RAmmStein: Regime Adaptation in Mean-reverting Markets with Stein Thresholds -- Optimal Impulse Control in Concentrated AMMs

This paper introduces RAmmStein, a deep reinforcement learning framework that optimizes liquidity provision in concentrated Automated Market Makers by solving an impulse control problem via a Hamilton-Jacobi-Bellman quasi-variational inequality, thereby significantly reducing rebalancing frequency and gas costs while maximizing net returns through regime-aware, mean-reversion-informed decision-making.

Pranay Anchuri2026-03-10🤖 cs.LG

Benchmarking GNN Models on Molecular Regression Tasks with CKA-Based Representation Analysis

This paper benchmarks four GNN architectures on molecular regression tasks, demonstrating that a hierarchical fusion framework combining GNNs with molecular fingerprints outperforms standalone models by over 7% in RMSE, while CKA analysis reveals that GNN and fingerprint embeddings occupy highly independent latent spaces despite high convergence among isotopic GNN architectures.

Rajan, Ishaan Gupta2026-03-10🤖 cs.LG

MrBERT: Modern Multilingual Encoders via Vocabulary, Domain, and Dimensional Adaptation

The paper introduces MrBERT, a family of efficient, open-source multilingual encoders built on the ModernBERT architecture that achieves state-of-the-art performance in specific languages and specialized domains while leveraging Matryoshka Representation Learning to reduce inference and storage costs.

Daniel Tamayo, Iñaki Lacunza, Paula Rivera-Hidalgo, Severino Da Dalt, Javier Aula-Blasco, Aitor Gonzalez-Agirre, Marta Villegas2026-03-10🤖 cs.LG

Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments

This paper addresses the scarcity of expert textual relevance labels in large-scale app store search by leveraging a specialized, fine-tuned LLM to generate millions of high-quality labels, which, when used to augment the production ranker, significantly improves both offline metrics and real-world conversion rates, particularly for tail queries lacking reliable behavioral data.

Evangelia Christakopoulou, Vivekkumar Patel, Hemanth Velaga, Sandip Gaikwad, Sean Suchter, Venkat Sundaranatha2026-03-10🤖 cs.LG

End-to-end Differentiable Calibration and Reconstruction for Optical Particle Detectors

This paper introduces the first end-to-end differentiable optical particle detector simulator that unifies simulation, calibration, and reconstruction into a single gradient-based framework, demonstrating improved accuracy, speed, and flexibility for analyzing large-scale neutrino detectors compared to traditional methods.

Omar Alterkait, César Jesús-Valls, Ryo Matsumoto, Patrick de Perio, Kazuhiro Terao2026-03-10🤖 cs.LG

Attn-QAT: 4-Bit Attention With Quantization-Aware Training

This paper introduces Attn-QAT, the first systematic 4-bit quantization-aware training framework for attention mechanisms that ensures stable FP4 training and inference by matching low-precision recomputation in the backward pass and correcting implicit precision assumptions, thereby eliminating quality drops and delivering up to 1.5x speedup on FP4-capable GPUs without relying on outlier-mitigation heuristics.

Peiyuan Zhang, Matthew Noto, Wenxuan Tan, Chengquan Jiang, Will Lin, Wei Zhou, Hao Zhang2026-03-10🤖 cs.LG

How Well Do Multimodal Models Reason on ECG Signals?

This paper introduces a reproducible, scalable framework for evaluating multimodal models on ECG signals by decomposing reasoning into "Perception" (verified via code generation) and "Deduction" (verified via retrieval against clinical criteria) to address the limitations of existing manual or superficial evaluation methods.

Maxwell A. Xu, Harish Haresamudram, Catherine W. Liu, Patrick Langer, Jathurshan Pradeepkumar, Wanting Mao, Sunita J. Ferns, Aradhana Verma, Jimeng Sun, Paul Schmiedmayer, Xin Liu, Daniel McDuff, Emily B. Fox, James M. Rehg2026-03-10🤖 cs.LG

Leveraging Model Soups to Classify Intangible Cultural Heritage Images from the Mekong Delta

This paper proposes a robust framework combining the hybrid CoAtNet architecture with model soups ensembling to effectively classify Intangible Cultural Heritage images from the Mekong Delta, achieving state-of-the-art performance on the ICH-17 dataset by reducing variance and enhancing generalization in data-scarce, high-similarity settings.

Quoc-Khang Tran, Minh-Thien Nguyen, Nguyen-Khang Pham2026-03-10🤖 cs.LG

Embedding interpretable 1\ell_1-regression into neural networks for uncovering temporal structure in cell imaging

This paper proposes a hybrid neural network architecture that embeds an interpretable, 1\ell_1-regularized vector autoregressive model within a convolutional autoencoder to effectively extract and visualize sparse temporal dynamics from two-photon calcium imaging data while preserving non-sparse spatial information.

Fabian Kabus, Maren Hackenberg, Julia Hindel, Thibault Cholvin, Antje Kilias, Thomas Brox, Abhinav Valada, Marlene Bartos, Harald Binder2026-03-10🤖 cs.LG

CGL: Advancing Continual GUI Learning via Reinforcement Fine-Tuning

This paper introduces CGL, a continual GUI learning framework that mitigates catastrophic forgetting by dynamically balancing Supervised Fine-Tuning and Reinforcement Learning through an entropy-guided proportion adjustment mechanism and a specialized gradient surgery strategy, validated by a new AndroidControl-CL benchmark.

Zhenquan Yao, Zitong Huang, Yihan Zeng, Jianhua Han, Hang Xu, Chun-Mei Feng, Jianwei Ma, Wangmeng Zuo2026-03-10🤖 cs.LG

Information Routing in Atomistic Foundation Models: How Task Alignment and Equivariance Shape Linear Disentanglement

This paper introduces Compositional Probe Decomposition (CPD) to demonstrate that linear disentanglement of geometric and compositional information in atomistic foundation models is primarily driven by task alignment rather than architecture, revealing a significant performance gradient where models trained on specific properties like HOMO-LUMO gaps outperform energy-trained models and exhibit symmetry-dependent information routing.

Joshua Steier2026-03-10🤖 cs.LG