Author

Shriram Chennakesavalu

3 papers indexed here

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Open access Aug 2026

Efficient, Few-Shot Directed Evolution with Energy Rank Alignment.

This work uses a general-purpose "post-training" algorithm grounded in statistical physics that employs quantitative experimental rankings to directly produce a sampler for diverse, high fitness sequences with fewer data points than competing methods.

Sebastian Ibarraran, Shriram Chennakesavalu, Frank Hu et al. · Journal of Chemical Informat... · 0 citations

#machine learning Preprint Sep 2026

Training Large Language Models for Small-Molecule Design with Synthetic Task Scaling

This work investigates whether LLMs can learn molecular design strategies from cheaper synthetic tasks that generalize to expensive molecular lead optimization settings and suggests that scaling post-training using synthetic tasks is an effective strategy for adapting LLMs to high-cost experimental scenarios that are t...

Frank Hu, Shriram Chennakesavalu, Zi-Cheng Wang et al. · 0 citations

#machine learning Preprint Sep 2026

Frontier LLMs are effective batch optimizers: Assessing reasoning models in continuous and discrete settings

It is found that while LLMs are competitive zero-shot batch optimizers for numerical test functions, their performance is brittle compared to classical non-LLM optimization approaches.

Frank Hu, Shriram Chennakesavalu, David Graff · 0 citations