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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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• 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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• Nature Will unveils Natura-1, a 50B MoE pretrained on 1M hours of human video: Nature Will's Natura-1 is a 50-billion-parameter MoE pretrained on one million hours of first-person human video. The model was then adapted with limited robot data to control 20-DoF robot hands.

  Robotics • Pandaily

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