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This paper introduces CaRE, a compute-aware evaluation protocol for masked diffusion language models that standardizes step counts, metrics, and stochasticity. It demonstrates that previous comparisons conflate algorithmic improvements with evaluation artifacts, showing temperature explains most MAUVE variance and compute-matched comparisons reverse published rankings.
PlaceMem proposes a compute-aware memory plane for lifelong agents, using versioned memory capsules that unify semantic content and reusable runtime state to enable correction-aware reuse and avoid redundant computation.
This paper introduces a compute-aware evaluation framework for adversarial robustness of LLMs, proposing risk-compute curves and metrics based on FLOPs to better assess attack costs, finding that alignment training has non-monotonic effects and compute costs vary across models and harm categories.