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A recent NYT article by @CadeMetz [1] claims that neural network distillation was first developed in 2015 by a team of Google researchers in…

A recent NYT article by @CadeMetz [1] claims that neural network distillation was first developed in 2015 by a team of Google researchers including G. Hinton [2]. Not true! I published the technique i

researchdavid-ha--x

Model Releases

A Reliable Context-Aware and Temporal Planning Framework for Autonomous Driving

DGX agent

arXiv:2607.04689v1 Announce Type: cross Abstract: Safe operation of autonomous vehicles in dense urban traffic depends on perception and planning that remain reliable when onboard sensing is degraded.

model-releasesarxiv-cs-cv

7 Jul 2026

A Retrieval-Augmented Framework for Detecting and Resolving Pragmatic Ambiguities in Natural Language Requirements

arXiv:2607.04436v1 Announce Type: cross Abstract: Natural language requirements (NLRs) are essential for bridging communication gaps among diverse stakeholders in software development. However, the in

A short tour of operator learning theory: Convergence rates, statistical limits, and open questions

arXiv:2603.00819v2 Announce Type: replace-cross Abstract: This paper surveys recent developments at the intersection of operator learning, statistical learning theory, and approximation theory. First,

A simplex-based measure of symmetry

arXiv:2607.03815v1 Announce Type: cross Abstract: For compact convex sets L,K subset R^n, denote by lambda_K(L) the smallest size of a homothet of K that contains L. We define a measure of symmetry ba

A Simulation Framework for Electromagnetic Signal Injection Attacks on Image Sensors

arXiv:2408.05124v2 Announce Type: replace-cross Abstract: Image sensors are fundamental to many intelligent systems, allowing visual perception and AI-driven decision-making. However, their integrity

A Sliding-Window-Based Reinforcement Learning for Dynamic Assembly Flow Shop Scheduling with Multi-Product Delivery

arXiv:2607.02941v1 Announce Type: new Abstract: Multi-product kitting delivery imposes significant challenges for real-time scheduling in hybrid manufacturing systems that integrate processing and ass

applicationsarxiv-cs-ai

A Spiking Sequence Generator for Polar Trajectories on Neuromorphic Hardware

arXiv:2607.02753v1 Announce Type: cross Abstract: Neuromorphic controllers for size, weight, and power-constrained systems require neural architectures that are both energy-efficient and interpretable

researcharxiv-cs-ro

A Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning

arXiv:2607.03600v1 Announce Type: cross Abstract: Adversarial robustness in Unsupervised Domain Adaptation (UDA) remains a significant challenge due to noisy pseudo labels and inherent distributional

A Structural Interpretation of GELU and Threshold-Transmission Activations via the First-Order Loss Function

model-releasesarxiv-cs-lg

arXiv:2607.03664v1 Announce Type: new Abstract: The Gaussian Error Linear Unit is usually motivated as the expected output of an input-dependent stochastic Bernoulli gate. This work gives a complement

A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective

arXiv:2503.23650v2 Announce Type: replace Abstract: Reinforcement learning (RL), with its ability to explore and optimize policies in complex, dynamic decision-making tasks, has emerged as a promising

agentsarxiv-cs-lg

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving

arXiv:2509.08269v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly integrated with evolutionary computation to support optimization tasks. This survey primarily fo

A Technical Survey of Reinforcement Learning Techniques for Large Language Models

arXiv:2507.04136v2 Announce Type: replace Abstract: This survey offers a comprehensive foundation on the integration of RL with language models, highlighting prominent algorithms such as Proximal Poli

A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition

arXiv:2204.02803v2 Announce Type: replace-cross Abstract: Sign language recognition from monocular video or 2D pose sequences is challenging, both because 3D information must be inferred from 2D obser

applicationsarxiv-cs-ai

A Unified Algebraic Framework for Classification Performance Evaluation

arXiv:2607.04028v1 Announce Type: cross Abstract: We propose a unified algebraic framework for classification performance evaluation that encompasses binary, multiclass, multilabel, ordinal, hierarchi

researcharxiv-cs-ai

A Unified Causal-Origin Taxonomy of Distributional Shifts in Reinforcement Learning

arXiv:2606.16933v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) systems often degrade when operating conditions differ from those previously encountered, reflecting distributiona

