Can frontier LLMs earn $1 million from real-world freelance software engineering?
arXiv:2607. 14108v1 Announce Type: cross Abstract: This paper introduces tool efficiency, a new quantitative metric to evaluate the rate of useful tool calls in an LLM agent trajectory.
arXiv:2601. 06401v2 Announce Type: replace Abstract: Large language models are becoming increasingly significant in financial applications.
arXiv:2606. 27406v1 Announce Type: cross Abstract: Software engineering, whether performed by humans or by AI agents, requires reasoning about how software behaves.
OpenAI’s B2B Signals research shows how frontier enterprises deepen AI adoption, scale Codex-powered agentic workflows, and build durable competitive advantage.
arXiv:2606. 05548v1 Announce Type: cross Abstract: The rapid proliferation of Agent Development Kits (ADKs), SDK-level frameworks for building LLM-powered autonomous agents, has outpaced any empirical understanding of how framework choice affects agent performance.
SWE-bench Verified is increasingly contaminated and mismeasures frontier coding progress. Our analysis shows flawed tests and training leakage.
OpenAI and Intuit have entered a $100M+ multi-year partnership to launch Intuit app experiences in ChatGPT and expand Intuit’s use of OpenAI’s frontier models to power personalized financial tools.
arXiv:2608. 11683v1 Announce Type: new Abstract: AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow.
By combining commercial experience and deep LLM expertise with OpenAI’s models, Rox makes every seller a top 1% seller.
API Partnership with Stack Overflow Stack Overflow and OpenAI today announced a new API partnership that will empower developers with the collective strengths of the world’s leading knowledge platform for highly technical content with the world’s most popular LLM models for AI development.
arXiv:2606. 11672v1 Announce Type: cross Abstract: This paper explores the value of agentic AI tools for cybersecurity purposes.
AI agents are increasingly deployed for professional investment research, yet no benchmark captures the complexity of the full investor workflow. Existing benchmarks mainly target financial data extraction, a narrow slice that current models have largely saturated, while reference-based metrics and generic LLM-as-a-judge scoring fall short on the open-ended, long-form answers that real analyst queries demand.