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arXiv 最近完成扫描:2026-11-15 18:02 · 全站最近入库:2026-11-14 15:09各信源进度 →

统计口径与数据新鲜度

统计 2026-11-14 18:48 — 2026-11-15 18:48(北京时间) · 点击数字查看对应简报;AI 相关包含在新增论文内。

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找到 3 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-15 18:01(北京时间)
版本更新 · v4arXiv:2602.12162

Amortized Molecular Optimization via Group Relative Policy Optimization

Muhammad bin Javaid, Hasham Hussain, Ashima Khanna-Reiter, Berke Kişin, Jonathan Pirnay, Alexander Mitsos, Dominik G. Grimm, Martin Grohe

原文摘要

In structurally constrained molecular optimization, state-of-the-art methods treat every new input structure as a separate instance, restarting an expensive oracle-driven search from scratch and scaling poorly to settings with many starting structures or expensive oracles. While amortized approaches that learn a transferable policy could in principle remove this bottleneck, existing methods struggle to generalize to diverse structural constraints at inference time. We present AMORTIX, a Graph Transformer model that natively supports such constraints, optimizing molecular structures in a single rollout with zero inference-time oracle calls. A central challenge for amortized training in this domain is that optimization difficulty varies drastically across starting structures. We address this by centering rewards within groups of completions that share the same starting structure. We evaluate on goal-directed scaffold decoration, including single- and multi-target kinase inhibitor design, and on a few-shot prodrug case study. AMORTIX outperforms both amortized baselines and search-based instance optimizers on kinase scaffold decoration, and ablations across 23 objectives show that group-relative centering improves over REINFORCE and PPO and is substantially more consistent across scaffold splits. The prodrug case study further demonstrates transfer of learned modification rules to unseen drug structures.

cs.LG

新论文 · v1arXiv:2611.11030

Building and Auditing Chemical Datasets with LLMs: A General LLM-as-a-Judge Framework Applied to Homogeneous Catalytic PET Depolymerisation

Muhammad bin Javaid, Karim K. Ben Hicham, Enkhnyam Battulga, Alexander Mitsos, Martin Grohe

原文摘要

Large language models can rapidly extract structured multivariate experimental data from the chemistry literature, yet verifying thousands of generated records without ground truth remains difficult. We present a general, citation-grounded extraction and verification methodology centered on an independent "LLM-as-a-judge", applied to the literature on homogeneous catalytic poly(ethylene terephthalate) (PET) depolymerisation. In this pipeline, an extractor LLM generates schema-enforced records from research papers with citations to document chunks tagged with universally unique identifiers (UUIDs); an independent LLM judge then reads the full paper alongside each record, flags unsupported fields, and proposes cited corrections. Prompt ablations show that requiring chunk-level citations not only provides provenance down to specific paragraphs and tables but also improves extraction accuracy. Before deploying the judge at scale, we benchmarked it against manual evaluation on extractions from a hand-curated reference set of 295 ionic-liquid-catalysed PET glycolysis experiments. The judge agreed with human evaluation on 85.7% of reviewed records and was significantly more effective than a heuristic string-matching baseline at catching errors across related experiments. Deployed across 3,942 extracted records from 561 papers, the framework accepted 67.5% of them as written. Its proposed corrections concentrated on quantitative fields, where the extractor often conflated related experiments. We release the extracted dataset with the complete LLM judge audit logs, as well as the Chemistry Data Extractor Toolkit, an open-source application implementing this end-to-end pipeline in a code-free, browser-based environment for document parsing, customizable schema and prompt design, model-agnostic extraction and auditing, interactive provenance-highlighted review, and reproducible dataset bundling.

cs.CL · cs.AI · cs.DL

版本更新 · v2arXiv:2611.05048

图相似性与最优传输

Graph Similarity Meets Optimal Transport

Jens Breitung, Timo Gervens, Martin Grohe

AI 速读 基于摘要

该论文研究用最优传输松弛双射对齐来定义图距离时,最优传输版本与双射版本之间的整性间隙,并给出若干距离的上下界与计算复杂性结果。

cs.DM

查看原文摘要

A natural way to compare two graphs is to align their vertex sets by a bijection and to measure the difference of the aligned adjacency matrices in a matrix norm. Depending on the norm, this yields, for example, the edit distance, the cut distance, the spectral distance, and the local edit distance. To make these distances applicable to graphs of different order, one can consider blowups of the graphs or, equivalently, relax bijections to fractional alignments, that is, optimal transport plans between the uniform distributions on the vertex sets, in the spirit of Gromov-Wasserstein distances. We investigate how much the optimal transport versions can differ from their bijective counterparts, that is, we quantify their integrality gaps. For the edit distance, we improve the best known upper bound on the integrality gap from 3 to 2 and the lower bound from 6/5 to 4/3. For the cut distance, we approach Lovász's conjecture that $δ_\square \le 2δ_\square^{\rm OT}$ from both sides. We improve the previously best known upper bound $δ_\square \le 32 \cdot (δ_\square^{\rm OT})^{1/67}$ (Borgs, Chayes, Lovász, Sós and Vesztergombi, Adv. Math. 2008) to $δ_\square \le C_\varepsilon \cdot (δ_\square^{\rm OT})^{1-\varepsilon}$ for every $\varepsilon > 0$, and we construct graphs $G,H$ with $δ_\square(G,H) = 2δ_\square^{\rm OT}(G,H)$, establishing a factor-2 lower bound that matches Lovász's conjecture. For the local edit and spectral distances, we obtain upper bounds of order $\sqrt{t\ln(1/t)}$ and $\sqrt{t}$, respectively, where $t$ is the corresponding optimal transport distance, and we show that, unlike for the edit and cut distances, no linear upper bounds are possible. Finally, we show that computing the optimal transport distances is NP-hard, and we give hardness of approximation results for the cut, spectral, and local edit distances.