11 papers
One Qubit Can Beat One Bit: Quantum Advantage for Post-Training Quantization
Yuma Ichikawa, Moeto Mishima
One-bit post-training quantization represents each weight using only its sign, requiring all deployment contexts to share the same binary weight matrix even when their activation s…
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…
Tensor-Network Population Annealing
Takumi Oshima, Yuma Ichikawa, Koji Hukushima
We propose a hybrid sampling method, tensor-network population annealing (TNPA), which combines tensor-network (TN) initialization with population annealing (PA). We apply this met…
OneComp: One-Line Revolution for Generative AI Model Compression
Yuma Ichikawa, Keiji Kimura, Akihiro Yoshida +11
Deploying foundation models is increasingly constrained by memory footprint, latency, and hardware costs. Post-training compression can mitigate these bottlenecks by reducing the p…