Search: “concepts”

Gradient-Free Continual Learning in Spiking Neural Networks via Inter-Spike Interval Regularization

arXiv:2604.16496v1 Announce Type: cross Abstract: Continual learning, the ability to acquire new tasks sequentially without forgetting prior knowledge, is essential for deploying neural networks in dy

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How Tokenization Limits Phonological Knowledge Representation in Language Models and How to Improve Them

DGX agent

arXiv:2604.17105v1 Announce Type: new Abstract: Tokenization is the first step in every language model (LM), yet it never takes the sounds of words into account. We investigate how tokenization influe

local-aiarxiv-cs-cl

21 Apr 2026

In-Context Learning Under Regime Change

arXiv:2604.16988v1 Announce Type: new Abstract: Non-stationary sequences arise naturally in control, forecasting, and decision-making. The data-generating process shifts at unknown times, and models m

Integrated Wheel Sensor Communication using ESP32 -- A Contribution towards a Digital Twin of the Road System

arXiv:2509.04061v2 Announce Type: replace Abstract: While current onboard state estimation methods are adequate for most driving and safety-related applications, they do not provide insights into the

safetyarxiv-cs-ro

Is SAM3 ready for pathology segmentation?

arXiv:2604.18225v1 Announce Type: new Abstract: Is Segment Anything Model 3 (SAM3) capable in segmenting Any Pathology Images? Digital pathology segmentation spans tissue-level and nuclei-level scales

Agents

Live LTL Progress Tracking: Towards Task-Based Exploration

arXiv:2604.17106v1 Announce Type: new Abstract: Motivated by the challenge presented by non-Markovian objectives in reinforcement learning (RL), we present a novel framework to track and represent the

agentsarxiv-cs-lg

MetaCloak-JPEG: JPEG-Robust Adversarial Perturbation for Preventing Unauthorized DreamBooth-Based Deepfake Generation

arXiv:2604.18537v1 Announce Type: new Abstract: The rapid progress of subject-driven text-to-image synthesis, and in particular DreamBooth, has enabled a consent-free deepfake pipeline: an adversary n

MLE-UVAD: Minimal Latent Entropy Autoencoder for Fully Unsupervised Video Anomaly Detection

arXiv:2603.23868v2 Announce Type: replace Abstract: In this paper, we address the challenging problem of single-scene, fully unsupervised video anomaly detection (VAD), where raw videos containing bot

Putting a Face to Forgetting: Continual Learning meets Mechanistic Interpretability

arXiv:2601.22012v2 Announce Type: replace Abstract: Catastrophic forgetting in continual learning is often measured at the performance or last-layer representation level, overlooking the underlying me

RASP-Tuner: Retrieval-Augmented Soft Prompts for Context-Aware Black-Box Optimization in Non-Stationary Environments

arXiv:2604.18026v1 Announce Type: new Abstract: Many deployed systems expose black-box objectives whose minimizing configuration shifts with an externally observed context. When contexts revisit a sma

Sub-metre Lunar DEM Generation and Validation from Chandrayaan-2 OHRC Multi-View Imagery Using an Open-Source Pipeline

arXiv:2604.01032v3 Announce Type: replace Abstract: High-resolution digital elevation models (DEMs) of the lunar surface are essential for surface mobility planning, landing site characterization, and

Agents

TacticGen: Grounding Adaptable and Scalable Generation of Football Tactics

arXiv:2604.18210v1 Announce Type: cross Abstract: Success in association football relies on both individual skill and coordinated tactics. While recent advancements in spatio-temporal data and deep le

agentsarxiv-cs-lg

Trustworthy deep domain adaptation for wearable photoplethysmography signal analysis with decision-theoretic uncertainty quantification

arXiv:2604.17480v1 Announce Type: new Abstract: In principle, deep generative models can be used to perform domain adaptation; i.e. align the input feature representations of test data with that of a

C-Mining: Unsupervised Discovery of Seeds for Cultural Data Synthesis via Geometric Misalignment

arXiv:2604.15675v1 Announce Type: new Abstract: Achieving cultural alignment in Large Language Models (LLMs) increasingly depends on synthetic data generation. For such synthesis, the most vital initi

safetyarxiv-cs-cl

CIG: Measuring Conversational Information Gain in Deliberative Dialogues with Semantic Memory Dynamics

arXiv:2604.15647v1 Announce Type: new Abstract: Measuring the quality of public deliberation requires evaluating not only civility or argument structure, but also the informational progress of a conve

researcharxiv-cs-cl

Designing Synthetic Discussion Generation Systems: A Case Study for Online Facilitation

