Search: “framework”

AgentBetta: Verification-Driven Adaptive Configuration of an AI Nano-Agent through Selective Expansion and Verified Contraction

arXiv:2609.23512v1 Announce Type: new Abstract: Large language model agents are typically deployed with predefined configurations, although the required model capability, context, tools, permissions,

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Agentic Explainable Artificial Intelligence (Agentic XAI) Approach To Explore Better Explanation: A Case Study in Decision Support for Rice Cultivation in Japan

DGX agent

arXiv:2512.21066v4 Announce Type: replace Abstract: Explainable artificial intelligence (XAI) reveals how explanatory variables relate to a response variable, yet communicating XAI outputs to layperso

safetyarxiv-cs-ai

23 Sept 2026

AIGC Video Detection based on the fusion of spatial-frequency-optical flow multimodal features

arXiv:2609.26274v1 Announce Type: new Abstract: The rapid evolution of generative AI (e.g., Sora, Hunyuan) makes it essential to develop effective detection strategies that can generalize across ever-

ASTRA-SR: Atmospheric Seeing and Turbulence Restoration for Astronomical Image Super-Resolution

arXiv:2609.26731v1 Announce Type: new Abstract: Ground-based planetary imaging suffers from atmospheric turbulence, sensor noise, and limited sampling, making restoration a joint denoising, deblurring

researcharxiv-cs-cv

AT3D-AD: Anomaly Type-Aware 3D Anomaly Detection via Hierarchical Point-Language Alignment

arXiv:2609.25930v1 Announce Type: new Abstract: Detecting and localizing 3D point-cloud defects is essential for industrial inspection. However, existing methods often suffer from imprecise localizati

BAS-OPD: Budget-Aware Selective On-Policy Self-Distillation for Fine-Grained Multimodal Perception

arXiv:2609.25891v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) often struggle with fine-grained visual perception when processing complete images, as critical evidence may on

local-aiarxiv-cs-cv

Bearings: Self-Supervised Soundfield Embeddings from First-Order Ambisonics

arXiv:2609.23152v1 Announce Type: cross Abstract: Recently proposed self-supervised audio encoders learn powerful general-purpose representations of sound scenes, yet they are spatially blind. To supp

local-aiarxiv-cs-ai

Beyond Predictable Paths: Redefining AI Security Incident Reporting for Agents

arXiv:2609.24515v1 Announce Type: cross Abstract: AI agents are being deployed rapidly, accompanied by a growing number of AI-specific attacks and corresponding incidents. As incident reporting become

agentsarxiv-cs-ai

Beyond Static Charts: Can Language and Vision Language Models Generate Interactive Data Visualization Interfaces?

arXiv:2609.26208v1 Announce Type: new Abstract: Data visualization is central to analytical reasoning, but real-world analysis increasingly requires language-driven interactive interfaces rather than

BigO(Bench): Can LLMs Generate Code with Controlled Time and Space Complexity?

arXiv:2503.15242v3 Announce Type: replace Abstract: We introduce BigO(Bench), a novel coding benchmark designed to evaluate the capabilities of generative language models in understanding and generati

Brain-Token Learning: Microstate-Based Tokenization and Multi-Scale Interaction for Long-Horizon EEG Sequence Modeling

arXiv:2609.24324v1 Announce Type: new Abstract: Electroencephalography (EEG) provides a non-invasive window into dynamic brain activity, yet modeling long-horizon EEG sequences remains challenging due

researcharxiv-cs-ai

CableVLA: Simulation-Privileged Global-Local Representation Learning for Cable Routing

model-releasesarxiv-cs-ro

arXiv:2609.25606v1 Announce Type: new Abstract: Cable routing requires coordinated control of global cable topology and changing local contacts. We present CableVLA, an end-to-end multimodal vision-la

Calibrated Confidence Expression for Radiology Report Generation

arXiv:2603.29492v2 Announce Type: replace Abstract: Safe deployment of Large Vision-Language Models (LVLMs) in radiology report generation requires not only accurate predictions but also clinically in

researcharxiv-cs-cl

Capability-Aware Arbitration for Semantic Intent-Based Shared Control

arXiv:2609.25369v1 Announce Type: new Abstract: Shared control often allocates robot authority based on confidence in inferred human intent, assuming reliable autonomous execution. When this assumptio

safetyarxiv-cs-ro

Cellular-Communication-Level Interpretability for Pathology Foundation Models via Graph Distillation on Microenvironment

arXiv:2609.26073v1 Announce Type: new Abstract: Pathology foundation models (PFMs) provide strong tile-level representations but remain difficult to interpret at the cellular and microenvironmental sc

researcharxiv-cs-cv

ChainDoRA: Tensor-Train Factorized Weight-Decomposed Low-Rank Adaptation for Parameter-Efficient LLM Fine-Tuning

arXiv:2609.25058v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) adapts large language models (LLMs) to downstream tasks while updating only a small fraction of their pretrained

