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cs.LG2026
KV-streams for Efficient Compaction in Agentic Reinforcement Learning
Emiliano Penaloza, Dane Malenfant, Dheeraj Vattikonda +15
Scaling the horizon of agentic LLMs is bottlenecked by the need to fit ever longer context traces in GPU memory. Context compaction has been the most popular mechanism to alleviate…
cs.LG2026
Dialectics of Alignment: Harnessing Unsafe Knowledge for Dynamic Safety Routing
Maryam Hashemzadeh, Jerry Huang, Minseon Kim +2
The prevailing paradigm in large language model (LLM) alignment operates via erasure, filtering unsafe data or training models to strictly refuse harmful prompts. While effective a…
cs.LG2025
Learning to Extract Context for Context-Aware LLM Inference
Minseon Kim, Lucas Caccia, Zhengyan Shi +4
User prompts to large language models (LLMs) are often ambiguous or under-specified, and subtle contextual cues shaped by user intentions, prior knowledge, and risk factors strongl…