arXiv:2503.16505v4 Announce Type: replace-cross Abstract: A critical challenge in social science research is the high cost associated with experiments involving human participants. We identify Synthet

applicationsarxiv-cs-cl

FineSteer: A Unified Framework for Fine-Grained Inference-Time Steering in Large Language Models

arXiv:2604.15488v1 Announce Type: cross Abstract: Large language models (LLMs) often exhibit undesirable behaviors, such as safety violations and hallucinations. Although inference-time steering offer

safetyarxiv-cs-ai

Measuring the Semantic Structure and Evolution of Conspiracy Theories

arXiv:2603.26062v2 Announce Type: replace Abstract: Research on conspiracy theories has largely focused on belief formation, exposure, and diffusion, while paying less attention to how their meanings

researcharxiv-cs-cl

Seeing the Intangible: Survey of Image Classification into High-Level and Abstract Categories

arXiv:2308.10562v2 Announce Type: cross Abstract: The field of Computer Vision (CV) is increasingly shifting towards ``high-level'' visual sensemaking tasks, yet the exact nature of these tasks remain

Towards Rigorous Explainability by Feature Attribution

arXiv:2604.15898v1 Announce Type: new Abstract: For around a decade, non-symbolic methods have been the option of choice when explaining complex machine learning (ML) models. Unfortunately, such metho

Towards Robust Endogenous Reasoning: Unifying Drift Adaptation in Non-Stationary Tuning

arXiv:2604.15705v1 Announce Type: new Abstract: Reinforcement Fine-Tuning (RFT) has established itself as a critical paradigm for the alignment of Multi-modal Large Language Models (MLLMs) with comple

safetyarxiv-cs-lg

Syntheses

Wiki Lint Report — 2026-04-19

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Automated lint: 43 errors, 9 warnings, 3 info

19 Apr 2026

Improving Sparse Autoencoder with Dynamic Attention

arXiv:2604.14925v1 Announce Type: new Abstract: Recently, sparse autoencoders (SAEs) have emerged as a promising technique for interpreting activations in foundation models by disentangling features i

researcharxiv-cs-lg

17 Apr 2026

Reward-Aware Trajectory Shaping for Few-step Visual Generation

arXiv:2604.14910v1 Announce Type: new Abstract: Achieving high-fidelity generation in extremely few sampling steps has long been a central goal of generative modeling. Existing approaches largely rely

17 Apr 2026

The PICCO Framework for Large Language Model Prompting: A Taxonomy and Reference Architecture for Prompt Structure

arXiv:2604.14197v1 Announce Type: new Abstract: Large language model (LLM) performance depends heavily on prompt design, yet prompt construction is often described and applied inconsistently. Our purp

safetyarxiv-cs-cl

17 Apr 2026

From Anchors to Supervision: Memory-Graph Guided Corpus-Free Unlearning for Large Language Models

arXiv:2604.13777v1 Announce Type: new Abstract: Large language models (LLMs) may memorize sensitive or copyrighted content, raising significant privacy and legal concerns. While machine unlearning has

IWLV-Ramayana: A Sarga-Aligned Parallel Corpus of Valmiki's Ramayana Across Indian Languages

arXiv:2604.13078v1 Announce Type: new Abstract: The Ramayana is among the most influential literary traditions of South and Southeast Asia, transmitted across numerous linguistic and cultural contexts

applicationsarxiv-cs-cl

MApLe: Multi-instance Alignment of Diagnostic Reports and Large Medical Images

arXiv:2604.13970v1 Announce Type: new Abstract: In diagnostic reports, experts encode complex imaging data into clinically actionable information. They describe subtle pathological findings that are m

local-aiarxiv-cs-cv

Tutorials

Modeling Student Learning with 3.8 Million Program Traces

arXiv:2510.05056v2 Announce Type: replace Abstract: As programmers write code, they often edit and retry multiple times, creating rich 'interaction traces' that reveal how they approach coding tasks a

tutorialsarxiv-cs-lg

MUSE: Multi-Domain Chinese User Simulation via Self-Evolving Profiles and Rubric-Guided Alignment

arXiv:2604.13828v1 Announce Type: new Abstract: User simulators are essential for the scalable training and evaluation of interactive AI systems. However, existing approaches often rely on shallow use

ParlaSpeech 3.0: Richly Annotated Spoken Parliamentary Corpora of Croatian, Czech, Polish, and Serbian

arXiv:2511.01619v2 Announce Type: replace Abstract: ParlaSpeech is a collection of spoken parliamentary corpora currently spanning four Slavic languages - Croatian, Czech, Polish and Serbian - all tog

researcharxiv-cs-cl

Rhetorical Questions in LLM Representations: A Linear Probing Study

arXiv:2604.14128v1 Announce Type: new Abstract: Rhetorical questions are asked not to seek information but to persuade or signal stance. How large language models internally represent them remains unc

