that reason before scoring, reporting up to 4.9% gains on public RM benchmarks and highlighting safety evaluation gaps.
Ulysses splits sequences across GPUs and exchanges K/V via all-to-all to reduce long-context attention bottlenecks and track throughput.
Microsoft introduces Copilot Cowork as a research preview, focusing on long-running, multi-step work and human-in-the-loop execution.
Separate time-series gains from LLM backbone ability versus tokenizer/decoder bias using controlled swaps and LLM-free baselines.
Overview of dynamic chunking for Diffusion Transformers, adapting compute by timestep and spatial detail to improve the cost-quality tradeoff.
Overview of PCN: iterative inference, fixed-point convergence (dv≈0), links to backprop equivalence/approximation, and compute bottlenecks.
Summarizes prompt group-aware training that aligns predictions across equivalent prompts, reducing variance and improving average zero-shot Dice.
Review across seven venues (2020–2025) argues consensus labeling can erase sociotechnical signals; proposes rules for distribution labels.
A curated link roundup from recently collected official updates and tech news.