mlsanityData to 14 Oct 2026

CFAgentBench: A Reproducible Environment and Benchmark for Autonomous Construction-Finance Agents

arXiv 2606.22000 ↗First posted 20 Jun 2026cs.AI cs.CL cs.LGcited by 2

On the map: LLM agents and tool use → Financial trading and forecasting agents

Abstract (arXiv)

We introduce CFAgentBench, a reproducible, self-hostable environment and benchmark for autonomous construction-finance agents: a CFO/controller-class agent operating across the real software stack a US construction finance team runs - ERP, project management, email, documents, pay applications, payroll, certified payroll, lien waivers, and bank/treasury portals. It contains 1,014 machine-gradeable task specifications across 8 domains and 77 families, every family grounded in a real source; a self-validated subset of 40 tasks (54 with a project-management extension) is compiled into oracle-validated executable evaluators, the runnable suite reported here. Following WebArena, the benchmark runs on an executable environment rather than static traces: 35 mock applications (31 reconciled to one company book, plus 4 PM platforms) over 9 archetypes, each implementing a uniform self-hostable app contract, so every task is graded by functional correctness - a state diff plus forbidden-side-effect checks plus required-output regexes - with an LLM judge used only for reply quality, never as reward. A distinguishing principle is a money-movement guard: 278 instances embed a payment, payroll, e-signature, or e-filing step where the correct behavior is to stop and stage for human approval, and executing even the correct transaction fails the task. The public split (n=711) is sized for a 95% Wilson half-width of +/-4.1%; a private, contamination-protected split (n=303) is reserved for remote scoring. In a first three-model open-weight sweep (k=5), the strongest agent reaches pass^1 = 0.67 but only pass^5 = 0.38 - losing 43% of its successes when required to repeat them under temperature-0 decoding. The within-model pass^1 to pass^5 collapse and sharp per-domain heterogeneity are clear evidence that single-attempt accuracy overstates deployable construction-finance competence.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 7 of its 10 references that are in the map. First posted on arXiv 20 Jun 2026.

Builds on

7 of 10 references.

References · 7

  1. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
  2. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
  3. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,947
  4. 2024τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domainscited by 1,402
  5. 2023Gorilla: Large Language Model Connected with Massive APIscited by 1,332
  6. 2024AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agentscited by 401
  7. 2025SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?cited by 279

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Benchmarking LLM Agents for Wealth-Management Workflows
  2. 2026FORCE-Bench: A Benchmark, Dataset, and Evaluation Harness for Agentic AI in Enterprise Finance
  3. 2026BankerToolBench: Evaluating AI Agents in End-to-End Investment Banking Workflows
  4. 2026ττ-Bench: An Environment for End-To-End, Realistic Agent Construction
  5. 2026FinProBench: Evaluating Financial AI Agents with Role-Grounded Rubrics Derived from Professional Deliverables
  6. 2026EnterpriseClawBench: Benchmarking Agents from Real Workplace Sessions
  7. 2026OpenFinGym: A Verifiable Multi-Task Gym Environment for Evaluating Quant Agents
  8. 2026AgentSWE: Can Coding Agents Build the Agent You Actually Want?