arXiv:2605.22066v1 Announce Type: cross Abstract: Reconstructing 4D (3D+t) cardiac geometry from sparse 2D echocardiography is highly desirable yet fundamentally challenged by geometric ambiguity and temporal discontinuity. To tackle these issues, we propose Echo4DIR, a novel test-time 4D implicit reconstruction framework. Specifically, we learn robust 3D shape priors from statistical shape models (SSMs) via a cardiac […]
Short-Term-to-Long-Term Memory Transfer for Knowledge Graphs under Partial Observability
arXiv:2605.22142v1 Announce Type: cross Abstract: Reinforcement learning under partial observability requires deciding what information to retain, yet most memory-based approaches do not explicitly model short-term-to-long-term transfer of symbolic observations. We study this transfer process in a temporal knowledge-graph memory setting and cast it as a neuro-symbolic value-based decision problem: for each observed triple, the agent […]
What are the Right Symmetries for Formal Theorem Proving?
arXiv:2605.22257v1 Announce Type: cross Abstract: Formal theorem provers based on large language models (LLMs) are highly sensitive to superficial variations in problem representation: semantically equivalent statements can exhibit drastically different proof success rates, revealing a failure to respect structural symmetries inherent in formal mathematics. This raises a central question: what are the right symmetries for […]
Advancing Mathematics Research with AI-Driven Formal Proof Search
arXiv:2605.22763v1 Announce Type: new Abstract: Large language models (LLMs) increasingly excel at mathematical reasoning, but their unreliability limits their utility in mathematics research. A mitigation is using LLMs to generate formal proofs in languages like Lean. We perform the first large-scale evaluation of this method’s ability to solve open problems. Our most capable agent autonomously […]
The Attribution Impossibility: No Feature Ranking Is Faithful, Stable, and Complete Under Collinearity
arXiv:2605.21492v1 Announce Type: cross Abstract: No feature ranking can be simultaneously faithful, stable, and complete when features are collinear. For collinear pairs, ranking reduces to a coin flip. We prove this impossibility, quantify it for four model classes, resolve it via ensemble averaging (DASH), and machine-verify it with 305 Lean 4 theorems. We characterize the […]
Multivariate Financial Forecasting using the Chronos Time Series Foundation Models
arXiv:2605.21504v1 Announce Type: cross Abstract: Using Chronos-2, an open-source time-series foundation model, we evaluate pretrained time-series models for economic and financial forecasting with an emphasis on whether multivariate (MV) inputs improve accuracy relative to univariate (UV) baselines. The study covers two panels — the Magnificent-7 equities and U.S. Treasury interest rates — as well as […]
Tackle CSM in JPEG Steganalysis with Data Adaptation
arXiv:2605.21523v1 Announce Type: cross Abstract: Steganalysis models excel on benchmark datasets but struggle in the wild when analyzed images are produced by a processing pipeline unseen during training. This problem known as Cover Source Mismatch (CSM) is particularly hard in realistic settings where practitioners (1) have access to only a small, unlabeled dataset, (2) are […]
RefusalBench: Why Refusal Rate Misranks Frontier LLMs on Biological Research Prompts
arXiv:2605.21545v1 Announce Type: cross Abstract: Frontier large language models are increasingly deployed as orchestration backbones for biological research workflows, yet no shared evidence base exists for comparing their refusal behaviour on legitimate research prompts. RefusalBench, introduced here, is a matched-triple benchmark of 141 prompts in 47 bundles that holds task framing constant while varying only […]
Addressing the Synergy Gap: The Six Elements of the Design Space
arXiv:2605.21635v1 Announce Type: cross Abstract: AI is now embedded in healthcare, finance, policy, and many other domains, yet genuine human-AI synergy – combined performance that exceeds what either party achieves alone – is uncommon. Meta-analyses show that AI assistance tends to improve human performance compared to working alone, but studies finding true synergy are scarce. […]
Hierarchical Variational Policies for Reward-Guided Diffusion
arXiv:2605.21661v1 Announce Type: cross Abstract: Adapting pretrained diffusion models to downstream objectives such as inverse problems often requires expensive test-time guidance or optimization. We propose a principled framework for generating high-quality reward-aligned samples at substantially reduced inference cost. Our approach formulates test-time adaptation as a hierarchical variational model, where control is amortized into a lightweight […]
Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems
arXiv:2605.21723v1 Announce Type: cross Abstract: This paper studies heterogeneous multi-team collaboration through dynamic robot allocation, where robots are treated as transferable resources. Leveraging Hamilton’s rule from ecology as an altruistic decision-making mechanism, we propose a multi-team collaborative resource allocation framework with heterogeneous capabilities, transfer costs, and capability-dependent contributions. The resulting allocation problem is combinatorial and […]
The Shape of Testimony: A Scalable Framework for Oral History Archive Comparison
arXiv:2605.21623v1 Announce Type: new Abstract: Researchers in Holocaust studies have often distinguished between two styles of oral survivor testimony: the USC Shoah Foundation’s interviews tend to follow a structured, interviewer-guided format, whereas the Yale Fortunoff Video Archive generally favors a more free-form, open-ended style. This distinction has influenced both scholarly research and the development of […]