RealClawBench: Live benchmark framework built from real developer-agent sessions
RealClawBench is a new benchmark framework that converts real OpenClaw developer-agent sessions into reproducible, automatically scored evaluation tasks. It addresses realism gaps in existing agent benchmarks through reconstructed execution environments and deterministic verifiable scorers, releasing 281 executable tasks sampled to preserve the source session distribution. Evaluation of 14 contemporary models shows the best system solves only 65.8% of tasks, indicating substantial headroom on realistic developer-agent workloads.
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Claw-Anything: Benchmark for Always-On Personal Assistants with Broad Digital World Access
Claw-Anything is a new benchmark designed to evaluate LLM agents acting as always-on personal assistants with access to long-horizon activity histories, interdependent backend services, and multi-device GUI/CLI interaction. The benchmark simulates months of user activity to create complex, noisy world states and evaluates both reactive and proactive assistance. GPT-5.5 achieves only 34.5% pass@1, revealing a substantial capability gap versus prior narrower benchmarks. An accompanying automated data-generation pipeline produces 2,000 training environments and yields a 23.7% improvement over the base model.
Claw-SWE-Bench: A benchmark for evaluating agent harnesses on multilingual coding tasks
Researchers introduce Claw-SWE-Bench, a multilingual SWE-bench-style benchmark and adapter protocol designed to fairly compare heterogeneous agent harnesses ("claws") on GitHub issue-resolution tasks. The benchmark contains 350 instances across 8 languages and 43 repositories, with an 80-instance Lite subset for cost-efficient validation. Key findings show adapter design dominates raw model choice: a minimal adapter scores 19.1% Pass@1 versus 73.4% for a full adapter using the same GLM 5.1 backbone, and harness choice and model choice each shift Pass@1 by roughly 27-29 percentage points. The work also introduces cost accounting as a first-class evaluation axis alongside accuracy.
BigCodeBench: The Next Generation of HumanEval
Hugging Face introduces BigCodeBench, a new code generation benchmark designed to succeed HumanEval by offering more challenging and diverse programming tasks. The benchmark aims to better evaluate LLMs on real-world coding scenarios involving complex function calls and library usage. A leaderboard accompanies the release to track model performance across the community.
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.
AARRI-Bench evaluates frontier LLMs and agents on granular research-intern-level tasks
Researchers introduce AARR (Act As a Real Researcher), a new benchmark series targeting whether AI agents can emulate the professionalism, thoroughness, and nuanced judgment of human researchers in granular research scenarios—not just macro-level task execution. The first benchmark, AARRI-Bench, tests frontier models and agentic harnesses, finding that even the best configuration (Mini-SWE-Agent with Claude Opus 4.7) achieves only 68.3% success, frequently missing subtle but critical details obvious to human researchers. The work argues that closing the gap requires deeper modeling of research behavior rather than more complex scaffolding.
AutoLab benchmark evaluates frontier models on ultra long-horizon iterative research and engineering tasks
AutoLab is a new benchmark of 36 expert-curated tasks across system optimization, puzzle-solving, model development, and CUDA kernel optimization, designed to test agents on sustained closed-loop improvement under wall-clock budgets rather than single-turn or short-horizon settings. Evaluation of 17 frontier models finds that persistence in iterative benchmarking and feedback incorporation — not initial attempt quality — is the dominant success predictor. Claude Opus 4.6 stands out as the strongest performer, while most models including proprietary ones either terminate early or exhaust budgets with minimal progress. The benchmark, harness, and task artifacts are open-sourced.
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.
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.