Search: “framework”

BaCP: Backbone Contrastive Pruning for Preserving Representations in Extremely Sparse Neural Networks

arXiv:2610.02524v1 Announce Type: new Abstract: Unstructured pruning at extreme sparsity often suffers from representational collapse, causing sharp drops in accuracy. To address this, we study Backbo

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Bandits via Additive Quantized Representations

DGX agent

arXiv:2610.02440v1 Announce Type: new Abstract: Contextual bandits require balancing nonlinear reward modeling with online efficiency. Tree ensembles and neural methods capture nonlinearities but requ

researcharxiv-cs-lg

5 Oct 2026

Beyond Entropy: Self-Diagnostic Multi-Role Token Optimization for Video Reasoning

arXiv:2610.03400v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards has substantially advanced multimodal reasoning, yet it remains fundamentally limited by ambiguous token-

local-aiarxiv-cs-cv

Agents

Beyond Predefined Sinks: Security-Aware Dependency Analysis for LLM Agents

arXiv:2610.03014v1 Announce Type: cross Abstract: Large language model (LLM)-based agents increasingly connect model-generated decisions to security-sensitive software capabilities such as command exe

agentsarxiv-cs-ai

Beyond Single Videos: Benchmarking and Active Evidence Seeking for E-Commerce Cross-Video Reasoning

arXiv:2610.03099v1 Announce Type: cross Abstract: E-commerce videos are information-dense and frequently compared by consumers evaluating products and merchants assessing marketing strategies. However

Bifidelity Karhunen-Loeve Expansion Surrogate with Active Learning for Random Fields

arXiv:2511.03756v2 Announce Type: replace-cross Abstract: We present a bifidelity Karhunen--Loeve expansion (KLE) surrogate model for field-valued quantities of interest (QoIs) under uncertain inputs.

BitNest: Bit-Nested Speculative Decoding for Memory-Efficient LLM Inference Acceleration

arXiv:2610.02800v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by using a lightweight draft to propose multiple tokens for parallel verification. However, e

Capability Scaling-Down Laws for LLM Compression

arXiv:2610.02462v1 Announce Type: cross Abstract: LLM compression reduces inference costs and memory requirements, but selecting a method and configuration remains largely empirical because comparable

researcharxiv-cs-cl

Causal Representation Learning with Instantaneous and Lagged Relations via Nonstationarity

arXiv:2610.03452v1 Announce Type: new Abstract: Causal representation learning for time-series data aims to identify latent states and their causal relations from observations. In this setting, an imp

tutorialsarxiv-cs-lg

CLEAN: Psychometrically Consistent Incremental Cognitive Diagnosis under Concept-Space Expansion via Architectural Isolation

arXiv:2610.02278v1 Announce Type: new Abstract: Cognitive diagnosis (CD) is a fundamental task in intelligent education that profiles learner proficiency over knowledge concepts. In real-world learnin

tutorialsarxiv-cs-lg

CLIMB: Confidence-Guided Complementary Evidence for Multimodal Retrieval-Augmented Generation

arXiv:2610.03421v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong visual reasoning abilities, but knowledge-intensive visual question answering often requires

safetyarxiv-cs-cl

Hardware

Cocoon: A System Architecture for Differentially Private Training with Correlated Noises

arXiv:2510.07304v2 Announce Type: replace-cross Abstract: Machine learning (ML) models memorize and leak training data, causing serious privacy issues to data owners. Training algorithms with differen

hardwarearxiv-cs-ai

Confidence-Gated Cloud-Edge Cascade Triage via Variational Risk Minimization for Medical Imaging

arXiv:2610.02269v1 Announce Type: cross Abstract: Emergency chest X-ray (CXR) triage has a structural modality gap: reports arrive after triage decisions, yet multimodal foundation models require imag

researcharxiv-cs-cv

Applications

Conformal Prediction for Time Series with Deep Sequence Models

arXiv:2610.02357v1 Announce Type: cross Abstract: Recent advances in deep learning for time series prediction have amplified the need for reliable uncertainty quantification. Conformal prediction has

applicationsarxiv-cs-lg

ConvoDrift: A Multi-Turn Conversational Dataset for Modeling Stylistic Tone Evolution

arXiv:2610.02873v1 Announce Type: cross Abstract: The evolution of linguistic style in conversations is an underexplored issue in NLP. Most style-control datasets focus on sentences or assume a static

safetyarxiv-cs-ai

CORNAV: Construction-Aware Reasoning for Robot Navigation on Active Worksites

arXiv:2610.03622v1 Announce Type: new Abstract: The construction industry faces persistent labor shortages, low productivity that costs the global economy over $1.6 trillion annually, and one of the h

local-aiarxiv-cs-ro

Counterfactual Predictions in Scientific Emulators Without Controlled Experiments

arXiv:2610.02252v1 Announce Type: cross Abstract: Many scientific questions require reasoning about what was never observed: What if the conditions, interventions, or history had been different? Model

