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Weights to Code: Extracting Interpretable Algorithms from the Discrete Transformer

arXiv:2601.05770v3 Announce Type: replace-cross Abstract: Algorithm extraction aims to synthesize executable programs directly from models trained on algorithmic tasks, enabling de novo recovery of ex

researcharxiv-cs-cl

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Welfare, Improvability, and Variance: A Principal-Agent Approach to Optimal Benchmark Item Aggregation

DGX agent

arXiv:2605.30916v1 Announce Type: new Abstract: AI benchmarks have well-documented limitations, with prior work examining contamination, saturation, and construct underspecification. Aggregation has r

model-releasesarxiv-cs-lg

1 Jun 2026

What Am I Missing? Question-Answering as Hidden State Probing

arXiv:2605.31561v1 Announce Type: new Abstract: Test-time reasoning has become a significant field of study since the introduction of chain-of-thought reasoning in large language models (LLMs). Howeve

safetyarxiv-cs-cl

Local Ai

What changes after deployment? A survey on On-device Learning in TinyML

arXiv:2605.31226v1 Announce Type: cross Abstract: Machine learning models on microcontroller-class devices (TinyML) face a fundamental challenge: post-deployment distribution change undermines static

local-aiarxiv-cs-ai

What Does Preference Learning Recover from Pairwise Comparison Data?

arXiv:2602.10286v2 Announce Type: replace Abstract: Pairwise preference learning is central to machine learning, with recent applications in aligning language models with human preferences. A typical

What Gets Unmasked First? Trajectory Analysis of Diffusion Models for Graph-to-Text Generation

arXiv:2605.31564v1 Announce Type: cross Abstract: We present the first systematic study of masked diffusion language models (MDLMs) for graph-to-text generation. We analyze MDLM generation trajectorie

What is Missing? Explaining Neurons Activated by Absent Concepts

arXiv:2603.09787v2 Announce Type: replace Abstract: Explainable artificial intelligence (XAI) aims to provide human-interpretable insights into the behavior of deep neural networks (DNNs), typically b

What Makes LVLMs Hallucinate Less? Unveiling the Architectural Factors Behind Hallucination Robustness

arXiv:2605.30911v1 Announce Type: cross Abstract: Hallucination remains one of the key challenges undermining the reliability of Large Vision-Language Models (LVLMs). But what makes an LVLM hallucinat

When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks?

arXiv:2605.30719v1 Announce Type: cross Abstract: We study when large language models (LLMs) can serve as effective black-box policy optimizers for reinforcement learning (RL) tasks, i.e., when can we

When Are Multimodal Predictions Biologically Supported? A Diagnostic Evaluation Framework

arXiv:2605.31504v1 Announce Type: new Abstract: Multimodal models in oncology can produce accurate predictions, but accurate prediction does not reveal whether the model has learned biology that is sh

Local Ai

When English Rewrites Local Knowledge: Global Narrative Dominance in Large Language Models

arXiv:2605.30481v1 Announce Type: new Abstract: Large language models (LLMs) are widely used as cross-lingual knowledge interfaces. However, culturally grounded questions often reflect globally domina

local-aiarxiv-cs-cl

When LLMs Learn to Be Consistently Wrong: A Multi-Model Study of Linear Representations of Synthetic Deception

arXiv:2605.30381v1 Announce Type: cross Abstract: Deceptive alignment, in which models maintain accurate internal representations while deliberately producing false outputs, remains a central challeng

When More Data Doesn't Help: Limits of Adaptation in Multitask Learning

arXiv:2601.20774v2 Announce Type: replace Abstract: Multitask learning and related frameworks have achieved tremendous success in modern applications. In multitask learning problem, we are given a set

Who Gets Credit or Blame? Attributing Accountability in Modern AI Systems

arXiv:2506.00175v5 Announce Type: replace-cross Abstract: Modern AI systems are typically developed through multiple stages-pretraining, fine-tuning rounds, and subsequent adaptation or alignment, whe

Why Linear Recurrent Memory Works in Partially Observable Reinforcement Learning

arXiv:2605.31261v1 Announce Type: cross Abstract: The family of linear recurrent neural networks has shown strong performance as recurrent memory units in partially observable reinforcement learning.

