Search: “framework”

arXiv:2607.28032v1 Announce Type: new Abstract: Creating photorealistic animatable head avatars from a single image remains a fundamental challenge in digital human synthesis. While recent 3D Gaussian

hardwarearxiv-cs-cv

31 Jul 2026

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Research

Structuring Quantitative Image Analysis with Object Prominence

DGX agent

arXiv:2409.00216v2 Announce Type: replace Abstract: When photographers or media professionals compose an image, they make deliberate choices about what to foreground and what to background, shaping ho

researcharxiv-cs-cv

31 Jul 2026

AIGen: Automating AI Bill of Materials Generation Through Hybrid MLOps Integration

arXiv:2607.26652v1 Announce Type: new Abstract: The responsible development and deployment of artificial intelligence (AI) systems requires rigorous documentation of their constituent artifacts, e.g.,

CMT-RAG: Complementary Memory Traces for Multi-turn Multi-hop RAG

model-releasesarxiv-cs-cl

arXiv:2607.26470v1 Announce Type: new Abstract: Multi-turn information-seeking conversations require both multi-hop reasoning and long-range dependency tracking across turns. However, existing RAG sys

Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering

arXiv:2607.27077v1 Announce Type: new Abstract: Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often

Explainable and Resource-Efficient Spatial Reasoning in Multimodal LLMs for Decision-Critical Applications

arXiv:2607.27145v1 Announce Type: new Abstract: As Multimodal Large Language Models (MLLMs) are increasingly deployed in decision-critical pipelines such as robotics, embodied AI, and safety monitorin

safetyarxiv-cs-cv

Heterogeneous Element-Aware Cross-Version Differencing of Scientific Documents via Layout-Aware Alignment and Structure-Aware Reasoning

arXiv:2607.14117v2 Announce Type: replace Abstract: Cross-version differencing of scientific documents is essential in scholarly publishing and technical documentation, but remains challenging because

safetyarxiv-cs-cl

Invariant Extended Kalman Filtering with Partial Orientation Measurement Integration: Theoretical Derivations and Application to Autonomous Surface Vessels

arXiv:2506.10850v2 Announce Type: replace Abstract: Autonomous surface vessels (ASVs) are increasingly vital for marine science, offering robust platforms for underwater mapping and inspection. Accura

agentsarxiv-cs-ro

Latent-IM: Latent Interaction Management for Speech LLMs

arXiv:2607.26928v1 Announce Type: new Abstract: Classical spoken dialogue systems often separated dialogue management from response realization: a policy selected the next dialogue action, and a gener

safetyarxiv-cs-cl

Multi-Objective Compliance-Integrated Coevolution For Simulated And Real-World Deployment Of Multi-Robot Marine Autonomy

arXiv:2607.26279v1 Announce Type: new Abstract: Collaborative robots are well-suited to maritime missions that benefit from coordination, such as the exploration of unknown reef structures, inspection

safetyarxiv-cs-ro

Parameterized Fair Resource Allocation under Diversity Constraints

arXiv:2607.26485v1 Announce Type: cross Abstract: Resource allocation across multiple agent groups arises in many applications including e-commerce recommendation systems, housing assignment, and cour

Reeling It In: Flexible Needle Pick Up via Thread Manipulation for Autonomous Suturing

model-releasesarxiv-cs-ro

arXiv:2607.26337v1 Announce Type: new Abstract: Suture-needle pickup is necessary for autonomous suturing, as a needle can be unexpectedly dropped or strategically released to adjust the grasping conf

AI Deployment and Cyber Governance Failures in Public-Sector Organizations: A Typological Analysis

arXiv:2607.25368v1 Announce Type: new Abstract: The intersection of artificial intelligence adoption, cybersecurity governance, and public sector institutional constraints has not been examined as a u

Building Large-Scale English-Romanian Literary Translation Resources with Open Models

arXiv:2509.07829v4 Announce Type: replace-cross Abstract: Literary translation has recently gained attention as a distinct and complex task in machine translation research, yet translation by small op

Dataset Poisoning Attacks on Behavioral Cloning Policies

arXiv:2511.20992v2 Announce Type: replace Abstract: Behavior Cloning (BC) is a popular framework for training sequential decision policies from expert demonstrations via supervised learning. As these

Distilling Temporal Search and Reasoning: Evolving LLMs for Future Prediction via Harness-Assisted Efficient Data Synthesis

arXiv:2607.25554v1 Announce Type: new Abstract: Future event prediction carries broad social impact yet remains challenging. SOTA approaches augment LLMs with external agent frameworks whose predictiv

agentsarxiv-cs-ai

DocAnnot -- Accelerating the Creation of Key Information Extraction Datasets with GenAI-Powered Auto-annotation

arXiv:2607.24745v1 Announce Type: cross Abstract: Key Information Extraction (KIE) is vital for many document applications, but creating training datasets is traditionally a time-consuming manual proc

