Search: “framework”

Unsupervised Post-Training of Foundation Models: A Survey

arXiv:2608.24982v1 Announce Type: new Abstract: Foundation-model post-training usually relies on human labels, preference data, stronger teachers, or executable verifiers. We study Unsupervised Post-T

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VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning

DGX agent

arXiv:2608.26105v1 Announce Type: new Abstract: Native visual reasoning treats visual generation as the medium of reasoning itself: visual states (i.e. images and videos) are not merely inputs to be u

safetyarxiv-cs-cv

27 Aug 2026

VDAR-Router: Adaptive LLMs Routing via Verbalized Query Difficulty Analysis Retrieval

arXiv:2607.18098v2 Announce Type: replace Abstract: Large language models are increasingly used in practical systems, making efficient model selection important for reducing deployment cost. LLM routi

researcharxiv-cs-cl

VINCENT: Validated Interaction Network for Cross-drug Explanation of Therapeutics

arXiv:2608.25841v1 Announce Type: new Abstract: Drug synergy prediction estimates whether two drugs produce a stronger joint effect than expected from their individual activities. For drug combination

Agents

VirTooS: A ROS 2 - Unity Virtualization Toolkit for Fleet Management of Autonomous Mobile Robots

arXiv:2608.26066v1 Announce Type: new Abstract: In this paper, we present VirTooS, a Python/C# toolkit designed to implement fleet-management tasks on teams of Autonomous Mobile Robots (AMRs). VirTooS

agentsarxiv-cs-ro

VISTA: Visually Inferred Spatial ConTact Attention for Contact-Rich Manipulation

arXiv:2608.25872v1 Announce Type: new Abstract: Contact-rich manipulation requires precise interaction feedback. While vision-centric imitation learning is prevalent, external visual observations prov

safetyarxiv-cs-ro

When Personality Meets Quantization: A Layer-wise MBTI Analysis of Quantized LLMs

arXiv:2608.25977v1 Announce Type: new Abstract: Personality is increasingly important in large language models (LLMs), as it shapes users' trust, engagement, and emotional experiences. While the Myers

researcharxiv-cs-cl

Where to Look Matters: On-Policy Self-Distillation for Long-Video Understanding

arXiv:2608.25356v1 Announce Type: new Abstract: Vision-language models (VLMs) have made substantial progress in long-video understanding, with standard backbone models typically answering questions fr

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Why Does Graph Learning Fail to Fully Benefit from a Text Teacher?

arXiv:2608.25741v1 Announce Type: cross Abstract: Graph neural networks (GNNs) are widely used to represent complex interactions and relationships among entities. We investigate a multimodal model tha

safetyarxiv-cs-cl

Why ML-based cough models do not generalize: a systematic cross-dataset evaluation for tuberculosis screening

arXiv:2608.25846v1 Announce Type: cross Abstract: Cough acoustics are promising for non-invasive tuberculosis (TB) screening, yet whether machine learning (ML) models capture disease-related acoustics

A Bayesian Learning Approach for Drone Coverage Network: A Case Study on Cardiac Arrest in Scotland

arXiv:2603.23134v2 Announce Type: replace Abstract: Drones are becoming popular as a complementary system for Emergency Medical Services (EMS). Although several pilot studies and flight trials have sh

applicationsarxiv-cs-lg

A Data-dependent Early Stopping Rule using Rademacher Complexity with L1-norm

arXiv:2608.24210v1 Announce Type: new Abstract: Training neural networks requires balancing the trade-off between fitting the training data and achieving robust performance on unseen inputs. This abil

A Hybrid Two-Stage Machine Learning Pipeline for Fault Detection and Classification in Power Transmission Systems

model-releasesarxiv-cs-lg

arXiv:2608.23726v1 Announce Type: cross Abstract: Rapid and accurate fault detection in high-voltage transmission networks is essential for grid reliability and equipment protection. Transmission faul

A Multimodal Foundation Model for Longitudinal Patient Representation and Scalable Insight Generation in Oncology

arXiv:2608.24688v1 Announce Type: new Abstract: Precision oncology necessitates a longitudinal model of patient state that captures cancer evolution and treatment over time, integrating multimodal obs

applicationsarxiv-cs-lg

A Robust Task-Level Control Architecture for Learned Dynamical Systems

arXiv:2511.09790v2 Announce Type: replace-cross Abstract: Dynamical system (DS)-based learning from demonstration (LfD) is a powerful tool for generating motion plans in the operation ('task') space o

A Structural FHMM for Interpretable Disease Trajectories in T2DM

arXiv:2608.24328v1 Announce Type: new Abstract: In this work, we propose a structural variant of the Factorial Hidden Markov Model (FHMM) for the analysis of disease trajectories in patients with Type

