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ArborEnum: Decision Tree Rashomon Sets over Continuous Features

arXiv:2608.04310v1 Announce Type: new Abstract: The Rashomon effect describes the phenomenon that many models can achieve nearly equivalent performance on the same learning task, with significant rami

Local Ai

Architectural Implications of Agentic AI Workflows

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DGX agent

arXiv:2608.04458v1 Announce Type: new Abstract: Agentic AI is emerging in datacenters, but its architectural implications remain unexplored. We organize agentic workflows in a taxonomy and present its

local-aiarxiv-cs-ai

6 Aug 2026

Argus: A General-Purpose Agentic Runtime for Long-Horizon Reasoning

arXiv:2608.05144v1 Announce Type: new Abstract: Long-horizon reasoning requires an agentic runtime that can persist when evidence supports its current approach and pivot when measurements reveal failu

Arnold: A multi-task, multi-embodiment muscle transformer policy

arXiv:2508.18066v2 Announce Type: replace-cross Abstract: Controlling high-dimensional and nonlinear musculoskeletal models of the human body is a foundational scientific challenge. Recent machine lea

ArtAnno: Annotating Implicit Semantics in Artworks through LLM Agent-Driven Bidirectional Human-AI Augmentation

arXiv:2608.05026v1 Announce Type: cross Abstract: High-quality annotation of artworks is essential for computational art research, yet extracting implicit semantics remains challenging due to the reli

agentsarxiv-cs-ai

ArtChart: Faithful Artistic Chart Generation with Integrated Text Rendering

model-releasesarxiv-cs-cv

arXiv:2607.16060v2 Announce Type: replace Abstract: Artistic charts combine data visualization with expressive marks, textures, and typography, but they are difficult for image generators: an output i

ATLAS: Adaptive Topological Learning with Abstract Successors for Continual Learning

arXiv:2608.04334v1 Announce Type: cross Abstract: Contemporary model-free reinforcement learning algorithms can achieve very high performance, but have low sample efficiency and are not robust to chan

Attention, Anomalies! Handling Attention Layers in Unsupervised Federated Outlier Detection

arXiv:2608.04753v1 Announce Type: new Abstract: Attention layers are the backbone of today's most powerful and impactful models. Models with multi-million and billion parameters rely on contextual kno

Attention-based representations for multi-task computation

arXiv:2608.04243v1 Announce Type: new Abstract: Multi-head attention layers produce vector representations that support multiple downstream tasks. We establish bounds on the number of heads required i

Attention Fusion for Bridge Deck Delamination Detection

arXiv:2512.20113v4 Announce Type: replace Abstract: Subsurface delaminations in reinforced concrete bridge decks escape conventional visual inspection, and the two principal sensing techniques used to

safetyarxiv-cs-cv

Attention-Only White-Box Transformer via LeJEPA-Based Self-Supervised Pretraining

model-releasesarxiv-cs-lg

arXiv:2608.04213v1 Announce Type: new Abstract: Existing studies on self-supervised learning for white-box networks typically decouple the derivation of white-box networks via optimization algorithms

AudioDER: A Deduplication-Enhanced Reasoning Dataset for Post-Training Large Audio-Language Models

arXiv:2606.14591v2 Announce Type: replace-cross Abstract: Recent advances in pretrained large audio-language models (LALMs) have demonstrated strong capabilities across speech, sound, and music. To ad

AudioScape-TTA: A Structured Soundscape Benchmark for Fine-Grained Text-to-Audio Evaluation

arXiv:2608.04479v1 Announce Type: cross Abstract: Text-to-audio (TTA) generation has recently achieved remarkable progress in synthesizing realistic audio from natural language descriptions. However,

Automatic Statistical Test for Rationally Expressible Algorithms by Selective Inference, with Applications to Feature Selection

model-releasesarxiv-cs-lg

arXiv:2608.04667v1 Announce Type: cross Abstract: Selective inference (SI) provides statistically valid p-values for hypotheses selected by applying an algorithm to the data, correcting for the bias t

AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering

arXiv:2411.04440v1 Announce Type: cross Abstract: Protein engineering is important for biomedical applications, but conventional approaches are often inefficient and resource-intensive. While deep lea

agentsarxiv-cs-ai

Bag-of-Visual-Words for Spatial Mapping of Lung Adenocarcinoma Growth Patterns

arXiv:2608.05074v1 Announce Type: new Abstract: Spatial mapping of lung adenocarcinoma (LUAD) growth patterns across whole slide images (WSIs) requires resolving architectural context at the region le

