Search: “concepts”

Mechanical Conscience: A Mathematical Framework for Dependability of Machine Intelligenc

arXiv:2605.03847v1 Announce Type: new Abstract: Distributed collaborative intelligence (DCI), encompassing edge-to-edge architectures, federated learning, transfer learning, and swarm systems, creates

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Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent

DGX agent

arXiv:2602.19837v3 Announce Type: replace-cross Abstract: Humans are highly effective at utilizing prior knowledge to adapt to novel tasks, a capability that standard machine learning models struggle

agentsarxiv-cs-lg

7 May 2026

Multi-Source Human-in-the-Loop Digital Twin Testbed for Connected and Autonomous Vehicles in Mixed Traffic Flow

arXiv:2603.17751v3 Announce Type: replace Abstract: In the emerging mixed traffic environments, Connected and Autonomous Vehicles (CAVs) have to interact with surrounding human-driven vehicles (HDVs).

agentsarxiv-cs-ro

On the Hardness of Junking LLMs

arXiv:2605.05116v1 Announce Type: new Abstract: Large language models (LLMs) are known to be vulnerable to jailbreak attacks, which typically rely on carefully designed prompts containing explicit sem

Shape2Animal: Creative Animal Generation from Natural Silhouettes

arXiv:2506.20616v3 Announce Type: replace Abstract: Humans possess a unique ability to perceive meaningful patterns in ambiguous stimuli, a cognitive phenomenon known as pareidolia. This paper introdu

applicationsarxiv-cs-cv

Smart Passive Acoustic Monitoring: Embedding a Classifier on AudioMoth Microcontroller

arXiv:2605.03412v1 Announce Type: cross Abstract: Passive Acoustic Monitoring (PAM) is an efficient and non-invasive method for surveying ecosystems at a reduced cost. Typically, autonomous recorders

agentsarxiv-cs-ai

Sparsity is Combinatorial Depth: Quantifying MoE Expressivity via Tropical Geometry

arXiv:2602.03204v2 Announce Type: replace Abstract: While Mixture-of-Experts (MoE) architectures define the state-of-the-art, their theoretical success is often attributed to heuristic efficiency rath

TNStream: Applying Tightest Neighbors to Micro-Clusters to Define Multi-Density Clusters in Streaming Data

arXiv:2505.00359v2 Announce Type: replace Abstract: In data stream clustering, systematic theory of stream clustering algorithms remains relatively scarce. Recently, density-based methods have gained

applicationsarxiv-cs-lg

Understanding LoRA as Knowledge Memory: An Empirical Analysis

arXiv:2603.01097v2 Announce Type: replace Abstract: Continuous knowledge updating for pre-trained large language models (LLMs) is increasingly necessary yet remains challenging. Although inference-tim

6G Needs Agents: Toward Agentic AI-Native Networks for Autonomous Intelligence

model-releasesarxiv-cs-ai

arXiv:2605.01546v1 Announce Type: cross Abstract: Sixth-generation (6G) networks are increasingly envisioned as AI-native infrastructures integrating communication, sensing, and computing into a unifi

AI Expert Twin: Capturing Expert Cognition for Human-Centred, Practice-Based Learning

arXiv:2605.01401v1 Announce Type: cross Abstract: Tacit knowledge embedded in expert practice remains difficult to capture, formalise, and scale. While AI-driven educational systems have advanced pers

applicationsarxiv-cs-ai

Can Semantic Methods Enhance Team Sports Tactics? A Methodology for Football with Broader Applications

arXiv:2601.00421v2 Announce Type: replace Abstract: This paper explores how semantic-space reasoning, traditionally used in computational linguistics, can be extended to tactical decision-making in te

Client-Conditional Federated Learning via Local Training Data Statistics

arXiv:2603.11307v2 Announce Type: replace Abstract: Federated learning (FL) under data heterogeneity remains challenging: existing methods either ignore client differences (FedAvg), require costly clu

Complexity Horizons of Compressed Models in Analog Circuit Analysis

arXiv:2605.02285v1 Announce Type: new Abstract: The deployment of Large Language Models (LLMs) for specialized engineering domains, such as circuit analysis, often faces a trade-off between reasoning

agentsarxiv-cs-ai

Complexity is not an intrinsic property of a problem. It's a property of the relationship between a problem and an observer. Once you know t…

researchfrancois-chollet--x

Complexity is fundamentally relative rather than absolute—it depends on the knowledge, perspective, and tools available to a particular observer rather than being an inherent feature of the problem it

