Leaderboards, eval harnesses and ablations — the contested business of deciding which model is actually better.
arXiv:2610.17131v2 Announce Type: cross Abstract: Dynamic spatial audio reasoning requires a model to associate changing acoustic observations with the correct source and recover its spatial state at...
arXiv:2610.17354v1 Announce Type: cross Abstract: Current evaluation protocols for medical image segmentation remain largely anchored in sample-wise comparisons, e.g., Dice and Hausdorff distance, wh...
arXiv:2610.17309v1 Announce Type: cross Abstract: We introduce StartLux-Decision, decision models from 0.8B to 27B parameters that combine efficient single-pass prediction with adaptive, confidence-g...
arXiv:2610.19388v1 Announce Type: new Abstract: Massively multilingual speech modeling has made remarkable progress in recent years, yet existing training resources and benchmarks still underrepresen...
arXiv:2610.19996v1 Announce Type: new Abstract: Large language models often produce fluent but factually incorrect output. Gray-box detectors identify such hallucinations from the token-probability s...
arXiv:2610.14541v1 Announce Type: cross Abstract: Emerging strong Vision-Language Models (VLMs) have advanced navigation foundation models. However, these models still have unsatisfactory performance...
The paper introduces an exhaustive dataset of univariate canonical skeletons for neural symbolic regression, generated deterministically via a constrained context-free grammar. These skeletons serve as generic structural representations that eliminate redundant expressions and provide uniform operator distribution, thereby avoiding simplicity bias. The authors also present a sampling strategy for clean numerical data and benchmark 150 SR problems across three tiers, demonstrating challenges for current state‑of‑the‑art methods.
In-Context Batch Black-box Optimization via Flow Matching introduces FMTR, an amortized method that learns a continuous, history-conditioned proposal distribution for variable-size batch sampling in expensive black-box optimization settings. FMTR is trained offline using conditional flow matching on Thompson-sampling argmaxes refined through a horizon-dependent Langevin pushforward map, enabling adaptive exploration-exploitation over the optimization horizon. At test time, FMTR generates batches without task-specific surrogate refitting or numerical acquisition optimization, achieving strong performance on synthetic and real-world benchmarks while significantly reducing batch selection computational cost.
arXiv:2610.20174v1 Announce Type: new Abstract: Reinforcement Learning on Mixed-domain preference data (Mix-RL) has become the mainstream approach for training general-purpose Generative Reward Model...
arXiv:2605. 12461v2 Announce Type: replace-cross Abstract: We propose an algorithm to sample from composite log-concave distributions over $\mathbb{R}^d$, i.
arXiv:2610.15029v1 Announce Type: cross Abstract: Automated disease classification from patient-reported symptoms requires balancing the speed of lightweight discriminative models against the context...
arXiv:2609.20827v2 Announce Type: replace Abstract: Effective discharge education is personalized: a clinician adapts the discharge plan to each patient's literacy, recall, and personality. Before la...
arXiv:2610.20327v1 Announce Type: new Abstract: India's 22 scheduled languages are spoken by over a billion people in conversations that mix languages, often within one turn, overlap freely, and take...
NeuroDETR is a novel multiscale discrete spatiotemporal tokenization framework designed for asynchronous EEG event detection. It combines structured tokenization, event-aware feature refinement, and a sparse attention module called FOCAL to capture both brief and sustained neural responses while preserving spatial hierarchy across EEG channels. Experiments on eight diverse EEG datasets show that NeuroDETR achieves state‑of‑the‑art performance in jointly classifying events and estimating their onsets and offsets.
arXiv:2610.16859v2 Announce Type: replace Abstract: For repository-grounded coding agents, a functionally correct patch is not necessarily an acceptable one: developers also constrain how the task mu...
arXiv:2610.19564v1 Announce Type: new Abstract: A spatial answer depends on the reference frame specified in language and its supporting visual evidence. Item accuracy alone obscures whether correctn...
arXiv:2610.17387v1 Announce Type: cross Abstract: Day-ahead (DA) electricity price forecasting is usually posed per market, with a separate model fitted to each grid's own history. This caps accuracy...
arXiv:2610.17424v1 Announce Type: cross Abstract: Respecting a-priori known symmetries of underlying data is a key principle in designing efficient neural architectures. Convolutional neural networks...
arXiv:2610.15482v1 Announce Type: cross Abstract: Large Language Models accelerate software development but introduce severe security vulnerabilities. This systematic review investigates security ris...
arXiv:2610.15555v1 Announce Type: cross Abstract: Training-free story visualization without reference images must preserve recurring entities across frames while scaling to long narratives. Existing...