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Related Articles from SNS
Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
Announce Type: replace Abstract: Generalist robot policies increasingly benefit from large-scale pretraining, but offline data alone is insufficient for robust real-world deployment. Deployed robots encounter distribution shifts, long-tail failures, task variations, and human correction opportunities that fixed demonstration datasets cannot fully capture. We present Learning While Deploying (LWD), a fleet-scale offline-to-online reinforcement learning framework for continual post-training of...
Harnessing Generalist Agents for Contextualized Time Series
Announce Type: new Abstract: Time series are often embedded in rich contexts that are essential for holistic modeling. Moreover, real-world practitioners often require end-to-end workflows for analyzing temporal dynamics, where widely studied tasks such as forecasting are only one step in a broader solution loop. While generalist AI agents offer a promising interface for such workflows under complex contexts, they still operate primarily in textual spaces that are not fully aligned with...
Can Generalist Agents Automate Data Curation?
arXiv:2606.04261v1 Announce Type: new Abstract: Curating training data is among the most consequential yet labor-intensive parts of modern AI development: practitioners iteratively propose, implement, evaluate, and revise data policies against noisy benchmark feedback. We ask whether generalist coding agents can automate this data-curation loop. We introduce *Curation-Bench*, an agent-centric benchmark that fixes the model, training recipe, and evaluation suite while giving agents...
Image Generators are Generalist Vision Learners
Announce Type: replace Abstract: Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent capabilities of language understanding and reasoning from generative pretraining. While it has long been conjectured that the ability to create visual content implies an ability to understand it, there has been limited evidence that generative vision models have developed strong understanding capabilities.
FindIt: A Format-Informed Visual Detection Benchmark for Generalist Multimodal LLMs
Announce Type: new Abstract: Multimodal large language models (MLLMs) are predominantly evaluated on free-form vision-language tasks such as visual question answering, captioning, and summarization. However, their practical use is rapidly expanding to more structured computer vision settings, where users prompt models to perform localization-centric tasks such as object detection, often within larger agentic or decision-making systems.
Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages
arXiv:2605.30529v1 Announce Type: new Abstract: Sentence-embedding models for semantic search are overwhelmingly developed and evaluated on English corpora. When applied to clinical retrieval in other languages -- particularly retrieval of ICD-10-CM / CIE-10 codes -- recall degrades in ways often masked by aggregate benchmarks. We study whether large generative language models can serve as data factories to close this gap.
Embody4D: A Generalist Data Engine for Embodied 4D World Modeling
arXiv:2605.01799v2 Announce Type: replace Abstract: Embodied agents require robust and comprehensive 3D spatiotemporal representations to support spatial reasoning, manipulation understanding, and downstream decision making. However, existing robot data are typically captured from fixed or sparse viewpoints, providing only partial and view-dependent observations, which limits multi-view perception and generalization across viewpoints. Given the difficulty of collecting additional viewpoints...
AirDreamer: Generalist Drone Navigation with World Models
new Abstract: Navigating a drone in unseen and cluttered environments requires reliable generalization to unseen scene layouts and understanding of environmental structure relative to the robot's capabilities. Previous methods, which assume the same environment configuration, often rely heavily on human-designed perception pipelines and predefined rules to guide the robot toward the target. This process is environment-dependent and generalizes poorly across environments.
Nvidia-Backed Robotics Startup Generalist AI Valued at $2 Billion
Pete Florence, Andy Zeng, and Andy Barry
A co-proteomic view of metabolite-specific interactions in the Botrytis cinerea-Arabidopsis pathosystem
To successfully infect their myriad hosts, generalist plant pathogens must tolerate a vast arsenal of plant specialized defense metabolites. To understand how host-specific metabolites influence plant-generalist pathogen interactions, we conducted a co-proteomic analysis of both Arabidopsis thaliana and Botrytis cinerea proteomes from the same samples during early infection. The Arabidopsis proteomic responses to Botrytis center around induction and suppression of defense metabolite...