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103 papers, 5 topic clusters

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3 papers with a successful agent run21 sandboxed reproduction runs

Latest papers

Showing 1–20 of 103

Apr 25, 2026cs.LG260425.0003

Bayesian Optimization: A Pure-Python Sequential Model-Based Optimization Library

Authors pending

Bayesian optimization via Gaussian Process surrogate — installs with pip install bayesian-optimization, runs in <5s with no GPU.

Reference implementationRuns97% match · pre-ledger4 attempts

Apr 25, 2026cs.LG260425.0002

Fundamental Machine Learning Algorithms Implemented from Scratch in Python

Authors pending

A from-scratch NumPy implementation of 20+ ML algorithms with runnable example scripts.

Reference implementationRuns3 attempts

Apr 25, 2026cs.LG260425.0001

micrograd: A tiny scalar-valued autograd engine and neural net library

Authors pending

micrograd is a ~200-line pure-Python autograd engine + neural net library that backpropagates through scalar operations.

Reference implementationRuns40% match · pre-ledger4 attempts

Apr 22, 2026Formal Sciences260423.0001

FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels

Sina Gholami, Abdulmoneam Ali, Tania Haghighi, Ahmed Arafa +1 more

FedSIR is a federated learning framework that identifies noisy labels and clients by analyzing spectral properties of feature representations, then uses clean clients to help relabel corrupted samples. The method combines spectral analysis with noise-aware training strategies to achieve robust collaborative learning despite label noise.

CodeRun failed1 attempt

Apr 22, 2026Formal Sciences260423.0002

Closing the Domain Gap in Biomedical Imaging by In-Context Control Samples

Ana Sanchez-Fernandez, Thomas Pinetz, Werner Zellinger, Günter Klambauer

This paper proposes CS-ARM-BN, a meta-learning method that uses negative control samples to adapt deep learning models to new experimental batches in biomedical imaging, closing the domain gap caused by batch effects. The approach achieves 0.935±0.018 accuracy on drug mechanism-of-action classification, recovering performance from 0.862±0.060 back to near training-domain levels of 0.939±0.005.

Apr 22, 2026Formal Sciences260423.0003

Global Offshore Wind Infrastructure: Deployment and Operational Dynamics from Dense Sentinel-1 Time Series

Thorsten Hoeser, Felix Bachofer, Claudia Kuenzer

This paper presents a global dataset of Sentinel-1 SAR time series tracking offshore wind infrastructure deployment and operations from 2016-2025, with 15,606 time series and expert-annotated benchmarks for monitoring wind farm construction and operational dynamics. The dataset enables independent, high-resolution global monitoring of the rapidly expanding offshore wind sector.

Apr 22, 2026Formal Sciences260423.0004

Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling

Yiming Bian, Joshua M. Akey

Stream-CQSA enables exact self-attention computation on long sequences by decomposing attention into independent subsequence computations that fit within arbitrary memory budgets, allowing billion-token sequences to run on a single GPU without approximation. This removes the assumption that full query, key, and value tensors must fit in device memory, addressing the quadratic memory bottleneck of standard attention.

Apr 22, 2026Formal Sciences260423.0005

Convergent Evolution: How Different Language Models Learn Similar Number Representations

Deqing Fu, Tianyi Zhou, Mikhail Belkin, Vatsal Sharan +1 more

This paper reveals that different language model architectures (Transformers, RNNs, LSTMs) converge on learning similar periodic number representations with periods at 2, 5, and 10, despite being trained differently. The authors identify a two-tiered hierarchy of these features and explain when models learn geometrically separable representations useful for modular arithmetic.

Apr 22, 2026Formal Sciences260423.0006

Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories

Ali Rayat, Yaohang Li, Gia-Wei Chern

This paper introduces gauge-equivariant graph neural networks that embed local gauge symmetries directly into message passing for learning on lattice gauge theories. The approach enables principled machine learning for systems with site-dependent symmetries and nonlocal observables.

Apr 22, 2026Formal Sciences260423.0007

Working Memory Constraints Scaffold Learning in Transformers under Data Scarcity

Pranava Madhyastha, Dagmar Adamcova

This paper integrates working memory constraints into Transformer models through cognitively-inspired attention mechanisms and shows that these constraints improve grammatical accuracy and human alignment when training data is limited. The results suggest that mimicking human cognitive limitations can serve as a useful inductive bias for learning robust language representations.

Apr 22, 2026Formal Sciences260423.0008

Physics-Conditioned Synthesis of Internal Ice-Layer Thickness for Incomplete Layer Traces

Zesheng Liu, Maryam Rahnemoonfar

This paper develops a physics-conditioned neural network to complete incomplete ice-layer thickness measurements from radar data by synthesizing missing layer traces. The approach combines geometric and temporal learning with physical climate model features to recover fragmented or absent layers while maintaining consistency with observed data.

Apr 22, 2026Formal Sciences260423.0009

Efficient Multi-Cohort Inference for Long-Term Effects and Lifetime Value in A/B Testing with User Learning

Dario Simionato, Andrea Tonon, Mingxue Wang, Weiguo Wang +2 more

This paper proposes a method to estimate long-term treatment effects and lifetime value changes in A/B tests for streaming platforms where user churn is costly. It uses inverse-variance weighted estimation across multiple cohorts and parametric decay modeling to capture both steady-state impact and cumulative user value within short experiments.

