arXiv AI
EvoFlux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents
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arXiv AI
Jun 12
Evoflux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents
arXiv:2606. 12674v1 Announce Type: new Abstract: Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents.
arXiv Machine Learning
Aug 12
FlowScout: From Execution Feedback to Reliable Tool-Using Agent Workflows
arXiv:2608. 10039v1 Announce Type: new Abstract: Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures.
arXiv AI
Sep 30
WEFT: Scaling Tool-Use Post-Training for General-Purpose Agents
arXiv:2609.36887v1 Announce Type: new Abstract: Recent efforts to scale tool-use post-training have largely centered on the synthesis of executable environments, which constitute only one component o...
arXiv AI
Jul 17
MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers
arXiv:2607. 14642v1 Announce Type: new Abstract: As Model Context Protocol (MCP) servers emerge as the core infrastructure for connecting LLMs with external tools, existing benchmarks leverage real-world MCP servers to evaluate LLM agents' tool-using capabilities.
arXiv AI
5d ago
When Simulation Lies: A Sim-to-Real Benchmark and Domain-Randomized RL Recipe for Tool-Use Agents
The paper introduces RobustBench‑TC, a benchmark that tests tool‑use language agents under 22 real‑world perturbations affecting observations, actions, rewards, and transitions. Experiments on 21 models show that observation noise has minimal impact, while reward‑relevant and transition noise drastically reduce accuracy, and model size alone does not mitigate these gaps. The authors propose ToolRL‑DR, a domain‑randomized reinforcement‑learning recipe that improves robustness, retaining most clean accuracy and narrowing performance gaps even on unseen transition perturbations.
arXiv AI
1d ago
Jev-LiteMAS: Separating Decisions from Generation in Multi-Agent Workflows
Jev-LiteMAS is a multi‑agent framework that separates decision‑making from generative execution in large‑language‑model workflows. By using a lightweight decision model (Jev) to dynamically select experts and screen artifacts, it closes the planning and review gaps left by pure generative planners and reviewers. Experiments on four benchmarks show that Jev‑LiteMAS cuts generative‑LLM token usage by 12.7–54.6% while keeping accuracy within 1.34 percentage points and reducing latency in most settings.