optimization

The paper introduces NS-RIS, a scalable Newton-Schulz retraction-based algorithm for learning hidden quantum Markov models on the Stiefel manifold, providing the first mathematical performance guarantee and empirical evidence that HQMMs can outperform EM-trained HMMs on non-quantum-generated data.

Introduces CEDAR, an autonomous method that uses LLM agents with Monte Carlo Tree Search to discover complex systems satisfying user-specified behavioral goals, reducing human effort and enabling goal-directed design.

This paper presents a deep reinforcement learning approach for solving vehicle routing problems, demonstrated through three industrial truck planning case studies. The proposed method achieves over 10% cost reduction compared to baseline results and discusses generalization to more VRP variants.

The author shares an agent-design-review Skill for systematically diagnosing and optimizing Agent architectures, covering Prompt, tool permissions, context, security, memory, evaluation, cost, observability, and more, and outputting evidence-based P0/P1/P2 issues to help avoid common pitfalls.

A deep-dive educational article exploring dynamic programming as a unifying principle behind shortest-path algorithms, neural network training, and context-free grammar parsing, tying together automata, optimal control, and linear programming.

Tony Finch revisits his Gregorian-to-Julian day conversion algorithm, incorporating tricks from Ben Joffe to produce a more efficient and robust formula.

An AI agent called KISS Sorcar achieved a verified 1.59x geometric-mean speedup on SQLite across four benchmarks in under 8 hours for less than $150, passing over 1 million SQLite tests. This highlights the growing capability of coding agents.

RPCS3's ARM port now runs 60% faster and draws 25% less power after fixing a busy-wait timer bug, replacing x86 pause with ARM ISB, and reworking LLVM code generation. The gains come from low-level ARM optimizations driven by a cheap Android handheld test device.

Jimmy Ostler explores tail-call interpreters in Rust, implementing and benchmarking various VM dispatch techniques including switch dispatch, subroutine threading, and tail-call optimized machines.

This position paper argues that many LLM failures stem from evaluating outputs independently and proposes a self-consistency framework that treats diverse techniques as special cases of consistency optimization.

This paper proposes Hyper-ES, a subspace-based evolution strategy framework for LLM reasoning that obtains descent directions via lightweight gradient-based fine-tuning and then uses CMA-ES to merge layer-wise DARE-TIES coefficients, consistently outperforming GRPO-LoRA while requiring fewer gradient updates.

This paper introduces a scenario-weighted adversarially robust posture optimization engine for military asset allocation, proposing CEV and RobustCEV optimizers that outperform greedy baselines under adversarial threat uncertainty.

ReASearch is a unified framework where a single tool-using agent internalizes the search policy for optimizing prompts, programs, and ML workflows, outperforming specialized baselines across 14 tasks.

The article investigates whether recurring LLM workloads can be replaced by automatically synthesized deterministic pipelines of typed ML/NLP operators, and asks for feedback on feasibility and approaches.

A deep-dive blog post walks through a Rust performance PR in GreptimeDB that makes Prometheus read conversion 10x faster, with benchmark reproduction and code analysis.

Tests et réglages détaillés pour optimiser DeepSeek-V4-Flash-0731 en GGUF sur une RTX 3090, atteignant ~15 tok/s à 128K de contexte grâce à différentes quantifications et paramètres de chargement.

An in-depth technical blog post examining Ruby Hash memory usage, comparing it to Structs, and exploring historical implementation changes and potential optimizations for shrinking hashes.

This paper proposes SCORE, a self-concordance-inspired quasi-Newton method for training physics-informed neural networks (PINNs). It uses a decrement-coupled shifted secant geometry to improve final accuracy on nonlinear PDE benchmarks without requiring Hessian computations.

This paper introduces a constrained mixed-strategy GroupDRO framework for equitable system-prompt selection, assigning weights to existing prompts to minimize worst-case information-quality loss across demographic groups and metrics. Experiments across five LLMs on bilingual medical and finance benchmarks show consistent reductions in worst-case quality drops while preserving average performance.

LaPrune introduces a differentiable sparse-selection layer that independently controls budget and mask hardness at million scale, using a LapSum barrier and normalized second-moment constraint to approximate hard top-k selection while preserving selected mass.