optimization

MIT Press's 634-page book "Algorithms for Optimization" is available as a free CC-BY-NC-ND PDF, with the second edition (featuring Julia implementations and new chapters on DCP, surrogate models, and multidisciplinary optimization) releasing January 5, 2027.

A research blog post finding that frontier LLMs can natively read and write neural network weights — diagnosing trained policies, editing weights to fix reasoning loops and boost Qwen3.5-0.8B's MATH-500 from 7.9% to 48.1%, and hand-writing small game-playing networks — a capability the authors call Weight-Space Programming.

Cloudflare details how a targeted build planner and new scheduler for its DNS Zone Builder cut peak memory use by 85% and halved the delay between a DNS change and its data reaching Quicksilver, as zone sizes grow toward hundreds of millions of records.

The paper proposes a surrogate-assisted genetic programming framework for lifelong learning of scheduling rules in dynamic flexible job shop scheduling, using an adaptive two-stage training strategy and task-similarity-based weighting to mitigate catastrophic forgetting while adapting to new tasks.

该论文研究随机舍入（stochastic rounding）如何影响低精度训练中的预测风险，针对两层线性网络推导了匹配的上下界，并给出了随计算量、模型规模、训练时长与精度变化的 scaling laws，揭示前向与后向舍入对最优计算缩放的不同影响。

该论文研究具有已知有向反馈图的对抗性多臂老虎机的一阶遗憾保证，通过固定点采样规则和两层聚合方法，将强可观察图的期望遗憾改进至接近最优的 Õ(√(αL*) + α)，弥合了最坏情况与一阶保证之间的图依赖差距。

论文提出 DecompGym 基准，包含来自九类问题族的 81 个实例，用于评估 12 个 GPT、Claude、Gemini 编码智能体设计与实现 Benders 分解和列生成求解器的能力，并比较了独立重生成与反思两种失败修复策略的效果。

A deep-dive blog post exploring how GCC lowers switch statements, showing its transformation into jump tables (including offset handling and a ~8-entries-per-label sparsity threshold) and falling back to decision trees of comparisons for sparser cases.

A researcher shares that agent loops behave like stochastic gradient descent without gradients, reporting overnight DeepSeek optimizations achieving 2.32ms/step—beating their estimated 2.5ms floor—with observations on diminishing gains, steady ~46% experiment acceptance rates, exponential decay of improvements, and the open challenge of discrete optimization across agent swarms.

LFM3-1.7B and LFM3-VL-2B now run fully offline on iPhone and Android via the open-source Private Mind app, with detailed notes on what it took to export LFM3's novel architecture (gated convolutions, hybrid attention, sliding-window cache tweaks) to ExecuTorch, achieving ~40 tok/s on an iPhone 16 Pro GPU.

该论文指出权重归一化（WN）训练的收益主要源于其诱导出的有效学习率（ELR）轨迹，而非归一化本身，并据此提出 AIR（仿射平方根倒数衰减）调度策略。在 128B token 的 MoE 预训练实验中，AIR 在训练损失、下游性能和超参数鲁棒性上均优于 cosine 等调度方案。

There is ongoing work that will make the OXC parser approximately 3 times faster, according to a teaser tweet hinting at an upcoming release.

该论文确定了在重尾噪声（有界中心 p 阶矩，1<p≤2）下，平滑凸目标的裁剪 SGD 最后一次迭代的时间一致（time-uniform）最优收敛速率，证明了无需已知时间跨度的调度可达 O~(t^{-2(p-1)/(3p-1)})，且不存在更优的规则，回答了未知停止时间对收敛的代价问题。

RadonBench is a new benchmark comparing classical and learning-guided search methods (exhaustive extension, greedy local search, PatternBoost, AlphaZero, AlphaEvolve) for constructing maximum-cardinality Radon-free lattice configurations. None of the audited runs reached the open targets of 11 points in Z^3 or 21 points in Z^4, but the study establishes reproducible near-miss baselines for AI-assisted mathematical discovery.

Garry Tan mentions efforts to slim down GStack to reduce token usage and task latency, with a reply noting CI pipeline times dropping from 7 to 3 minutes while asking about latency spikes from 20 running subagents.

A Stanford CS312 (Deep Learning Alchemy) instructor posts exam-style prediction questions testing deep learning intuition, asking whether learning-rate or weight-decay rescaling can compensate for an 8× batch size increase in an AdamW-trained Transformer.

提出了 PACE（研究证明代理），支持在应用数学中联合进行猜想修订与证明开发，包含研究循环、独立验证器与证据追踪记忆，并发布了聚焦优化领域的 ResearchProofBench 基准，包含 51 个已知证明任务和 6 个开放问题案例。

该论文提出 OptABA-PC，将条件独立性事实的修复形式化为最小代价校正问题，并在回答集编程（ASP）中实现，从而在 Causal ABA 基础上提供带健全性/完备性保证的可审查修复对象与用户异议的正式纠正机制。

提出 CA-DPSGD 框架，利用近似二阶信息构建曲率感知的梯度裁剪空间，以缓解 DPSGD 中的裁剪偏差问题，并在图像和句子分类基准上验证其优于现有裁剪方法。

A paper analyzing LoRA's sensitivity to learning-rate choices and proposing Init[A²], a new initialization scheme with variance scaling that suppresses initialization-induced noise, stabilizing asymmetric LoRA+ training and widening the stable learning-rate region for LLM fine-tuning.