AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
We have 5 of 10 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
It is observed that interaction-induced feature changes correlate with approximation error, while low-frequency interpolation errors are phase-sensitive and show more predictable phase progression, which motivates CAST, a reconstruction-coupled inference framework.
Feed-forward 3D vision models such as VGGT have achieved remarkable progress, unifying camera estimation and dense scene reconstruction in a single pass. However, their quadratic global attention makes long image sequences expensive, while existing sparse methods may favor highly attended yet value-redundant regions. T...
JustQuant is proposed, a simple yet effective framework that moves the complexity of low-bit quantization from deployment-time operators into the training process and introduces Theseus QAD, a quantization-aware distillation method that progressively applies multi-level supervision, analogous to the gradual replacement...
A causal interpretability framework is introduced that reveals that the tokens encoding semantics at the input need not be those that maintain them during generation, but also provides a causal view of internal mechanisms in diffusion transformers.
Visual token pruning reduces the inference cost of multimodal large language models, but a fixed token ratio is poorly matched to text-rich inputs. In OCR-centric tasks, decisive evidence can be a small number, label, or field whose relevance is specified by the question; indiscriminate pruning can erase that evidence...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.