Large Language Models (LLMs) exhibit remarkable capabilities in natural language understanding and reasoning, but suffer from hallucination: the generation of f...
Large language models specialized for code (CodeLLMs) have demonstrated remarkable capabilities in generating code snippets, documentation, and test cases. Howe...
The transition from hand-crafted heuristics to data-driven evolutionary algorithms faces a fundamental dilemma: achieving neural plasticity without sacrificing ...
The automated translation of C code to Java code is a notoriously difficult task, fraught with challenges stemming from fundamental paradigm shifts (procedural ...
With the rapid advancement of large language models (LLMs) in code generation, their applications in hardware design are receiving growing attention. However, e...
In this paper, we introduce GraphSecure, a web application that provides advanced analysis and visualisation of security scanning results. GraphSecure enables u...
Rollout-training disaggregation is emerging as the standard architecture for Reinforcement Learning (RL) post-training, where memory-bound rollout and compute-b...
The testing phase is an essential part of software development, but manually creating test cases can be time-consuming. Consequently, there is a growing need fo...
Current AI code generation systems suffer from significant latency bottlenecks due to CPU-GPU data transfers during compilation, execution, and testing phases. ...
Actively secure arithmetic MPC is now practical for real applications, but performance and usability are still limited by framework-specific compilation stacks,...
We introduce StereoSpace, a diffusion-based framework for monocular-to-stereo synthesis that models geometry purely through viewpoint conditioning, without expl...
Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physicall...
