Laurent Bindschaedler — accepted AI-conference papers

2026

The Invisible Lottery: How Subtle Cues Steer Algorithm Choice in LLM Code Generation

Akanksha Narula, Mofasshara Rafique, Laurent Bindschaedler

ICML 2026poster

Large language models (LLMs) now generate substantial production code, often for tasks with multiple valid algorithmic solutions. The hidden risk is that incidental prompt cues can steer \emph{which} algorithm is selected, even when all outputs pass the same tests. Prompt sensitivity is well studied…

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2025

Cache Saver: A Modular Framework for Efficient, Affordable, and Reproducible LLM Inference

Nearchos Potamitis, Lars Henning Klein, Bardia Mohammadi, Chongyang Xu, Attreyee Mukherjee, Niket Tandon +2

EMNLP 2025

Inference constitutes the majority of costs throughout the lifecycle of a large language model (LLM). While numerous LLM inference engines focusing primarily on low-level optimizations have been developed, there is a scarcity of non-intrusive client-side frameworks that perform high-level optimizati

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