CL-Bench is the first expert-validated benchmark for continual learning in frontier LLMs across six real-world domains, showing limited gains and that naive in-context learning outperforms dedicated memory systems.
Available at http://arxiv.org/abs/2403.05175 (accessed 14 December 2025)
4 Pith papers cite this work, alongside 37 external citations. Polarity classification is still indexing.
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Pith papers citing it
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2026 4representative citing papers
RPSFT improves the in-domain versus out-of-domain performance trade-off during LLM supervised fine-tuning by penalizing rotations in pretrained singular subspaces as a proxy for loss-sensitive directions.
TRACED is a biophysical model that quantifies cell size distribution, extracellular diffusivity, tortuosity, and cell density in human gliomas from time-dependent diffusion MRI data.
citing papers explorer
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Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments
CL-Bench is the first expert-validated benchmark for continual learning in frontier LLMs across six real-world domains, showing limited gains and that naive in-context learning outperforms dedicated memory systems.
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Rotation-Preserving Supervised Fine-Tuning
RPSFT improves the in-domain versus out-of-domain performance trade-off during LLM supervised fine-tuning by penalizing rotations in pretrained singular subspaces as a proxy for loss-sensitive directions.
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TRACED: In vivo imaging of extracellular intrinsic diffusivity, tortuosity, cell size distribution and cell density in human glioma patients
TRACED is a biophysical model that quantifies cell size distribution, extracellular diffusivity, tortuosity, and cell density in human gliomas from time-dependent diffusion MRI data.
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