Paint-Anything: Unified Any-Color Control for Image Generation and Editing
arXiv:2609.20816v2 Announce Type: replace-cross Abstract: Professional design requires any-color control: the ability to specify an object’s target color with any…
RBS-Attention: Radius-Bounded Sparse Prefill for Long-Context Large Language Models
arXiv:2609.20971v1 Announce Type: new Abstract: Long-context large language model inference is increasingly limited by prefill, where dense self-attention processes the…
PACE: Precise AI Cinematic Expression
arXiv:2609.19853v2 Announce Type: replace-cross Abstract: Between a screenplay and a film sits a planning problem that is spatial first: who…
TERMon: Detecting Persistent Behavioral Threats in Edge AI via Hardware-Native Ternary Runtime Monitor
arXiv:2609.21713v1 Announce Type: cross Abstract: Edge AI accelerators are increasingly deployed in safety-critical environments, where model outputs may control physical…
CIPL: A Channel-Aware Framework for Recoverable Privacy Leakage in LLM Agents
arXiv:2609.21686v1 Announce Type: cross Abstract: Privacy leakage in LLM agents is commonly evaluated within individual components such as memory, retrieval,…
Large Language Model Agents for Evidence Based Genetic Disease Severity Classification
arXiv:2609.19569v2 Announce Type: replace-cross Abstract: Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening,…
Regularized Emphatic Temporal-Difference Learning: Stability under Constant Stepsizes
arXiv:2609.19170v1 Announce Type: new Abstract: Emphatic temporal-difference learning (ETD) stabilizes the expected off-policy TD update and changes its projection geometry,…
Dreaming the Sound of Contact: Leveraging Video and Audio Generation for Zero-Shot Force-Aware Manipulation and Data Generation
arXiv:2609.19137v2 Announce Type: replace-cross Abstract: Video generation models have advanced rapidly and can now synthesize plausible videos of robot manipulation…
Cross-Modal Attention Acts as a Frequency Filter: Why Verbose Prompts Improve Robustness in Vision-Language Models
arXiv:2609.20139v1 Announce Type: cross Abstract: Vision-language models (VLMs) are fragile under image corruption. We find that the wording of the…