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arXiv:2508. 02721v2 Announce Type: replace-cross Abstract: While powerful, the inherent non-determinism of large language model (LLM) agents limits their application in structured operational environments where procedural fidelity and predictable execution are strict requirements.
The paper introduces OSCAR, an LLM‑based framework that translates business descriptions into accurate optimization models while verifying and improving them through a simulator, coder, and reviewer. OSCAR uses a cost‑ordered escalation strategy to select among LLMs of varying price and capability, achieving 95–100% accuracy on benchmark problems with local, open‑weight models. The framework also provides competitive guarantees and token‑cost advantages over existing LLMs like Codex and Claude Code.
arXiv:2607. 20468v1 Announce Type: new Abstract: AI agents are increasingly used to automate research and development tasks, yet existing benchmarks typically evaluate them on prescribed workflows or narrow action spaces.
arXiv:2511. 02734v3 Announce Type: replace Abstract: Current evaluations of Large Language Model (LLM) agents primarily emphasize task completion, often overlooking resource efficiency and adaptability.
The paper investigates how to design portfolios of agentic AI workflows that vary in reasoning strategy, verification structure, and compute cost. It proposes a portfolio-and-selector framework where multiple workflow executions are run and the best output is chosen, balancing additional compute with potential gains in accuracy. The authors develop exact and approximate optimization methods, evaluate them on three datasets, and show modest improvements over the best single workflow.
The paper introduces AgenticBBO-Bench, a cross‑domain benchmark for evaluating large language model agents on black‑box optimization tasks such as synthetic functions, hyperparameter tuning, database tuning, chip design, and molecular design. Experiments show that agentic BBO outperforms direct LLM‑based methods in all five domains and beats the best numerical optimizers in four. The study also analyzes the impact of optimization tools, task semantics, prior knowledge, and the LLM’s role, finding that numerical tools are not consistently helpful, task semantics are broadly useful, and numerical optimizers can capitalize on agent‑generated search trajectories.