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cs.CL2026

LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers

Tao Feng, Fangxu Yu, Haozhen Zhang +9

No single large language model (LLM) is optimal across all queries and budget constraints, making model routing essential for cost-effective deployment. Existing routers adopt dive…

cs.CL2026

ExpWeaver: LLM Agents Learn from Experience via Latent RAG

Tao Feng, Tianyang Luo, Jingjun Xu +5

Experience learning has achieved promising results in enhancing LLM agent planning and reasoning by integrating past interactions as reusable knowledge. However, existing methods r…

cs.CL2026

ExpGraph: Model-Agnostic Experience Learning with Graph-Structured Memory for LLM Agents

Tao Feng, Chongrui Ye, Tianyang Luo +8

Large language model (LLM) agents have shown strong capabilities in reasoning, tool use, and multi-step interaction, but they often solve tasks from scratch and fail to reuse succe…

cs.CL2026

ElasticMem: Latent Memory as a Learnable Resource for LLM Agents

Tao Feng, Chongrui Ye, Tianyang Luo +5

Long-term memory is essential for LLM agents to reason coherently across extended interactions, personalize responses, and reuse past experience. However, existing memory-augmented…

cs.CL2026

Auto-Dreamer: Learning Offline Memory Consolidation for Language Agents

Chongrui Ye, Yuxiang Liu, Yu Wang +5

Language agents increasingly operate over streams of related tasks, yet existing memory systems struggle to convert accumulated experience into reusable knowledge. Retrieval-augmen…

cs.CL2026

LangFlow: Continuous Diffusion Rivals Discrete in Language Modeling

Yuxin Chen, Chumeng Liang, Hangke Sui +4

Continuous diffusion has been the foundation of high-fidelity, controllable, and few-step generation of many data modalities such as images. However, in language modeling, prior co…