Multimodal A

Passive long-video models 'watch it all,' processing frames uniformly so cost grows with duration regardless of query difficulty. This work treats perception as reasoning, with native active perception that selectively attends to relevant frames for efficient omni-modal understanding.

Post-training of reasoning models often combines supervised distillation with reinforcement learning from verifiable rewards, but distillation relies on costly chain-of-thought annotations. This work proposes rubric-conditioned self-distillation to rethink reward supervision while cutting annotation cost.

Existing multi-speaker dialogue systems bind speakers to utterances through structured supervision such as per-turn tags, multi-stream transcriptions, or learnable speaker embeddings. This work generates multi-speaker audio scenes by drawing on in-the-wild reference priors for more natural synthesis.

Embodied Vision-Language-Action (VLA) models are typically obtained by fine-tuning powerful pretrained VLMs on robotics data, yet how much commonsense and factual knowledge they retain is unclear. This work measures that retention, revealing how much fine-tuning erodes prior world knowledge.

Delirium is a common, serious ICU complication linked to higher morbidity, longer stays, and greater costs, yet early detection is hard. This work uses pervasive ambient sensing information from the ICU to stratify patients' delirium risk, supporting earlier intervention.

Text-rich images often hold privacy-sensitive, transactional, or decision-relevant information. As multimodal generators synthesize realistic text and layouts, this work builds a multi-domain benchmark for detecting AI-generated text-rich images from GPT-Image-2, assessing detector reliability.

Existing 3D scene understanding in VLMs relies on complex, model-specific geometry encoders or large training budgets for spatial reasoning. OneCanvas instead uses panoramic reprojection, letting VLMs reason about 3D scenes efficiently without dedicated geometry encoders or heavy training.

Despite the wide adoption and success of Vision Transformers, understanding of their dimensional and representational geometry remains limited. The Transformer Geometry Observatory (TGO-I) studies ViTs through spectral geometry, observing and analyzing the structure of their representation spaces.

Autonomous UAV operations on ships need reliable vision-based relative pose estimation, yet at-sea validation is costly, weather-dependent, and risky. This paper presents hardware- and vision-in-the-loop validation of deep monocular pose estimation for autonomous maritime UAV flight.

Accurate survival prediction is essential for personalized treatment in head and neck cancer but is challenging given heterogeneous, high-dimensional multimodal clinical data. ChronoSurv is a clinical pathway-guided graph framework that integrates multimodal data to improve survival analysis.

On-policy self-distillation trains a model on its own rollouts, using a frozen copy to give dense token-level targets conditioned on a reference. This work decouples perception from reasoning—seeing before reasoning—to make multimodal on-policy self-distillation resilient to shortcut learning.

LLMs are increasingly used as judges for scalable evaluation, yet LLM-as-a-Judge systems show systematic biases decoupled from semantic quality, notably verbosity bias. This work uses positive-unlabeled learning to quantify and audit LLM evaluation, helping detect and correct such biases.

Forecast errors in high-resolution numerical weather prediction such as HRRR often stem from unresolved planetary boundary layer processes, convection, and terrain-induced circulations. This work uses a hybrid LSTM–Vision Transformer architecture to predict HRRR forecast errors from vertically structured features.

Existing multimodal deception detection relies on end-to-end black boxes that offer no transparent reasoning. ThinkDeception is a progressive reinforcement learning framework that explicitly captures subtle cross-modal cues and produces interpretable reasoning trajectories for deception detection.

Automated assessment in software engineering education has advanced, but giving quality feedback on architecture deliverables remains hard. CAPRA is a multi-agent LLM system that scales detailed feedback on software architecture deliverables.

Modern vision-language models struggle with strategic reasoning. RTSGameBench uses real-time strategy games to benchmark VLMs on planning and situational judgment, probing their strategic reasoning abilities.

Test-time scaling via sequential revision has become a powerful paradigm. REVES proposes revision- and verification-augmented training that strengthens a model ability to revise and verify its own outputs, making extra test-time compute more effective.

Multimodal emotion-cause pair extraction requires reliable pairing of emotions and their causes. This work learns robust pair confidence, yielding emotion-cause extraction that is more resilient to noise and ambiguity.

Large instruction-following models are powerful but costly to deploy, especially in finance. This work distills capabilities using synthetic data to build lightweight models that understand financial language efficiently.

Multimodal information extraction spans many tasks but suffers from scarce data in low-resource settings. SAMA proposes semantic anchor-aligned augmentation to unify and improve multimodal information extraction under low-resource conditions.

NVIDIA introduced NVIDIA XR AI, a framework for developers to build AI agents for AR glasses and wearable XR devices. It targets the gap between ready hardware and the work of integrating live, real-time AI experiences. Capabilities are per NVIDIA's own announcement; third-party verification pending.

The paper proposes VERITAS, a generator-verifier framework pairing a pre-trained generalist robot policy with a gradient-free visual verifier that evaluates actions at inference time, improving performance without extra training and enabling self-improvement.

Dataset distillation synthesizes compact training sets for data-centric machine learning. This paper rethinks distillation for classification, asking whether distilled sets actually outperform coresets (real-data subsets) and under what conditions.

The paper proposes a quality-aware self-distillation method for GUI grounding, where vision-language models predict precise screen coordinates, addressing how naive on-policy self-distillation can degrade coordinate-token teacher signals.

Vision-language-action models combine vision-language backbones with expressive generative action heads trained via flow matching on large robotic datasets. Despite strong performance, the paper studies uncertainty quantification for these flow-based VLA models.

The paper proposes STAR, a spatiotemporal adaptive reward allocation method for text-to-image RL post-training, replacing a single scalar advantage applied uniformly with rewards that account for the temporal and spatial structure of generation.

Game generation is an emerging coding-agent application requiring natural-language specs to become playable interactive systems. GameCraft-Bench evaluates whether agents can build games end-to-end inside a real game engine, where scripts, scenes, assets, rendering and runtime must cohere.

Language and multimodal foundation models generalize by aligning heterogeneous data under a unified formulation and training at scale. This technical report investigates applying that recipe to robotic manipulation, arguing alignment unlocks scale for manipulation foundation models.

LLM agents in interactive environments are sensitive to prompts, yet prompt engineering stays manual and task-specific. The paper decomposes the observation-to-action pipeline and proposes an environment-grounded automated prompt optimization framework for LLM game agents.

AI clinical documentation tools increasingly summarize and reformat radiology reports with LLMs. Using 450 chest X-ray reports from the Indiana University dataset, the paper measures resulting information degradation, showing erosion of clinical uncertainty and cross-modal alignment in AI-rewritten reports.