Accelerating the Adoption of Residential Solar Power Systems: Policy Analysis using a Dynamic Structural Model

arXiv:2608.23796v1 Announce Type: cross Abstract: Problem definition: Solar electricity generation is a strategic component of energy portfolios designed to meet growing demand and reduce carbon emiss

Adaptive GR(1) Specification Repair for Liveness-Preserving Shielding in Reinforcement Learning

arXiv:2511.02605v3 Announce Type: replace Abstract: Shielding is widely used to enforce safety in reinforcement learning (RL), ensuring that an agent's actions remain compliant with formal specificati

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Agents

AHEAD: Adaptive Hindsight with Environment-Augmented Distillation for Agentic RL

arXiv:2608.24114v1 Announce Type: new Abstract: Training multi-turn LLM agents with reinforcement learning typically relies on trajectory-level rewards, which assign a uniform advantage to every step

agentsarxiv-cs-ai

ASemConsist: Adaptive Semantic Feature Control for Training-Free Identity-Consistent Generation

arXiv:2512.23245v3 Announce Type: replace Abstract: Recent text-to-image diffusion models have significantly improved visual quality and text alignment. However, generating a sequence of images while

safetyarxiv-cs-cv

AURASeg: Attention-Guided Upsampling with Residual-Assisted Boundary Refinement for Drivable-Area Segmentation

arXiv:2510.21536v5 Announce Type: replace-cross Abstract: Free-space segmentation is essential for autonomous robots to identify drivable regions and navigate safely across indoor, outdoor, and road-s

local-aiarxiv-cs-cv

Benchmarking LLM Judges for Voice-Agent Evaluation: Reliability, Calibration, and Human Oversight

arXiv:2608.24314v1 Announce Type: new Abstract: Evaluating conversational voice agents at scale re- quires reliable assessment methods that capture both observ- able interaction quality and the contex

Beyond Static Interpretability: Anticipating Post-SFT Mechanisms from Pre-SFT Parameters for Better Tuning

arXiv:2608.24482v1 Announce Type: cross Abstract: Mechanistic Localization bridges mechanistic interpretability and post-training optimization by isolating critical parameters via interpretative appro

BioKERN: Biological Kernel Regularization for Histology-to-Transcriptomics Neighborhood Retrieval

arXiv:2608.24823v1 Announce Type: new Abstract: Spatially resolved biology requires representations that preserve biological neighborhood structure rather than only exact cross-modal correspondences.

Bridging Adversarial and Collaborative Learning for AI-Generated Image Quality Assessment

arXiv:2608.24372v1 Announce Type: new Abstract: AI-generated image quality assessment (AIGIQA) requires jointly reasoning about perceptual fidelity and prompt alignment, two quality dimensions that ar

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CAFE: Self-Improving Search Agents Need Co-Evolving Feedback

arXiv:2608.24794v1 Announce Type: new Abstract: Outcome-supervised search agents learn when and how to retrieve evidence, but terminal rewards neither localize intermediate errors nor redirect an ongo

Can large language models assist choice modelling? Insights into prompting strategies and current models' capabilities

arXiv:2507.21790v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are becoming widely used to support various workflows across different disciplines, yet their potential in discre

Causal Modelling of Support Interventions for Student Competency Assessment

arXiv:2608.24632v1 Announce Type: new Abstract: Accurate assessment of student competencies is essential for enabling educators to identify individual needs, design targeted interventions, and evaluat

researcharxiv-cs-ai

ChorusTIC: Training-Free Multivariate Time Series Classification via Chorus In-Context Learning

arXiv:2608.24033v1 Announce Type: cross Abstract: Time series classification underpins applications in healthcare, sensing, and industrial monitoring. Although time series foundation models support fo

COCI: Conference Organisers and Content Identifier

arXiv:2608.24559v1 Announce Type: cross Abstract: Despite the critical role of grey literature in scholarly communication, artefacts such as Calls for Papers (CfPs) remain largely isolated from modern

researcharxiv-cs-ai

CoDrift: Compositional Drifting for Offline Reinforcement Learning

arXiv:2608.23939v1 Announce Type: new Abstract: Offline reinforcement learning is intrinsically multi-objective: a policy must remain compatible with the behavioral support of a fixed dataset while pr

Comparative Assessment of Deep Learning Architectures for Underwater Subsurface Kelp Forest Segmentation with The Kelp-o-Tron

arXiv:2608.24594v1 Announce Type: new Abstract: Submerged kelp forests are vital coastal ecosystems that support marine biodiversity and ecosystem dynamics, yet accurate underwater kelp segmentation r

researcharxiv-cs-cv

Compression Trinity: Exploring Sparsity, Quantization, and Low-Rank Approximations for LLM Compression

arXiv:2608.24070v1 Announce Type: new Abstract: Prohibitive computational and environmental costs impede the scalable deployment of Large Language Models (LLMs). Traditional compression techniques (sp

