MultiClin introduces a clinical ASR benchmark that evaluates models' robustness to multiscript variability. It shows that multiscript-aware evaluation outperforms conventional single-reference methods, and script unification yields the best ASR performance, while inconsistent script mappings increase orthographic uncertainty.
ChLogic evaluates how well large language models maintain logical reasoning when English logical structures are expressed in Chinese. It reveals a persistent English-Chinese performance gap, with back-translation improving results on general items but harming performance on difficult problems. The benchmark highlights the impact of surface realization, translation artifacts, and model-specific behaviors on multilingual reasoning.
The Informath project demonstrates symbolic informalization to convert formal mathematical proofs into fluent, precise natural language. It uses Dedukti as a hub connecting proof systems like Agda, Lean, and Rocq, with Grammatical Framework ensuring linguistic correctness across multiple languages.
ContextRL introduces an indirect auxiliary objective to improve long-horizon reasoning and multimodal performance in LLMs. It rewards models for selecting the context that supports a query-answer pair, using contrastive context data from coding agent trajectories and image-based visual questions. ContextRL achieves +2.2% and +1.8% gains over standard methods on long-horizon and visual QA benchmarks, with gains attributed to the selection objective, not data augmentation.
The Informath project demonstrates symbolic informalization to convert formal mathematics into fluent, precise natural language. It uses Dedukti as a proof system hub and Grammatical Framework for linguistic correctness across multiple languages, enabling human-readable outputs from AI-generated proofs.
BinTrack is a fully open-source spatial question answering agent that uses binary search over a robot's trajectory to locate answers. It achieves up to 22.8% higher accuracy than other open-source methods and matches closed-source model performance on the most challenging global category of the SpaceLocQA benchmark. The system also offers over 1.5x faster inference and introduces GangnamLoop, a real-world outdoor benchmark collected with a quadruped robot.
CrossMaps is a real-time, confidence-aware semantic mapping pipeline that uses RGB-D data to create language-queryable maps. It integrates multi-scale CLIP embeddings with a dual-memory architecture—Short-Term and Long-Term Memory—to aggregate visual observations and promote coherent, confident cells as persistent semantic landmarks. The system enables natural language queries to guide rover navigation via semantic heatmaps.
FusionRS introduces the first large-scale RGB-infrared-text dataset for remote sensing vision-language modeling. It aligns RGB and infrared images with IR-aware captions, enabling dual-modal vision-language foundation models. Experiments show improved RGB-IR alignment, retrieval, and captioning, with ablation studies confirming the critical role of modality-specific textual supervision.
CrossMaps is a real-time, confidence-aware semantic mapping pipeline that uses RGB-D data to create language-queryable maps. It integrates multi-scale CLIP embeddings with a dual-memory architecture—Short-Term and Long-Term Memory—to aggregate visual observations and promote coherent, confident cells as persistent semantic landmarks. The system enables natural language queries to guide rover navigation via semantic heatmaps.
A new multi-center benchmark enables abdominal disease diagnosis and report generation from non-contrast CT by synthesizing contrast-enhanced findings. The dataset includes paired NCCT-CECT studies and reports from two centers, showing NCCT achieves average multi-organ AUCs of 69.1% internally and 63.1% externally. The benchmark and code are publicly released to support research into safer, contrast-free abdominal imaging workflows.
A new method called filtered conformal ellipsoids provides prediction sets for multivariate time series by using a frozen state-space filter to generate predictive means and covariances, then applying split-conformal calibration to Mahalanobis scores. The approach achieves coverage under dependence through contraction in an observable predictive-law quotient, with theoretical bounds derived under Gaussian-projection and observability conditions, and shows sharper ellipsoids on graph-native traffic benchmarks compared to static and non-filter baselines.
This survey presents a mathematical framework for analyzing geometric data, integrating differential geometry, statistics, and machine learning. It outlines a unified pipeline for shape representation, geodesic metrics, statistical analysis, and geometry-aware learning, enabling the study of shape variability and structural trajectories across populations and time. Applications span biology, medicine, anthropology, and computer vision, highlighting challenges in handling nonlinear and unaligned geometric variation.