Apr 22, 2026Formal Sciences260423.0010

Personalized electric vehicle energy consumption estimation framework that integrates driver behavior with map data

Sreechakra Vasudeva Raju Rachavelpula, Sangwhan Cha

This paper presents a personalized BEV energy consumption framework that combines driver behavior prediction using Bidirectional LSTM with physics-based energy modeling and map contextual features. The system accurately estimates State-of-Charge depletion by capturing individual driving patterns across diverse road conditions.

Apr 22, 2026Formal Sciences260423.0011

Coverage, Not Averages: Semantic Stratification for Trustworthy Retrieval Evaluation

Andrew Klearman, Radu Revutchi, Rohin Garg, Rishav Chakravarti +2 more

This paper proposes semantic stratification, a structured evaluation framework for retrieval systems that organizes documents into entity-based clusters and systematically generates queries to ensure comprehensive coverage. It addresses hidden biases in current heuristic evaluation approaches and provides formal guarantees for more trustworthy retrieval assessment.

Apr 22, 2026Formal Sciences260423.0012

V-tableR1: Process-Supervised Multimodal Table Reasoning with Critic-Guided Policy Optimization

Yubo Jiang, Yitong An, Xin Yang, Abudukelimu Wuerkaixi +6 more

V-tableR1 is a reinforcement learning framework that trains multimodal language models to perform rigorous, step-by-step reasoning on tables rather than relying on pattern matching. It uses a critic model to provide feedback on visual reasoning chains and a novel optimization algorithm (PGPO) to improve performance.

Apr 22, 2026Formal Sciences260423.0013

Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems

Beining Wu, Jun Huang

This paper proposes a lifecycle-aware federated continual learning framework for distributed autonomous fleets that addresses both immediate forgetting during training and long-term cumulative drift through layer-selective rehearsal and rapid recovery strategies. The approach is validated theoretically and empirically, including on a real rover testbed.

Apr 22, 2026Formal Sciences260423.0014

AAC: Admissible-by-Architecture Differentiable Landmark Compression for ALT

An T. Le, Vien Ngo

AAC introduces a differentiable landmark-selection module for ALT shortest-path heuristics that maintains admissibility by construction through row-stochastic mixtures of triangle-inequality bounds. The method achieves near-optimal landmark coverage on road networks while being 1.2–1.5× faster than classical farthest-point sampling at matched memory.

Apr 22, 2026Formal Sciences260423.0015

F\textsuperscript{2}LP-AP: Fast \& Flexible Label Propagation with Adaptive Propagation Kernel

Yutong Shen, Ruizhe Xia, Jingyi Liu, Yinqi Liu

F²LP-AP is a training-free label propagation method that adapts to both homophilous and heterophilous graphs using geometric median prototypes and local clustering coefficients. It achieves competitive accuracy with GNNs while being significantly more computationally efficient.

Apr 22, 2026Formal Sciences260423.0016

Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health

Peter Collett, Alexander Johannes Stasik, Simone Casolo, Signe Riemer-Sørensen

This paper proposes a Simulation-Based Inference (SBI) framework using neural networks for fast Bayesian condition monitoring of industrial equipment, specifically heat exchangers. The approach achieves 82× speedup over MCMC while maintaining comparable diagnostic accuracy and uncertainty quantification.

Apr 22, 2026Formal Sciences260423.0017

Near-Future Policy Optimization

Chuanyu Qin, Chenxu Yang, Qingyi Si, Naibin Gu +5 more

Near-Future Policy Optimization (NPO) improves reinforcement learning with verifiable rewards by learning from a policy's own future checkpoints, which are both higher quality and closer to the current policy than external sources. The method achieves significant performance gains on vision-language models, improving from 57.88 to 63.15 on Qwen3-VL-8B-Instruct.

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103 papers, 5 topic clusters

Latest papers

Bayesian Optimization: A Pure-Python Sequential Model-Based Optimization Library

Fundamental Machine Learning Algorithms Implemented from Scratch in Python

micrograd: A tiny scalar-valued autograd engine and neural net library

FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels

Closing the Domain Gap in Biomedical Imaging by In-Context Control Samples

Global Offshore Wind Infrastructure: Deployment and Operational Dynamics from Dense Sentinel-1 Time Series

Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling

Convergent Evolution: How Different Language Models Learn Similar Number Representations

Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories

Working Memory Constraints Scaffold Learning in Transformers under Data Scarcity

Physics-Conditioned Synthesis of Internal Ice-Layer Thickness for Incomplete Layer Traces

Efficient Multi-Cohort Inference for Long-Term Effects and Lifetime Value in A/B Testing with User Learning

Personalized electric vehicle energy consumption estimation framework that integrates driver behavior with map data

Coverage, Not Averages: Semantic Stratification for Trustworthy Retrieval Evaluation

V-tableR1: Process-Supervised Multimodal Table Reasoning with Critic-Guided Policy Optimization

Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems

AAC: Admissible-by-Architecture Differentiable Landmark Compression for ALT

F\textsuperscript{2}LP-AP: Fast \& Flexible Label Propagation with Adaptive Propagation Kernel

Fast Bayesian equipment condition monitoring via simulation based inference: applications to heat exchanger health

Near-Future Policy Optimization

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Efficient LLM Training

Inference-Time Model Improvement

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Machine Learning Optimization Methods

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