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Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning

- Effective contact-rich manipulation requires robots to synergistically leverage vision, force, and proprioception. However, Reinforcement Learning agents struggle to learn in such multisensory...

ThreadFuzzer: Fuzzing Framework for Thread Protocol

- With the rapid growth of IoT, secure and efficient mesh networking has become essential. Thread has emerged as a key protocol, widely used in smart-home and commercial systems, and serving as a core...

Selective Rotary Position Embedding

- Position information is essential for language modeling. In softmax transformers, Rotary Position Embeddings (\textit{RoPE}) encode positions through \textit{fixed-angle} rotations, while in linear...

How Learning Rate Decay Wastes Your Best Data in Curriculum-Based LLM Pretraining

- Due to the scarcity of high-quality data, large language models (LLMs) are often trained on mixtures of data with varying quality levels, even after sophisticated data curation. A natural approach to...

Sparse shepherding control of large-scale multi-agent systems via Reinforcement Learning

- We propose a Reinforcement Learning framework for sparse indirect control of large-scale multi-agent systems, where few controlled agents shape the collective behavior of many uncontrolled agents....

Differentiable Filtering for Learning Hidden Markov Models

- Hidden Markov Models (HMMs) are fundamental for modeling sequential data, yet learning their parameters from observations remains challenging. Classical methods like the Baum-Welch algorithm are...

Cost-Effective Communication: An Auction-based Method for Language Agent Interaction

- Multi-agent systems (MAS) built on large language models (LLMs) often suffer from inefficient "free-for-all" communication, leading to exponential token costs and low signal-to-noise ratios...

Optical Network Digital Twin -- Practical Use Cases and Architecture

- With the widespread adoption of AI, machine-to-machine communications are rapidly increasing, reshaping the requirements for optical networks. Recent advances in Gaussian noise modeling for digital...

Parameter-Efficient Conditioning for Material Generalization in Graph-Based Simulators

- Graph network-based simulators (GNS) have demonstrated strong potential for learning particle-based physics (such as fluids, deformable solids, and granular flows) while generalizing to unseen...

Atlas-Alignment: Making Interpretability Transferable Across Language Models

- Interpretability is crucial for building safe, reliable, and controllable language models, yet existing interpretability pipelines remain costly and difficult to scale. Interpreting a new model...

Deliberation via Matching

- We study deliberative social choice, where voters engage in small-group discussions to output collective preferences that are then aggregated by a social choice rule. We introduce a simple...

NeuronMLP: Efficient LLM Inference via Singular Value Decomposition Compression and Tiling on AWS Trainium

- Emerging AI accelerators have started to gain attention and offer new opportunities for efficient inference of large language models (LLMs). Trainium, an AI accelerator recently developed by Amazon...

AgentBound: Securing Execution Boundaries of AI Agents

- Large Language Models (LLMs) have evolved into AI agents that interact with external tools and environments to perform complex tasks. The Model Context Protocol (MCP) has become the de facto standard...

Identifying the Periodicity of Information in Natural Language

- Recent theoretical advancement of information density in natural language has brought the following question on desk: To what degree does natural language exhibit periodicity pattern in its encoded...

OREN: Octree Residual Network for Real-Time Euclidean Signed Distance Mapping

- Reconstructing signed distance functions (SDFs) from point cloud data benefits many robot autonomy capabilities, including localization, mapping, motion planning, and control. Methods that support...

Decomposed Attention Fusion in MLLMs for Training-Free Video Reasoning Segmentation

- Multimodal large language models (MLLMs) demonstrate strong video understanding by attending to visual tokens relevant to textual queries. To directly adapt this for localization in a training-free...

Leveraging Teleconnections with Physics-Informed Graph Attention Networks for Long-Range Extreme Rainfall Forecasting in Thailand

- Accurate rainfall forecasting, particularly for extreme events, remains a significant challenge in climatology and the Earth system. This paper presents novel physics-informed Graph Neural Networks...

Are Video Models Emerging as Zero-Shot Learners and Reasoners in Medical Imaging?

- Recent advances in large generative models have shown that simple autoregressive formulations, when scaled appropriately, can exhibit strong zero-shot generalization across domains. Motivated by this...

Test-Time Matching: Unlocking Compositional Reasoning in Multimodal Models

- Frontier AI models have achieved remarkable progress, yet recent studies suggest they struggle with compositional reasoning, often performing at or below random chance on established benchmarks. We...

Shaken or Stirred? An Analysis of MetaFormer's Token Mixing for Medical Imaging

- The generalization of the Transformer architecture via MetaFormer has reshaped our understanding of its success in computer vision. By replacing self-attention with simpler token mixers, MetaFormer...

Robust stability of event-triggered nonlinear moving horizon estimation

- In this work, we propose an event-triggered moving horizon estimation (ET-MHE) scheme for the remote state estimation of general nonlinear systems. In the presented method, whenever an event is...