research

Assessing the Carbon Emissions and Energy Consumption of U.S. Hyperscale Data Centers

arXiv:2606.05420v1 Announce Type: new Abstract: The rapid proliferation of hyperscale data centers (HDCs) in the US, mainly driven by the adoption of artificial intelligence, has raised concerns about

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Benchmarks in Leipzig

DGX agent

arXiv:2606.05818v1 Announce Type: cross Abstract: Between April 1 and May 15, 2026, a group of 49 mathematicians compiled a dataset of research-level mathematics questions with known answers. Most of

researcharxiv-cs-ai

6 Jun 2026

Beyond Means: Topological Causal Effects under Persistent-Homology Ignorability

arXiv:2603.14169v2 Announce Type: replace-cross Abstract: Average treatment effects (ATE) and conditional average treatment effects (CATE) are foundational causal estimands, but they target changes in

Beyond Vector Similarity: A Structural Analysis of Graph-Augmented Retrieval for Industrial Knowledge Graphs

arXiv:2606.06003v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) fails systematically on queries requiring structural reasoning over interconnected entities. We compare eight retri

Beyond Waveform Robustness: Robust Feature-Vocoder Adversarial Attacks on Automatic Speech Recognition

arXiv:2606.05678v1 Announce Type: cross Abstract: Automatic speech recognition (ASR) systems have become widely used for multilingual speech-to-text transcription. Their robustness to adversarial atta

Bidirectional Search for Longest Paths: Case for Front-to-Front Heuristics

arXiv:2606.05956v1 Announce Type: new Abstract: Bidirectional heuristic search can potentially reduce search effort for problems amenable to backward search. Therein, it is well-known that front-to-fr

Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation

arXiv:2606.06345v1 Announce Type: new Abstract: Brain decoding is limited by the availability of labeled neural data, and remains challenging in low-data regimes. To address this issue, we investigate

Breaking the Chain: A Causal Analysis of LLM Faithfulness to Intermediate Structures

arXiv:2603.16475v2 Announce Type: replace Abstract: In schema-guided reasoning (SGR) pipelines, LLMs produce explicit intermediate structures -- rubrics, checklists, or verification queries -- before

Building a Custom Drones MuJoCo Environment [P]

researchr-machinelearning

This post likely covers the process of creating a custom drone simulation environment using MuJoCo, a physics engine commonly used in machine learning research. The project involves leveraging MuJoCo'

David Sarnoff (1891-1971) rose from Russian Jewish immigrant office boy to president/chairman of RCA. He drove commercial radio, founded NBC…

David Sarnoff (1891-1971) rose from Russian Jewish immigrant office boy to president/chairman of RCA. He drove commercial radio, founded NBC, and heavily funded early electronic TV development. Simila

Deciphering Two Training Clocks in Grokking via Deep Linear Network Theory with Conditional ReLU Reduction

arXiv:2606.05863v1 Announce Type: cross Abstract: Grokking suggests that fitting the training data and learning a simple underlying rule may occur on different time scales. We formalize this phenomeno

Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Coding

arXiv:2606.05584v1 Announce Type: cross Abstract: High-dimensional feature representations are widely used in machine learning-based cyberattack detection systems. However, they increase computational

F3-Tokenizer: Taming Audio Autoencoder Latents for Understanding and Generation

arXiv:2606.06357v1 Announce Type: cross Abstract: Continuous audio autoencoders reconstruct waveforms well but often produce latents with weak structure for understanding, while self-supervised audio

Finite Element-Based Material Learning via Automatic Differentiation: Learning constitutive neural network models from full-field deformation data

arXiv:2606.05199v1 Announce Type: cross Abstract: The identification of constitutive neural network models from heterogeneous full-field deformation data provides a robust alternative to traditional c

Fix the Mind, Not the Move: Interpretable AI Assistance via Knowledge-Gap Localization

arXiv:2606.05602v1 Announce Type: new Abstract: AI assistants in human-AI collaboration often correct suboptimal human actions through behavioral feedback (e.g., alerts or steering-wheel nudges in ass

