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6,549 papers
A simple, sequence-only pipeline can match and surpass leading methods by combining 330 interpretable sequence descriptors with TabPFN, a tabular foundation model that performs in-context prediction in a single forward pass without gradient-based training or hyperparameter search.
This paper proposes a trainable Neural Cellular Automata (NCA) based surrogate model for learning long time PDE dynamics that achieves the lowest long-horizon relative errors on the majority of the experiments.
This work proposes a multi-domain generative domain mapping approach based on Star Generative Adversarial Networks (StarGAN) to improve fault detection on data-scarce wind turbines and proposes a proxy metric that detects poor performance at training time, despite an absence of anomalies.
LFPG-RL is developed and evaluated, which integrates link-flow propagation guidance (LFPG) into proximal policy optimization (PPO), and results support the contention that the method is a more efficient and accurate online OD demand calibration method compared to existing ones.
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A dynamic explanation of how data statistics and architecture jointly shape token embeddings in language models is provided, and an implicit bias in the space of data statistics is revealed: training proceeds from simpler, low-order statistical relations toward increasingly complex, context-dependent ones.
Tail-Replay is presented, a prefix caching mechanism that enables unconstrained token-level prefix reuse in hybrid large language models and is evaluated on three Gated DeltaNet-based hybrid models using the LongBench and RULER benchmarks.
This work discovers that certain attention heads exhibit sequential consistency in their attention patterns, which can be persistently identified using a coefficient-of-variation-based algorithm, and proposes CateKV, a hybrid KV cache method that retains only critical token information for consistent heads, thereby reducing KV cache size and computational overhead.
BCPPO (Bachelier-Inspired Constrained Proximal Policy Optimization), a proximal policy optimization (PPO) method, supports a practical balance among reward, caution around cost predictions that vary across trained critics, and policy-only deployment.
CAESAR-LDAR is presented, an error-controlled multivariate learned compressor that augments a shared CAESAR-V backbone with two complementary mechanisms: a trainable orthogonal transform that reorganizes dependence across aligned latent channels, and a causal autoregressive hierarchical prior that captures local spatial structure left after transformation.
The threshold part of Question 4 of the COLT 2025 open problem "Data Selection for Regression Tasks" is resolved, and an erroneous claim circulating in a recent unrefereed preprint is correct.
Memory-augmented drafting is introduced for long-context SD, equipping a strong independent draft with compressed draft-side KV memory and incrementally updates this memory to retain distant information and exact recent context.
Localized extreme precipitation is a major trigger of urban flash floods and landslides, yet producing nowcasts that combine fine spatial detail with probabilistic uncertainty remains challenging. Here we introduce exPreCast-ENS, a conditional residual diffusion framework that transforms the deterministic 4 km radar nowcaster exPreCast into a 1 km probabilistic ensemble while correcting systematic forecast errors. Conditioning on both the forecast and preceding radar observations lets the ensemble-mean correct the baseline rather than perturb it, while members represent unresolved fine-scale variability. Over the Korean Peninsula, skill improves with ensemble size. In two high-impact events in 2023, a 30-member ensemble recovers 38-47% of heavy-rain pixels missed by exPreCast while retaining approximately 95% of its correct detections and alarming on under 1% of the pixels it correctly left clear. The method generates a 1-h forecast in 3.4 s on a single GPU and yields consistent improvements on the French regional MeteoNet radar dataset.
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.
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