arXiv:2603.13361v1 Announce Type: cross Abstract: Functional magnetic resonance imaging (fMRI) enables noninvasive investigation of brain function, while short clinical scan durations, arising from human and non-human factors, usually lead to reduced data quality and limited statistical power for neuroimaging research. In this paper, we propose BrainCast, a novel spatio-temporal forecasting framework specifically tailored for whole-brain […]
Adapting Critic Match Loss Landscape Visualization to Off-policy Reinforcement Learning
arXiv:2603.14589v1 Announce Type: cross Abstract: This work extends an established critic match loss landscape visualization method from online to off-policy reinforcement learning (RL), aiming to reveal the optimization geometry behind critic learning. Off-policy RL differs from stepwise online actor-critic learning in its replay-based data flow and target computation. Based on these two structural differences, the […]
Agentic LLM Workflow for MR Spectroscopy Volume-of-Interest Placements in Brain Tumors
arXiv:2603.13371v1 Announce Type: cross Abstract: Magnetic resonance spectroscopy (MRS) provides clinically valuable metabolic characterization of brain tumors, but its utility depends on accurate placement of the spectroscopy volume-of-interest (VOI). However, VOI placement typically has a broad operating window: for a given tumor there are multiple possible VOIs that would lead to high-quality MRS measurements. Thus, […]
Agora: Teaching the Skill of Consensus-Finding with AI Personas Grounded in Human Voice
arXiv:2603.07339v2 Announce Type: replace-cross Abstract: Deliberative democratic theory suggests that civic competence: the capacity to navigate disagreement, weigh competing values, and arrive at collective decisions is not innate but developed through practice. Yet opportunities to cultivate these skills remain limited, as traditional deliberative processes like citizens’ assemblies reach only a small fraction of the population. […]
MAD: Microenvironment-Aware Distillation — A Pretraining Strategy for Virtual Spatial Omics from Microscopy
arXiv:2603.13401v1 Announce Type: cross Abstract: Bridging microscopy and omics would allow us to read molecular states from images-at single-cell resolution and tissue scale-without the cost and throughput limits of omics technologies. Self-supervised pretraining offers a scalable approach with minimal labels, yet how to encode single-cell identity within tissue environments-and the extent of biological information such […]
FuXiWeather2: Learning accurate atmospheric state estimation for operational global weather forecasting
arXiv:2603.15358v1 Announce Type: cross Abstract: Numerical weather prediction has long been constrained by the computational bottlenecks inherent in data assimilation and numerical modeling. While machine learning has accelerated forecasting, existing models largely serve as “emulators of reanalysis products,” thereby retaining their systematic biases and operational latencies. Here, we present FuXiWeather2, a unified end-to-end neural framework […]
Agent Privilege Separation in OpenClaw: A Structural Defense Against Prompt Injection
arXiv:2603.13424v1 Announce Type: cross Abstract: Prompt injection remains one of the most practical attack vectors against LLM-integrated applications. We replicate the Microsoft LLMail-Inject benchmark (Greshake et al., 2024) against current generation models running inside OpenClaw, an open source multitool agent platform. Our proposed defense combines two mechanisms: agent isolation, implemented as a privilege separated two-agent […]
Lore: Repurposing Git Commit Messages as a Structured Knowledge Protocol for AI Coding Agents
arXiv:2603.15566v1 Announce Type: cross Abstract: As AI coding agents become both primary producers and consumers of source code, the software industry faces an accelerating loss of institutional knowledge. Each commit captures a code diff but discards the reasoning behind it – the constraints, rejected alternatives, and forward-looking context that shaped the decision. I term this […]
Spatially Grounded Long-Horizon Task Planning in the Wild
arXiv:2603.13433v1 Announce Type: cross Abstract: Recent advances in robot manipulation increasingly leverage Vision-Language Models (VLMs) for high-level reasoning, such as decomposing task instructions into sequential action plans expressed in natural language that guide downstream low-level motor execution. However, current benchmarks do not assess whether these plans are spatially executable, particularly in specifying the exact spatial […]
Beyond Local Code Optimization: Multi-Agent Reasoning for Software System Optimization
arXiv:2603.14703v1 Announce Type: cross Abstract: Large language models and AI agents have recently shown promise in automating software performance optimization, but existing approaches predominantly rely on local, syntax-driven code transformations. This limits their ability to reason about program behavior and capture whole system performance interactions. As modern software increasingly comprises interacting components – such as […]
Reconciling In-Context and In-Weight Learning via Dual Representation Space Encoding
arXiv:2603.13459v1 Announce Type: cross Abstract: In-context learning (ICL) is a valuable capability exhibited by Transformers pretrained on diverse sequence tasks. However, previous studies have observed that ICL often conflicts with the model’s inherent in-weight learning (IWL) ability. By examining the representation space learned by a toy model in synthetic experiments, we identify the shared encoding […]
Learning Question-Aware Keyframe Selection with Synthetic Supervision for Video Question Answering
arXiv:2603.14953v1 Announce Type: cross Abstract: Large multimodal models (LMMs) have recently demonstrated remarkable performance in video question answering (VideoQA), yet reasoning over video remains challenging due to high inference cost and diluted information. Keyframe selection offers efficiency and sharper reasoning but suffers from sparse supervision and redundant frame choices when relying only on image-text similarity. […]