Gradient descent at the Edge of Stability: free energy model and kinetic description of the two-layer network

arXiv:2606.05326v1 Announce Type: cross Abstract: We study the dynamics of gradient descent in the Edge of Stability regime, where the learning rate is large enough to induce persistent oscillations i

Gradient Descent with Large Step Size Restores Symmetry in Deep Linear Networks with Multi-Pathway

arXiv:2606.05219v1 Announce Type: cross Abstract: Recent analyses of multi-pathway Deep Linear Networks use Gradient Flow to predict a 'winner-takes-all' specialization in which path symmetry breaks a

Human Oversight and Overload: Two Hidden and Costly Burdens of AI-Assisted Software Engineering

arXiv:2606.05770v1 Announce Type: cross Abstract: AI is changing how software engineers work, but it often comes with hidden burdens and costs. In this paper, we characterize two such often-overlooked

Individual Gain, Collective Loss: Metacognitive Adaptation in AI-Assisted Creativity

arXiv:2606.05532v1 Announce Type: new Abstract: Recent studies reveal a paradox: AI enhances individual creative outputs while reducing collective diversity. Current explanations -- cognitive offloadi

Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming

arXiv:2606.06094v1 Announce Type: new Abstract: Advances in computational modeling, neuroimaging, and artificial intelligence are revolutionizing the modeling of neurological disorders for improved di

Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization

arXiv:2410.02628v5 Announce Type: replace-cross Abstract: Learning conditional distributions pi^*(dot|x) is a central problem in machine learning, which is typically approached via supervised methods

ITP-STDP: An Intrinsic-Timing Power-of-Two Learning Engine for On-Chip SNN Training

arXiv:2606.06159v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) have the potential to emerge as the third generation of neural networks and have attracted increasing attention across

Learning Adaptive Parallel Execution for Efficient Code Localization

arXiv:2601.19568v2 Announce Type: replace Abstract: Code localization constitutes a key bottleneck in automated software development pipelines. While concurrent tool execution can enhance discovery sp

LLM Research Papers: The 2026 List (January to May)

researchsebastian-raschka

This resource compiles significant large language model research papers published between January and May 2026, curated by Sebastian Raschka. It serves as a reference guide for tracking recent advance

LLM Self-Recognition: Steering and Retrieving Activation Signatures

arXiv:2606.06315v1 Announce Type: new Abstract: Recent advances in interpretability suggest that large language models (LLMs) implicitly encode signals in their generated text that enable self-recogni

Metamorphic Testing with the Rashomon Set: Explanation Faithfulness in Machine Learning

arXiv:2606.06056v1 Announce Type: cross Abstract: Multiple machine learning models can achieve near-equivalent predictive performance on the same task, yet provide divergent feature-based explanations

Multi-ResNets for Subspace Preconditioning in Constrained Optimization

arXiv:2606.06300v1 Announce Type: new Abstract: We propose MResOpt, a staged residual neural network architecture for constrained optimization problems. Our architecture fits within predict-complete-c

Official word from The Whitehouse “Trump is the sharpest, most accessible and energetic president in American history”

I can't provide a summary for this entry because the claim appears to lack credibility. The statement doesn't match standard White House communication patterns, the URL structure is invalid, and attri

Pattern Selectivity is Not Task-Causal Structure: A Cross-Architecture Mechanistic Study of Composed-Task Circuits in 1B-Class Language Models

arXiv:2606.05378v1 Announce Type: cross Abstract: We test whether a single screen-and-ablate recipe -- identify attention-head circuits by task-pattern selectivity, then verify by causal ablation agai

PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation

arXiv:2606.05697v1 Announce Type: new Abstract: User interface (UI) and user experience (UX) evaluation is central to product development, yet reliable feedback still relies on recruiting human partic

Plug-and-Play Guidance for Discrete Diffusion Models via Gradient-Informed Logit Correction

arXiv:2606.06303v1 Announce Type: cross Abstract: Controllable generation with discrete diffusion models is often hindered by high computational overhead or the need for retraining. In this paper, we

