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The paper proposes ReLOBGen, a limit order book message generator that ensures replayability by construction by selecting referenced orders from the current resting book and masking invalid tokens, achieving 100% replayability in 500-message rollouts and a 2.7-3.6x speedup per replayed message over the LOBS5 baseline.
The paper introduces Coffee, a plug-and-play framework that guides discrete diffusion models at inference time using compiled finite-state objectives, avoiding exponential enumeration of token completions while supporting hard constraints and learned soft objectives across symbolic, language, and biological benchmarks.
MIT researchers have developed PottsMPNN, a machine-learning framework that incorporates physical principles to improve protein sequence generation and stability prediction, enabling the design of novel proteins beyond native sequences.
Explores a storyboard-planned approach for AI cinematics that builds sequence structure before generating shots individually, resulting in more coherent video compared to single-prompt generation, while noting current weaknesses like identity drift and interaction physics.
This paper introduces the Structured Recurrent Mixer (SRM), an architecture enabling algebraic conversion between parallel training and recurrent inference without specialized kernels. Experiments show SRMs achieve significantly higher throughput and concurrency compared to Transformers, with effective performance in reinforcement learning tasks.