Category
4,920 papers
Normalized Low-Rank Adaptation (NoRA) is introduced, a simple yet effective method that normalizes the down-projection matrices during training, improving standard LoRA without requiring repeated normalization throughout training.
TSPFN is introduced, a foundation model that redesigns TabPFN's architecture for time series data and yields a unified, generalizable framework capable of learning the specificities of medical time series.
LiFT, a language-informed cross-modal framework built on Flow Matching for trend-guided 3D molecular generation across both de novo design and scaffold hopping, and suggests that language-derived chemical priors provide effective trend-level guidance for 3D molecular generation.
This work develops a self-contained methodology for learning parameter-efficient rotational transformations based on Riemannian optimization and empirically validate the proposed rotation-based steering scheme, demonstrating its superiority in intervention efficiency.
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This work introduces a method that maintains near-baseline accuracy, induces usage-based modularity by sparsely routing inputs to neuron groups, and encourages specialization of these modules, such that their activations are correlated with input classes.
CastClaw is presented, a human-in-the-loop autonomous forecasting system built through forecasting-oriented harness engineering that connects data, specialized models, analytical tools, user input, and a versioned execution record in one runtime.
Results provide initial evidence that KV states can serve as transferable computational representations rather than strictly model-local caches, and motivate context mobility as a systems abstraction for reducing redundant prefill across heterogeneous LLM and multi-agent inference workflows.
Gradient descent (GD) is explicit Euler for gradient flow, but a state-accurate continuous-time surrogate need not remain accurate after differentiation. At every fixed nonresonant step size, ordinary automatic differentiation exactly differentiates the executed hard-ReLU GD program. We prove that, over a fixed finite horizon, the GD states converge and these exact discrete derivatives approach an event-free regional propagator, whereas the derivative of the limiting flow also contains speed-normalized activation-event transfers. A prepoint Stieltjes representation separates the absolutely continuous regional Hessian from atomic interface curvature; one nonzero gradient jump produces an exactly rank-one endpoint discrepancy, and global convexity prevents complete multi-event cancellation whenever an event is strict. Nevertheless, a standard family of globally 1-strongly convex residual-ReLU squared-loss risks realizes arbitrarily large reciprocal sensitivity ratios on open initialization sets, with a uniform transversality margin. The same discrete-versus-flow decomposition extends to parameters and reverse-mode adjoints; resolved smoothing in the scalar or autonomous-normal regime and consistent event localization recover the flow sensitivity. The results concern deterministic full-batch, finite-horizon dynamics with a stable finite itinerary of separated same-direction transverse events; they are consistency theorems, not prevalence claims for large-scale training.
This model interleaves reasoning, tool calls, and returns in one left-to-right generation, trained by a supervised warm-up and then outcome-level reinforcement learning against a programmatic reward read directly off the gold call chain, which leaves no learned critic and no judge in the training loop.
An externally anchored leading-order effective field linking empirical dynamics, an interpretable mechanism and neural computation is identified linking empirical dynamics, an interpretable mechanism and neural computation in closed-loop human-AI systems.
A Mean-Calibrated Kernel UCB (MCK-UCB) algorithm is proposed that turns each incomplete sales record into a reliable guide for both inventory and price decisions, using data from past rounds with similar market conditions.
This work calls on the stream-learning community to make bounded resource usage a first-class design objective alongside drift adaptation, and proposes concrete steps toward this goal, including an API through which stream learners can explicitly expose and respect resource budgets.
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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
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