A Unified Framework for In-Context Learning with Causal and Masked Language Models

arXiv:2607.04081v1 Announce Type: new Abstract: In-context learning (ICL) has emerged as a central capability of pretrained language models, yet its theoretical analysis has focused primarily on causa

A Unified Framework for Quantized and Continuous Strong Lottery Tickets

arXiv:2607.03860v1 Announce Type: new Abstract: The Strong Lottery Ticket Hypothesis (SLTH) asserts that sufficiently overparameterized, randomly initialized neural networks contain sparse subnetworks

A User-driven Design Framework for Robotaxi

arXiv:2602.19107v3 Announce Type: replace Abstract: Robotaxis are emerging as a promising form of urban mobility, but removing human drivers fundamentally reshapes passenger-vehicle interaction and ra

safetyarxiv-cs-ro

A Vision Based System for Guided and Collaborative Reconstruction of Fragmented Documents

arXiv:2607.03621v1 Announce Type: new Abstract: This paper presents the development and evaluation of a collaborative system for real-time reconstruction of fragmented paper documents in the context o

ACE: Agentic Control for Embodied Manipulation via Zero-shot Workflow Reasoning

arXiv:2607.04162v1 Announce Type: cross Abstract: Open-ended tabletop manipulation requires agents to not only understand natural language but also adapt to dynamic environments and execution failures

ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI

arXiv:2607.04426v1 Announce Type: new Abstract: Embodied AI is moving from isolated perception or action modules toward physical agents that understand, plan under goals, act through robot bodies, mon

agentsarxiv-cs-ro

ACPO: Adaptive Credit Policy Optimization via Fine-Grained Surrogate Entropy

arXiv:2607.03126v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has substantially improved the reasoning ability of large language models (LLMs), but sparse outcome rewards still make to

Activation-Deactivation: A General Framework for Robust Post-hoc Explainable AI

arXiv:2510.01038v2 Announce Type: replace Abstract: Perturbation-based explainability methods face criticism due to their reliance on out-of-distribution mutants. This raises doubts about the quality

researcharxiv-cs-ai

Active Learning on Adversarially Corrupted Graphs

arXiv:2607.04869v1 Announce Type: new Abstract: Motivated by real-world scenarios where malicious entities tamper with existing networks, we define a model where an adversary seeks to hide a set of co

applicationsarxiv-cs-lg

Adaptive Entropy-Driven Sensor Selection in a Camera-LiDAR Particle Filter for Single-Vessel Tracking

arXiv:2603.08457v2 Announce Type: replace-cross Abstract: Robust single-vessel tracking from fixed coastal platforms is hindered by modality-specific degradations: cameras suffer from illumination and

Adaptive Inference Batching using Policy Gradients

arXiv:2607.05272v1 Announce Type: cross Abstract: Inference serving systems must balance throughput and latency under bursty, heterogeneous workloads, yet the industry standard remains static batching

Adaptive Loss Balancing for Multi-Task Bioacoustic Classification of Bird Species and Call Types

arXiv:2607.03304v1 Announce Type: cross Abstract: Reliable analysis of bird vocalisations in passive acoustic monitoring requires models handling multiple, imbalanced annotation targets. We extend Bir

Adaptive Margin RLHF via Preference over Preferences

arXiv:2509.22851v4 Announce Type: replace-cross Abstract: Margin-based optimization is fundamental to improving generalization and robustness in classification tasks. In the context of reward model le

Adaptive Partitioning and Learning for Stochastic Control of Diffusion Processes

arXiv:2512.14991v2 Announce Type: replace Abstract: We study reinforcement learning for controlled diffusion processes with unbounded continuous state spaces, bounded continuous actions, and polynomia

Adaptive Time-step Training for Enhancing Spike-Based Neural Radiance Fields

arXiv:2507.23033v2 Announce Type: replace Abstract: Spiking Neural Networks (SNNs) provide an energy-efficient computing paradigm for neural rendering, but existing spike-based Neural Radiance Field (

Local Ai

AdaptiveSD A Stability-Aware, Runtime-Adaptive Speculative Decoding Framework with Multi-Policy Orchestration for CPU-Constrained LLM Inference

arXiv:2607.03876v1 Announce Type: new Abstract: With the rise of small quantized GGUF-based language models and their increasing use for on-device inference tasks, we have seen the growing need for an

local-aiarxiv-cs-lg

AdaptiveSplat:Texture Aware Controllable 3D Gaussian Allocation for Feed-Forward Reconstruction

arXiv:2607.04256v1 Announce Type: new Abstract: Current feed-forward 3D reconstruction methods predict pixel aligned Gaussian primitives, resulting in highly redundant representations. A natural solut