Wind Turbine Maintenance Log Labelling Framework: LLM-Driven Data Correction and Enrichment via Semantic Extraction of Reliability Intelligence

arXiv:2605.31281v1 Announce Type: new Abstract: As wind turbine fleets age, data-driven reliability engineering is essential to optimise their operation and maintenance for service life extension and

researcharxiv-cs-cl

World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry

arXiv:2604.01985v2 Announce Type: replace-cross Abstract: General-purpose world models promise scalable policy evaluation, optimization, and planning, yet achieving the required level of robustness re

World2Act: Latent Action Post-Training from World Model Dynamics

arXiv:2603.10422v2 Announce Type: replace Abstract: World Models (WMs) offer a promising mechanism for post-training Vision-Language-Action (VLA) policies by providing dynamics priors that improve gen

safetyarxiv-cs-cv

WristCompass: Kinematic Coupling as a Learnable Visual Concept for Ego-Camera Orientation

model-releasesarxiv-cs-cv

arXiv:2605.30671v1 Announce Type: new Abstract: Recovering ego-camera orientation from manipulation video is a prerequisite for disentangling hand motion from camera motion, a key step in imitation le

XLGoBench: Detecting cross-lingual skill gaps with algorithmic tasks

arXiv:2605.30788v1 Announce Type: cross Abstract: We introduce a set of synthetic algorithmic tasks to detect cross-lingual gaps in the abilities of large language models. Our benchmark is commensurat

XOResNet: Exclusive-OR Meta-Residuals Facilitate Deep Spiking Neural Networks Learning

arXiv:2605.30362v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) hold promise for demonstrating superior learning and representation capabilities in deep models. Given the tremendous s

Local Ai

YARD: Y-Architecture Register Decoding for Efficient Hallucination Mitigation in Large Vision-Language Models

arXiv:2605.31429v1 Announce Type: new Abstract: Contrastive decoding (CD) seeks to mitigate hallucinations in Large Vision-Language Models (LVLMs) by contrasting the output distributions of a standard

local-aiarxiv-cs-cv

Your Multimodal Speech Model Says I Have a Face for Radio

model-releasesarxiv-cs-cl

arXiv:2605.30472v1 Announce Type: new Abstract: As large neural models have become better at language tasks, researchers are increasingly building multi- and omnimodal models that handle more modaliti

Your Teacher Can't Help You Here: Combating Supervision Fidelity Decay in On-Policy Distillation

arXiv:2605.30833v1 Announce Type: cross Abstract: On-policy distillation transfers reasoning capabilities by training a student model on its own generated trajectories using token-level feedback from

ZAPS-DA: Zero-Phase Action Policy Smoothing with Decoupled Actor for Continuous Control in Reinforcement Learning

arXiv:2605.30612v1 Announce Type: cross Abstract: Continuous control policies trained with off-policy reinforcement learning frequently exhibit high-frequency action jitter, rendering direct deploymen

safetyarxiv-cs-lg

Zero Collapse: A Failure Mode of Policy Gradient Methods in Discontinuous Reward Environments

arXiv:2605.30896v1 Announce Type: new Abstract: Bidding in repeated auctions is a central challenge for reinforcement learning (RL), combining continuous control with the strategic complexities of dig

safetyarxiv-cs-lg

Zeroth-Order Non-Log-Concave Sampling with Variance Reduction and Applications to Inverse Problems

arXiv:2605.30573v1 Announce Type: new Abstract: Sampling from high-dimensional, non-log-concave distributions with unnormalized densities remains a fundamental challenge in machine learning, particula

3DVLA: Enhancing Vision-Language-Action Models via 3D Spatial and Instance Understanding

arXiv:2605.29416v1 Announce Type: cross Abstract: Vision-Language-Action models have achieved remarkable progress in robotic manipulation, yet they suffer from a critical limitation: a lack of 3D scen

4DPC^2hat: Towards Dynamic Point Cloud Understanding with Failure-Aware Bootstrapping

arXiv:2602.03890v2 Announce Type: replace Abstract: Point clouds provide a compact and expressive representation of 3D objects, and have recently been integrated into multimodal large language models

A comparative study of transformer-based embeddings for topic coherence

arXiv:2605.28832v1 Announce Type: cross Abstract: Topic modeling is a branch of Natural Language Processing (NLP) that aims to organize large collections of texts into coherent groups according to wor

A Complete Loss Landscape Analysis of Regularized Deep Matrix Factorization

arXiv:2506.20344v3 Announce Type: replace-cross Abstract: Despite its wide range of applications across various domains, the optimization foundations of deep matrix factorization (DMF) remain largely

A Composable Multimodal Framework for cine CMR-Text-Driven Prediction of Heart Failure Outcomes

arXiv:2502.16548v3 Announce Type: replace-cross Abstract: Objective. Heart failure is one of the leading causes of death worldwide, with millions of deaths each year, according to data from the World