CID: Measuring Feature Importance Through Counterfactual Distributions

arXiv:2511.15371v3 Announce Type: replace Abstract: Assessing the importance of individual features in Machine Learning is critical to understand the model's decision-making process. While numerous me

local-aiarxiv-cs-lg

Code Plans, Diffusion Renders: Open-Ended Generative World Modeling

arXiv:2609.26458v1 Announce Type: new Abstract: We introduce extbf{CoDeR}, a new paradigm for world modeling. Unlike existing video world models that implicitly represent world dynamics through visual

agentsarxiv-cs-cv

CoEvo: Oracle-Grounded Self-Evolution of a Single Model for Multi-Step Causal Reasoning

arXiv:2609.26094v1 Announce Type: new Abstract: Multi-step causal reasoning requires chaining inferences where each step constrains the next. An early error propagates silently, and a correct answer r

safetyarxiv-cs-lg

Combinatorial Network-Based Manifold Topological Deep Learning for Image Analysis

model-releasesarxiv-cs-cv

arXiv:2609.25453v1 Announce Type: new Abstract: Medical image analysis remains fundamentally challenging because of the intricate geometric and topological structures present in medical data. Conventi

CompKV: Compensation-Aware KV Selection for Long-Context LLM Inference

arXiv:2609.26300v1 Announce Type: new Abstract: Despite their strong performance, large language models (LLMs) are bottlenecked by KV cache memory traffic during long-context inference. Sparse attenti

Compressing Long Context into Answer-Aligned Memory Embeddings for LLM Inference

arXiv:2609.25537v1 Announce Type: new Abstract: Large language model (LLM) inference is constrained by the quadratic scaling of self-attention and the linear scaling of the KV cache, increasing latenc

Applications

Convex AI Compositionality and the Governance of AI System Populations

arXiv:2609.24784v1 Announce Type: new Abstract: AI governance increasingly requires providers and public authorities to reason about multiple AI instantiations, alternative versions, and deployment co

applicationsarxiv-cs-ai

CREDO: Variance-Guided Rubric Evolution for Replay-Corrected Credit Assignment

arXiv:2609.24174v1 Announce Type: new Abstract: Long-horizon language agents receive sparse terminal feedback, while intermediate rubrics provide structured but potentially misspecified assessments of

local-aiarxiv-cs-ai

CricRAG: Retrieval Augmented Vision-Language Models for Personalized Cricket Coaching

arXiv:2609.26056v1 Announce Type: new Abstract: Vision-Language Models (VLMs) offer promising capabilities for automated sports coaching but face a fundamental limitation: they implicitly compare agai

Decoding Order Matters in Autoregressive Speech Synthesis

arXiv:2601.08450v2 Announce Type: replace-cross Abstract: Autoregressive speech synthesis often adopts a left-to-right order, yet generation order is a modelling choice. We investigate decoding order

researcharxiv-cs-ai

Deep Reinforcement Learning on Item-Compatibility Graphs for One-Dimensional Bin Packing

arXiv:2609.25397v1 Announce Type: new Abstract: The one-dimensional bin packing problem (1D-BPP) is a classical NP-hard combinatorial optimization problem with applications ranging from logistics and

Deepfakes and Synthetic Media: Generation, Detection, and Governance

arXiv:2609.25017v1 Announce Type: new Abstract: Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains,

DeepSPoC: A Deep Learning Based Sequential Propagation of Chaos

arXiv:2408.16403v2 Announce Type: replace Abstract: Classical particle methods based on propagation of chaos (PoC) have been developed for solving mean-field stochastic differential equations and thei

Delving into Asymmetric Information Dynamics for High-Fidelity Virtual Try-On

arXiv:2609.25881v1 Announce Type: new Abstract: Virtual try-on (VTON) requires precise pixel-level fidelity, yet mainstream Diffusion Transformers (DiTs) often suffer from texture degradation and stru

researcharxiv-cs-cv

Diffusion-Induced Spatial Attention Overlapping Community Detection

arXiv:2609.26737v1 Announce Type: cross Abstract: Detection of overlapping communities is essential for modelling networks in which nodes participate simultaneously in multiple structural or functiona

