Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv:2610.27800v1 Announce Type: new Abstract: Face presentation attack detection is typically studied under fixed camera or handheld acquisition, whereas smart glass capture introduces a more chall...
arXiv:2610.27874v1 Announce Type: new Abstract: Hyperspectral Image (HSI) reconstruction has made substantial progress with the deep unfolding framework by decomposing the problem into a projection m...
arXiv:2610.27958v1 Announce Type: new Abstract: Hyperspectral images provide a rich representation of the underlying spectrum for each pixel, allowing for a pixel-wise classification/segmentation int...
arXiv:2610.28120v1 Announce Type: new Abstract: Feedforward reconstruction of dynamic driving scenes requires integrating observations across multiple views and time steps into a coherent 3D represen...
arXiv:2610.28191v1 Announce Type: new Abstract: Incorrect exposure degrades both image brightness and recoverable scene detail. Existing sRGB-based correction methods are limited by the non-linear im...
arXiv:2610.28346v1 Announce Type: new Abstract: Autonomous video editing from open-world resources remains a challenging problem in multimedia content creation. Retrieval-based methods are constraine...
arXiv:2610.28630v1 Announce Type: new Abstract: Game engines and coding agents can specify the layout, motion, and camera of a scene, yet turning such coarse structure into realistic video requires a...
arXiv:2605.00675v3 Announce Type: replace Abstract: Medical imaging datasets are often characterized by extreme class imbalances, where rare pathologies are significantly underrepresented compared to...
arXiv:2605.20682v3 Announce Type: replace Abstract: Small defects in unfamiliar product categories can be difficult to distinguish from benign appearance variations in a global view. Additional reaso...
arXiv:2607.25962v2 Announce Type: replace Abstract: Recent generative models can produce images with few obvious visual artifacts, weakening detectors and explanations that rely only on surface appea...
arXiv:2609.32180v2 Announce Type: replace Abstract: Audio-visual segmentation (AVS) aims to segment sounding objects at the pixel level by integrating auditory and visual cues. However, existing meth...
arXiv:2610.27563v1 Announce Type: cross Abstract: Stochastic optimization with high-fidelity nonlinear models is often computationally prohibitive because each objective evaluation requires sampling-...
arXiv:2610.26096v2 Announce Type: replace Abstract: We present PnP-Transformer, a neural pose solver designed as an iterative-free, differentiable alternative to classical PnP-RANSAC algorithms. PnP-...
arXiv:2610.26853v2 Announce Type: replace Abstract: Procedural generation plays a key role in synthetic data creation and game development, offering greater controllability, editability, and diversit...
MAS-Guard is a unified framework designed to safeguard both single- and multi-agent large language model (LLM) systems. It introduces a local Agent Guardrail (A-Guard) layer that uses task-specific experts to evaluate agentic events, and a system-level Control Plane that applies deterministic structural checks and semantic assessments to routes, capabilities, and message content. The framework enforces interventions—allow, reflection, confirmation, and block—across benchmarks, reducing attack success rates by 71.5‑86.3% while maintaining utility on benign tasks.
UniWALM is a unified world-action-language model that jointly learns video, action, and language in a single shared representation, using a mixture-of-transformers backbone with diffusion heads that co-denoise across modalities. By generating language as part of the same pass as action, the model closes a language‑as‑memory loop without an external planner, and introduces Flow Matching with Correction to maintain reliable action streams. Experiments show UniWALM outperforms baselines on compositional pick‑and‑place tasks and memory‑dependent benchmarks, and dramatically improves success on a real‑world Find‑the‑Item task when using generated textual memory.
TeCoP is a training‑free decoder that accelerates diffusion large language model (dLLM) decoding by decoupling prediction readiness from context utility. It uses tentative context pipelining and draft‑and‑verify to reuse stable but uncommitted predictions as provisional cross‑block context, with commitment based on confidence and stability checks. Across four dLLMs on GSM8K, Minerva MATH, and MBPP, TeCoP cuts forward evaluations by up to 87% and boosts throughput up to 7.5‑fold without KV caching and 11.77‑fold with caching, outperforming Fast‑dLLM and DSB while maintaining accuracy within one percentage point.
The paper surveys how code‑mixed (CM) text and speech systems transfer knowledge between CM data and the native languages involved, a process that can go in both directions. It introduces a taxonomy of transfer direction, route, and modality, and finds that most work transfers from native languages to CM, while the reverse is newer and mainly aligns the two languages. The study notes that text and speech methods overlap but rarely cite each other, and that native data often outperforms new architectures on shared speech benchmarks, highlighting open questions about data mixing and evaluation across scripts.
SciDiagBench is a new benchmark designed to evaluate scientific diagram generation in a way that mirrors human peer‑review practices. It provides a dataset of 165 diagrams from 10 domains, systematically classified into 11 distinct types such as graphical abstracts and technical roadmaps. The benchmark also introduces a preference‑guided calibration method that incorporates human preferences into type‑specific evaluation criteria, aiming to improve the reliability of automated assessments.
Navalia is a framework that advances cowork agents by combining verifiable task synthesis with long‑horizon post‑training. It selects supporting evidence from corporate financial reports, builds executable computation chains, and uses independent LLM review and programmatic checks to ensure evidence consistency and answer correctness. The verified tasks are then used for trajectory collection, supervised fine‑tuning, and reinforcement learning, yielding consistent performance gains over the base model across five financial and office‑task benchmarks.