Large language models can support medical question answering, but privacy, cost, and reliability still limit their clinical use in focused domains. Can rubric-based reinforcement learning bring a locally deployable model close to much larger systems? We study Group Relative Policy Optimization (GRPO) for heart-focused...
Arash Ahmadi, Parisa Masnadi Khiabani, Sarah Sharif et al.· 0 citations
Proactive multimodal assistants must decide when guidance is needed and what to say, while tracking progress through extended procedures and helping users recover when they depart from the expected plan. Progress is limited by benchmarks that score isolated clips, cover narrow domains, or omit realistic deviations. We...
Kaustav Kundu, Ritvik Shrivastava, Maxim Arap et al.· 0 citations
LLMs and agentic systems are increasingly deployed in social environments, making normative competence critical for safe and appropriate behavior. However, existing approaches either assess normative judgment in text alone or reduce it to choosing among a fixed set of candidate actions. We argue both are insufficient....
Sichao Li, Sai Ma, Daniel Kilov et al.· 0 citations
Deformable object manipulation (DOM) is challenging due to high-dimensional, partially observable states that evolve through long-horizon, topology-changing interactions with multiple valid manipulation modes. We introduce Instant-Fold, an in-context imitation learning framework for DOM. Given a single human demonstrat...
Yilong Wang, Cheng Qian, Edward Johns· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
Adaptive patching has been proposed for time-series forecasting: allocate finer patches where the sequence looks locally informative. We compare three adaptive patching methods with tuned uniform counterparts that use one fixed patch size throughout the input, while preserving each method's backbone family and shared t...
Federico Zucchi, Yi Xie, Chao Zhang et al.· 0 citations
How can a population of agents self-orchestrate and self-adapt into stronger collective intelligence without centralized control? Inspired by Friedrich Hayek's economic theory of decentralized coordination in markets, we study this question through an agent economy in which agents compete via auctions for the right to...
Zhenting Qi, Huangyuan Su, Ao Qu et al.· 0 citations
Reasoning language models (RLMs) achieve strong performance on complex reasoning tasks, but still exhibit substantial multilingual reasoning gaps, largely due to language-understanding failures when interpreting non-English inputs. Translation into English can mitigate these failures by expressing non-English inputs in...
A benchmark score is a measurement whose uncertainty has two sources: an aleatoric component from stochastic decoding and an epistemic component from the finite problem set. Using 16 models on 99 recent LeetCode-style problems with two prompts and five runs each (15,840 judged runs in a fully crossed design), we estima...
Yongxi Zhou, Lai Yun Choi, Jiaxi Wen et al.· 0 citations
We propose to turn generalist multi-embodiment value functions into reusable models for robot design. Instead of running a new reinforcement learning co-design loop for each robot, we first train an embodiment-aware policy and value function across many robot designs. After training, the frozen value function is used a...
Video Diffusion Models (VDMs) synthesize high-quality videos, but precise camera and motion control remains challenging when large viewpoint shifts, changing occlusions, or complex dynamics reveal unseen regions. Diffusion priors alone can yield structural inconsistencies, while warped guidance can contain incomplete g...
Jiayi Wu, Haoming Cai, Cornelia Fermuller et al.· 0 citations
Explainability of deep learning algorithms is critical for computer-vision applications with high-stakes decisions. Concept bottleneck models (CBM) have recently shown promising performance to provide explainable and accurate predictions for classification problems, based on a bottleneck of high-level concepts. Existin...
Cl\'ement B\'enard, Manon Arfib, Christophe Labreuche et al.· 0 citations
Can a dataset be recognized from the spurious correlations it induces during training? We argue that datasets leave dataset-specific traces in a model's learned semantic correlation structure: incidental regularities that are predictive within a dataset, but not causal for the underlying task, can be internalized durin...
Andrada Gobeaja, Ionut Hodoroaga, Marius Leordeanu et al.· 0 citations
