arXiv:2601.19275v1 Announce Type: cross Abstract: Tactile memory, the ability to store and retrieve touch-based experience, is critical for contact-rich tasks such as key insertion under uncertainty. To replicate this capability, we introduce Tactile Memory with Soft Robot (TaMeSo-bot), a system that integrates a soft wrist with tactile retrieval-based control to enable safe and robust manipulation. […]
Robust Uncertainty Estimation under Distribution Shift via Difference Reconstruction
arXiv:2601.19341v1 Announce Type: cross Abstract: Estimating uncertainty in deep learning models is critical for reliable decision-making in high-stakes applications such as medical imaging. Prior research has established that the difference between an input sample and its reconstructed version produced by an auxiliary model can serve as a useful proxy for uncertainty. However, directly comparing reconstructions […]
Bridging Visual and Wireless Sensing: A Unified Radiation Field for 3D Radio Map Construction
arXiv:2601.19216v1 Announce Type: cross Abstract: The emerging applications of next-generation wireless networks (e.g., immersive 3D communication, low-altitude networks, and integrated sensing and communication) necessitate high-fidelity environmental intelligence. 3D radio maps have emerged as a critical tool for this purpose, enabling spectrum-aware planning and environment-aware sensing by bridging the gap between physical environments and electromagnetic signal […]
From Answer Givers to Design Mentors: Guiding LLMs with the Cognitive Apprenticeship Model
arXiv:2601.19053v1 Announce Type: cross Abstract: Design feedback helps practitioners improve their artifacts while also fostering reflection and design reasoning. Large Language Models (LLMs) such as ChatGPT can support design work, but often provide generic, one-off suggestions that limit reflective engagement. We investigate how to guide LLMs to act as design mentors by applying the Cognitive […]
Axe: A Simple Unified Layout Abstraction for Machine Learning Compilers
arXiv:2601.19092v1 Announce Type: cross Abstract: Scaling modern deep learning workloads demands coordinated placement of data and compute across device meshes, memory hierarchies, and heterogeneous accelerators. We present Axe Layout, a hardware-aware abstraction that maps logical tensor coordinates to a multi-axis physical space via named axes. Axe unifies tiling, sharding, replication, and offsets across inter-device distribution […]
GMS-CAVP: Improving Audio-Video Correspondence with Multi-Scale Contrastive and Generative Pretraining
arXiv:2601.19606v1 Announce Type: cross Abstract: Recent advances in video-audio (V-A) understanding and generation have increasingly relied on joint V-A embeddings, which serve as the foundation for tasks such as cross-modal retrieval and generation. While prior methods like CAVP effectively model semantic and temporal correspondences between modalities using contrastive objectives, their performance remains suboptimal. A key […]
Sim-and-Human Co-training for Data-Efficient and Generalizable Robotic Manipulation
arXiv:2601.19406v1 Announce Type: cross Abstract: Synthetic simulation data and real-world human data provide scalable alternatives to circumvent the prohibitive costs of robot data collection. However, these sources suffer from the sim-to-real visual gap and the human-to-robot embodiment gap, respectively, which limits the policy’s generalization to real-world scenarios. In this work, we identify a natural yet […]
GradPruner: Gradient-Guided Layer Pruning Enabling Efficient Fine-Tuning and Inference for LLMs
arXiv:2601.19503v1 Announce Type: cross Abstract: Fine-tuning Large Language Models (LLMs) with downstream data is often considered time-consuming and expensive. Structured pruning methods are primarily employed to improve the inference efficiency of pre-trained models. Meanwhile, they often require additional time and memory for training, knowledge distillation, structure search, and other strategies, making efficient model fine-tuning challenging […]
FROST: Filtering Reasoning Outliers with Attention for Efficient Reasoning
arXiv:2601.19001v1 Announce Type: cross Abstract: We propose FROST, an attention-aware method for efficient reasoning. Unlike traditional approaches, FROST leverages attention weights to prune uncritical reasoning paths, yielding shorter and more reliable reasoning trajectories. Methodologically, we introduce the concept of reasoning outliers and design an attention-based mechanism to remove them. Theoretically, FROST preserves and enhances the […]
Dynamic Cogeneration of Bug Reproduction Test in Agentic Program Repair
arXiv:2601.19066v1 Announce Type: cross Abstract: Bug Reproduction Tests (BRTs) have been used in many agentic Automated Program Repair (APR) systems, primarily for validating promising fixes and aiding fix generation. In practice, when developers submit a patch, they often implement the BRT alongside the fix. Our experience deploying agentic APR reveals that developers similarly desire a […]
LLM-Assisted Logic Rule Learning: Scaling Human Expertise for Time Series Anomaly Detection
arXiv:2601.19255v1 Announce Type: cross Abstract: Time series anomaly detection is critical for supply chain management to take proactive operations, but faces challenges: classical unsupervised anomaly detection based on exploiting data patterns often yields results misaligned with business requirements and domain knowledge, while manual expert analysis cannot scale to millions of products in the supply chain. […]
StableQAT: Stable Quantization-Aware Training at Ultra-Low Bitwidths
arXiv:2601.19320v1 Announce Type: cross Abstract: Quantization-aware training (QAT) is essential for deploying large models under strict memory and latency constraints, yet achieving stable and robust optimization at ultra-low bitwidths remains challenging. Common approaches based on the straight-through estimator (STE) or soft quantizers often suffer from gradient mismatch, instability, or high computational overhead. As such, we […]