CSIR: Contextually and Socially Informed Robots for Efficient Person Goal Navigation

model-releasesarxiv-cs-ro

arXiv:2610.02750v1 Announce Type: new Abstract: We address the Person Goal Navigation (PersonNav) problem, enabling robots to search for people under realistic constraints on information accessibility

CUEing User Simulators: Calibrated User Embeddings for Multi-Turn Benchmarking

arXiv:2610.02460v1 Announce Type: new Abstract: Recent benchmarks rely on user simulators to evaluate AI agents in multi-turn interaction. While existing simulation techniques demonstrate surface fide

safetyarxiv-cs-cl

DAWIS: Data Assimilation with Windowed Inverse Sampling via Multitask Interpolants

arXiv:2610.03314v1 Announce Type: cross Abstract: Flow- and diffusion-based generative models have recently emerged as flexible and highly efficient forecasting models for dynamical systems. When comb

Decoupling Memory from Context: Structured Memory for Token-Efficient Test-Time Continual Learning

arXiv:2610.02687v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in enterprise, scientific, and medical applications, where agents must incorporate domain-specifi

Demonstration-Guided Observation Attacks on Black-Box Safe Reinforcement Learning Controllers for Robotic Systems

arXiv:2602.16543v2 Announce Type: replace Abstract: Safe reinforcement learning (Safe RL) learns robotic controllers that optimize task rewards under safety constraints, yet observation perturbations

Depth Hypothesis Guided Iterative Refinement for Event-Image Monocular Depth Estimation

arXiv:2610.03439v1 Announce Type: new Abstract: Event cameras hold excellent dynamic properties, showing great potential for monocular depth estimation (MDE). However, existing methods mainly improve

local-aiarxiv-cs-cv

Designing the Future of User Feedback for Generative AI

arXiv:2610.02631v1 Announce Type: new Abstract: Post-deployment feedback from users can be a cost-effective, scalable, and representative means to monitor and improve generative AI systems and feature

DeskForge: Dense Supervision from Desktop Environments for Computer-Use Agents

arXiv:2610.02320v1 Announce Type: cross Abstract: Computer-use agents need to reliably ground action targets in complex desktop scenes, where multiple applications, overlapping windows, and visually s

Distributionally Robust Survival Models under Subpopulation Shift and Outlier Contamination

arXiv:2610.02868v1 Announce Type: cross Abstract: Learning robust survival models under distribution shift is an important but challenging problem in many applications. In heterogeneous populations, a

DIVINE: Simple Cross-Market Stock Pretraining via Diverse Indicator Reconstruction

arXiv:2610.02866v1 Announce Type: new Abstract: Financial time-series pretraining typically learns from masked observations, contrastive relations, or future outcomes---yet existing objectives struggl

DuoMatching: Joint-Marginal Distribution Matching for Few-Step Video Generation

arXiv:2610.03543v1 Announce Type: new Abstract: Streaming video generation has benefited from distribution matching distillation (DMD), which matches the joint distribution of video frames to a video

safetyarxiv-cs-cv

Hardware

Efficient Memory Crystallization for Graph Learning under Non-Stationary Distribution Shifts

arXiv:2610.02795v1 Announce Type: new Abstract: Deep graph learning models deployed in real-world systems often need to cope with non-stationary environments, where the underlying graph distribution d

hardwarearxiv-cs-lg

Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching

model-releasesarxiv-cs-cl

arXiv:2509.20810v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit strong reasoning capabilities in complex tasks. However, they still struggle with hallucinations and factual er

Error Propagation in Dynamic Programming: From Stochastic Control to American Option Pricing

arXiv:2509.20239v2 Announce Type: replace-cross Abstract: This paper investigates theoretical and methodological foundations for stochastic optimal control (SOC) in discrete time. We start formulating

Evaluating LLM-as-a-Judge Beyond Score Alignment: A Psychometric Analysis of Residual Judging Difficulty

arXiv:2610.02877v1 Announce Type: new Abstract: Large language models (LLMs) are widely used as automatic judges, with validity typically assessed via alignment with human scores. However, aggregate a

safetyarxiv-cs-cl

Evaluation of Sampling Strategies and Physics-Informed Kolmogorov--Arnold Networks in Unbounded Domains

arXiv:2512.12074v2 Announce Type: replace Abstract: Physics-informed neural networks (PINNs) have emerged as an effective approach for solving partial differential equations (PDEs) by incorporating ph

