Search: “models”

Structured Pruning of Large Language Models via Power Transformation and Sign-Preserving Score Aggregation with Adaptive Feature Retention

arXiv:2607.08027v1 Announce Type: cross Abstract: This paper proposes an improved structured pruning method for large language models (LLMs) that addresses key challenges in adapting Adaptive Feature

10 Jul 2026

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An Hybrid Quantum-Classical Diffusion Model for Image Generation

DGX agent

arXiv:2607.07072v1 Announce Type: new Abstract: Quantum diffusion models provide a physics-consistent route to generative learning by formulating noising and denoising directly on quantum states. Howe

researcharxiv-cs-lg

9 Jul 2026

Fast segmentation of watermarked texts from large language models through an epidemic change-point framework

arXiv:2509.21160v2 Announce Type: replace-cross Abstract: With the growing use of large language models, concerns over content authenticity have spurred a variety of watermarking schemes. These scheme

Generalist Vision-Language Models for Fast Radio Burst detection: a zero-shot benchmark against a specialized detector

arXiv:2607.07382v1 Announce Type: new Abstract: Fast Radio Bursts (FRBs) are millisecond-duration radio transients whose automated detection increasingly relies on highly specialized deep learning mod

LoCA: Spatially-Aware Low-Rank Convolutional Adaptation of Vision Foundation Models

arXiv:2607.06918v1 Announce Type: cross Abstract: Pre-trained Vision Foundation Models (VFMs) provide strong visual representations for diverse downstream tasks. The key challenge of VFM adaptation st

Applications

Physical activities enable scalable foundation modelling for broad-spectrum health prediction

arXiv:2607.06954v1 Announce Type: new Abstract: Wearable and mobile sensing technologies have demonstrated strong potential for health inference; however, most sensor models are designed for specific

applicationsarxiv-cs-lg

ReMoDEx: A Local-to-Global Relevance-Based Model Decision Explainability Framework for large-Scale Image Datasets

arXiv:2607.06889v1 Announce Type: cross Abstract: Deep learning image classifiers achieve strong predictive performance yet remain opaque in how decisions are formed. A model may predict correctly whi

local-aiarxiv-cs-ai

Reward Valuation in Vision Language Models: Causal Mechanisms Underlying Anhedonia

arXiv:2607.06626v1 Announce Type: new Abstract: Recent Vision-Language Models capture increasingly complex aspects of human cognition. Here we ask whether this alignment extends to reward valuation, w

local-aiarxiv-cs-lg

Riemannian Geometry for Pre-trained Language Model Embeddings

arXiv:2607.07047v1 Announce Type: cross Abstract: Understanding the geometric structure of pre-trained language model embeddings matters for interpretability and safety. We ask whether sentence-level

Siamese Neural Network for Label-Efficient Critical Phenomena Prediction in 3D Percolation Models

arXiv:2507.14159v2 Announce Type: replace-cross Abstract: Predicting critical phenomena from limited labeled data remains a challenging task in statistical physics. As percolation theory provides a ca

TF-Engram: A Train-Free Engram with SSD-Backed Memory for Large Language Models

model-releasesarxiv-cs-cl

arXiv:2607.07388v1 Announce Type: new Abstract: Large Language Models (LLMs) store factual knowledge and domain-specific patterns implicitly in dense Transformer parameters, making knowledge expansion

Vision Language Action (VLA) Models for Unmanned Aerial Robotics and Bimanual Manipulation: A Review

arXiv:2607.06706v1 Announce Type: cross Abstract: Vision Language Action (VLA) models unify visual perception, natural-language understanding, and action generation within a single foundation model, a

researcharxiv-cs-ai

Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents

arXiv:2607.05775v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly evaluated on their ability to use tools, plan multi-step tasks, coordinate with other agents, and ope

ExplAIner: A Declarative Query Language for Explaining Classification Models

arXiv:2607.06407v1 Announce Type: new Abstract: The XAI community has studied a wide range of queries and scores for explaining predictions of ML models. From a data management perspective, this proli

researcharxiv-cs-ai

Applications

Learning 4D Geometric Priors for Inference-Efficient World Action Models

arXiv:2607.05468v1 Announce Type: cross Abstract: World Action Models (WAMs) have shown strong potential for robotic manipulation by jointly modeling visual future dynamics and executable action seque

applicationsarxiv-cs-ai

Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems

arXiv:2607.06094v1 Announce Type: new Abstract: Faults on a cyber-physical system (CPS) are too rare and unrepresentative to characterise, or even to select a model on, so detection must instead model

