4 citations · 13 across the 30 of their papers we have counts for
33 papers
AlphaDiana: A Measurement System for Agents in Verifiable Reasoning Tasks
Zhanke Zhou, Zongze Li, Weikai Huang +16
Agents pair reasoning models with harnesses that organize model calls, execute tools, load skill instructions, and retain history in memory. Understanding agent performance require…
LinearPFN: Amortized Variable Selection for Linear Models with Interactions
Louis Schiekiera, Max Zimmer, Christophe Roux +3
Spike-and-slab regression is a standard Bayesian formulation of variable selection: it returns a posterior distribution over which candidate effects are active rather than a single…
Optimal Gradient-Norm Minimization in Non-Euclidean Hölder-Smooth Convex Optimization
Nico Pelleriti, Maryam Shiran, David Martínez-Rubio +2
Minimizing gradients of a convex function is an important problem across optimization and learning tasks. The gradient provides a directly computable certificate of approximate sta…
Neural Field Tokenizations with Hierarchy and Spatial Locality Priors
Alonso Urbano, David W. Romero, Max Zimmer +1
Neural fields parameterize data as functions from coordinates to values, providing a unified framework for representation learning across modalities. Existing approaches are domina…
What Do Evolutionary Coding Agents Evolve?
Nico Pelleriti, Sree Harsha Nelaturu, Zhanke Zhou +4
Recent work pairs LLMs with evolutionary search to iteratively generate, modify, and select code using task-specific feedback. These systems have produced strong results in mathema…
Fast Isotopy Computation for T-Curves
Zoe Geiselmann, Michael Joswig, Lars Kastner +5
A T-curve of degree is given by a regular unimodular triangulation of together with a sign distribution on its lattice points. By Viro's Patchworking Theorem, thi…
The Agentic Researcher: A Practical Guide to AI-Assisted Research in Mathematics and Machine Learning
Max Zimmer, Nico Pelleriti, Christophe Roux +1
AI tools and agents are reshaping how researchers work, from proving theorems to training neural networks. Yet for many, it remains unclear how these tools fit into everyday resear…
ECHOSAT: Estimating Canopy Height Over Space And Time
Jan Pauls, Karsten Schrödter, Sven Ligensa +7
Forest monitoring is critical for climate change mitigation. However, existing global tree height maps provide only static snapshots and do not capture temporal forest dynamics, wh…
When Does Sparsity Mitigate the Curse of Depth in LLMs
Dilxat Muhtar, Xinyuan Song, Sebastian Pokutta +4
Recent work has demonstrated the curse of depth in large language models (LLMs), where later layers contribute less to learning and representation than earlier layers. Such under-u…
From Associations to Activations: Comparing Behavioral and Hidden-State Semantic Geometry in LLMs
Louis Schiekiera, Max Zimmer, Christophe Roux +2
We investigate the extent to which an LLM's hidden-state geometry can be recovered from its behavior in psycholinguistic experiments. Across eight instruction-tuned transformer mod…
Limits of combinatorial patchworking
Zoe Geiselmann, Michael Joswig, Lars Kastner +5
It is shown that there are real plane algebraic curves of degree eight that cannot be realized as T-curves, i.e., via combinatorial patchworking. In fact, this holds for several re…
Lower Bounds for Frank-Wolfe on Strongly Convex Sets
Jannis Halbey, Daniel Deza, Max Zimmer +3
We present a constructive lower bound of for Frank-Wolfe (FW) when both the objective and the constraint set are smooth and strongly convex, showing that…
SparseSwaps: Tractable LLM Pruning Mask Refinement at Scale
Max Zimmer, Christophe Roux, Moritz Wagner +2
The resource requirements of neural networks can be significantly reduced through pruning - the removal of seemingly less important parameters. However, for LLMs, full retraining t…
Objective Coefficient Rounding and Almost Symmetries in Binary Programs
Dominik Kuzinowicz, Paweł Lichocki, Gioni Mexi +3
This article investigates the interplay of rounding objective coefficients in binary programs and almost symmetries. Empirically, reducing the number of significant bits through ro…
Don't Be Greedy, Just Relax! Pruning LLMs via Frank-Wolfe
Christophe Roux, Max Zimmer, Alexandre d'Aspremont +1
Pruning is a common technique to reduce the compute and storage requirements of Neural Networks. While conventional approaches typically retrain the model to recover pruning-induce…
Neural Sum-of-Squares: Certifying the Nonnegativity of Polynomials with Transformers
Nico Pelleriti, Christoph Spiegel, Shiwei Liu +3
Certifying nonnegativity of polynomials is a well-known NP-hard problem with direct applications spanning non-convex optimization, control, robotics, and beyond. A sufficient condi…
A Free Lunch in LLM Compression: Revisiting Retraining after Pruning
Moritz Wagner, Christophe Roux, Max Zimmer +1
Post-training pruning can substantially reduce LLM inference costs, but it often degrades quality unless the remaining weights are adapted. Since global retraining is expensive at…
Computational Algebra with Attention: Transformer Oracles for Border Basis Algorithms
Hiroshi Kera, Nico Pelleriti, Yuki Ishihara +2
Solving systems of polynomial equations, particularly those with finitely many solutions, is a crucial challenge across many scientific fields. Traditional methods like Gröbner and…
RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization
Alonso Urbano, David W. Romero, Max Zimmer +1
Real world data often exhibits unknown, instance-specific symmetries that rarely exactly match a transformation group fixed a priori. Class-pose decompositions aim to create di…
DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications
Ibrahim Fayad, Max Zimmer, Martin Schwartz +6
Significant efforts have been directed towards adapting self-supervised multimodal learning for Earth observation applications. However, most current methods produce coarse patch-s…
Approximating Latent Manifolds in Neural Networks via Vanishing Ideals
Nico Pelleriti, Max Zimmer, Elias Wirth +1
Deep neural networks have reshaped modern machine learning by learning powerful latent representations that often align with the manifold hypothesis: high-dimensional data lie on l…
Capturing Temporal Dynamics in Large-Scale Canopy Tree Height Estimation
Jan Pauls, Max Zimmer, Berkant Turan +4
With the rise in global greenhouse gas emissions, accurate large-scale tree canopy height maps are essential for understanding forest structure, estimating above-ground biomass, an…
Neural Discovery in Mathematics: Do Machines Dream of Colored Planes?
Konrad Mundinger, Max Zimmer, Aldo Kiem +2
We demonstrate how neural networks can drive mathematical discovery through a case study of the Hadwiger-Nelson problem, a long-standing open problem at the intersection of discret…
Estimating Canopy Height at Scale
Jan Pauls, Max Zimmer, Una M. Kelly +6
We propose a framework for global-scale canopy height estimation based on satellite data. Our model leverages advanced data preprocessing techniques, resorts to a novel loss functi…