Design-OS: A Specification-Driven Framework for Engineering System Design with a Control-Systems Design Case

arXiv:2603.20151v2 Announce Type: replace-cross Abstract: Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit

agentsarxiv-cs-ai

EFGPP: Exploratory framework for genotype-phenotype prediction

arXiv:2605.02954v1 Announce Type: cross Abstract: Predicting complex human traits from genetic data is challenging because different genetic, clinical, and molecular data sources often contain differe

Erase Persona, Forget Lore: Benchmarking Multimodal Copyright Unlearning in Large Vision Language Models

model-releasesarxiv-cs-cv

arXiv:2605.03547v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs), trained on web-scale data, risk memorizing and regenerating copyrighted visual content such as characters and logo

How Language Models Process Negation

model-releasesarxiv-cs-cl

arXiv:2605.03052v1 Announce Type: new Abstract: We study how Large Language Models (LLMs) process negation mechanistically. First, we establish that even though open-weight models often provide wrong

Learning Generalizable Action Representations via Pre-training AEMG

arXiv:2605.03462v1 Announce Type: new Abstract: A fundamental role in decoding human motor intent and enabling intuitive human-computer interaction is played by electromyography (EMG). However, its ge

Model Routing as a Trust Problem: Route Receipts for Adaptive AI Systems

arXiv:2605.01710v1 Announce Type: new Abstract: AI products often route requests through version aliases, service tiers, tool choices, regional endpoints, fallback rules, or safety handling before res

NORA: A Harness-Engineered Autonomous Research Agent for End-to-End Spatial Data Science

arXiv:2605.02092v1 Announce Type: new Abstract: The automation of scientific research workflows has emerged as a transformative frontier in artificial intelligence, yet existing autonomous research ag

Privacy Preserving Machine Learning Workflow: from Anonymization to Personalized Differential Privacy Budgets in Federated Learning

arXiv:2605.02372v1 Announce Type: cross Abstract: The growing development of artificial intelligence based solutions, together with privacy legislation, has driven the rise of the so-called privacy pr

Random test functions, H^{-1} norm equivalence, and stochastic variational physics-informed neural networks

arXiv:2605.03542v1 Announce Type: cross Abstract: The dual norm characterisation of weak solutions of second-order linear elliptic partial differential equations is mathematically natural but computat

SERE: Structural Example Retrieval for Enhancing LLMs in Event Causality Identification

arXiv:2605.03701v1 Announce Type: new Abstract: Event Causality Identification (ECI) requires models to determine whether a given pair of events in a context exhibits a causal relationship. While Larg

safetyarxiv-cs-cl

Toward Structural Multimodal Representations: Specialization, Selection, and Sparsification via Mixture-of-Experts

arXiv:2605.03348v1 Announce Type: new Abstract: We propose S3 (Specialization, Selection, Sparsification), a framework that rethinks multimodal learning through a structural perspective. Instead of en

VisInject: Disruption != Injection -- A Dual-Dimension Evaluation of Universal Adversarial Attacks on Vision-Language Models

model-releasesarxiv-cs-ai

arXiv:2605.01449v1 Announce Type: cross Abstract: Universal adversarial attacks on aligned multimodal large language models are increasingly reported with attack success rates in the 60-80% range, sug

Adaptive Pluralistic Alignment: A pipeline for dynamic artificial democracy

arXiv:2605.01642v1 Announce Type: new Abstract: Prevailing alignment methods target a fixed set of preferences and therefore risk forcing value lock-in as societal norms evolve over time. We introduce

ALIGNS: Unlocking nomological networks in psychological measurement through a large language model

arXiv:2509.09723v3 Announce Type: replace Abstract: Psychological measurement is critical to many disciplines. Despite advances in measurement, building nomological networks, theoretical maps of how c

safetyarxiv-cs-cl

Anticipation-VLA: Solving Long-Horizon Embodied Tasks via Anticipation-based Subgoal Generation

arXiv:2605.01772v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as a powerful paradigm for embodied intelligence, enabling robots to perform tasks based on natural l

Atlassian looks to the pit lane for the winning edge in the enterprise AI race

Enterprise productivity has long been measured by output and headcount, yet the tools and overhauls meant to improve it keep delivering diminishing returns. The discipline software teams quietly perfe

applicationssiliconangle

Autonomous Drift Learning in Data Streams: A Unified Perspective

arXiv:2605.01295v1 Announce Type: new Abstract: In the pursuit of autonomous learning systems, the foundational assumption of stationarity, the premise that data distributions and model behaviors rema

