arXiv:2603.01771v3 Announce Type: replace-cross Abstract: Neural networks (NNs) often have critical behavioural trade-offs that are set at design time with hyperparameters-such as reward weights in reinforcement learning or quantile targets in regression. Post-deployment, however, user preferences can evolve, making initial settings undesirable, necessitating potentially expensive retraining. To circumvent this, we introduce the task of Hyperparameter […]
Differential Privacy in Generative AI Agents: Analysis and Optimal Tradeoffs
arXiv:2603.17902v1 Announce Type: cross Abstract: Large language models (LLMs) and AI agents are increasingly integrated into enterprise systems to access internal databases and generate context-aware responses. While such integration improves productivity and decision support, the model outputs may inadvertently reveal sensitive information. Although many prior efforts focus on protecting the privacy of user prompts, relatively […]
CLeAN: Continual Learning Adaptive Normalization in Dynamic Environments
arXiv:2603.17548v1 Announce Type: cross Abstract: Artificial intelligence systems predominantly rely on static data distributions, making them ineffective in dynamic real-world environments, such as cybersecurity, autonomous transportation, or finance, where data shifts frequently. Continual learning offers a potential solution by enabling models to learn from sequential data while retaining prior knowledge. However, a critical and underexplored […]
Loc3R-VLM: Language-based Localization and 3D Reasoning with Vision-Language Models
arXiv:2603.18002v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have made impressive progress in connecting vision and language, but they still struggle with spatial understanding and viewpoint-aware reasoning. Recent efforts aim to augment the input representations with geometric cues rather than explicitly teaching models to reason in 3D space. We introduce Loc3R-VLM, a framework […]
interwhen: A Generalizable Framework for Verifiable Reasoning with Test-time Monitors
arXiv:2602.11202v2 Announce Type: replace-cross Abstract: Reasoning models produce long traces of intermediate decisions and tool calls, making test-time verification increasingly important for ensuring correctness. Existing approaches either verify only the final answer, which misses early errors, or rely on branch-and-verify strategies that explore multiple trajectories at substantially higher compute cost. We introduce interwhen, a single-trajectory […]
ScheduleMe: Multi-Agent Calendar Assistant
arXiv:2509.25693v3 Announce Type: replace Abstract: Recent advancements in LLMs have contributed to the rise of advanced conversational assistants that can assist with user needs through natural language conversation. This paper presents a ScheduleMe, a multi-agent calendar assistant for users to manage google calendar events in natural language. The system uses a graph-structured coordination mechanism where […]
Learning Coordinate-based Convolutional Kernels for Continuous SE(3) Equivariant and Efficient Point Cloud Analysis
arXiv:2603.17538v1 Announce Type: cross Abstract: A symmetry on rigid motion is one of the salient factors in efficient learning of 3D point cloud problems. Group convolution has been a representative method to extract equivariant features, but its realizations have struggled to retain both rigorous symmetry and scalability simultaneously. We advocate utilizing the intertwiner framework to […]
Stepwise Think-Critique: A Unified Framework for Robust and Interpretable LLM Reasoning
arXiv:2512.15662v3 Announce Type: replace Abstract: Human beings solve complex problems through critical thinking, where reasoning and evaluation are intertwined to converge toward correct solutions. However, most existing large language models (LLMs) treat the reasoning and verification as separate processes: they either generate reasoning without explicit self-checking or rely on external verifiers to detect errors post […]
Chain of Mindset: Reasoning with Adaptive Cognitive Modes
arXiv:2602.10063v2 Announce Type: replace Abstract: Human problem-solving is never the repetition of a single mindset, by which we mean a distinct mode of cognitive processing. When tackling a specific task, we do not rely on a single mindset; instead, we integrate multiple mindsets within the single solution process. However, existing LLM reasoning methods fall into […]
I Know What I Don’t Know: Latent Posterior Factor Models for Multi-Evidence Probabilistic Reasoning
arXiv:2603.15670v2 Announce Type: replace Abstract: Real-world decision-making, from tax compliance assessment to medical diagnosis, requires aggregating multiple noisy and potentially contradictory evidence sources. Existing approaches either lack explicit uncertainty quantification (neural aggregation methods) or rely on manually engineered discrete predicates (probabilistic logic frameworks), limiting scalability to unstructured data. We introduce Latent Posterior Factors (LPF), a […]
Physics-Informed Evolution: An Evolutionary Framework for Solving Quantum Control Problems Involving the Schr”odinger Equation
arXiv:2502.05228v2 Announce Type: replace-cross Abstract: Physics-Informed Neural Networks (PINNs) have demonstrated that embedding physical laws directly into the learning objective can significantly enhance the efficiency and physical consistency of neural network solutions. Inspired by this principle, we ask a natural question: can physical information be similarly embedded into the fitness function of evolutionary algorithms? In […]
Efficient Diffusion as Low Light Enhancer
arXiv:2410.12346v3 Announce Type: replace-cross Abstract: The computational burden of the iterative sampling process remains a major challenge in diffusion-based Low-Light Image Enhancement (LLIE). Current acceleration methods, whether training-based or training-free, often lead to significant performance degradation, highlighting the trade-off between performance and efficiency. In this paper, we identify two primary factors contributing to performance degradation: […]