Search: “concepts”

Embedding Prediction Helps Image Generation

arXiv:2610.02203v1 Announce Type: new Abstract: In diffusion transformers, a class label or a text prompt is embedded once, and the same condition is reused at every denoising step. We ask whether pre

Feature Selective Model Collapse in Diffusion Models: Total Replacement versus Fixed-Budget Training

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DGX agent

arXiv:2610.01318v1 Announce Type: cross Abstract: Model collapse arises when generative models are trained on synthetic data produced by earlier models. The phenomenon has attracted considerable atten

model-releasesarxiv-cs-ai

2 Oct 2026

Agents

Form and Void: Entangled Composition through an Autonomous AI Agent

arXiv:2610.02045v1 Announce Type: new Abstract: Positive and negative space is a fundamental principle in visual composition, supporting visually coherent forms and layered semantic relationships. Gen

agentsarxiv-cs-cv

LLM-Assisted Discovery of Typed Semantic Links for Ontology Network Construction

arXiv:2610.01393v1 Announce Type: cross Abstract: Constructing typed, justified semantic links between ontologies is essential for enabling interoperability across heterogeneous and interdisciplinary

Meta-Multi-Agent Reinforcement Learning for Fast Adaptation of Interactive Policies with Applications to Autonomous Driving

arXiv:2610.00705v1 Announce Type: new Abstract: This paper develops a meta-multi-agent reinforcement learning (meta-MARL) framework to enable fast adaptation of interactive policies in a multi-agent s

safetyarxiv-cs-ai

Rethinking Memorization Mitigation in Diffusion Models: Reinforcing Text Conditioning

arXiv:2610.01723v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved remarkable progress in image synthesis, yet can exhibit memorization by closely reproducing individual trai

safetyarxiv-cs-cv

UnifiedAttack: Evaluating the Safety of Large Multimodal Models in Synergistic Harmful Image-Text Generation

model-releasesarxiv-cs-cv

arXiv:2610.00341v1 Announce Type: cross Abstract: As Large Multimodal Models (LMMs) transition toward natively unified architectures, evaluating their safety in synergistic harmful image-text generati

BadAction: Backdoor Attacks on Interactive Video Generation via Action-Guided Triggers

arXiv:2609.39047v1 Announce Type: new Abstract: Interactive video generation (IVG) models have achieved remarkable progress in producing controllable visual content guided by user-defined actions, yet

Contrastive Representation Shaping for LLM Unlearning

arXiv:2601.22028v2 Announce Type: replace Abstract: Most LLM unlearning methods aim to approximate retrain-from-scratch behaviors with minimal distribution shift, often via alignment-style objectives

safetyarxiv-cs-lg

Faithful Dual-constrained Erasure for Robust LLM Safety Alignment

model-releasesarxiv-cs-ai

arXiv:2609.39279v1 Announce Type: cross Abstract: Machine unlearning has emerged as a crucial mechanism for removing hazardous knowledge and enforcing safety alignment in Large Language Models (LLMs).

Local Ai

MetaSteer: Context-Conditioned, nonlinear Steering via Attention-Projection Adaptation

arXiv:2609.38718v1 Announce Type: new Abstract: Steering large language models typically relies on linear, context-independent interventions in activation space, an assumption that recent work has cha

local-aiarxiv-cs-cl

Non-Invasive Inspection of Water Canals Using Dronar

arXiv:2609.40178v1 Announce Type: new Abstract: Open concrete canals play a vital role in water transportation, serving as primary water infrastructure for millions of people across the Phoenix, Arizo

researcharxiv-cs-ro

Agents

PathAnchor: Path-Structured Evidence for Scientific Agents

arXiv:2609.38766v1 Announce Type: new Abstract: Scientific agents can retrieve relevant passages yet still lose functional order, mix evidence across sources, or state conclusions that exceed the retr

agentsarxiv-cs-ai

SCALE: Synthetic Calibration via Agreement Labeling in Embedding Space

arXiv:2609.38705v1 Announce Type: new Abstract: Foundation models for computational pathology are usually evaluated using AUC and accuracy, while calibration is often left untested. This matters becau

Seeing as Humans Do: Learning from Motion to Segment Anything Without Supervision

arXiv:2609.39785v1 Announce Type: new Abstract: The Segment Anything Model (SAM) relies heavily on massive manual annotations, creating a fundamental bottleneck for model scaling. While unsupervised m

tutorialsarxiv-cs-cv

Amadeus: When Models of People Meet

arXiv:2609.35835v1 Announce Type: cross Abstract: With the sheer constant advancements raining down in the field of Artificial Intelligence, one particular possibility that may cross our mind is wheth

tutorialsarxiv-cs-lg

Causal and Interpretable Structures in LLM Compositional Tasks

model-releasesarxiv-cs-ai

arXiv:2609.35970v1 Announce Type: cross Abstract: Large language models are able to solve tasks whose answers depend on not only individual input tokens, but also on relations among them. How is such

i would like to learn deeply about fine-tuning local models before burning money

model-releasesr-localllama

There's so many new techniques like RL, RL LoRA, QLoRA, CPT LoRA. I believe i would have a usecase for them, but i don't know where to learn, youtube is filled with bad quality tutorials if i just sea

