Dataset Condensation with Color Compensation
arXiv:2508.01139v2 Announce Type: replace-cross Abstract: Dataset condensation always faces a constitutive trade-off: balancing performance and fidelity under extreme compression. Existing…
Image2Net: Datasets, Benchmark and Hybrid Framework to Convert Analog Circuit Diagrams into Netlists
arXiv:2508.13157v1 Announce Type: cross Abstract: Large Language Model (LLM) exhibits great potential in designing of analog integrated circuits (IC) because…
Finite Automata Extraction: Low-data World Model Learning as Programs from Gameplay Video
arXiv:2508.11836v1 Announce Type: new Abstract: World models are defined as a compressed spatial and temporal learned representation of an environment.…
Visual Perception Engine: Fast and Flexible Multi-Head Inference for Robotic Vision Tasks
arXiv:2508.11584v2 Announce Type: replace-cross Abstract: Deploying multiple machine learning models on resource-constrained robotic platforms for different perception tasks often results…
OpenMoCap: Rethinking Optical Motion Capture under Real-world Occlusion
arXiv:2508.12610v1 Announce Type: cross Abstract: Optical motion capture is a foundational technology driving advancements in cutting-edge fields such as virtual…
SSPO: Self-traced Step-wise Preference Optimization for Process Supervision and Reasoning Compression
arXiv:2508.12604v1 Announce Type: cross Abstract: Test-time scaling has proven effective in further enhancing the performance of pretrained Large Language Models…
Note on Selection Bias in Observational Estimates of Algorithmic Progress
arXiv:2508.11033v2 Announce Type: replace-cross Abstract: Ho et. al (2024) attempts to estimate the degree of algorithmic progress from language models.…
Grounding Rule-Based Argumentation Using Datalog
arXiv:2508.10976v1 Announce Type: new Abstract: ASPIC+ is one of the main general frameworks for rule-based argumentation for AI. Although first-order…
PTQAT: A Hybrid Parameter-Efficient Quantization Algorithm for 3D Perception Tasks
arXiv:2508.10557v2 Announce Type: replace-cross Abstract: Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT) represent two mainstream model quantization approaches. However, PTQ…
When Punctuation Matters: A Large-Scale Comparison of Prompt Robustness Methods for LLMs
arXiv:2508.11383v1 Announce Type: cross Abstract: Large Language Models (LLMs) are highly sensitive to subtle, non-semantic variations in prompt phrasing and…