We argue that learning visual representations without labels requires a training signal jointly complete across three non-overlapping objectives: semantic invariance across augmented views, patch-level spatial prediction, and representational non-degeneracy. We formalize these as the observation, prediction, and regularization principles and prove (i) that combining observation and prediction without regularization admits the constant encoder as a global minimizer under negative-free alignment; (ii) that the two objectives are gradient-complementary and structurally non-conflicting at the encoder output; and (iii) that the momentum encoder converges to the same fixed point as the online encoder and provides no collapse guarantee at convergence. Contrastive alignment provides only self-limiting collapse resistance, formalized via an explicit gradient-decay argument. Dropping prediction withholds the spatial training signal by construction; dropping observation forfeits cross-view semantic invariance by construction; at the scale we study, no pair substitutes for the third. Every major self-supervised method is a special case of a single unified energy decomposition. We pair every theoretical claim with a controlled experiment, including a patch-retrieval evaluation for the spatial consequence of prediction.
This work designs and operationalizes a governance-aware, multi-tenant AI sandbox that supports structured experimentation and produces reusable evaluation evidence across stakeholders and yields lessons learned and practical considerations that inform deployment and future evolution of governance-aware sandbox platforms.
Muhammad Waseem, M. Islam, Md Nasir Uddin Shuvo et al.· arXiv.org· 0 citations
Multi-Objective In-context Knowledge Editing (MO-IKE), a multi-objective RL algorithm that formulates prompt construction for in-context knowledge editing as a Constrained Markov Decision Process, enabling more balanced and globally coherent prompt construction.
Xu-Zhong Wang, Maiqi Jiang, Tejal Nair et al.· 1 citation
The Colombo Tea Auction (CTA) plays a vital role in determining global tea prices, yet the relationship between local weather conditions and price behavior across different tea catalogues has not been thoroughly explored. In this study, we develop a novel, structured dataset by extracting information from 105 weekly broker reports spanning late 2023 to 2026, and combined with region-specific weather data. Our analysis focuses on four main tea catalogues of Sri Lankan tea: High Grown, Low Grown, Off-Grade, and Dust. To better understand the factors influencing tea prices, we apply Granger causality analysis alongside tree-based machine learning models: Random Forest, XGBoost, LightGBM, and Gradient Boosting. Our results show that while market dynamics are primary drivers, weather conditions also have significant effects. Notably, Low Grown tea shows strong sensitivity to precipitation and sunshine duration (p<0.05) across 1-3-week lags. Off-Grade and Dust catalogues also exhibit significant responses to temperature variations. Catalogue-specific modelling outperformed unified approaches, with LightGBM emerging as the superior model for three out of four catalogues. Overall, this study highlights the importance of considering both localized weather patterns and catalogue-level differences when forecasting tea prices, offering a more precise and practical framework for the tea industry.
H. Mallawarachchi, Senilka Madurapperumage, Nadil Kulathunge et al.· 0 citations
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SatDL is presented, a data-redistribution framework designed to minimize total end-to-end learning time and onboard energy consumption in satellite-based distributed learning, and develops a Distributor-Critic framework that jointly models and optimizes data-transfer delay and training time.
Hao Wu, Kin Whye Chew, Yi-Zhan Han et al.· 0 citations
LUCAID is an agentic AI system for precision lung cancer pathology that combines diagnostic reasoning with nine modules that cover the full routine workflow, from quality control, tumor detection and segmentation, histological subtyping, tumor microenvironment profiling, tumor cellularity quantification, and predictive biomarker scoring to automated structured report generation.
M. Eich, K. Standvoss, Timo Milbich et al.· 0 citations
This work presents \textsc{GTA-RAG}, a graph-trajectory-augmented RL framework for multi-turn retrieval-augmented reasoning that consistently outperforms RL-based RAG baselines with both Qwen2.5-3B and Qwen2.5-7B backbones, while substantially improving evidence-chain coverage.
Jun Chen, Yongchao Liu, Pengyu Qiu et al.· 0 citations
The core design principle of DELE-w0.5 is to model how the physical world changes under robot actions, rather than how its visual appearance evolves frame by frame, which enables cheaper training and low-latency inference.
Fenghao Lei, Zhixiong Huang, Long Yang et al.· 0 citations
Physics-informed neural networks applied to the level-set formulation of interface advection commonly augment the residual and initial-condition losses with an eikonal regulariser, penalising the deviation of $\|\nabla\phi\|$ from unity. A previous two-dimensional study identified this weight as the dominant hyperparameter and found its optimum shifts by four orders of magnitude between rigid-body and deforming flows, but left open whether these principles transfer to three dimensions and whether single-seed results survive run-to-run variability. We answer both by repeating the weight selection across four 3D benchmarks (translating sphere, rotating sphere, slotted sphere, reversed vortex), sweeping six weights with three seeds at full training budget under a pre-registered selection rule. The ordering transfers: the selected weight tracks how far the exact solution departs from the signed-distance property, spanning four decades from $10^{-1}$ where it holds exactly to $10^{-5}$ where the interface is stretched. Values transfer only benchmark by benchmark; two of four carry over unchanged and two do not, so inheritance must be verified. The multi-seed protocol reveals that at small weights the seed-to-seed standard deviation equals the error itself, and the regulariser reduces it by more than an order of magnitude, buying reproducibility as well as accuracy. We benchmark against a fifth-order WENO solver on identical grids and error measures; the classical scheme is more accurate on all four problems, by two orders of magnitude on smooth rigid advection, with a margin that narrows with geometric difficulty and is smaller in volume conservation than in the field norm. Finally, we show that the relative $L_2$ error cannot certify the preservation of thin features, and report a feature-restricted measure that can.
This work formalizes multi-model human evaluation as a best-arm identification problem in a multi-armed bandit setup with correlated arms, where pulling an arm corresponds to human-evaluating a model, and proves the optimality of the proposed algorithms and shows that it improves discrimination between top-performing models.
Vilém Zouhar, Julia Kreutzer, A. Lavie et al.· 0 citations
A scheme to train a better transformation for a given image dataset is developed, using isometric tensor networks, inspired by quantum many-body theory, to parameterize the basis, and train it with Riemannian optimization.
Shiwen An, Zhongyi Ni, Huanhai Zhou et al.· 0 citations
This paper uses machine learning methods to address the question of how to efficiently establish dualities of supersymmetric quiver gauge theories for Seiberg dualities of supersymmetric quiver gauge theories and finds that for quivers with a modest number of quiver nodes, different network architectures tend to outperform deterministic algorithms.
J. Heckman, S. Meynet, Alessandro Mininno et al.· arXiv.org· 1 citation
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
MIT News · Artificial Intelligence· news.mit.eduAug 24, 2026