Benchmarking local Hebbian learning rules for memory storage and prototype extraction

model-releasesarxiv-cs-lg

arXiv:2605.01074v1 Announce Type: cross Abstract: Associative memory or content-addressable memory is an important component function in computer science and information processing, and at the same ti

Local Ai

Bridging the Experimental Last Mile: Digitizing Laboratory Know-How for Safe AI-Assisted Support

arXiv:2604.16345v2 Announce Type: replace-cross Abstract: While advances in materials informatics have accelerated the development of Self-Driving Laboratories (SDLs), human-led experiments remain sta

local-aiarxiv-cs-ai

Disciplined Diffusion: Text-to-Image Diffusion Model against NSFW Generation

arXiv:2605.01113v1 Announce Type: new Abstract: Text-to-image (T2I) diffusion models have the ability to build high-quality pictures from text prompts, but they pose safety concerns because they can g

GA-VisAgent: A Multi-Agent application for code generation and visualization in interactive learning

arXiv:2605.01299v1 Announce Type: new Abstract: Geometric Algebra (GA) presents challenges to learners due to its highly abstract mathematical structure and complex operational rules, as translating a

agentsarxiv-cs-lg

H-Probes: Extracting Hierarchical Structures From Latent Representations of Language Models

arXiv:2605.00847v1 Announce Type: new Abstract: Representing and navigating hierarchy is a fundamental primitive of reasoning. Large language models have demonstrated proficiency in a wide variety of

applicationsarxiv-cs-cl

Model-Based Proactive Cost Generation for Learning Safe Policies Offline with Limited Violation Data

arXiv:2605.01356v1 Announce Type: new Abstract: Learning constraint-satisfying policies from offline data without risky online interaction is crucial for safety-critical decision making. Conventional

Multi-Scale Gaussian-Language Map for Zero-shot Embodied Navigation and Reasoning

arXiv:2605.01736v1 Announce Type: new Abstract: Understanding the geometric and semantic structure of environments is essential for embodied navigation and reasoning. Existing semantic mapping methods

OceanPile: A Large-Scale Multimodal Ocean Corpus for Foundation Models

model-releasesarxiv-cs-cl

arXiv:2605.00877v1 Announce Type: cross Abstract: The vast and underexplored ocean plays a critical role in regulating global climate and supporting marine biodiversity, yet artificial intelligence ha

Parking Assistance for Trailer-Truck Transport Vehicles Using Sensor Fusion and Motion Planning

arXiv:2605.02716v1 Announce Type: new Abstract: Autonomous driving technology has rapidly evolved over the past decade, offering significant improvements in transportation efficiency, safety, and cost

safetyarxiv-cs-ro

Representation learning from OCT images

arXiv:2605.02589v1 Announce Type: new Abstract: Optical Coherence Tomography (OCT) has become one of the most used imaging modality in ophthalmology. It provides high-resolution, non-invasive visualiz

Retrieval-Guided Generation for Safer Histopathology Image Captioning

arXiv:2605.00893v1 Announce Type: new Abstract: Generative vision-language models can produce fluent medical image captions but remain prone to hallucination, over-specific diagnostic claims, and fact

Retrieval with Multiple Query Vectors through Anomalous Pattern Detection

arXiv:2605.01965v1 Announce Type: new Abstract: A classical vector retrieval problem typically considers a single query embedding vector as input and retrieves the most similar embedding vectors from

ROBOPOL: Social Robotics Meets Vehicular Communications for Cooperative Automated Driving

arXiv:2512.24129v2 Announce Type: replace Abstract: On the way toward full autonomy, sharing roads between automated and autonomous vehicles in so-called mixed traffic is unavoidable. Moreover, even i

agentsarxiv-cs-ro

SRA: Span Representation Alignment for Large Language Model Distillation

arXiv:2605.01205v1 Announce Type: new Abstract: Cross-Tokenizer Knowledge Distillation (CTKD) enables knowledge transfer between a large language model and a smaller student, even when they employ dif

safetyarxiv-cs-cl

The grip of grammar on meaning uncertainty: cross-linguistic evidence, neural correlates, and clinical relevance

arXiv:2605.01537v1 Announce Type: new Abstract: Isolated word meanings are inherently uncertain. This uncertainty reduces when they are combined and anchored in context. We propose that grammar compre

applicationsarxiv-cs-cl

The Pragmatic Frames of Spurious Correlations in Machine Learning: Interpreting How and Why They Matter

arXiv:2411.04696v5 Announce Type: replace Abstract: Learning correlations from data forms the foundation of today's machine learning (ML) and artificial intelligence research. While contemporary metho