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Repro - Attention's forward pass and Frank-Wolfe
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Jude Ighomena
JIghomena
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JIghomena/icml26-XjSd2CtV20Organizations
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Repro - Softmax as Linear Attention in the Large-Prompt Regime: a Measure-base
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Review and verify research claims using LLM pass@k results
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Repro - Optimal and Scalable MAPF via Multi-Marginal Optimal Transport and Sch
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Repro - Parsimonious Learning-Augmented Online Metric Matching
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Repro - Online Conformal Prediction via Universal Portfolio Algorithms
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Repro - Convergence Analysis of the Lion Optimizer in Centralized and Distribu
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Repro - Alignment-Sensitive Minimax Rates for Spectral Algorithms with Learned
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Repro - Set-Preserving Calibration from Conformal P-Values to E-Values
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Repro - (Doubly) Exponential Lower Bounds for Follow the Regularized Leader in
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Repro - A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithm
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Repro - Anytime Detection of Strategic Deviations in Multi-Agent Systems
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Repro - Randomized Feasibility Methods for Constrained Optimization with Adapt
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Repro - Why Deep Jacobian Spectra Separate: Depth-Induced Scaling and Singular
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Repro - Unveiling the Structure of Do-Calculus Reasoning via Derivation Graphs
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Repro - Mirror Descent Under Generalized Smoothness
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Repro - The Catastrophic Failure of *the* k-Means Algorithm in High Dimensions
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Repro - A Tight Theory of Error Feedback Algorithms in Distributed Optimizatio
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Repro - Fast Estimation for Forest Matrix of Signed Graphs
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Repro - A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversaria
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Repro - Welfare-Optimal Classification with Accuracy Auctions
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Repro - On Densest $k$-Subgraph Mining and Diagonal Loading: Optimization Land
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Display ICML 2026 reproduction results for daVinci-Dev paper
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Repro - Maximin Relative Improvement: Fair Learning as a Bargaining Problem
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Explore reproduction results for PipeSD research paper
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Repro - Online Packet Scheduling with Deadlines and Learning
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Review reproducibility verdicts for research claims
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Repro - Inference of Online Newton Methods with Nesterov's Accelerated Sketchi
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Reproduce research claims with an LLM and get a verdict