Agents

LLMs Learn to Evade Latent Monitors from Prior Feedback Alone

arXiv:2609.36490v1 Announce Type: cross Abstract: Latent space monitors aim to detect undesired behaviors in LLM agents by inspecting an agent's internal activations rather than its outputs. However,

agentsarxiv-cs-ai

Beyond Token Scale: Chunk-Level Sparse Autoencoders for Reliable Semantic Feature Discovery

arXiv:2609.35521v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) expose features that help us understand and steer language models, but faithful reconstruction does not guarantee informati

researcharxiv-cs-lg

Feedback-Robust AI for Patient Knowledge Graphs

arXiv:2609.33248v1 Announce Type: new Abstract: Patient knowledge graphs from bedside monitoring should type their relations and state whether the data support their signs. In anesthesia and intensive

safetyarxiv-cs-lg

Model-Agnostic Online Certificate-Driven Calibration for Time Series Forecasting Under Distribution Shift

arXiv:2609.31960v1 Announce Type: new Abstract: Time series out-of-distribution generalization requires forecasters to remain reliable when deployment dynamics differ from training conditions due to c

researcharxiv-cs-lg

TACO: Training-free Sound Prompted Segmentation via Semantically Constrained Audio-visual CO-factorization

arXiv:2412.01488v4 Announce Type: replace-cross Abstract: Large-scale pre-trained audio and image models demonstrate an unprecedented degree of generalization, making them suitable for a wide range of

researcharxiv-cs-lg

Beyond Bag-of-Words: Diagnosing Compositional Binding Failures in Vision-Language Models

arXiv:2602.02043v2 Announce Type: replace-cross Abstract: Modern vision-language models struggle with basic compositional reasoning, failing to bind attributes to objects or relations to their referen

ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models

arXiv:2609.30434v1 Announce Type: new Abstract: Pre-trained vision-language models such as CLIP can recognize new categories via prompting, but they often struggle when labeled data are scarce or the

Towards Automated Lexicography: Generating and Evaluating Definitions for Learner's Dictionaries

arXiv:2601.01842v2 Announce Type: replace Abstract: Dictionary definitions are an essential resource for learning word senses, but manually creating them is costly. We thus study dictionary definition

researcharxiv-cs-cl

Why Better Cross-Lingual Alignment Fails for Better Cross-Lingual Transfer: Case of Encoders

arXiv:2603.18863v2 Announce Type: replace Abstract: Cross-lingual alignment is often assumed to improve cross-lingual transfer by bringing representations of different languages closer together. Howev

Signal2Symbol: Neuro-Symbolic Temporal Reasoning for Explainable Physiological Time-Series Anomaly Detection

arXiv:2609.26820v1 Announce Type: cross Abstract: Physiological time series such as electrocardiograms (ECG) and electroencephalograms (EEG) exhibit complex temporal structure, substantial acquisition

24 Sept 2026

Applications

Mirror Skin: In Situ Visualization of Robot Touch Intent on Robotic Skin

arXiv:2512.11472v3 Announce Type: replace-cross Abstract: Effective communication of robot touch intent is essential for safe and predictable physical human-robot interaction. While intent communicati

applicationsarxiv-cs-ro

23 Sept 2026

Brain-to-Image Generation: Reconstructing Visual Stimuli from EEG using Generative Adversarial Networks

arXiv:2609.22282v1 Announce Type: new Abstract: Reconstructing visual stimuli from electroencephalography (EEG) is difficult because scalp measurements have high temporal but limited spatial resolutio

22 Sept 2026

Functional Emotion Without Character: Large Language Models, Aristotelian Disposition, and the Limits of Behavioral Alignment

arXiv:2609.22362v1 Announce Type: new Abstract: Debates about whether artificial systems can feel are often forced between two unsatisfactory positions: behavioral equivalence is treated as sufficient

22 Sept 2026

DiaVLo: Diagnosing Behaviours of Vision-Language Models

arXiv:2609.22008v1 Announce Type: cross Abstract: Vision-language models (VLMs) rely on storing and transferring appropriate information across their sub-components. Verifying that the VLMs exhibit de