Additive Causal Construction for Transferable and Reconfigurable Cross-System Learning in Multi-Source Image Fusion

arXiv:2607.02572v1 Announce Type: cross Abstract: In multi-source image fusion scenarios, heterogeneous inputs are typically driven by distinct generative mechanisms and can be viewed as a composition

ADP: Adversarial Dynamics Priors for Physically Grounded Humanoid Locomotion

arXiv:2607.03454v1 Announce Type: cross Abstract: In this paper, we propose Adversarial Dynamics Priors (ADP) for perturbation-resilient humanoid locomotion control. Existing motion prior-based method

Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities

arXiv:2607.03887v1 Announce Type: cross Abstract: The increasing complexity and frequency of software vulnerabilities demand efficient methods to analyze and prioritize threats. Traditional approaches

researcharxiv-cs-ai

Advances in Neural Controlled Differential Equations

arXiv:2607.05280v1 Announce Type: new Abstract: Many real-world systems evolve continuously, yet most machine learning models interpret time series as discrete sequences. Continuous-time approaches in

applicationsarxiv-cs-lg

Adversarial LassoNet: Robust Feature Selection via Stability-Driven Sparse Learning

model-releasesarxiv-cs-lg

arXiv:2607.03839v1 Announce Type: new Abstract: Sparse feature selection is critical for high-dimensional machine learning, yet traditional ell_1-regularized methods are often brittle under observatio

Aerial Manipulation: Contact, Medium Coupling, and the Geometry of Readiness

arXiv:2607.04719v1 Announce Type: new Abstract: Aerial robots are increasingly moving from remote observation toward physical interaction with objects, surfaces, structures, loads, and surrounding flo

researcharxiv-cs-ro

Agent Data Injection Attacks are Realistic Threats to AI Agents

arXiv:2607.05120v1 Announce Type: cross Abstract: AI agents act on behalf of user prompts, consuming external data and taking actions based on the agent context. Prior research on AI agent security ha

Agent-driven Long-tail Simulation for Autonomous Driving

model-releasesarxiv-cs-cv

arXiv:2607.04331v1 Announce Type: cross Abstract: Evaluating autonomous driving systems in closed-loop settings requires realistic and interactive simulation, yet existing simulators largely rely on l

Local Ai

Agent Reinforcement Learning via Pivotal-Aware Self-Feedback Retry

arXiv:2607.03702v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong decision-making capabilities in long-horizon interactive tasks, yet they still struggle to effective

local-aiarxiv-cs-ai

Agent Step Value: State-Transition Measurement with State-Grounded LLM Evaluators

arXiv:2607.04419v1 Announce Type: new Abstract: Most agent evaluations collapse a multi-step trace into a final answer, a success flag, or a trajectory-level score. These aggregates obscure the diagno

AgentFoX: LLM Agent-Guided Fusion with eXplainability for AI-Generated Image Detection

arXiv:2603.23115v2 Announce Type: replace Abstract: The realism of AI-generated images (AIGI) poses increasing challenges for reliable forensic detection, where heterogeneous expert detectors may prod

agentsarxiv-cs-cv

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments

arXiv:2607.05174v1 Announce Type: new Abstract: Language agents, i.e., LLM agents, progress rapidly and are increasingly deployed in production environments. This trend underscores the urgent need for

Agentic AI-RAN: Enabling Intent-Driven, Explainable and Self-Evolving Open RAN Intelligence

arXiv:2602.24115v2 Announce Type: replace Abstract: Open RAN (O-RAN) exposes rich control and telemetry interfaces across the Non-RT RIC, Near-RT RIC, and distributed units, but also makes it harder t

agentsarxiv-cs-lg

Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directions

arXiv:2607.03233v1 Announce Type: cross Abstract: The rapid growth of publicly available digital information has rendered manual open-source intelligence (OSINT) analysis insufficient for modern intel

agentsarxiv-cs-ai

Agentic Artificial Intelligence for Multistage Physics Experiments at a Large-Scale User Facility Particle Accelerator

arXiv:2509.17255v2 Announce Type: replace-cross Abstract: We present the first language-model-driven agentic artificial intelligence (AI) system to autonomously execute multi-stage physics experiments