FDE-Gym is a new benchmark that evaluates AI agents on 95 end‑to‑end delivery tasks drawn from real customer scenarios across 18 sectors. Unlike traditional benchmarks that use well‑specified tasks, FDE-Gym places agents in simulated customer environments where they must clarify ambiguous requirements, interact with stakeholders, and uncover unstated constraints to improve a customer’s success metric. In tests, the best model configuration achieved a mean score of 0.56 and solved only 14 of the 95 tasks, highlighting a significant gap between well‑specified problem solving and real‑world AI delivery.
arXiv:2610.14539v1 Announce Type: new Abstract: Real-world business requests often leave key requirements unstated because frontline practitioners struggle to articulate rules they know from experien...
The paper investigates whether large language model (LLM) coding agents can function as forward‑deployed engineer (FDE) agents by discovering customer requirements and executing deployment plans in a real enterprise text‑to‑SQL scenario. In a simulated engagement, agents must uncover acceptance criteria, data permissions, and hardware constraints before planning and delivering a post‑training recipe. Across 15 models, the best agents uncovered over 80% of hidden requirements, yet only 25% of runs met all acceptance criteria, revealing bottlenecks in discovery, execution reliability, compute usage, and benchmark relevance.
xDailyBench is a new benchmark comprising 248 tasks across 51 real‑life scenarios, designed to evaluate large language models on everyday professional consultation. The tasks are based on actual user requests and assessed with detailed binary rubrics that capture both explicit instructions and implicit needs inferred from context. In tests of 11 leading models, the best achieved a 75.6% task‑level score, yet all models struggled more with implicit requirements, showing gaps of at least 9 percentage points.
The paper introduces $ au^ au$-Bench, a benchmark that turns the construction of AI agents into a measurable task. In this environment a developer agent receives real business records, client requirements, a production API, an existing codebase, and constraints on cost and models, and must deliver a complete customer‑service agent. The benchmark evaluates performance by deploying the agent against simulated users, revealing that current state‑of‑the‑art models achieve only 23.9% success while an expert‑written reference scores 82.2%.
ASI‑Bench is a new benchmark that evaluates AI systems on their ability to conduct innovative exploration and autonomous scientific research across 11 domains, using 60 project‑level tasks. It progressively removes human methodological guidance to test whether AI can independently select methods, execute research, and produce verifiable results. Results from 18 state‑of‑the‑art agent–model configurations show a sharp performance drop when guidance is reduced, indicating current systems still rely heavily on human input.
arXiv:2609.37658v1 Announce Type: new Abstract: LLM agents are increasingly expected to support enterprise workflows, where tasks often involve missing information, uncertainty, feedback, and long-te...
StartupBench is a new benchmark that evaluates general-purpose agents on end-to-end workflows derived from AI startup products that have proven market adoption. It translates real-world product workflows into deliverable-oriented tasks and assesses them with detailed rubrics. The study finds that even the best models complete only about 30% of these tasks, highlighting challenges such as complex instruction following and domain expertise.
arXiv:2607. 08964v1 Announce Type: new Abstract: AI agents have become capable of autonomously completing short, well-specified tasks.
arXiv:2607. 13034v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires.
arXiv:2607. 01647v1 Announce Type: cross Abstract: Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society.
StartupBench is a benchmark that evaluates general‑purpose agents on end‑to‑end workflows derived from real AI startup products that have proven market adoption. It translates these product workflows into deliverable‑oriented tasks and assesses them with detailed rubrics that capture complex requirements. Even the best current models complete only about 30% of the tasks, highlighting failures in instruction following and domain expertise.
Agentick is a unified benchmark for sequential decision‑making agents that evaluates RL, LLM, VLM, hybrid, and human agents on 37 procedurally generated tasks across six capability categories, four difficulty levels, and five observation modalities via a single Gymnasium‑compatible interface. It includes a Coding API, oracle reference policies, pre‑built SFT datasets, a composable agent harness, and a live leaderboard. An evaluation of 27 configurations and over 90,000 episodes shows no single approach dominates, with GPT‑5 mini leading overall, PPO excelling in planning and multi‑agent tasks, and the reasoning harness boosting LLM performance by 3‑10×, while ASCII observations outperform natural language.