arXiv AI
Salesforce Koa: An Enterprise Language Model for Agentic Tool Use
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arXiv Machine Learning
Oct 19
Lean4AgenticRL: Advancing Agentic RL with Verifiable Multi-level Process Rewards
Lean4AgenticRL is a new framework that introduces multi‑level process rewards grounded in the Lean4 formalization schema to improve agentic reinforcement learning for large language models. It constructs a problem‑specific workflow system that balances capability and controllability, enabling verification of agent behavior and providing rewards at low‑, mid‑, and high‑levels. Experiments show that models trained with Lean4AgenticRL, such as LAR‑27B, outperform comparable models, achieving 77.3% on TerminalBench‑2.1 and surpassing the base model by an average of 9.89%.
arXiv AI
Jul 28
CRAFT: Learn the Schema, Execute the Plan
arXiv:2607. 22642v1 Announce Type: new Abstract: Enterprise coding agents translate natural-language analytical requests into executable code over proprietary APIs, schemas, and metric definitions.
arXiv AI
Jun 30
Pushing Forward Pareto Frontiers of Proactive Agents with Behavioral Agentic Optimization
arXiv:2602. 11351v2 Announce Type: replace Abstract: Proactive large language model (LLM) agents aim to actively plan, query, and interact over multiple turns, enabling efficient task completion beyond passive instruction following and making them essential for real-world, user-centric applications.
arXiv AI
Aug 24
AgentMercury: Your Agent Can Synthesize Verifiable Environments for Business Scenarios at scale
AgentMercury is a scalable framework that synthesizes executable environments from high‑level business scenarios instead of task‑specific benchmarks. It creates a persistent world with entities, services, tools, and invariants, allowing diverse tasks and interaction trajectories to emerge naturally. The authors generated 4,783 environments across 14 industries and 50 countries, and training reinforcement‑learning agents on them improved performance on enterprise workflows and out‑of‑domain benchmarks, while the construction process itself can be learned to increase authoring success.
arXiv AI
Oct 1
CompoWorld: Compositional Environment Scaling for General Agents
arXiv:2609.33665v2 Announce Type: replace Abstract: Automatically generated environments provide a scalable source of interaction data for training general agents. However, existing approaches mainly...
arXiv AI
Oct 2
It Takes Workflows to Evolve Better Workflows
The paper "It Takes Workflows to Evolve Better Workflows" introduces FloWright, a method that uses a hierarchical, structure‑aware reward system to allow one or more roles in a multi‑agent workflow to self‑evolve without extra models or data. It also proposes DataWright, an adaptive data hardening technique that transforms existing datasets into more challenging workflow‑level tasks. Experiments on document, slide, chart, code, math, and finance tasks show that small open models trained with FloWright can improve performance by up to +7.41%, with co‑evolving multiple roles yielding the largest gains. "whyItMatters":"The work demonstrates that optimizing beyond the workflow generator—by enabling multiple agents to co‑evolve—can substantially enhance the effectiveness of multi‑agent workflows for complex real‑world tasks."