Category
4,920 papers
This tutorial review synthesizes recent developments on diffusion models for SBI, covering design choices for training, inference, and evaluation, and highlights opportunities created by various concepts such as guidance, score composition, flow matching, consistency models, and joint modeling.
This work proposes SpIDER (Spatially Informed Dense Embedding Retrieval), a dense retrieval approach that integrates LLM-based reasoning with graph-based exploration of the codebase and introduces SpIDER-Bench, a graph-structured benchmark curated from SWEPolyBench, SWEBench-Verified and Multi-SWE-bench, spanning Python, Java, JavaScript and TypeScript repositories.
Extensive experiments on MS-COCO across three representative diffusion backbones demonstrate that TPSO substantially improves diversity, boosting performance from 1.10 to 4.18, while maintaining image quality with only a modest inference-time overhead.
DCC is the first data-centric ML compiler for PIM systems that jointly co-optimizes data rearrangements and compute code in a unified tuning process to enable high performance execution.
Reach audiences
Advertise in front of researchers, engineers, and readers.
SPADE (Soil moisture Pattern and Anomaly DEtection), which is the first LLM-based framework specifically developed for soil moisture time-series analysis, is proposed, which is the first LLM-based framework specifically developed for soil moisture time-series analysis.
Rank-One Safety Injection (ROSI), a white-box method that amplifies a model's safety alignment by permanently steering its activations toward the refusal-mediating subspace, is proposed, suggesting that targeted, interpretable weight steering is a cheap and potent mechanism to improve LLM safety, complementing more resource-intensive fine-tuning paradigms.
Language-Guided Tuning is introduced, a framework that employs multi-agent Large Language Models to automatically optimize configurations through natural language reasoning to demonstrate substantial improvements over traditional optimization methods while maintaining high interpretability.
Long-context reasoning requires accurately identifying relevant information in extensive, noisy input contexts. In this work, we propose PERK (Parameter Efficient Reasoning over Knowledge), a scalable approach for learning to encode long contexts using gradient updates at test time. Specifically, PERK employs two nested optimization loops in a meta-training phase. The inner loop rapidly encodes contexts into a low-rank adapter (LoRA) that serves as a parameter-efficient memory module for the base model. Concurrently, the outer loop learns to use the updated adapter to accurately recall and reason over relevant information from the encoded long context. Our evaluations on several long-context reasoning tasks show that PERK significantly outperforms the standard long-context finetuning, achieving average absolute performance gains of up to 20% for Qwen-2.5 (0.5B&7B) on synthetic and real-world long-context reasoning. PERK also maintains its advantages across model scales and families. Compared to specialized long-context LLMs, PERK matches or surpasses their performance. Finally, our analyses show PERK is more robust to reasoning complexity, length extrapolation, and the positions of relevant information in contexts. https://perk-long-context.web.app
We propose a novel test for assessing partial effects in Fr\'echet regression with responses lying on the Bures-Wasserstein manifold. Under the null hypothesis, we show that the statistic admits a degenerate V-statistic approximation whose limiting distribution is a weighted mixture of chi-squared random variables, with weights determined by the eigenvalues of an integral operator associated with a reproducing kernel Hilbert space (RKHS) kernel. We establish the asymptotic validity and consistency of the proposed test. Its finite-sample performance is examined through simulation studies. We apply the proposed test to study the effect of age, while controlling for other covariates, on gene co-expression structure in single-cell data.
This paper addresses the problem of optimally estimating the watermark proportion in mixed-source texts, and proposes efficient estimators for this class of methods, and derive minimax lower bounds for any measurable estimator based on pivotal statistics, showing that their estimators achieve these lower bounds.
RNop represents a shift in mRNA optimization methodology: by infusing explicit and interpretable knowledge, the"black-box"mRNA design can be transformed into a predictable, explainable engineering problem.
A framework of mirror descent linearized augmented Lagrangian methods that employs two-point stochastic zeroth-order gradient estimators and exploits non-Euclidean mirror descent geometry is proposed to solve nonconvex constrained stochastic zeroth-order optimization problems with exact constraints and stochastic objective evaluations.
Education Innovation
Machine Intelligence
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
General Science
GPT-Lab
· gpt-lab.eu
Sep 3, 2026
Adaptive AI agents can help make BIM data more machine-readable by navigating IFC models, interpreting inconsistent information, and mapping it to defined standards. In this blog, Alok Rawat shares findings from a real-world pilot in construction workflows. The post Adaptive AI Agents in Construction Workflows appeared first on GPT-Lab.
Climate & Sustainability
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
