Search: “concepts”

VSI: Visual Subtitle Integration for Keyframe Selection to enhance Long Video Understanding

arXiv:2508.06869v4 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) demonstrate exceptional performance in vision-language tasks, yet their processing of long videos is

13 Apr 2026

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DGX agent

arXiv:2604.09501v1 Announce Type: new Abstract: Norm, the formal theoretical linguist, and Claudette, the computational language scientist, have a lovely time discussing whether modern language models

tutorialsarxiv-cs-cl

13 Apr 2026

ADAG: Automatically Describing Attribution Graphs

arXiv:2604.07615v1 Announce Type: new Abstract: In language model interpretability research, extbf{circuit tracing} aims to identify which internal features causally contributed to a particular outp

AI generates well-liked but templatic empathic responses

arXiv:2604.08479v1 Announce Type: new Abstract: Recent research shows that greater numbers of people are turning to Large Language Models (LLMs) for emotional support, and that people rate LLM respons

applicationsarxiv-cs-cl

Beyond Social Pressure: Benchmarking Epistemic Attack in Large Language Models

arXiv:2604.07749v1 Announce Type: new Abstract: Large language models (LLMs) can shift their answers under pressure in ways that reflect accommodation rather than reasoning. Prior work on sycophancy h

ChopGrad: Pixel-Wise Losses for Latent Video Diffusion via Truncated Backpropagation

arXiv:2603.17812v2 Announce Type: replace-cross Abstract: Recent video diffusion models achieve high-quality generation through recurrent frame processing where each frame generation depends on previo

Detecting HIV-Related Stigma in Clinical Narratives Using Large Language Models

arXiv:2604.07717v1 Announce Type: new Abstract: Human immunodeficiency virus (HIV)-related stigma is a critical psychosocial determinant of health for people living with HIV (PLWH), influencing mental

From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps

arXiv:2411.18084v2 Announce Type: replace-cross Abstract: Mobile apps are essential in daily life but frequently employ deceptive patterns, such as visual emphasis or linguistic nudging, to manipulate

Learning is Forgetting: LLM Training As Lossy Compression

arXiv:2604.07569v1 Announce Type: cross Abstract: Despite the increasing prevalence of large language models (LLMs), we still have a limited understanding of how their representational spaces are stru

tutorialsarxiv-cs-cl

Nearest Neighbor Projection Removal Adversarial Training

arXiv:2509.07673v4 Announce Type: replace Abstract: Deep neural networks have exhibited impressive performance in image classification tasks but remain vulnerable to adversarial examples. Standard adv

researcharxiv-cs-cv

Physics-Informed Spectral Modeling for Hyperspectral Imaging

arXiv:2508.21618v2 Announce Type: replace-cross Abstract: We present PhISM, a physics-informed deep learning architecture that learns without supervision to explicitly disentangle hyperspectral observ

Team Fusion@ SU@ BC8 SympTEMIST track: transformer-based approach for symptom recognition and linking

arXiv:2604.06424v1 Announce Type: cross Abstract: This paper presents a transformer-based approach to solving the SympTEMIST named entity recognition (NER) and entity linking (EL) tasks. For NER, we f

TEMPER: Testing Emotional Perturbation in Quantitative Reasoning

arXiv:2604.07801v1 Announce Type: new Abstract: Large language models are trained and evaluated on quantitative reasoning tasks written in clean, emotionally neutral language. However, real-world quer

What do Language Models Learn and When? The Implicit Curriculum Hypothesis

arXiv:2604.08510v1 Announce Type: new Abstract: Large language models (LLMs) can perform remarkably complex tasks, yet the fine-grained details of how these capabilities emerge during pretraining rema

tutorialsarxiv-cs-cl

Why Are We Lonely? Leveraging LLMs to Measure and Understand Loneliness in Caregivers and Non-caregivers

arXiv:2604.07834v1 Announce Type: new Abstract: This paper presents an LLM-driven approach for constructing diverse social media datasets to measure and compare loneliness in the caregiver and non-car

