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I’ve been asked many times over why I keep posting when so many of the replies I receive are personal attacks. The answer is actually really…

I’ve been asked many times over why I keep posting when so many of the replies I receive are personal attacks. The answer is actually really simple. As scientists, we cannot concede to silence. If we

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Learning the Supports for Categorical Critic in Reinforcement Learning

DGX agent

arXiv:2607.01880v1 Announce Type: new Abstract: Value functions are an essential component in actor-critic based deep reinforcement learning (RL). Conventionally, these functions are trained as a regr

researcharxiv-cs-lg

3 Jul 2026

LLM Priors for ERM over Programs

arXiv:2510.14331v3 Announce Type: replace Abstract: We study program-learning methods that are efficient in both samples and computation. Classical learning theory suggests that when the target admits

Local exponential stability of mean-field Langevin descent-ascent and associated particle system

arXiv:2602.01564v2 Announce Type: replace Abstract: We study the mean-field Langevin descent-ascent (MFL-DA), a coupled optimization dynamics on the space of probability measures for entropically regu

Low-Latency Task-Oriented Image Transmission with Opportunistic Spectrum Access

arXiv:2607.01921v1 Announce Type: cross Abstract: Communication systems designed for reliable data reconstruction, rather than task-oriented communication, typically rely on separate source and channe

Many Voices, One Reward: Multi-Role Rubric Generation for LLM Judging and Reward Modeling

arXiv:2607.01830v1 Announce Type: new Abstract: Reliable reward and preference signals are critical for evaluating and optimizing large language models on open-ended tasks. Rubric-based judges offer a

Mechanistic Interpretability and Causal Feature Steering of Neural Quantum States via Sparse Autoencoders

arXiv:2607.01336v1 Announce Type: cross Abstract: Neural Quantum States (NQS) are a remarkably expressive class of variational ansatze for quantum many-body wavefunctions, yet little is understood abo

Message Passing Based Two-Timescale Bayesian Learning for Joint Channel and Memory Hardware Impairments Tracking

arXiv:2607.01660v1 Announce Type: new Abstract: Hardware impairments in massive multiple-input multiple-output (MIMO) receivers introduce inter-symbol memory and inter-element coupling, severely degra

Multi-Head Recurrent Memory Agents

arXiv:2607.01523v1 Announce Type: cross Abstract: Recurrent memory agents extend LLMs to arbitrarily long contexts by iteratively consolidating input into a fixed-size memory window. Despite their sca

Multi-Rate Nonlinear Model Predictive Control for Wall-Supported Bipedal Locomotion of Quadrupedal Robots

arXiv:2607.01574v1 Announce Type: new Abstract: This paper presents a novel layered planning and control framework based on multi-rate nonlinear model predictive control (MR-NMPC) that enables quadrup

researcharxiv-cs-ro

NAVER LABS Europe Submission to the Instruction-following 2026 Short Track

arXiv:2607.01960v1 Announce Type: new Abstract: In this paper, we describe NAVER LABS Europe's submission to the instruction-following speech processing short track at IWSLT 2026. We participate again

NeuroBridge: Bridging Multi-Task MRI Knowledge for Neurodegenerative Disease Diagnosis

arXiv:2607.01401v1 Announce Type: cross Abstract: INTRODUCTION: Accurate MRI-based identification of Alzheimer's disease (AD), mild cognitive impairment (MCI), and related dementias remains challengin

Neuron-Aware Active Few-Shot Learning for LLMs

arXiv:2607.02423v1 Announce Type: cross Abstract: Active Few-Shot Learning (AFSL) adapts LLMs to specialized domains by identifying the most valuable unlabeled samples for annotation and use as few-sh

Object-centric LeJEPA

arXiv:2607.02404v1 Announce Type: cross Abstract: Image encoders trained with LeJEPA can deliver strong features for downstream tasks, but, like other image-level self-supervised methods, typically re

On the Dimension-Free Approximation of Deep Neural Networks for Symmetric Korobov Functions

arXiv:2511.12398v2 Announce Type: replace Abstract: Deep neural networks have been widely used as universal approximators for functions with inherent physical structures, including permutation symmetr