Behavioral Skill Reconstruction: Reconstructing Hidden Functionality from LLM Agent Skills

arXiv:2608.04192v1 Announce Type: cross Abstract: Closed source agent skills may encode proprietary instructions, scripts, constants, and data. Providers may offer their capabilities as services while

agentsarxiv-cs-ai

Benchmarking Deep Learning Models for Dense Event Classification of Offshore Wind Infrastructure in Sentinel-1 Time Series

arXiv:2608.04706v1 Announce Type: new Abstract: Monitoring of offshore wind energy infrastructure life cycles, especially during the deployment phase, is an important contribution for stakeholders to

applicationsarxiv-cs-lg

Beyond Boundary Frames: Talking-Head Inbetweening via Context-Aware Motion Modeling

arXiv:2512.03590v3 Announce Type: replace Abstract: Existing talking-head generation methods primarily target open-ended generation rather than bridging two existing video segments. In this paper, we

Beyond Global Routing Aggregation: Phase-Aware Expert Merging for MoE Vision-Language Models

arXiv:2608.04454v1 Announce Type: new Abstract: Mixture-of-experts vision-language models (MoE-VLMs) increase model capacity with sparse expert activation, yet deployment requires storing the full exp

Beyond Linear Dynamics: Neural Bilinear Dynamical Models for Time Series Forecasting

arXiv:2608.04471v1 Announce Type: cross Abstract: Time series in real-world applications are often generated by nonlinear dynamical systems, making accurate forecasting challenging. Existing approache

applicationsarxiv-cs-ai

Local Ai

Beyond Motion Cues and Structural Sparsity: Revisiting Small Moving Target Detection

arXiv:2509.07654v2 Announce Type: replace Abstract: Small moving target detection is crucial for many defense applications but remains highly challenging due to low signal-to-noise ratios, ambiguous v

local-aiarxiv-cs-cv

Beyond Reprojection Error: Camera Calibration with 3D Targets

arXiv:2608.05066v1 Announce Type: new Abstract: In 3D reconstruction, camera calibration is an essential element for achieving high fidelity and accuracy of the reconstructed geometry. While existing

Beyond Semantic Equivalence: Logical Graphs for LLM Uncertainty Quantification

arXiv:2607.16868v2 Announce Type: replace Abstract: Large Language Models often produce confidently stated yet unreliable outputs, posing critical challenges for deployment in safety-sensitive applica

Beyond the Dirac Delta: Mitigating Diversity Collapse in Reinforcement Fine-Tuning for Versatile Image Generation

arXiv:2601.12401v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning large-scale generative models, such as diffusion and flow model

Beyond the QBER Threshold: A Temporal QBER Based Machine Learning Framework for Multi Attack Detection in BB84 QKD

arXiv:2608.04047v1 Announce Type: cross Abstract: Conventional BB84 Quantum Key Distribution (QKD) systems rely on a fixed 11% Quantum Bit Error Rate (QBER) threshold to detect eavesdropping. However,

Local Ai

Bi-Level Reinforcement Learning Pathway for Sim-to-Real Optimality

arXiv:2510.17709v2 Announce Type: replace-cross Abstract: Training Reinforcement Learning (RL) policies using simulation models before deployment in real-world environments is a common strategy when r

local-aiarxiv-cs-ai

BIM-Native Tokenization for Constraint-Aware Room Layout Synthesis

model-releasesarxiv-cs-cv

arXiv:2512.04832v3 Announce Type: replace Abstract: We present a BIM-native tokenization for room-level layout synthesis in Building Information Modeling (BIM) scenes. The core contribution is represe

Binding Biometrics with AI Agent Identifiers for Delegation of Authority

arXiv:2608.04292v1 Announce Type: new Abstract: The proliferation of agentic artificial intelligence (AI) systems has raised serious questions about the accountability for tasks performed by AI agents

agentsarxiv-cs-cv

BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning

model-releasesarxiv-cs-lg

arXiv:2608.05104v1 Announce Type: new Abstract: Deep neural networks have shown impressive success in NLP tasks owing to their complex structure and huge number of edges. Achieving state-of-the-art pe

