6 accepted papers
Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning capabilities of large language models. While existing analyses identify that RLVR-induced changes are sparse, they primarily focus on the **magnitude** of these updates, largely overlooking their **direct…
The widespread adoption of encrypted network protocols has made traffic encryption ubiquitous, creating substantial challenges for network management and security. This paper introduces a novel encrypted traffic classification system, ANASETC, which combines traffic burst features with Neural Archit…
Preference learning is critical for aligning large language models (LLMs) with human values, with the quality of preference datasets playing a crucial role in this process. While existing metrics primarily assess data quality based on either *explicit* or *implicit* reward margins, their single-mar…
Auction plays a crucial role in many modern trading environments, including online advertising and public resource allocation. As the number of competing bidders increases, learning Bayesian Nash Equilibrium (BNE) in auctions faces significant scalability challenges. Existing methods often experienc…
Auction games have been widely used in plenty of trading environments such as online advertising and real estate. The complexity of real-world scenarios, characterized by diverse auction mechanisms and bidder asymmetries, poses significant challenges in efficiently solving for equilibria. Traditiona…
Vehicle Re-Identification (ReID) has attracted lots of research efforts due to its great significance to the public security. In vehicle ReID, we aim to learn features that are powerful in discriminating subtle differences between vehicles which are visually similar, and also robust against differen…