Directional Total Variation-Regularized Implicit Neural Representations (DTV-INR) for Continuous Super-Resolution in Degraded Imaging Domains

model-releasesarxiv-cs-cv

arXiv:2609.25429v1 Announce Type: new Abstract: In this paper, we introduce the Directional Total Variation-Regularized Implicit Neural Representation (DTV-INR), an advanced variational paradigm that

Discrete vs. Continuous: A Comprehensive Study of Unified Audio Understanding in LALMs

arXiv:2609.22851v2 Announce Type: cross Abstract: Large Audio Language Models (LALMs) utilize either continuous features or discrete tokens, yet the optimal representation paradigm for general audio u

Disentangling Heterogeneous Traffic Dynamics for Multi-Step Traffic Forecasting via Adaptive Spectral Decomposition

arXiv:2609.25777v1 Announce Type: new Abstract: Accurate multi-step traffic forecasting remains challenging because observed traffic signals contain heterogeneous temporal dynamics with different char

Divergent strategies and convergent outcomes in autonomous materials discovery

arXiv:2609.23957v1 Announce Type: new Abstract: Scientific agents are mostly evaluated on whether they complete tasks or recover known results; we instead study variation across repeated open-ended ca

agentsarxiv-cs-ai

DocMIDE: Learning Multi-Hop Implicit Derivation in Visually Rich Documents

arXiv:2609.24092v1 Announce Type: new Abstract: Real-world document processing systems rely on rigid, predefined schemas, yet critical target fields often lack direct visual counterparts on the page.

DTOC: Dynamic Tool Output Compression for Adaptive Context Management in AI Agents

arXiv:2609.26121v1 Announce Type: cross Abstract: As agent capabilities have grown, practical limitations increasingly stem from constrained context windows rather than model capacity. Common strategi

DynaForge: Planning-Guided Residual Learning for Dynamic Manipulation Demonstration Generation

arXiv:2609.25631v1 Announce Type: new Abstract: Dynamic object manipulation is essential for robots operating in real-world environments, yet methods for generating high-quality demonstrations remain

safetyarxiv-cs-ro

EDGEGEN: Improving Tool-Calling Agents Beyond Happy Paths with Synthetic Edge Case Generation

arXiv:2609.24115v1 Announce Type: new Abstract: Tool-calling LLM agents are increasingly deployed in enterprise applications. However, effective evaluation and optimization require high-quality, diver

ELEMENT: Episodic and Lifelong Exploration via Maximum Entropy

arXiv:2412.03800v2 Announce Type: replace Abstract: Reinforcement learning agents depend on reward signals whose density is rarely under the designer's control, and when such signals are absent, an ag

agentsarxiv-cs-lg

EMERGE: Resolution-Agnostic Point Cloud Generation with Equivariant Graph-Based Diffusion

arXiv:2609.26039v1 Announce Type: cross Abstract: Point cloud generation has emerged as a crucial task for accurately capturing and reproducing the complexity of the physical world. However, existing

EMGBlend: Heterogeneity-Aware Self-Supervised Pretraining for Gesture and Force Decoding

arXiv:2609.25582v1 Announce Type: new Abstract: Public surface electromyography (EMG) datasets vary widely in electrode layout, channel count, frequency support, and size. Simply mixing them for pretr

Empirical Auditing of Edge-Private Graph Generators

arXiv:2609.25155v1 Announce Type: cross Abstract: We empirically audit privacy leakage by testing whether outputs from edge-neighbouring inputs remain distinguishable, using statistically valid lower

ENPIRE: Agentic Robot Policy Self-Improvement in the Real World

arXiv:2606.19980v2 Announce Type: replace Abstract: Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bo

eXplaining to Learn (eX2L): Regularization Using Contrastive Visual Explanation Pairs for Distribution Shifts

arXiv:2605.06368v2 Announce Type: replace-cross Abstract: Despite extensive research into mitigating distribution shifts, many existing algorithms yield inconsistent performance, often failing to outp

Fairly Compensated Distributed Information Retrieval and Augmentation for AI Agents

arXiv:2609.22601v1 Announce Type: cross Abstract: The increasing reliance of autonomous AI agents on external and distributed knowledge sources introduces a fundamental challenge for decentralized inf

safetyarxiv-cs-ai

Applications

Faithful Faithfulness Evaluations: Challenges & Pitfalls Learned from a Breast MRI Case Study

arXiv:2609.25978v1 Announce Type: cross Abstract: Saliency maps are widely used to explain deep learning predictions in medical imaging, yet visually plausible explanations do not necessarily reflect

applicationsarxiv-cs-lg

FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in Federated Learning

arXiv:2502.16396v2 Announce Type: replace Abstract: Federated learning systems are increasingly threatened by data poisoning attacks, where malicious clients compromise global models by contributing t