15 papers · 1 filter
Dual-Layer Agentic Memory with Fast Write Routing and Slow Consolidation
Wenzhi Li, Dong Nie, Rui Lan +7
Large language model (LLM) agents operate in dynamic environments where knowledge continuously evolves. Existing memory systems typically treat external memory as a monotonically g…
Do Not Waste Your Rollouts: Recycling Search Experience for Efficient Test-Time Scaling
Xinglin Wang, Jiayi Shi, Shaoxiong Feng +8
Test-Time Scaling enhances the reasoning capabilities of Large Language Models by allocating additional inference compute to broaden the exploration of the solution space. However,…
Silencer: From Discovery to Mitigation of Self-Bias in LLM-as-Benchmark-Generator
Peiwen Yuan, Yiwei Li, Shaoxiong Feng +7
LLM-as-Benchmark-Generator methods have been widely studied as a supplement to human annotators for scalable evaluation, while the potential biases within this paradigm remain unde…
Speculative Decoding for Multi-Sample Inference
Yiwei Li, Jiayi Shi, Shaoxiong Feng +8
We propose a novel speculative decoding method tailored for multi-sample reasoning scenarios, such as self-consistency and Best-of-N sampling. Our method exploits the intrinsic con…
UniCBE: An Uniformity-driven Comparing Based Evaluation Framework with Unified Multi-Objective Optimization
Peiwen Yuan, Shaoxiong Feng, Yiwei Li +7
Human preference plays a significant role in measuring large language models and guiding them to align with human values. Unfortunately, current comparing-based evaluation (CBE) me…
LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient
Peiwen Yuan, Shaoxiong Feng, Yiwei Li +7
The rapid advancement of large language models (LLMs) has led to a surge in both model supply and application demands. To facilitate effective matching between them, reliable, gene…