Confidently Wrong, Silently So: Auditing Undetectable Failures of a Deployed On-Device Language Model

arXiv:2608.23663v1 Announce Type: cross Abstract: Aligning deployed language models requires knowing when their outputs can be trusted, yet on-device models now ship to hundreds of millions of devices

Continual Visual Learning under Evolving Semantic Concept Shift

model-releasesarxiv-cs-cv

arXiv:2608.23903v1 Announce Type: new Abstract: Visual foundation models are commonly adapted under the assumption that the appearance of incoming data may change while the semantic meaning of the pre

Coverage Planning for Robotic Tooth Preparation in Densely Constrained Environments

arXiv:2608.24155v1 Announce Type: new Abstract: Tooth preparation refers to the controlled removal of tooth structure to create an optimal substrate for fixed restorations and is a core procedure in r

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CRISP: Calibration-Aware Visual State Space Duality for Remote Sensing Semantic Segmentation

arXiv:2608.23746v1 Announce Type: new Abstract: State space models, especially Visual State Space Duality (VSSD), have emerged as efficient linear-time alternatives to Transformers for dense visual ta

local-aiarxiv-cs-cv

CytoNet: A Foundation Model for the Human Cerebral Cortex at Cellular Resolution

arXiv:2511.01870v3 Announce Type: replace-cross Abstract: Studying the cellular architecture of the human cerebral cortex is essential for understanding how the brain is organized from the micro to th

researcharxiv-cs-ai

Data Leakage Inflates Generalizability of Power Outage Prediction Models

arXiv:2608.24665v1 Announce Type: new Abstract: Power outage prediction models are increasingly used in assessments of climate-driven infrastructure risk, yet current evaluation practices obscure whet

applicationsarxiv-cs-lg

Data Mixing as Mixture Experiment: Response Surface Methodology and Optimal Design for Large Language Model Pretraining

arXiv:2608.23922v1 Announce Type: new Abstract: Data mixing is a central design problem in large language model pretraining: given a fixed token budget, practitioners must decide how much data to allo

applicationsarxiv-cs-ai

DDMS: Discriminative Distillation of Multi-view Foundational Features into Single-view Models

arXiv:2608.23850v1 Announce Type: new Abstract: Foundational visual features such as DINO have played a critical role across modern computer vision, and have recently become key components in multi-vi

local-aiarxiv-cs-cv

Hardware

Deep Feature Pyramid Convolutional Networks with In-Place Activated Batch Normalization for Automated Skin Lesion Boundary Segmentation

arXiv:1812.00877v2 Announce Type: replace Abstract: Segmentation of skin lesion boundaries in dermoscopic imaging is an important prerequisite step for computer-aided diagnosis of malignant melanoma,

hardwarearxiv-cs-cv

Delayed Optimizer-State Transport Shapes Short-Horizon Training Decisions

arXiv:2608.24593v1 Announce Type: new Abstract: Adaptive optimizers retain gradient history in moment variables, allowing a local change in loss weighting to alter later updates. We examine whether th

local-aiarxiv-cs-lg

Differential Learning for Robust Prediction of Thermal Stability with Application to Energetic Materials

arXiv:2608.23874v1 Announce Type: cross Abstract: Predicting thermal stability during handling and storage is essential for the design of safe and reliable energetic materials. However, experimental m

DoublesEval: Diagnosing Multi-Agent Tactical Reasoning in Vision-Language Models via Professional Doubles Badminton

model-releasesarxiv-cs-cv

arXiv:2608.24439v1 Announce Type: new Abstract: Visual Language Models (VLMs) excel at describing visible scene content but struggle to reason about dynamic multi-agent interactions, where action sema

DriftAD: Visually-Guided Text Drift for Few-Shot Industrial Anomaly Detection

arXiv:2608.23723v1 Announce Type: new Abstract: Few-shot anomaly detection (FSAD) has recently benefited from vision-language models such as CLIP, which enable anomaly de?tection by aligning visual fe

researcharxiv-cs-cv

Dynamical System-Based Imitation Learning and Neuroadaptive Control for Trajectory Recovery in Autonomous Ships

arXiv:2608.23924v1 Announce Type: cross Abstract: Repetitive maritime operations can be effectively learned using the Imitation Learning (IL) paradigm, which transfers human expertise directly to Unma

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Agents

EgoErrorVQA: Assess Egocentric Comprehension Capabilities through Procedural Errors for Ego-Agentic AI

arXiv:2608.24134v1 Announce Type: new Abstract: The majority of our everyday activities are procedural and consist of sequences of interdependent steps. However, existing benchmarks for Visual Agents

agentsarxiv-cs-cv