As the number of cores within one processor will continue to increase, while per-core frequency is likely to stagnate, speculative execution in general and data value prediction in particular, will once again be a prime candidate for speeding up execution of applications with limited inherent parallelism. In order to succeed however, we need to improve the semantic information the applications will be able to provide to the hardware; in this paper we present initial results based on using formal contracts to convey such information to the run-time environment in order to guide the type and scope of speculative execution. Experiments with an application that is traditionally considered hard to parallelize -- Huffman decoding -- show the potential of such an approach, without the need to re-write the actual application. One of the strengths of this method is that it can make efficient use of large number of cores, opening a possible path for scaling applications with limited amount of parallelism on massively multi-core processors.