arXiv:2509.24296v2 Announce Type: replace
Abstract: The rapid advancement of Diffusion Large Language Models (dLLMs) introduces unprecedented vulnerabilities that are fundamentally distinct from Autoregressive LLMs, stemming from their iterative and p…
arXiv:2603.23997v2 Announce Type: replace
Abstract: Recovering high-fidelity 3D hand geometry from images is a critical task in computer vision, holding significant value for domains such as robotics, animation and VR/AR. Crucially, scalable applicati…
arXiv:2603.25681v1 Announce Type: new
Abstract: As large language models (LLMs) continue to advance, improving them solely through human supervision is becoming increasingly costly and limited in scalability. As models approach human-level capabilitie…
arXiv:2510.11233v2 Announce Type: replace
Abstract: Depression is a pressing global public health issue, yet publicly available Chinese-language resources for depression risk detection remain scarce and largely focus on binary classification. To addre…
arXiv:2603.24278v2 Announce Type: replace
Abstract: The dominant paradigm for high-fidelity 3D generation relies on a VAE-Diffusion pipeline, where the VAE’s reconstruction capability sets a firm upper bound on generation quality. A fundamental challe…
arXiv:2511.21740v4 Announce Type: replace
Abstract: Speech brain-computer interfaces (BCIs) aim to restore communication for people with paralysis by translating neural activity into text. Most systems use cascaded frameworks that decode phonemes befo…
arXiv:2601.00216v2 Announce Type: replace
Abstract: Current medical retrieval-augmented generation (RAG) approaches overlook evidence-based medicine (EBM) principles, leading to two key gaps: (1) the lack of PICO alignment between queries and retrieve…
arXiv:2603.25706v1 Announce Type: new
Abstract: Recent unified models have made unprecedented progress in both understanding and generation. However, while most of them accept multi-modal inputs, they typically produce only single-modality outputs. Th…
arXiv:2603.24709v1 Announce Type: cross
Abstract: Multi-step tool orchestration, where LLMs must invoke multiple dependent APIs in the correct order while propagating intermediate outputs, remains challenging. State-of-the-art models frequently fail o…
arXiv:2603.24755v1 Announce Type: cross
Abstract: Software development is iterative, yet agentic coding benchmarks overwhelmingly evaluate single-shot solutions against complete specifications. Code can pass the test suite but become progressively har…