Algorithms3 articles

Algorithms

Articles

Minimum Bayes Risk Decoding: How Utility Consensus and Distributional Reranking Outperform Mode-Seeking in Large Language Models
Autoregressive large language models generate text by estimating conditional probability distributions over token sequences. In conventional inference pipelines, generating the final output sequence relies almost entirely on search algorithms designed to locate high-probability trajectories: greedy decoding, beam search, or stochastic sampling with temperature and top-p filtering. However, theoretical and empirical analyses demonstrate that the most probable sequence under a model's distributio
August 22, 20261 min
Jacobi and Lookahead Decoding: How Parallel Fixed-Point Iteration Accelerates Autoregressive Inference Without Draft Models
Standard autoregressive generation in large language models operates as a strictly sequential process. To generate a sequence of $K$ tokens, an inference engine must execute $K$ successive forward passes through the network. In single-request serving regimes (batch size 1), each forward pass is heavily memory-bandwidth bound: the GPU must stream billions of model parameters from High Bandwidth Memory (HBM) into on-chip SRAM to process a single token, leaving tensor compute cores severely underut
August 21, 20261 min
Tree of Thoughts in Large Language Models: How Search Trees, State Evaluation, and Backtracking Expand Deliberate Reasoning
Standard autoregressive large language models generate text sequentially from left to right. At each step $t$, the network samples the next token according to a conditional probability distribution over the vocabulary: P(w_t | w_1, w_2, ..., w_{t-1}) While Chain-of-Thought (CoT) prompting (Wei et al., 2022) encourages models to output intermediate reasoning steps, the underlying computational process remains a linear path through token space. If the model makes an early logical error or choose
August 20, 20261 min

Algorithms

Articles

Minimum Bayes Risk Decoding: How Utility Consensus and Distributional Reranking Outperform Mode-Seeking in Large Language Models

Jacobi and Lookahead Decoding: How Parallel Fixed-Point Iteration Accelerates Autoregressive Inference Without Draft Models

Tree of Thoughts in Large Language Models: How Search Trees, State Evaluation, and Backtracking Expand Deliberate Reasoning