Parameter-Free Encoders Remain Viable for RDB Foundation Models

arXiv:2607.05476v1 Announce Type: new Abstract: Given a relational database (RDB) storing heterogeneous tabular information, how can we predict missing (or future) values in some target column of inte

Revisiting the Relation Between Language Model Perplexity and ASR Word Error Rate for Modern End-to-End Speech Recognition

arXiv:2607.05612v1 Announce Type: new Abstract: Language model (LM) perplexity (PPL) has historically been used as a proxy for automatic speech recognition (ASR) word error rate (WER), with prior work

researcharxiv-cs-cl

RPAM: A Principled Metric for Evaluating Associations in Language Models with High Predictive Validity in Downstream Outputs

arXiv:2607.05679v1 Announce Type: cross Abstract: Language models (LMs) exhibit problematic biases, such as stereotypes. Effectively analyzing and mitigating such biases requires accurate and generali

CrossHallu: Do Hallucination Signals Generalize Across Languages and Domains in Large Language Model's Internals?

arXiv:2607.04029v1 Announce Type: new Abstract: Recent hallucination detection techniques in large language models (LLMs) focus on directly extracting features from a model's internal representations

safetyarxiv-cs-cl

Deep Learning Network-Temporal Models For Traffic Prediction

arXiv:2603.11475v2 Announce Type: replace Abstract: Accurate prediction of multivariate time series is essential for emerging network intelligent control, observability, and management functions. Exis

Diffusion Models are Open-World Affordance Learners: Leveraging Generative Priors for 3D Affordance Learning

arXiv:2508.01651v2 Announce Type: replace Abstract: 3D affordance grounding aims to understand how diverse objects can be manipulated, making it a cornerstone of embodied interaction. However, prior w

DSWAM: A Dual-System World Action Foundation Model for Fine-Grained Robot Manipulation

arXiv:2607.04927v1 Announce Type: cross Abstract: World Action Models (WAMs) provide a promising alternative to Vision-Language-Action (VLA) policies by using video-based world modeling as dense super

safetyarxiv-cs-ai

Flex-Forcing: Towards a Unified Autoregressive and Bidirectional Video Diffusion Model

arXiv:2607.03509v1 Announce Type: new Abstract: Recent progress in large-scale generative models has substantially advanced video generation, yet existing methods remain constrained by a rigid inferen

safetyarxiv-cs-cv

GaussianArt: Unified Modeling of Geometry and Motion for Articulated Objects

arXiv:2508.14891v3 Announce Type: replace Abstract: Reconstructing articulated objects is essential for building digital twins of interactive environments. However, prior methods typically decouple ge

GLOW-FDG: Generalized cancer LesiOn Whole-body segmentation model for ^{18}F-FDG-PET/CT

arXiv:2607.03931v1 Announce Type: cross Abstract: Whole-body fluorodeoxyglucose positron emission tomography combined with computed tomography is widely used in cancer care, but manual lesion delineat

Harness-Aware Self-Evolving: Co-Evolving Model Weights, Harness, and Task Solutions

arXiv:2607.03935v1 Announce Type: new Abstract: Self-evolving frameworks usually optimize task solutions while treating the surrounding harness as fixed. We introduce Harness-Aware Self-Evolving (HASE

Agents

Heaviside Continuity of Rolling Coefficients for Eliminating Epistemic Entropy in Large Language Models

arXiv:2607.04562v1 Announce Type: new Abstract: Large language models (LLMs) generate fluent outputs that can be wrong. Unlike humans, who often exhibit cues when providing false information, LLMs pro

agentsarxiv-cs-ai

Is the Geometry Doing the Work? An Operating-Point Audit of Hierarchy in Hyperbolic Vision-Language Models

arXiv:2607.05268v1 Announce Type: new Abstract: Whether a hyperbolic representation model uses its geometry cannot be read off its curvature parameter: what matters is the dimensionless operating poin

Language Generation with Replay: A Learning-Theoretic View of Model Collapse

arXiv:2603.11784v2 Announce Type: replace Abstract: As scaling laws push the training of frontier large language models (LLMs) toward ever-growing data requirements, training pipelines are approaching

