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4,920 papers
This work develops an assumption-lean partial identification framework that uses such measurements as weak shadow variables, defined as outcome-informative proxies that are conditionally independent of missingness given the true outcome and observed covariates.
This work introduces a hybrid quantum graph learning architecture designed explicitly for unsupervised learning in the noisy intermediate-scale quantum (NISQ) regime that combines a variational quantum feature extraction layer with an edge-local and qubit-efficient quantum message-passing mechanism inspired by the Quantum Alternating Operator Ansatz (QAOA) framework.
This work presents a methodology for reducing stellar contamination and instrument-specific noise from exoplanet transmission spectra using neural networks, in particular the so-called Denoising AutoEncoders (DAEs), and demonstrates that DAEs outperform conventional correction methods in computational efficiency while maintaining high accuracy.
LSTR (Latent Sparse Transcoder Reasoning), a framework that turns sparse transcoders from post-hoc diagnostic tools into in-loop, intervenable transition components for latent reasoning, and suggests that sparse latent transitions can preserve the compression benefits of latent reasoning while making the resulting trajectories more inspectable and intervenable.
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The results suggest that explicitly regulating representation geometry is an effective complement to optimization balancing, and provide evidence that geometry-aware regularization can improve multimodal learning across diverse architectures and domains.
This work introduces a social-science-grounded set of ten hidden intention categories and shows that they are trivially inducible, providing the first systematic analysis of why hidden intentions are difficult to detect.
This tutorial demonstrates how duality-informed iterative schemes such as the alternating direction method of multipliers, and the primal-dual hybrid gradient can be learned and adapted through representative case studies.
SOCIAL CAPTION is introduced, a framework grounded in interaction theory to evaluate social understanding abilities of MLLMs along three dimensions: Social Inference, the ability to make accurate inferences about interactions; Holistic Social Analysis, the ability to generate comprehensive descriptions of interactions; Directed Social Analysis, the ability to generate relevant information from interactions.
It is found that sensitivity to demographic cues is distributed across internal units and varies substantially across models, suggesting that effective mitigation requires understanding distributed, task-specific mechanisms rather than manipulating a small set of identified neurons alone.
This paper examines whether synthetic training artifacts can support the complete SFT-to-RL cycle for competitive programming, and synthesizes tasks, verified solutions, and reliable test cases that serve as reward signals for reinforcement learning.
Under \(Q\)-function realizability and local regularity, it is established that soft control locally inherits the contraction of policy evaluation in a discounted-occupancy norm, and local contraction and finite-sample convergence with estimated ratios are established.
Combining occupancy-weighted FQE with fitted occupancy-ratio evaluation gives an end-to-end guarantee governed by the complexities and direct approximation errors of the value-function and occupancy-ratio classes, removing the need for Bellman completeness.
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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
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