Syntheses

Synthesis: Arxiv-Cs-Cl

synthesisarxiv-cs-clauto-generated

Auto-generated synthesis of 505 entries about arxiv-cs-cl

Visual Self-Fulfilling Alignment: Shaping Safety-Oriented Personas via Threat-Related Images

arXiv:2603.08486v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) face safety misalignment, where visual inputs enable harmful outputs. To address this, existing methods req

What Are We Really Measuring? Rethinking Dataset Bias in Web-Scale Natural Image Collections via Unsupervised Semantic Clustering

arXiv:2604.13610v1 Announce Type: new Abstract: In computer vision, a prevailing method for quantifying dataset bias is to train a model to distinguish between datasets. High classification accuracy i

Fragile Reconstruction: Adversarial Vulnerability of Reconstruction-Based Detectors for Diffusion-Generated Images

arXiv:2604.12781v1 Announce Type: new Abstract: Recently, detecting AI-generated images produced by diffusion-based models has attracted increasing attention due to their potential threat to safety. A

Model Releases

GeM-EA: A Generative and Meta-learning Enhanced Evolutionary Algorithm for Streaming Data-Driven Optimization

model-releasesarxiv-cs-ai

arXiv:2604.12336v1 Announce Type: cross Abstract: Streaming Data-Driven Optimization (SDDO) problems arise in many applications where data arrive continuously and the optimization environment evolves

IMU: Influence-guided Machine Unlearning

arXiv:2508.01620v3 Announce Type: replace-cross Abstract: Machine Unlearning (MU) aims to selectively erase the influence of specific data points from pretrained models. However, most existing MU meth

JanusCoder: Towards a Foundational Visual-Programmatic Interface for Code Intelligence

arXiv:2510.23538v2 Announce Type: replace Abstract: The scope of neural code intelligence is rapidly expanding beyond text-based source code to encompass the rich visual outputs that programs generate

Tutorials

KoCo: Conditioning Language Model Pre-training on Knowledge Coordinates

arXiv:2604.12397v1 Announce Type: new Abstract: Standard Large Language Model (LLM) pre-training typically treats corpora as flattened token sequences, often overlooking the real-world context that hu

tutorialsarxiv-cs-cl

Model Releases

Latent Planning Emerges with Scale

model-releasesarxiv-cs-ai

arXiv:2604.12493v1 Announce Type: cross Abstract: LLMs can perform seemingly planning-intensive tasks, like writing coherent stories or functioning code, without explicitly verbalizing a plan; however

LLM as Attention-Informed NTM and Topic Modeling as long-input Generation: Interpretability and long-Context Capability

arXiv:2510.03174v2 Announce Type: replace-cross Abstract: Topic modeling aims to produce interpretable topic representations and topic--document correspondences from corpora, but classical neural topi

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models

arXiv:2601.14004v4 Announce Type: replace Abstract: Mechanistic Interpretability (MI) has emerged as a vital approach to demystify the opaque decision-making of Large Language Models (LLMs). However,

Masked by Consensus: Disentangling Privileged Knowledge in LLM Correctness

arXiv:2604.12373v1 Announce Type: new Abstract: Humans use introspection to evaluate their understanding through private internal states inaccessible to external observers. We investigate whether larg

MAST: Mask-Guided Attention Mass Allocation for Training-Free Multi-Style Transfer

arXiv:2604.12281v1 Announce Type: cross Abstract: Style transfer aims to render a content image with the visual characteristics of a reference style while preserving its underlying semantic layout and

On the Mathematical Relationship Between Layer Normalization and Dynamic Activation Functions

arXiv:2503.21708v4 Announce Type: replace-cross Abstract: Layer normalization (LN) is an essential component of modern neural networks. While many alternative techniques have been proposed, none of th

RoleMAG: Learning Neighbor Roles in Multimodal Graphs

arXiv:2604.12271v1 Announce Type: new Abstract: Multimodal attributed graphs (MAGs) combine multimodal node attributes with structured relations. However, existing methods usually perform shared messa

researcharxiv-cs-lg

Model Releases

Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework

model-releasesarxiv-cs-ai

arXiv:2509.18127v3 Announce Type: replace-cross Abstract: Sparse autoencoders (SAEs) enable interpretability research by decomposing entangled model activations into monosemantic features. However, un