Local Ai

FlashEvaluator: Expanding Search Space with Parallel Sequence-Level Evaluation

arXiv:2603.02565v2 Announce Type: replace-cross Abstract: The Generator-Evaluator (G-E) framework generates K candidate sequences and uses an evaluator to select the highest-scoring one, which is wide

local-aiarxiv-cs-cl

Input-Aware Dynamic Backdoor Attack Against Quantum Neural Networks

arXiv:2607.11843v2 Announce Type: replace-cross Abstract: Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks r

Laplace-PSN-IRT: Uncertainty Quantification for Neural Item Response Theory Models of LLM Benchmarks

arXiv:2607.25257v1 Announce Type: cross Abstract: Item Response Theory (IRT) has recently been proposed as a framework for evaluating large language model (LLM) benchmarks by separating a model's late

Local Ai

Linear-LLM-SCM: Benchmarking LLMs for Coefficient Elicitation in Linear-Gaussian Causal Models

arXiv:2602.10282v2 Announce Type: replace Abstract: Large language models (LLMs) have shown potential in identifying qualitative causal relations, but their ability to perform quantitative causal reas

local-aiarxiv-cs-lg

LLM Scheming Inversely Scales with Pretraining Language Coverage

arXiv:2607.24769v1 Announce Type: new Abstract: With the growing capabilities of frontier models, AI alignment becomes increasingly critical in high-risk deployment settings. While recent work has emp

safetyarxiv-cs-ai

Medical world models in healthcare: foundations, applications, and challenges for trustworthy clinical translation

arXiv:2607.25242v1 Announce Type: new Abstract: Medical world models offer a framework for extending medical artificial intelligence beyond static prediction by representing evolving patient states an

safetyarxiv-cs-cv

Neural Network Learning of One-Bit Protocols for Qubit Measurement Simulation

arXiv:2607.23645v1 Announce Type: cross Abstract: Communication complexity provides a natural framework for quantifying the classical resources required to reproduce quantum statistics. In the qubit p

Tutorials

Normalizing Flows to Reconstruct Pseudo-PDFs

arXiv:2607.25282v1 Announce Type: cross Abstract: We investigate a normalizing-flow approach for reconstructing parton distribution functions (PDFs) from synthetic matrix-element data. Our framework c

tutorialsarxiv-cs-lg

Optimization with Dynamic Constraint Learning (DCL)

arXiv:2607.25719v1 Announce Type: new Abstract: We propose Dynamic Constraint Learning (DCL), a data-driven framework for constrained optimization when constraint functions are unknown and cannot be q

Applications

Rethinking Likelihood distributions: Student's t Likelihood Boosts Bayesian Neural Network Performance

arXiv:2607.25376v1 Announce Type: cross Abstract: In Bayesian neural networks (BNNs), variational inference is a widely adopted framework for modeling uncertainty in a distributional way, with the evi

applicationsarxiv-cs-ai

Right-sizing Recommendations (RSR): Cloud Workload Conformal Prediction for Virtual Machines in Data Center Operations

arXiv:2607.24773v1 Announce Type: new Abstract: Managing cloud infrastructure efficiently, especially in environments of large cloud providers or hyperscalers, requires optimizing the use of physical

S2A2: Audio-Visual Imitation Learning for Manipulation Tasks Using Acoustic Spatial Information

arXiv:2607.26047v1 Announce Type: new Abstract: Acoustic information provides rich cues about object location, material properties, and changes caused by contact or motion. This paper introduces a new

safetyarxiv-cs-ro

Score-Based Stabilization for Time-Dependent Problems

arXiv:2607.25119v1 Announce Type: new Abstract: We propose a score-based stabilization framework for numerical simulation of partial differential equations, in which a learned score model defines a st

Applications

SPARC Segmentation to Prediction via Affine Regression and Counterfactuals

arXiv:2607.25413v1 Announce Type: new Abstract: Transaction propensity prediction in B2B e commerce presents unique challenges distinct from B2C contexts, primarily due to the heterogeneous procuremen

applicationsarxiv-cs-lg

Hardware

Towards Embodied Cognition in Robots via Spatially Grounded Synthetic Worlds

arXiv:2505.14366v2 Announce Type: replace Abstract: We present a conceptual framework for training Vision-Language Models (VLMs) to perform Visual Perspective Taking (VPT), a core capability for embod

hardwarearxiv-cs-ai

Vector-Valued Distributional Reinforcement Learning Policy Evaluation: A Hilbert Space Embedding Approach

arXiv:2601.18952v2 Announce Type: replace Abstract: We propose an (offline) multi-dimensional distributional reinforcement learning framework (KE-DRL) that leverages Hilbert space mappings to estimate