A Conceptual Framework for Refining Influence Knowledge from Simulation Evidence in Cyber-Physical Systems

arXiv:2608.11221v1 Announce Type: new Abstract: Cyber-physical systems (CPS) are typically developed by multiple stakeholders who produce artefacts tailored to their specific domains of expertise. The

applicationsarxiv-cs-ai

Attractor Image-Based Deep Learning of Arterial Pulse Waves for Age Classification

arXiv:2608.12117v1 Announce Type: new Abstract: Arterial pulse waveform morphology evolves with age, reflecting structural and functional changes in the cardiovascular system. Thus, vascular age is a

researcharxiv-cs-lg

Class Activation Mapping in Explainable Computer Vision: A Method-Centered Review of CNN, Transformer, and Foundation-Model-Era Visual Explanations

arXiv:2608.12299v1 Announce Type: cross Abstract: Class activation mapping (CAM) is one of the most widely used visual explanation families in explainable artificial intelligence. Its purpose is intui

local-aiarxiv-cs-ai

Credo: Declarative Control of LLM Pipelines via Beliefs and Policies

arXiv:2604.14401v2 Announce Type: replace Abstract: Agentic AI systems are becoming commonplace in domains that require long-lived, stateful decision-making in continuously evolving conditions. As suc

Designing Agentic AI-Based Screening for Portfolio Investment

arXiv:2603.23300v2 Announce Type: replace-cross Abstract: We introduce a new agentic artificial intelligence (AI) platform for portfolio management. Our architecture consists of three layers. First, t

Safety

Do Not Forget the Obvious - RISC: A Risk-Informed Slice-Coverage Protocol for Safe Autonomous Driving

arXiv:2608.12051v1 Announce Type: new Abstract: Aggregate metrics may not fully reflect performance in insufficiently examined high-risk driving conditions. We propose RISC (Risk-Informed Slice Covera

safetyarxiv-cs-cv

Dynamics Models for Offline Hyperparameter Selection in Real-World RL

arXiv:2608.11349v1 Announce Type: cross Abstract: A key obstacle to deploying reinforcement learning in real-world systems is hyperparameter selection, particularly when simulators are unavailable and

applicationsarxiv-cs-ai

Low-Interaction-Rank Learning: Unifying Multiplicative Dual-Encoder Heads

arXiv:2608.11661v1 Announce Type: cross Abstract: A multiplicative dual-encoder network computes a real-valued output for a pair of inputs as the inner product of their separate encodings. This archit

tutorialsarxiv-cs-ai

Measure, Don't Optimize: Forecasting Recovery in LLM Unlearning

arXiv:2608.11408v1 Announce Type: new Abstract: Prior white-box studies show that large language models can retain latent traces of target knowledge after unlearning, even when the knowledge is no lon

researcharxiv-cs-cl

Multi-Agent Target-Existence Verification and Learned Mask Geometry Refinement: Winning Report of the MeViS-Text Track at the 8th LSVOS Challenge 2026

arXiv:2608.11458v1 Announce Type: new Abstract: We present the first-place solution to the MeViS-Text track of the 8th Large-scale Video Object Segmentation (LSVOS) Challenge 2026: referring video obj

agentsarxiv-cs-cv

RECAST: A Machine-Learning Framework for Correction and Super-Resolution of Coarse-Grid PDE Solvers

model-releasesarxiv-cs-lg

arXiv:2608.11572v1 Announce Type: new Abstract: Coarse-grid numerical solvers can substantially reduce the computational cost of time-dependent PDE simulation, but under-resolution often degrades both

Towards foundation-style models for energy-frontier heterogeneous neutrino detectors via self-supervised pre-training

arXiv:2604.07037v2 Announce Type: replace-cross Abstract: Accelerator-based neutrino physics is entering an energy-frontier regime in which interactions reach the TeV scale and produce exceptionally d

researcharxiv-cs-cv

Beyond a Bag of Features: Set-Level Instability in Sparse Autoencoders

arXiv:2608.11197v1 Announce Type: cross Abstract: Shani et al. (2026) show that LLM representations broadly recover human category boundaries, while failing to reflect fine-grained typicality structur

researcharxiv-cs-cl

Does Explanation Correctness Matter? Linking Computational XAI Evaluation to Human Understanding

arXiv:2603.25251v2 Announce Type: replace-cross Abstract: Explainable AI (XAI) methods are commonly evaluated using functional correctness metrics, sometimes termed faithfulness or fidelity, which est

Safety

Evaluation-Conditioned Training: Teaching Models to Generalize to Stronger Oversight Regimes

arXiv:2608.10209v1 Announce Type: new Abstract: Feedback signals used to train Large Language Models (LLMs) are the primary driver of their behavior and our main lever for instilling alignment with hu

safetyarxiv-cs-ai

Hierarchical Compositionality for An Assistive AI Agent

arXiv:2608.10330v1 Announce Type: new Abstract: AI agents are increasingly being developed to assist humans in various applications, and Large Language Models and other deep network architectures are

HSSBench: Benchmarking Humanities and Social Sciences Ability for Multimodal Large Language Models

arXiv:2506.03922v4 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant potential to advance a broad range of domains. However, current benchma

