8 citations · 16 across the 19 of their papers we have counts for
4 papers · 1 filter
LLM-as-an-Improver: Turning Verification into Better Candidates
Akiyoshi Tomihari, Yuma Ichikawa
Verifier-based selection improves LLM performance by generating multiple candidate solutions and using a verifier to select the most promising one. However, existing methods typica…
Addressable Recall Compaction for Long Context-Window Control in AI Agents
Thang Dang, Yuma Ichikawa, Sakina Fatima +1
Long-horizon LLM agents accumulate reasoning traces, actions, and tool observations that can eventually exceed a model's fixed context window. Existing compaction methods address t…
LOGOS: A Living Logic for AI Agent Teams That Evolve With Humans
Yuma Ichikawa, Yamato Arai, Kosaku Kimura +2
AI agents are evolving from answer engines into persistent teams that use tools, delegate work, learn from experience, and modify the artifacts that shape their future behavior. Th…
EVE-Agent: Evidence-Verifiable Self-Evolving Agents
Yamato Arai, Yuma Ichikawa
Self-evolving agents should not train on examples they cannot justify. Data-free self-evolving search agents offer a scalable route to systems that generate their own questions, an…