Using input-to-state stability, zeroth-order optimization achieves first-order convergence rates without extra dimension dependence when perturbations are small.
NoRA evaluates visual first-person normative reasoning by requiring models to generate actions with fact-reason-action support graphs, revealing current VLMs struggle to bind correct justifications to actions.
LensDesigner is a self-improving autonomous agent that uses retrieval, simulation, and curriculum learning to design optical lenses, significantly outperforming baselines on 120 diverse tasks.
Using summed last-k encoder layers and combining RAE with REPA, RAEv2 achieves state-of-the-art gFID of 1.06 in 80 epochs with 10x faster convergence and free guidance.
FinMTM introduces a bilingual multi-turn multimodal benchmark with 11,133 financial visual QA pairs to evaluate vision-language models on reasoning and agent tasks, revealing significant limitations in fine-grained perception and complex workflows.
NuMuon adds a nuclear-norm constraint to Muon updates, boosting weight compressibility and post-compression quality in billion-parameter LLMs while preserving convergence.
AsyncMesh enables fully asynchronous data and pipeline parallelism via weight look-ahead and sparse averaging to reduce communication overhead while matching synchronous training performance.
Look-Before-Move separates observation specification from motion execution for narrative-grounded camera planning, improving subject perception, intent consistency, and trajectory quality over baselines.
An asynchronous two-circuit system with spectral correction enables compressed LLM adaptation over decentralized GPUs, yielding up to 40× speedups with dense-level accuracy.