ARdena: Scenario-driven control of real-time LLM agents

arXiv:2607.22651v1 Announce Type: new Abstract: Large language models (LLMs) have enabled increasingly capable conversational agents, but reliably controlling their behavior in real-time interactive e

safetyarxiv-cs-ai

Are You Still the Agent I Authorized? Earned Authority under a Fixed Ceiling for Evolving Agents

arXiv:2607.23586v1 Announce Type: new Abstract: Long-lived AI agents increasingly evolve after deployment by retaining experience, acquiring skills and tools, revising workflows, delegating work, and

agentsarxiv-cs-ai

Attribution and Uncertainty Behavior of Learned Residual Gyro Correction for Gyro-Stellar Estimation

arXiv:2607.24608v1 Announce Type: new Abstract: This work investigates uncertainty decomposition and explainability in a deep learning-based framework for gyroscope bias correction. A 1-D Convolutiona

Beyond Directed Acyclic Graphs: Causal Zeros and Causal Differential Equations

arXiv:2607.22910v1 Announce Type: new Abstract: Pearl's structural causal model (SCM) framework, built on directed acyclic graphs (DAGs) and the do-calculus, is the dominant formal language for causal

Beyond Shapley: An Influence-Based Data Auditing Pipeline for LLM Alignment and Evaluation

arXiv:2607.22766v1 Announce Type: cross Abstract: The alignment of Large Language Models (LLMs) is increasingly bottlenecked by data quality. As datasets scale, massive preference and instruction-tuni

Tutorials

BeyondFusion: Self-Aligned Latent Diffusion for Calibration-Free Infrared Super-Resolution and Infrared-Visible Fusion

arXiv:2607.24110v1 Announce Type: new Abstract: Mobile infrared-visible imaging typically pairs a compact infrared sensor with a high-resolution visible camera for complementary perception. While cros

tutorialsarxiv-cs-cv

CameraAnything: Refilming Videos with Arbitrary Camera Control

model-releasesarxiv-cs-cv

arXiv:2607.24591v1 Announce Type: new Abstract: We introduce CameraAnything, the first unified framework for camera controlled video editing that enables joint control of both intrinsic and extrinsic

CodeEvo: Interaction-Driven Synthesis of Code-centric Data through Hybrid and Iterative Feedback

arXiv:2507.22080v2 Announce Type: replace-cross Abstract: Acquiring high-quality instruction-code pairs is essential for training Large Language Models for code generation. While automated synthesis h

agentsarxiv-cs-ai

Codifying the Judge: Scalable Evaluation via Program Distillation

arXiv:2607.22561v1 Announce Type: new Abstract: LLM-as-a-judge has become the standard for automated evaluation, but it suffers from high cost, significant latency, and opaque decisions -- limitations

CoopReflect: Towards Natural Language Communication for Cooperative Autonomous Driving via Multi-Agent Learning

arXiv:2505.18334v2 Announce Type: replace-cross Abstract: Past work has demonstrated that autonomous vehicles can drive more safely if they communicate with each other. However, this communication is

safetyarxiv-cs-ai

Applications

Deblur-Avatar: Animatable Avatars from Motion-Blurred Monocular Videos

arXiv:2501.13335v4 Announce Type: replace Abstract: We introduce a novel framework for modeling high-fidelity, animatable 3D human avatars from motion-blurred monocular video inputs. Motion blur is pr

applicationsarxiv-cs-cv

DecoupleMix: Decoupled Ratio Search and Convex Allocation for Scalable VLM Data Recipes

arXiv:2607.24516v1 Announce Type: cross Abstract: While data curation for Vision Language Models (VLMs) is increasingly active, public practice for constructing pretraining mixtures remains largely he

DeepLook: Deeper Thinking with Lookahead

arXiv:2607.22602v1 Announce Type: new Abstract: Inference-time scaling has emerged as a powerful paradigm for improving large language model reasoning, often delivering larger gains on difficult reaso

Disentangling Multi-View Scanning in Mamba for Network Traffic Anomaly Detection

arXiv:2607.22829v1 Announce Type: new Abstract: Network Traffic Anomaly Detection (NTAD) is a critical task in cybersecurity, yet timely and accurate anomaly detection remains challenging. Mamba has e

Distributed Convolutional Rank Regression over Decentralized Networks

arXiv:2607.23639v1 Announce Type: cross Abstract: This paper studies convolution rank regression (CRR) over decentralized distributed learning networks. We propose a novel decentralized CRR framework,