Inferential Capability Does Not Determine Legal Scope

arXiv:2608.10601v1 Announce Type: cross Abstract: Two instruments of EU digital law place inference at their centre and mean different things by it. Article 3(1) of the AI Act uses the capability to i

Safety

Injecting Hallucinations in Autonomous Vehicles: A Component-Agnostic Safety Evaluation Framework

arXiv:2510.07749v2 Announce Type: replace Abstract: Perception failures in autonomous vehicles (AV) remain a major safety concern because they are the basis for many accidents. To study how these fail

safetyarxiv-cs-ro

Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy

arXiv:2608.09992v1 Announce Type: cross Abstract: Controllable generation guided by external knowledge is a key requirement in modern generative deep learning applications, enabling the synthesis of s

On Solomonoff Induction in Large Language Models and the Limits of Self-Improving: The Singularity Is Not Near Without Symbolic Model Synthesis

arXiv:2601.05280v3 Announce Type: replace-cross Abstract: On the one hand, the question of whether large language models (LLMs) are Solomonoff induction estimators has become an explicit question at t

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information

arXiv:2608.10766v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) seeks to explain how an Artificial Intelligence (AI) system arrived at a particular decision. We propose ''Rul

The Gaussian-Multinoulli Restricted Boltzmann Machine: A Potts Model Extension of the GRBM

arXiv:2505.11635v2 Announce Type: cross Abstract: Many real-world tasks, from associative memory to symbolic reasoning, benefit from discrete, structured representations that standard continuous laten

The Illusion of Cross-Lingual Safety in Low-Resource Languages

arXiv:2608.11146v1 Announce Type: new Abstract: Safety alignment in large language models (LLMs) is largely developed in English, assuming these safeguards generalize across multilingual settings. How

local-aiarxiv-cs-cl

TRACE: Trustworthy Retrieval-Augmented Conversational Engine

arXiv:2608.10176v1 Announce Type: new Abstract: Public service chatbots are expected to deliver recommendations from an underlying public service directory, while also making sure that the recommendat

V-FiLLM: Verified Financial LLM Reasoning Benchmark

arXiv:2608.11047v1 Announce Type: new Abstract: While existing benchmarks have made substantial progress in evaluating LLMs across STEM domains, financial reasoning over structured data remains compar

Validated Synthetic Patient Generation for Small Longitudinal Cohorts: Coagulation Dynamics Across Pregnancy

arXiv:2604.07557v2 Announce Type: replace Abstract: Small longitudinal cohorts, common in maternal health, rare diseases, and early-phase trials, limit computational modeling because enrollment is slo

researcharxiv-cs-lg

Agentic Stage-One Stellarator Optimization: Autonomous Multi-Objective Search for Finite-Beta Equilibria

arXiv:2608.01344v2 Announce Type: replace Abstract: Stage-one stellarator design searches a high-dimensional family of three-dimensional plasma boundaries and fixed-boundary MHD equilibria for configu

local-aiarxiv-cs-ai

Analogical Learning for Cross-Scenario Generalization: Framework and Application to Intelligent Localization

arXiv:2504.08811v3 Announce Type: replace Abstract: Modern learning systems often struggle with joint learning across diverse scenarios and immediate adaptation to new ones, because they rely heavily

applicationsarxiv-cs-lg

Approximation Rates for Metaplectic Neural Networks

arXiv:2608.08872v1 Announce Type: new Abstract: In this paper we develop quantitative approximation results for shallow neural networks constructed using a dictionary based on metaplectic operators. F

researcharxiv-cs-lg

BDH-CQ: In-Context Learning with Recurrent Latent Reasoning

arXiv:2608.09888v1 Announce Type: cross Abstract: We introduce BDH-CQ, a reasoning model that combines in-context learning with recurrent latent reasoning. Inputs presented at inference time continuou

Beyond Hazard Resemblance: Contrastive Event Adjudication for Training-Free Video Anomaly Detection

arXiv:2608.09908v1 Announce Type: new Abstract: Video anomaly detection (VAD) aims to identify and temporally localize abnormal events in videos. Supervised methods learn anomaly decision boundaries f

local-aiarxiv-cs-cv

DoRF++: Spherical Representation Learning over Doppler Radiance Fields for Robust Wi-Fi Sensing

arXiv:2608.08381v1 Announce Type: new Abstract: Motivated by the IEEE 802.11bf effort to standardize advanced WLAN sensing, interest in Wi-Fi Channel State Information (CSI) for passive, device-free,

applicationsarxiv-cs-cv