Optimization story: Bloom inference

Incredibly Fast BLOOM Inference with DeepSpeed and Accelerate

Making thousands of open LLMs bloom in the Vertex AI Model Garden

Fast Inference on Large Language Models: BLOOMZ on Habana Gaudi2 Accelerator

ReLOBGen: Replayable Limit Order Book Message Generation

arXiv:2609.35867v1 Announce Type: new Abstract: We propose ReLOBGen, a method for generating limit order book (LOB) messages that are replayable by construction. Replayability is required for closed-...

Beyond the Node: Clade-level Selection for Efficient MCTS in Automatic Heuristic Design

arXiv:2602. 00549v2 Announce Type: replace Abstract: While Monte Carlo Tree Search (MCTS) shows promise in Large Language Model (LLM) based Automatic Heuristic Design (AHD), it suffers from a critical over-exploitation tendency under the limited computational budgets required for heuristic evaluation.

EvoDuet: Bilevel Co-Evolution of Web Searching and Task Solving for Scientific Discovery

arXiv:2609.40340v1 Announce Type: new Abstract: Evolutionary search with large language models (LLMs) can stall when progress requires external knowledge the model lacks. Supplying relevant documents...

Bayesian Optimization with Rich Auxiliary Information via LLMs

The paper introduces Bayesian Optimization (BO) techniques that incorporate rich auxiliary information—such as training curves, expert notes, images, and prior knowledge—using large language models (LLMs). Three new methods are proposed to integrate this auxiliary data into BO, and they are evaluated on hyperparameter optimization benchmarks and a real-world nuclear fusion task. The results show that these LLM-enhanced BO methods consistently outperform standard BO and existing LLM-based optimization approaches.

Scientific discovery as meta-optimization: a combinatorial optimization case study

Scientific discovery is fundamentally an optimization problem, defined by a vast "state space" of theories and experiments, and an evaluation criterion based on quality, novelty, and validity. Large language models (LLMs) have enabled automated exploration of this space, but we argue that simultaneous modification of the evaluation criteria is equally important.

Scientific discovery as meta-optimization: a combinatorial optimization case study

arXiv:2606. 26728v1 Announce Type: new Abstract: Scientific discovery is fundamentally an optimization problem, defined by a vast "state space" of theories and experiments, and an evaluation criterion based on quality, novelty, and validity.

DARTree: Speculative Diffusion Decoding with Autoregressive Draft Trees

Speculative decoding losslessly accelerates autoregressive language models by verifying multiple draft tokens in parallel. Diffusion-based drafters further reduce proposal latency by predicting an entire token block in parallel, but their position-wise distributions are marginal rather than conditioned on tokens selected along each draft path.

Principle-Evolvable Scientific Discovery via Uncertainty Minimization

arXiv:2602. 06448v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based scientific agents have accelerated scientific discovery, yet they often suffer from significant inefficiencies due to adherence to fixed initial priors.

LLM-Guided Program Evolution for Circle Packing: Breaking 10 Packomania Records for $28

The paper introduces Discovery Loop, a lightweight system that employs a large language model (LLM) to iteratively evolve optimization algorithms for the Packomania circle‑packing benchmark. Starting from a simple seed solver, the LLM proposes algorithmic improvements guided by a scoreboard and a history of prior ideas, evaluates each candidate against an independent verifier, and retains only successful changes. Within 15 iterations and a total LLM cost of $27.72, the system broke 10 Packomania records for N between 101 and 114, improving the best known solutions by 2.4%–5.4%. The work demonstrates that a cost‑efficient, LLM‑driven approach can rapidly advance state‑of‑the‑art solutions in a complex optimization domain, suggesting broader potential for democratizing automated scientific discovery.