3 citations · 5 across the 24 of their papers we have counts for
6 papers · 1 filter
From Pareto to Preference: Personalized Test-Time Scaling via Amortized Agentic Policy Discovery
Xinglin Wang, Zishen Liu, Tong Zheng +9
Test-time scaling (TTS) improves the reasoning capabilities of large language models by allocating additional inference computation. Existing approaches to improving TTS efficiency…
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…
Share More, Search Less: Collaborative Parallel Thinking for Efficient Test-Time Scaling
Xinglin Wang, Hao Lin, Shaoxiong Feng +9
Test-Time Scaling (TTS) enhances the reasoning capabilities of large language models by allocating additional inference compute to explore the solution space. However, existing par…
On Time, Within Budget: Constraint-Driven Online Resource Allocation for Agentic Workflows
Xinglin Wang, Zishen Liu, Shaoxiong Feng +9
Agentic systems increasingly solve complex user requests by executing orchestrated workflows, where subtasks are assigned to specialized models or tools and coordinated according t…
Learning More from Less: Unlocking Internal Representations for Benchmark Compression
Yueqi Zhang, Jin Hu, Shaoxiong Feng +9
The prohibitive cost of evaluating Large Language Models (LLMs) necessitates efficient alternatives to full-scale benchmarking. Prevalent approaches address this by identifying a s…
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,…