5arXiv cs.CL (Computation and Language)·
OmniaBench: A broad diagnostic benchmark for general AI agents across 354 domains
OmniaBench is a new benchmark for evaluating general AI agents across diverse scenarios, spanning 90 level-1 and 354 level-2 domains derived from app stores, product documents, and web retrieval. The benchmark contains 1,431 tasks with single-turn and multi-turn formats, a ten-dimensional capability taxonomy, and eight atomic difficulty factors for fine-grained analysis. Even frontier models like Claude Sonnet 5 and GPT-5.6-Sol achieve only ~58% Overall Pass@1, revealing persistent weaknesses in planning, constraint maintenance, and adaptive correction. The work addresses a gap in existing agent benchmarks that tend to cover narrow tool ecosystems or interaction formats.
Related guides (4)
Related events (8)
T1-Bench: Multi-scenario agent benchmark across 25 real-world domains
T1-Bench is a new benchmark for evaluating agentic LLM systems in realistic customer-facing, multi-domain environments, covering 25 domains of varying difficulty with interleaved multi-turn scenarios. The authors evaluate 12 proprietary and open-weight models and combine automatic evaluation with human judgments. The benchmark targets gaps in existing agent evals around task complexity, domain diversity, and compositional reasoning across multi-step interactions.
MacAgentBench: New benchmark for AI agents on real-world macOS desktop tasks
MacAgentBench introduces a 676-task benchmark across 25 macOS applications designed to evaluate computer use agents (CUAs) with framework augmentation and fine-grained multi-checkpoint scoring, addressing gaps in existing binary-evaluation benchmarks. Nearly 60% of tasks involve both GUI and CLI interaction, and the benchmark tests 16 models across three agent frameworks. The best result — Claude Opus 4.6 on the OpenClaw framework — achieves 73.7% Pass@1, with performance gains attributed primarily to the skill library rather than framework design. Fine-grained metrics reveal that models with similar Pass@1 scores can differ substantially in sub-goal completion, highlighting limitations of coarse evaluation.
PaperBench: OpenAI Benchmark for Evaluating AI Agents on Research Replication
OpenAI introduces PaperBench, a benchmark designed to evaluate AI agents' ability to replicate state-of-the-art AI research papers end-to-end. The benchmark targets a high-complexity capability: reproducing experimental results from frontier AI research, which requires code generation, experimental design, and scientific reasoning. This positions PaperBench as a tool for tracking progress toward autonomous AI research agents.
Benchmark Agent: Autonomous system for end-to-end benchmark construction
Researchers introduce Benchmark Agent, a fully autonomous agentic system that orchestrates the complete benchmark construction pipeline — from query analysis and subtask design to data annotation and quality control. The system was used to produce 15 benchmarks spanning text understanding, multimodal understanding, and domain-specific reasoning, with evaluation via human judges, LLM-as-a-judge, and consistency checks. The work addresses two persistent problems in the field: the labor intensity of benchmark creation and rapid performance saturation after release. Code and a demo will be publicly released.
UniClawBench: Capability-driven benchmark for proactive agents on real-world tasks
Researchers from HKU MMLab introduce UniClawBench, a benchmark for evaluating proactive AI agents across 400 bilingual real-world tasks organized around five foundational capabilities: Skill Usage, Exploration, Long-Context Reasoning, Multimodal Understanding, and Cross-Platform Coordination. Unlike prior benchmarks, it evaluates agents in live Docker containers with step-by-step checkpoints and a closed-loop multi-turn evaluation strategy using executor, supervisor, and user agents. The benchmark disentangles base model capabilities from agent framework design choices by testing state-of-the-art models across multiple frameworks. Code and benchmark are publicly released.
Automated Benchmark Auditing for AI Agents and Large Language Models (ABA)
The paper introduces Auto Benchmark Audit (ABA), an agentic framework that systematically audits AI benchmark tasks for issues such as ambiguous specifications, environment conflicts, and incorrect ground truths. Applied to 168 benchmarks across nine domains including NeurIPS publications, ABA identifies critical issues in over 25.7% of evaluated tasks. The authors demonstrate that filtering out flawed tasks materially shifts model rankings and improves average performance on SWE-bench Verified and Terminal-Bench 2 by 9.9% and 9.6% respectively, indicating that current benchmark scores are significantly distorted by task quality problems. The agentic tool and annotations are released publicly.
AssetOpsBench: Bridging the Gap Between AI Agent Benchmarks and Industrial Reality
IBM Research introduces AssetOpsBench, a benchmark designed to evaluate AI agents on industrial asset operations tasks, hosted on Hugging Face. The benchmark targets the gap between existing general-purpose agent benchmarks and real-world industrial deployment scenarios. It provides a playground environment for testing agent capabilities in enterprise/industrial contexts.
DeepSWE, ProgramBench, and ITBench-AA emerge as harder successors to SWE-bench for agent evaluation
Three new benchmarks — DeepSWE (by Datacurve), ProgramBench (Meta/Stanford/Harvard), and ITBench-AA (IBM/Artificial Analysis) — are positioned as more rigorous replacements for the SWE-bench family, which models have largely saturated. DeepSWE tests feature implementation using private codebases and human-written problems; ProgramBench evaluates agents' ability to recreate functional programs from scratch; ITBench-AA measures root-cause diagnosis in real-world IT incident scenarios. Current top performers include GPT-5.5 (70% on DeepSWE), Claude Opus 4.7 (46.7% on ITBench-AA), and Claude Opus 4.7 (3% on ProgramBench at the 95% pass threshold), illustrating that even frontier models have substantial headroom.