Hugging Face Trending Papers
CERA-MoA: Co-Evolving Routing Mechanisms with Continually Learning LLM Agents
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arXiv AI
Sep 17
CERA-MoA: Co-Evolving Routing Mechanisms with Continually Learning LLM Agents
CERA-MoA introduces a co-evolving router and independently learning agents for Mixture-of-Agents systems, allowing routing strategies to adapt to evolving agent capabilities. The framework uses a predictive familiarity estimator based on mid-layer hidden states to evaluate semantic competence, and a cumulative-threshold adaptive routing mechanism to activate a minimal agent subset. Experiments across multiple domains show that CERA-MoA outperforms static-agent routing and fixed-workflow fine-tuning baselines.
arXiv AI
Sep 23
ACLArena: Agent Continue Learning in Multi-stage Post-training
arXiv:2609.23989v1 Announce Type: new Abstract: Building general-purpose agents for industrial deployment requires integrating multiple capabilities, each typically acquired at a distinct stage of tr...
arXiv AI
Jul 7
Optimal-Agent-Selection: State-Aware Routing Framework for Efficient Multi-Agent Collaboration
arXiv:2511. 02200v2 Announce Type: replace Abstract: The emergence of multi-agent systems powered by large language models (LLMs) has unlocked new frontiers in complex task-solving, enabling diverse agents to integrate unique expertise, collaborate flexibly, and address challenges unattainable for individual models.
arXiv AI
1d ago
EvoRouter: Cost-Efficient Multi-Agent Routing via Insight-Guided On-Policy Distillation
arXiv:2610.35690v1 Announce Type: new Abstract: Serving web queries at scale with multi-agent systems requires jointly configuring large language models, collaboration modes, agent counts, interactio...
arXiv Machine Learning
Sep 2
TRIAGE: Three-level Routing and Intelligent Agent Guidance for Efficient Execution
TRIAGE introduces a three-level routing framework for Large Language Model agents that reduces token consumption by reusing historical execution trajectories. The system classifies queries into direct reuse, skill substitution, and full ReAct levels, achieving significant token savings in large-scale security monitoring and cross-domain benchmarks. An automatic skill extraction mechanism further refines reusable patterns, creating a positive feedback loop that improves efficiency over time.
arXiv AI
Jun 4
Adaptive Minds: Empowering Agents with LoRA-as-Tools
arXiv:2510. 15416v2 Announce Type: replace Abstract: We investigate a framework in which LoRA adapters are treated as callable tools that a base language model can dynamically select and invoke.