21 Sept 2026

FCA-Guided Counterfactual Explanations for Multi-Modal Breast Cancer Diagnosis: A Framework Achieving Perfect Validity with Emergent Sparsity

model-releasesarxiv-cs-ai

arXiv:2609.20067v1 Announce Type: new Abstract: Deep learning models for multi-modal breast cancer diagnosis achieve high predictive accuracy but remain clinically unacceptable without actionable, cou

18 Sept 2026

The Role of Fine-grained Harm Signals in LLM Safety

arXiv:2609.19366v1 Announce Type: new Abstract: Prior work has shown that internal harmfulness representations in large language models vary across risk categories, while sharing a common general harm

18 Sept 2026

Beyond frequency measures: Can contextual embeddings capture meaning change in scientific texts?

arXiv:2609.18804v1 Announce Type: new Abstract: Identifying technological trends is a core scientometric task, yet traditional frequency-based approaches struggle to capture substantial meaning shifts

researcharxiv-cs-cl

Applications

Human Resilience in the AI Era -- What Machines Can't Replace

arXiv:2510.25218v2 Announce Type: replace-cross Abstract: AI is changing work and decision making faster than many institutions can adapt their operating practices. We argue that this adaptation gap m

applicationsarxiv-cs-ai

PersonaPath: Towards Knowledge-Centric Personalized Learning Path Planning

model-releasesarxiv-cs-cl

arXiv:2609.18861v1 Announce Type: new Abstract: Adaptive learning systems commonly formulate learning path planning as Exercise-Centric (EC) recommendation, where the next step is inferred from item-l

Probabilistic Linear Explanations

arXiv:2609.19077v1 Announce Type: cross Abstract: Formal explainability provides mathematically grounded justifications for individual predictions. However, abductive explanations often exceed human c

Towards Generalizable Deepfake Detection via Real Distribution Bias Correction

arXiv:2603.14005v2 Announce Type: replace Abstract: To generalize deepfake detectors to future unseen forgeries, most existing methods attempt to simulate the dynamically evolving forgery types using

safetyarxiv-cs-cv

Tracing individual knowledge trajectories in a changing field: the case of general relativity and gravitation

arXiv:2609.18697v1 Announce Type: new Abstract: Historians have reconstructed the twentieth-century transformation of general relativity and gravitation (GRG) at the field level and through individual

What You Can't See Is Still What You Learn: A Preregistered Sixty-Society Confirmation That Evidence Masking Drives Compositional Generalization

arXiv:2609.17637v1 Announce Type: new Abstract: Restricting what a module can read may improve what a system learns to compute. We test this in a preregistered confirmation with sixty four-cell system

tutorialsarxiv-cs-ai

Coverage-Aware Virtual IMU Augmentation for Low-Resource Human Activity Recognition

arXiv:2609.16768v1 Announce Type: new Abstract: IMU-based human activity recognition (HAR) enables continuous, privacy-friendly monitoring of daily activities using wearable sensors. However, building

tutorialsarxiv-cs-ai

Hyperbolic Contrastive Learning with Entailment for Spatial Transcriptomics

arXiv:2609.16207v1 Announce Type: new Abstract: Spatial Transcriptomics (ST) has transformed biomedical research by enabling the spatial mapping of gene expression across tissue sections. However, hig

IMVS: Interactive Medical Volume Segmentation with Test-Time Adaptation - A New Method for Annotating Radiology Datasets

arXiv:2609.16775v1 Announce Type: new Abstract: Annotating large radiology datasets is bottlenecked by the manual effort of delineating structures slice-by-slice in 3D volumes. Interactive methods red

safetyarxiv-cs-cv

Interpreting and Steering LLM Agents for Social Simulations

arXiv:2609.16436v1 Announce Type: cross Abstract: Simulations based on large language models (LLMs) have proven to be powerful for understanding human behavior, making them valuable additions to the s

Local Ai

PiPS: Post-Hoc Prototypical Explanations for Interpretable Semantic Segmentation

arXiv:2609.16909v1 Announce Type: new Abstract: With the increasing deployment of deep neural networks in critical systems, such as medical diagnostics and autonomous vehicles, ensuring their interpre

local-aiarxiv-cs-cv

Test-Time Unlearning via Sparse Autoencoder

model-releasesarxiv-cs-cl

arXiv:2609.16229v1 Announce Type: cross Abstract: Machine unlearning aims to remove specific knowledge from a trained large language model (LLM) without retraining from scratch. Existing methods modif

The essay below from Aaronson is just unmoored from reality, sorry. It is just not true, if you try to approach the question in a data-orien…

The essay below from Aaronson is just unmoored from reality, sorry. It is just not true, if you try to approach the question in a data-oriented fashion, rather than via vibes and speculative fiction a

safetygary-marcus--x