EVEWorld: Physical Evolution Supervision for Embodied World Models

arXiv:2610.03374v1 Announce Type: new Abstract: Embodied world models enable scalable simulation of embodied interactions for robot learning. However, existing models are prone to Model Laziness, as t

safetyarxiv-cs-cv

EviDent-CBCT: Evidence-Bottlenecked Report Generation from Dental CBCT under Non-Exhaustive Report Supervision

arXiv:2610.02375v1 Announce Type: cross Abstract: Dento-maxillofacial cone-beam CT (CBCT) reports may contain dozens of tooth-specific, anatomical, and spatial findings from a single 3D scan. Learning

local-aiarxiv-cs-ai

EVOL: Simulator-Guided Evolutionary Expert Synthesis for Deployment-Free Learning Path Recommendation

arXiv:2610.03273v1 Announce Type: new Abstract: Reinforcement learning (RL) for learning path recommendation (LPR) faces two coupled obstacles. First, the policy must commit to a sequence of L concept

safetyarxiv-cs-ai

Evolving Hybrid Quantum-Classical Architectures for Image Classification

arXiv:2610.03220v1 Announce Type: cross Abstract: Hybrid quantum classical neural networks integrate parameterized quantum circuits (PQCs) with established deep learning architectures, but their perfo

Expected Utility Regret Rule: Minimax and Bayes Optimal Portfolio Choice

arXiv:2610.02290v1 Announce Type: cross Abstract: This study considers the problem of portfolio choice, where we recommend a portfolio to an investor to maximize the expected utility of their wealth.

FastOPD: On-Policy Distillation for Lightweight VLA Deployment

arXiv:2610.02832v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) foundation models have scaled rapidly to enhance manipulation performance and generalizability, but this scaling incurs h

safetyarxiv-cs-ai

Fed-ADApt: Federated Anytime Depth Adaptation for Resource-Aware Medical Image Segmentation

arXiv:2610.03474v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative training of medical image segmentation models without sharing raw patient data, yet existing approaches as

local-aiarxiv-cs-cv

ForestQuery: Boundary-Aware and Spatially Anchored Query Learning for Unified Forest Point Cloud Segmentation

arXiv:2610.03403v1 Announce Type: cross Abstract: Forest point cloud segmentation is fundamental for fine-grained 3D forest scene understanding, yet remains challenging due to irregular tree structure

tutorialsarxiv-cs-ai

From Fragments to Global Maps: Learning Vectorized Map Aggregation with Large Language Models

arXiv:2610.02513v1 Announce Type: cross Abstract: Large-scale vectorized HD maps provide structured road information that is essential for perception, localization, and planning in autonomous driving.

local-aiarxiv-cs-ai

From Mathematical to Executable Certificates for Machine Unlearning

arXiv:2610.02268v1 Announce Type: new Abstract: Machine unlearning is needed when data must be removed because of deletion requests, outdated records, or data-quality concerns, while retraining from s

FrugalEvo: Towards Cost-Aware LLM-Guided Program Evolution

arXiv:2610.03675v1 Announce Type: cross Abstract: LLM-guided evolutionary methods, such as AlphaEvolve, have emerged as powerful approaches for challenging computational optimization problems, such as

FUSEye: Training-Light Fisheye Detection with Overlapping Views and Zero-Initialized Adapters

model-releasesarxiv-cs-cv

arXiv:2610.02799v1 Announce Type: new Abstract: Fisheye cameras give mobile robots a single-sensor, low-cost view of their surroundings, yet the COCO-pretrained detectors that practitioners routinely

Generalizable single-cell perturbation response prediction using energy-guided flow matching

arXiv:2610.02232v1 Announce Type: cross Abstract: Predicting phenotypic and transcriptional responses to perturbations at single-cell resolution provides a powerful tool for probing biological systems

Generalization of Transformer-Based Neural Quantum States via In-Context Learning

arXiv:2610.03463v1 Announce Type: cross Abstract: Neural quantum states based on modern deep learning architectures have emerged as powerful representations for quantum many-body systems. In particula

Geometry Meets Physics: Data-Efficient Pre-Training for Unstructured Neural PDE Solvers

arXiv:2610.03363v1 Announce Type: new Abstract: Neural surrogate models for Partial Differential Equations (PDEs) on unstructured 3D geometries are often limited by poor generalization and the high co

tutorialsarxiv-cs-ai