On the Role of Directionality in Structural Generalization

arXiv:2607.02307v1 Announce Type: new Abstract: Several SLOG test categories explicitly involve directional distinctions (modifier position shifts, argument extraction positions), yet AM-Parser, the p

One way to live 400 years is to do (and experience) 5x more things per day

researchfrancois-chollet--x

This post explores a philosophical perspective on subjective time perception, suggesting that by engaging in more diverse activities and novel experiences daily, one can psychologically expand their l

Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning

arXiv:2604.02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation. However, current reranking models are typicall

PARTREP: Learning What to Repeat for Decoder-only LLMs

arXiv:2607.01792v1 Announce Type: new Abstract: While decoder-only LLMs excel at a vast array of natural language tasks, it suffers from an asymmetric information flow induced by causal attention: lat

Path planning for unmanned naval surface vehicles

arXiv:2607.01631v1 Announce Type: new Abstract: There nowadays is a myriad of approaches to real-time avoidance of fixed obstacles for unmanned surface vehicles (USVs) and, to a lesser extent, also th

researcharxiv-cs-ro

Pmeta-TLA: Backdoor Attacks for Speech Classification Models via Meta-Learning with Timbre Leakage Attack

arXiv:2607.01702v1 Announce Type: cross Abstract: Recently, speech classification methods have gained widespread adoption in intelligent gadgets. Current study indicates that backdoor attacks provide

Population-Based Multi-Objective Training of Discriminators for Semi-Supervised GANs

arXiv:2607.01907v1 Announce Type: cross Abstract: Semi-supervised generative adversarial networks (SSL-GANs) can exploit large unlabeled datasets while retaining a classifier in the discriminator, but

Predicting Early Stages Of Alzheimer's Disease And Identifying Key Biomarkers Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies

arXiv:2607.02142v1 Announce Type: cross Abstract: Alzheimers disease (AD) is a brain disorder that develops slowly and mainly affects memory, thinking, language, and daily activities. It is one of the

Predictive Conformal Slip Monitoring: An Empirical Evaluation of Rolling Split Conformal Prediction for Pre-Incident Traction Loss Detection

arXiv:2607.02124v1 Announce Type: new Abstract: Conventional traction control architectures intervene only after the adhesion limit of a tire has already been breached. This paper investigates whether

Privacy-Preserving and Verifiable Approximate Distributed Coded Computing

arXiv:2607.02187v1 Announce Type: new Abstract: Distributed machine learning enables collaborative model training without centralizing data, but it also exposes learning processes to privacy leakage a

Probing Chemical Language Models: Effects of Pre-training and Fine-tuning

arXiv:2607.02140v1 Announce Type: new Abstract: Chemical language models (CLMs) are trained with linearized representations such as SMILES, yet it remains unclear which chemically meaningful substruct

Probing Spectrum-Like Organization of States of Mind in Transformer Representation Spaces

arXiv:2512.22227v3 Announce Type: replace Abstract: We investigate whether graded states of mind form spectrum-like structure in transformer representation spaces. To do so, we construct a dataset of

ProWAFT: A ROMA-LPD Instance for Workload-Aware and Dynamic Fault Tolerance in FPGA-Based CNN Accelerators

arXiv:2607.01602v1 Announce Type: new Abstract: SRAM-based FPGAs provide an attractive platform for energy- and latency-constrained CNN inference at the network edge, yet transient faults can lead to

Q-GAIN: A Python Package for Machine Learning and Physically Informed Analysis Applications

arXiv:2607.02413v1 Announce Type: cross Abstract: Here we describe the quantum gas analysis and inference (Q-GAIN) Python package, which enables rapid deployment of machine learning (ML) and physics-i

QTALE: Quantization-Robust Token-Adaptive Layer Execution for LLMs

arXiv:2602.10431v4 Announce Type: replace Abstract: Large language models (LLMs) demand substantial computational and memory resources, posing challenges for efficient deployment. Two complementary ap

QuadRocket: An Aerial Robotic Testbed for Adaptive Thrust-Vector Control of Rocket-Like Vehicles

arXiv:2607.02474v1 Announce Type: new Abstract: This paper presents QuadRocket, a quadrotor-based rocket prototype that provides a low-cost, low-risk platform for validating advanced thrust-vector con

researcharxiv-cs-ro

Ravines in quantum cost landscapes: opportunities for improved VQA predictions

arXiv:2607.01329v1 Announce Type: cross Abstract: The geometric and topological structure of quantum cost landscapes (QCLs) governs the optimization and thus the predictive power of variational quantu

