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Related Articles from SNS
RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments
arXiv:2511.07317v2 Announce Type: replace Abstract: We introduce Reinforcement Learning (RL) with Adaptive Verifiable Environments (RLVE), an approach using verifiable environments that procedurally generate problems and provide algorithmically verifiable rewards, to scale up RL for language models (LMs). RLVE enables each verifiable environment to dynamically adapt its problem difficulty distribution to the policy model's capabilities as training progresses. In contrast, static data...
Proximity labelling of D1-like dopamine receptors reveals distinct cellular environments and uncovers trafficking proteins that regulate DA mediated behaviors in Drosophila
The neurotransmitter dopamine (DA) is central to synaptic regulation that support diverse behavioral functions, including both learning and forgetting. This multi-functional role of DA is due to receptor specific signaling in specific subcellular environments that remain uncharacterized. Here we utilized proximity labelling proteomics in human cells to characterize the proximal environments of two Drosophila D1-like DA receptors (Dop1R1 and Dop1R2) in basal and DA activation environments.
Environment-Division Multiple Access: an Enabler for AI-Native Multiple Access
arXiv:2606.07025v1 Announce Type: new Abstract: In this article, a new type of multiple access, termed Environment-Division Multiple Access (EDMA), is introduced and its interaction with AI-native communication networks is illustrated. In particular, the key properties of EDMA, such as utilizing the features of wireless propagation environments, integrating advanced flexible antennas, and proactively reconfiguring propagation environments, are described. The article also illustrates two...
Research at the Environment Agency
Research at the Environment Agency The Environment Agency’s research themes, partnerships and publications. We are committed to strengthening the impact of our knowledge and better integrating science into the heart of everything we do. Our work depends on science and research - whether it’s: - delivering healthier air, land and water to support nature recovery - contributing to sustainable growth - ensuring we are a nation resilient to climate change Research is carried out across the...
Cross-Environment Neural Reranking for Sample-Efficient Action Selection in Text-Based Agents
Announce Type: new Abstract: Large language model agents achieve strong performance on text-based benchmarks but incur prohibitive inference costs, motivating the use of compact neural rerankers for action selection. We investigate whether a single lightweight model can perform action selection across multiple diverse environments, a capability that would eliminate per-environment model maintenance. Training DeBERTa-v3 (184M-434M parameters) jointly on ALFWorld, WebShop, and ScienceWorld...
Scaling Multi-Agent Environment Co-Design with Diffusion Models
arXiv:2511.03100v2 Announce Type: replace Abstract: The agent-environment co-design paradigm jointly optimises agent policies and environment configurations in search of improved system performance. With application domains ranging from warehouse logistics to windfarm management, co-design promises to fundamentally change how we deploy multi-agent systems. However, current co-design methods struggle to scale.
Environment-Robust Representation Learning with Empirical Bayes
arXiv:2606.05365v1 Announce Type: cross Abstract: We consider multi-environment prediction problems. We assume the environments change the distribution of a latent variable, while the mechanisms generating observed covariates and targets remain stable conditional on that variable. For example, hospitals or clinical cohorts may differ in the prevalence of latent patient states, even though the relationships between those states, physiological measurements, and outcomes remain unchanged.
MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering
arXiv:2601.22859v3 Announce Type: replace Abstract: The evolution of Large Language Model (LLM) agents for software engineering (SWE) is constrained by the scarcity of verifiable datasets, a bottleneck stemming from the complexity of constructing executable environments across diverse languages. To address this, we introduce MEnvAgent, a Multi-language framework for automated Environment construction that facilitates scalable generation of verifiable task instances. MEnvAgent employs a...
Meridian: Metric-Semantic Primitive Matching for Cross-View Geo-Localization Beyond Urban Environments
Announce Type: new Abstract: Successful robot automation requires accurate global localization to support repeatability, task planning, goal specification, and safe operation. However, reliable localization in GNSS-denied environments remains an open problem. Overhead aerial imagery offers a promising solution, but existing approaches primarily target structured urban environments and have been rarely demonstrated in unstructured natural terrain.
AutoSUT: The Environment Semantics Gap in Structured CTI for Adversary Emulation
Announce Type: new Abstract: Structured Cyber Threat Intelligence (CTI) is increasingly used for adversary emulation, detection evaluation, and cyber range design. However, these workflows still require a target System Under Test (SUT) whose environment is not fully described by public CTI. We measure how much of that environment can be derived from MITRE ATT&CK Structured Threat Information Expression (STIX) bundles.