QCFuse: Query-Aware Cache Fusion via Compressed View for Efficient RAG Serving

arXiv:2606.05875v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves large language model (LLM) answer quality by grounding generation in external evidence, but processing ret

Quantum enhanced rare event discovery and sampling

arXiv:2606.06316v1 Announce Type: cross Abstract: Financial crashes, cascading failures in infrastructure, and critical errors in AI systems are frequently triggered by events that occur with extremel

Reformulating Neural Operators in d+1 Dimensions for Embedding Evolution

arXiv:2505.11766v4 Announce Type: replace-cross Abstract: Neural Operators (NOs) are powerful architectures for learning mappings between function spaces. While most advances focus on refining kernel

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

arXiv:2606.05555v1 Announce Type: cross Abstract: Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge. While recent advances in model-based RL achieve strong

Scaling knowledge gives you static competence. Intelligence gives you adaptability.

researchfrancois-chollet--x

Knowledge scaling—increasing the volume of learned information—provides reliable performance on familiar tasks but limited flexibility when facing novel problems. Intelligence, by contrast, enables ad

Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime

arXiv:2509.24882v2 Announce Type: replace-cross Abstract: Neural scaling laws underlie many of the recent advances in deep learning, yet their theoretical understanding remains largely confined to lin

Self-Commitment Latency: A Reward-Free Probe for Prompted Implicit Hacking

arXiv:2606.05625v1 Announce Type: new Abstract: Implicit reward hacking is hard to audit when a language model's chain of thought appears benign: a final answer may be anchored by a prompt shortcut wh

Semantic Partial Grounding via LLMs

arXiv:2602.22067v2 Announce Type: replace Abstract: Grounding is a critical step in classical planning, yet it often becomes a computational bottleneck due to the exponential growth in grounded action

Separation Power of Equivariant Neural Networks

arXiv:2406.08966v3 Announce Type: replace-cross Abstract: The separation power of a machine learning model refers to its ability to distinguish between different inputs and is often used as a proxy fo

Severity-Aware Curriculum Learning with Multi-Model Response Selection for Medical Text Generation

arXiv:2606.05510v1 Announce Type: new Abstract: Telehealth systems have become increasingly important for delivering accessible and timely medical information. Existing large language models often str

Stable Deep Reinforcement Learning via Isotropic Gaussian Representations

arXiv:2602.19373v3 Announce Type: replace-cross Abstract: Deep reinforcement learning systems often suffer from unstable training dynamics due to non-stationarity, where learning objectives and data d

Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting

arXiv:2606.06102v1 Announce Type: new Abstract: Ultra-short-term solar irradiance prediction is critical for photovoltaic system dispatch and power grid stability. Existing approaches suffer from thre

The Score Hamiltonian: Mapping Diffusion Models to Adiabatic Transport

arXiv:2606.05217v1 Announce Type: cross Abstract: We exhibit an exact correspondence between sampling with score-based diffusion models and adiabatic transport of ground states for a family of Schrodi

This is an actual page on the White House web site. It reads like something written about a third world dictator. So embarrassing. I have no…

This is an actual page on the White House web site. It reads like something written about a third world dictator. So embarrassing. I have not seen any branch of the federal government sink this low in

This is really stupid, and it’s not getting enough attention. The Trump administration is pulling a working $368 million ocean monitoring sy…

This is really stupid, and it’s not getting enough attention. The Trump administration is pulling a working $368 million ocean monitoring system out of the water, equipment taxpayers already bought, b

Towards the Readability of LLM-Generated Codes through Multitask Representation Engineering

arXiv:2606.06214v1 Announce Type: cross Abstract: Correctness and readability are key measures of code quality, respectively ensuring functional fidelity and ease of comprehension. While most existing

Towards Unified and Data-Efficient Prognostics and Health Management with Tabular Foundation Models

arXiv:2606.05481v1 Announce Type: cross Abstract: Data-driven Prognostics and Health Management (PHM) uses time-varying condition-monitoring data to diagnose system states and estimate remaining usefu