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Found 13 papers, 8 papers with code
Reinforcing the Diffusion Chain of Lateral Thought with Diffusion Language Models
DCoLT treats each intermediate step in the reverse diffusion process as a latent "thinking" action and optimizes the entire reasoning trajectory to maximize the reward on the correctness of the final answer with outcome-based Reinforcement Learning (RL).
Sparsity-Aware Near-Field Beam Training via Multi-Beam Combination
This paper proposes an adaptive near-field beam training method to enhance performance in multi-user and multipath environments.
STAR-1: Safer Alignment of Reasoning LLMs with 1K Data
This paper introduces STAR-1, a high-quality, just-1k-scale safety dataset specifically designed for large reasoning models (LRMs) like DeepSeek-R1.
Low-Complexity Near-Field Beam Training with DFT Codebook based on Beam Pattern Analysis
Extremely large antenna arrays (ELAAs) operating in high-frequency bands have spurred the development of near-field communication, driving advancements in beam training design.
CoLMDriver: LLM-based Negotiation Benefits Cooperative Autonomous Driving
Vehicle-to-vehicle (V2V) cooperative autonomous driving holds great promise for improving safety by addressing the perception and prediction uncertainties inherent in single-agent systems.
SoK: Knowledge is All You Need: Accelerating Last Mile Delivery for Automated Provenance-based Intrusion Detection with LLMs
Recently, provenance-based intrusion detection systems (PIDSes) have been widely proposed for endpoint threat analysis.
DEAL: Data-Efficient Adversarial Learning for High-Quality Infrared Imaging
The scarcity of high-quality infrared data, coupled with the challenges of dynamic, intricate degradations, makes it difficult to recover details using existing methods.
AttnGCG: Enhancing Jailbreaking Attacks on LLMs with Attention Manipulation
This paper studies the vulnerabilities of transformer-based Large Language Models (LLMs) to jailbreaking attacks, focusing specifically on the optimization-based Greedy Coordinate Gradient (GCG) strategy.
How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs
Different from prior studies, we shift our focus from evaluating standard performance to introducing a comprehensive safety evaluation suite, covering both out-of-distribution (OOD) generalization and adversarial robustness.
SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving
Monocular scene understanding is a foundational component of autonomous systems.
3D Semantic Scene Completion
3D Semantic Scene Completion from a single 2D image
A Competitive Method for Dog Nose-print Re-identification
Vision-based pattern identification (such as face, fingerprint, iris etc.)
Visual Attention-based Self-supervised Absolute Depth Estimation using Geometric Priors in Autonomous Driving
Although existing monocular depth estimation methods have made great progress, predicting an accurate absolute depth map from a single image is still challenging due to the limited modeling capacity of networks and the scale ambiguity issue.
Matrix Factorization on GPUs with Memory Optimization and Approximate Computing
Current MF implementations are either optimized for a single machine or with a need of a large computer cluster but still are insufficient.