ReContext: Recursive Evidence Replay as LLM Harness for Long-Context Reasoning

arXiv:2607.02509v1 Announce Type: new Abstract: Understanding and reasoning over long contexts has become a key requirement for deploying large language models (LLMs) in realistic applications. Althou

Regularized Variational and Spectral Log-Density-Ratio Estimation in the Gaussian Location Model

arXiv:2607.01895v1 Announce Type: new Abstract: We study ridge-regularized log-density-ratio estimation in the Gaussian location model with a common covariance matrix. By affine invariance, the model

Rethinking Complexity Metrics for LLM-Integrated Applications: Beyond Source Code

arXiv:2607.01903v1 Announce Type: new Abstract: LLM-integrated applications blend natural language prompts with program code, and much of their runtime behavior originates in the prompt layer rather t

Rethinking Speech-LLM Integration for ASR: Effective Joint Speech-Text Training by Interleaving

arXiv:2607.01733v1 Announce Type: new Abstract: Speech-LLM integration has shown promising results by leveraging extensive textual pretraining, yet its specific benefits for automatic speech recogniti

Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learning

arXiv:2607.01511v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning has emerged as an effective approach for activating latent reasoning capabilities in large language models. However, mo

Revisiting Decentralized Online Convex Optimization with Compressed Communication

arXiv:2607.01665v1 Announce Type: new Abstract: Decentralized online convex optimization (D-OCO) is a popular framework for distributed applications with streaming data. To tackle the communication bo

Robust and Explainable 3D Mode Shape Recognition Using Region-Aware Graph Neural Networks

arXiv:2607.01522v1 Announce Type: cross Abstract: Mode shape recognition is a fundamental task in automotive NVH development, yet it remains dependent on manual visual inspection by experienced engine

Role-Aware Neural Convex Divergence Heads for Asymmetric Representation Learning

arXiv:2607.01762v1 Announce Type: new Abstract: Many representation learning problems involve directed relations, such as lexical entailment, sentence entailment, ontology hierarchy, and citation link

RuleChef: Grounding LLM Task Knowledge in Human-Editable Rules

arXiv:2607.01293v1 Announce Type: new Abstract: We present RuleChef, a framework that uses large language models (LLMs) to generate executable rules for NLP tasks such as text classification, Named En

SA-HGNN: Sample-Adaptive Hyperbolic Graph Neural Network for EEG-Based Depression Recognition

arXiv:2607.02063v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have been widely used to capture spatial functional connectivity patterns to improve electroencephalography (EEG)-based d

Scalable and Distributed Silhouette Approximation

arXiv:2607.01993v1 Announce Type: cross Abstract: The silhouette is one of the most widely used measures to assess the quality of a k-clustering of a dataset of n elements. Its evaluation requires no

Scaling Latent Reasoning via Looped Language Models

arXiv:2510.25741v5 Announce Type: replace Abstract: Modern LLMs are trained to 'think' primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training a

Scaling to Multimodal and Multichannel Heart Sound Classification with Synthetic and Augmented Biosignals

arXiv:2509.11606v4 Announce Type: replace-cross Abstract: Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 17.9 million deaths each year. Early det

Scene-Conditioned PINN-GNN for Multipath RF Maps: Cross-Scene Generation and In-Scene Completion

arXiv:2607.01777v1 Announce Type: cross Abstract: Radio frequency (RF) maps provide a compact representation of multipath propagation characteristics and are fundamental to channel modeling, coverage

Self-Supervised Test-Time Tuning for Packet Loss Concealment

arXiv:2607.01823v1 Announce Type: cross Abstract: Packet loss concealment (PLC) reconstructs audio packets that are missing at the receiver, usually with a trained model whose parameters remain fixed

SemHash-LLM: A Multi-Granularity Semantic Hashing Framework for Document Deduplication

arXiv:2607.01601v1 Announce Type: new Abstract: Large scale document deduplication must preserve semantic equivalence while remaining efficient over massive corpora. We present SemHash LLM, a multi gr