A Computational Method for Solving the Stochastic Joint Replenishment Problem in High Dimensions

arXiv:2511.11830v2 Announce Type: replace-cross Abstract: We consider a discrete-time formulation for a class of high-dimensional stochastic joint replenishment problems. First, we approximate the pro

A Deep Learning Iterative Framework for Sentinel-1 Stripmap Enhancement Based on Azimuth Doppler Decomposition

arXiv:2605.29088v1 Announce Type: new Abstract: Synthetic Aperture Radar (SAR) imagery enables all-weather, day-and-night Earth observation; however, it remains difficult to interpret due to speckle n

Agents

A Deep Learning Model of Mental Rotation Informed by Interactive VR Experiments

arXiv:2512.13517v2 Announce Type: replace-cross Abstract: Mental rotation -- the ability to compare objects seen from different viewpoints -- is a fundamental example of mental simulation and spatial

agentsarxiv-cs-lg

Agents

A Domain-Informed Multi-Objective Framework for EEG Channel Selection in Motor Imagery BCIs

arXiv:2605.29943v1 Announce Type: cross Abstract: Motor imagery (MI) classification using electroencephalography (EEG) signals is essential for advancing brain-computer interfaces (BCIs). Traditional

agentsarxiv-cs-lg

A Dual-Path Architecture for Scaling Compute and Capacity in LLMs

model-releasesarxiv-cs-cl

arXiv:2605.30202v1 Announce Type: new Abstract: Looped transformers apply a shared block multiple times and have emerged as a parameter-efficient route to scaling compute in language models. However,

A Full-Pipeline Framework for Evaluating Membership Inference Attacks in Machine Learning

arXiv:2605.29454v1 Announce Type: new Abstract: While Membership Inference Attacks (MIAs) are the prevailing method for identifying training data, their application has expanded into privacy auditing

A Fully Convolutional Approach to Denoising Structural Dynamics Data from X-Ray Photon Correlation Spectroscopy

arXiv:2605.29975v1 Announce Type: new Abstract: We present a fully convolutional denoising autoencoder (FC-DAE) for denoising two-time intensity-intensity correlation functions (C_2) in X-ray photon c

safetyarxiv-cs-lg

A Geometric View of SRC: Learning Representations for Stable Residual Inference

arXiv:2605.29673v1 Announce Type: cross Abstract: Reconstruction-based inference assigns a class by comparing class-wise reconstruction residuals; Sparse Representation Classification (SRC) is a canon

safetyarxiv-cs-cv

A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms

arXiv:2605.30313v1 Announce Type: new Abstract: Simulation-based RL for contemporary robot control is increasingly organized around GPU-resident simulation: physics, rollout collection, and learning a

safetyarxiv-cs-ro

A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search

arXiv:2602.11171v2 Announce Type: replace-cross Abstract: Fine-tuning Large Language Models (LLMs) with Low-Rank Adaptation (LoRA) offers a resource-efficient way to personalize or specialize. However

Applications

A Matter of Interest: Understanding Interestingness of Math Problems in Humans and Language Models

arXiv:2511.08548v2 Announce Type: replace Abstract: The evolution of mathematics is shaped importantly by interestingness: researchers choose which problems to pursue, and students choose which proble

applicationsarxiv-cs-ai

A Minimal Bifurcation Model of Load Imbalance in a Softmax Mixture-of-Experts Router

arXiv:2605.29121v1 Announce Type: cross Abstract: We propose a minimal dynamical model of adaptive softmax routing for a two-expert Mixture-of-Experts (MoE) layer. The model is obtained as a mean-fiel

A Modular Architecture for Typologically Controlled Lexicon Generation

arXiv:2605.28824v1 Announce Type: new Abstract: Constructing artificial lexicons that are pronounceable, typologically plausible, and semantically structured remains an open challenge in computational

safetyarxiv-cs-cl

A new completely parameter-free clustering algorithm for unsupervised classification of BATSE gamma-ray bursts

arXiv:2605.30175v1 Announce Type: cross Abstract: Cluster analysis is a widely applied machine learning technique to understand the existing patterns in the population of gamma-ray bursts (GRBs), in o

A Novel Tensor Product-Based Neural Network for Solving Partial Differential Equations

arXiv:2605.29688v1 Announce Type: new Abstract: This paper presents the Tensor Product Network (TPNet), a novel neural architecture for efficient and accurate function approximation and PDE solving. T

A Predictive Law for On-Policy Self-Distillation From World Feedback

arXiv:2605.30070v1 Announce Type: cross Abstract: Moving beyond simple scalar rewards toward richer world feedback is a natural path to more scalable RL post-training. On-policy self-distillation (OPS