MambaLIE: Scene Light Intensity-Boosted Low-Light Image Enhancement with State Space Model

arXiv:2607.03013v1 Announce Type: cross Abstract: Images captured by consumer electronic devices, such as mobile phones and digital cameras, often suffer from low-light degradation due to sensor limit

local-aiarxiv-cs-ai

MCLMR: A Model-Agnostic Causal Learning Framework for Multi-Behavior Recommendation

arXiv:2603.25126v2 Announce Type: replace-cross Abstract: Multi-Behavior Recommendation (MBR) leverages multiple user interaction types (e.g., views, clicks, purchases) to enrich preference modeling a

safetyarxiv-cs-ai

Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models

arXiv:2409.16663v5 Announce Type: replace-cross Abstract: We propose the use of latent space generative world models to address the covariate shift problem in autonomous driving. A world model is a ne

safetyarxiv-cs-cv

Model Predictive Path Integral PID Control for Learning-Based Path Following

arXiv:2603.29499v2 Announce Type: replace-cross Abstract: Classical proportional--integral--derivative (PID) control remains widely used in industrial control systems, while model predictive control (

Out-of-Distribution Generalization of Risk Aversion in Language Models

arXiv:2607.02755v1 Announce Type: cross Abstract: Training AIs to be risk-averse in resources could offer a failsafe in the event that AIs turn out misaligned. Misaligned but risk-averse AIs would ten

Parameter Efficient Multimodal Instruction Tuning for Romanian Vision Language Models

arXiv:2512.14926v2 Announce Type: replace-cross Abstract: Focusing on low-resource languages is an essential step toward democratizing generative AI. In this work, we contribute to reducing the multim

Perceptual Flow Matching for Few-Step Generative Modeling

arXiv:2607.03524v1 Announce Type: new Abstract: We propose Perceptual Flow Matching (PFM), a simple yet effective framework for few-step generation in flow-matching models. Rather than performing velo

researcharxiv-cs-cv

PuzzleMoE: Efficient Compression of Large Mixture-of-Experts Models via Sparse Expert Merging and Bit-packed inference

arXiv:2511.04805v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models have shown strong potential in scaling language models efficiently by activating only a small subset of expert

researcharxiv-cs-ai

Reducing the Complexity of Deep Learning Models for EEG Analysis on Wearable Devices

arXiv:2606.12742v3 Announce Type: replace Abstract: Wearable healthcare devices are the fastest-growing Internet of Things (IoT) sector. Many automated healthcare services rely on two crucial biologic

Reward Granularity in RLVR: Comparing Process and Outcome Reward Structures for Mathematical Reasoning in Small Language Models

arXiv:2607.02869v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for improving mathematical reasoning in language models. Yet m

safetyarxiv-cs-lg

Stage-wise Attention-Guided Region Sequencing for Adversarial Attacks on Large Vision-Language Models

arXiv:2602.04356v2 Announce Type: replace Abstract: Targeted adversarial attacks on Large Vision-Language Models (LVLMs) test whether small image perturbations can steer model responses toward attacke

local-aiarxiv-cs-cv

TACG: Trajectory-Aware Commit Gating for Diffusion Language Model Decoding

model-releasesarxiv-cs-cl

arXiv:2607.03236v1 Announce Type: new Abstract: Diffusion language models (DLLMs) generate text by iteratively denoising masked positions, exposing a trajectory of predictive distributions rather than

Taming I2V models for Image HOI Editing: A Cognitive Benchmark and Agentic Self-Correcting Framework

arXiv:2606.19073v2 Announce Type: replace Abstract: Current image editing methods excel at static attributes but fail at complex Human-Object Interactions (HOI), a critical challenge unaddressed by ex

TestMate: Test-Time Domain Adaptation Aided by Lightweight Vision Foundation Model

arXiv:2607.03810v1 Announce Type: new Abstract: Test-Time Domain Adaptation (TTDA) aims to adapt Deep Neural Networks to distribution shifts using only streaming, unlabeled test data in real time. Cur

Algebraic Model Counting for Global Analysis of Optimal Decision Trees

arXiv:2607.02069v1 Announce Type: new Abstract: Ensuring model reliability in Explainable AI requires a global assessment of the hypothesis space. We propose a formal framework for the exhaustive anal

safetyarxiv-cs-ai

Automated grading of Linux/bash examinations using large language models: a four-level cognitive taxonomy approach

arXiv:2607.02432v1 Announce Type: new Abstract: Scalable and reliable grading of command-line examinations remains a challenge in computing education, where rising enrolments make manual marking diffi

Agents

Certified World Models as Sensing Clocks: Drift-Aware Deadlines for Active Perception

arXiv:2607.01537v1 Announce Type: new Abstract: Certified world models estimate how long their predictions remain valid. We turn this validity horizon into an operational sensing clock: a rule for whe

agentsarxiv-cs-lg

Generic Expert Coverage for Pruning SparseMixture-of-Experts Language Models

arXiv:2607.01710v1 Announce Type: new Abstract: Sparsely activated Mixture-of-Experts (MoE) language models contain substantial structured redundancy among routed experts, but pruning them without dow