Tag: #v1120 results
Enhances LLM-guided evolution by evolving programs that propose, improve, and perturb solutions iteratively. Achieves new SOTA on hexagon packing and autocorrelation inequality benchmarks. Reduces LLM cognitive load via structured parameterization.
Hierarchical Zero-Order optimization decomposes network depth for efficient ZO in DNNs. Reduces query complexity from O(ML^2) to O(ML log L). Matches backpropagation accuracy on CIFAR-10 and ImageNet.
Presents experimental framework comparing human-led, AI-led, and hybrid vibe coding groups. Humans deliver superior iterative instructions, preventing AI-led performance collapse. Hybrids thrive with human direction and AI evaluation.
Practical framework shifts organizations to agentic AI via domain-driven tasks and human-in-loop orchestration. Addresses challenges like workflow ownership and scaling. Emphasizes small AI-augmented teams with business alignment.
Global Temporal Retriever (GTR) is a plug-and-play module extending MTSF models' context via global pattern retrieval. Uses adaptive embeddings, dynamic alignment, and 2D convolution fusion. SOTA results on six datasets with low overhead; code on GitHub.
GRU-Mem introduces text-controlled gates to MemAgent for efficient long-context reasoning, preventing memory explosion and unnecessary computation. Update and exit gates manage recurrent memory loops via RL rewards. Achieves up to 400% faster inference on reasoning tasks.
Introduces an anatomy-preserving method using VAE and latent diffusion to generate multi-class brain segmentation masks from NCCT data. It learns anatomical latents from masks only, generating realistic samples with optional lesion control. Avoids artifacts seen in pixel-space models.
Surveys reveal divided stakeholder perceptions of GenAI in IT/EE disciplines at University of Oulu. Proposes conceptual framework with high-level requirements for responsible integration. Ensures EU AI Act compliance and addresses privacy, integrity concerns.
GameDevBench offers 132 multimodal game development tasks from tutorials. Agents struggle, with top solving 54.5%; tasks demand code and asset handling. Simple image/video feedback improves performance up to 47.7%.
Analyzes parameterized complexity of Bayesian Network Structure Learning using superstructure. Proves fixed-parameter tractability with feedback edge set parameterization. Extends to treewidth with additive representations and polytree learning.