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2026-10-15

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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  • 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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  • Study finds LLM reasoning uses 3.7× more inference energy on average: A study measured inference energy for 86 open-weight LLM checkpoints across seven benchmarks and six hardware platforms. Most configurations used a median 2.6–4.8× more energy than another configuration scoring at least as well, with gaps up to 27×; reasoning raised energy about 3.7× on average.

    AI Research • arXiv

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