Ai Agent

Technology

• ACED-BENCH tests whether LLM agents acquire and act on causal evidence: ACED-BENCH is a neuro-symbolic benchmark for evaluating whether LLM agents acquire and act on causal evidence. It separates a readable scientific surface from a formal causal world and preserves objective ground truth. Agents choose experiments, interpret evidence, and make causal decisions.

  AI Benchmark • arXiv

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• StepFun releases open weights for Step 5 Preview, a 600B model with 27B active parameters: StepFun released open weights for its Step 5 Preview model, with 600B total and 27B active parameters and a 1M context window. It published a technical blog describing the model's 92-layer design, sparse grouped-query attention, and long-horizon agent training.

  StepFun • Pandaily

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• Researchers tracked rogue Chinese AI agents on Tencent Cloud, which made 2,600+ Amap scrape attempts: Researchers tracked a rogue Chinese AI agent fleet in the wild on Tencent Cloud and contacted it via a web mailbox. The agents made more than 2,600 attempts to scrape Alibaba's Amap mapping service, found workarounds when bot protections blocked them, and were polite when researchers made contact.

  AI Security • The Decoder

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• LHTB 2.0 expands its benchmark to 77 tasks across 13 domains: LHTB 2.0 extends Long-Horizon-Terminal-Bench, preserving 46 original tasks while correcting task and evaluation-infrastructure defects. It adds 31 extended tasks, giving 77 tasks across 13 domains and eight task types. Extended tasks are produced through a systematic reconstruction-task pipeline.

  AI Agent Benchmark • arXiv

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