BrainBench: Benchmarking Large Language Models for Comprehensive EEG Understanding

arXiv:2608.04156v1 Announce Type: new Abstract: Electroencephalography (EEG) analysis extends beyond assigning predefined labels to recordings; it requires workflows connecting natural-language instru

Breadcrumbing Search Agents

arXiv:2608.04565v1 Announce Type: cross Abstract: LLM-based search agents are widely used for information-seeking tasks, but their reliance on external tool returns introduces a critical security risk

Breaking the Curse ofMultilinguality inMany-to-Many Speech-to-Text Translation via a Resource-AwareMixture of Speech Encoders

arXiv:2608.04586v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have achieved significant success in speech-to-text translation (S2TT). However, when processing multilingual

BridgeVLA++: A Data-Efficient, Generalizable, and Memory-Augmented Vision-Language-Action Framework for 3D Manipulation

arXiv:2608.05042v1 Announce Type: new Abstract: Leveraging pre-trained vision-language models (VLMs) to construct vision-language-action (VLA) models has emerged as a promising paradigm for 3D robot m

safetyarxiv-cs-ro

C^2MOE: Consistency and Complementarity-guided Mixture of Experts for Incomplete Multimodal Emotion Learning

arXiv:2608.04013v1 Announce Type: cross Abstract: Recent advances in Multimodal Emotion Recognition in Conversations (MERC) highlight its reliance on complete multimodal inputs. However, real-world da

applicationsarxiv-cs-ai

Caching for the Future: Scrub Jay Episodic Memory Principles for Agent Memory Systems

model-releasesarxiv-cs-cl

arXiv:2608.04746v1 Announce Type: new Abstract: LLM agents that persist across sessions accumulate stored memories whose validity varies enormously by content type, yet existing memory architectures t

Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning

arXiv:2608.04663v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning often adds social terms to individual rewards, yet the scale of those terms is usually chosen by hand. We

Calibrating Transformer Attention via Task-Space Sensitivity Feedback

arXiv:2512.20661v2 Announce Type: replace Abstract: Transformer-based pre-trained language models (PLMs) excel in text classification but suffer from attention dilution and attention sink effects, for

CAMP: A Cycle-Aware Multi-Scale Patch Mixer for Time Series Forecasting

arXiv:2608.04051v1 Announce Type: new Abstract: Real-world time series are often governed by recurring patterns, but their dominant periods may vary across datasets, forecasting settings, and individu

applicationsarxiv-cs-lg

Can Post-Training Transform LLMs into Causal Reasoners?

arXiv:2602.06337v2 Announce Type: replace-cross Abstract: Causal inference is essential for decision-making but remains challenging for non-experts. While large language models (LLMs) show promise in

Canonical Joint Energy-Based Model on CIFAR-10: failure modes and practical indistinguishability of Predictor-Corrector and SGLD samplers

arXiv:2608.05025v1 Announce Type: new Abstract: Joint Energy-Based Models (JEM) unify classification and generation within a single network and support out-of-distribution (OOD) detection. Canonical J

Capability-Gated Planning: Cost-to-Goal Discovery and the Limits of Myopic Experiment Selection

arXiv:2608.05085v1 Announce Type: cross Abstract: Systems that automate scientific discovery must repeatedly decide which experiment to run, which hypothesis to test, which tool to build, and when to

CARGO-VL: Counterfactual Arbitration with Risk-Constrained Group Optimization for Vision-Language Models

arXiv:2608.04509v1 Announce Type: new Abstract: Vision-language systems combine images with retrieved text, but these sources can disagree or jointly fail to support an answer. Reliable models must id

CARVE: Cross-Slice Anisotropic Reallocation of Visual Evidence for Efficient 3D Medical Volume Understanding

arXiv:2608.04515v1 Announce Type: cross Abstract: Slice-based MLLMs leverage mature 2D encoders by representing 3D volumes as sequences of 2D slices. However, this slice-wise formulation produces thou

Causal Evidence Extraction and Triangulation in Crisis Reports using Large Language Models: A ReliefWeb-based Study

model-releasesarxiv-cs-cl

arXiv:2608.04576v1 Announce Type: new Abstract: Humanitarian reports are long, noisy, and multi-topic, making it difficult to consolidate decision-relevant causal evidence. We present a ReliefWeb stud

Chain-of-Thought Monitoring Can Be Unreliable in Implicit-Influence Settings

arXiv:2608.04735v1 Announce Type: new Abstract: Chain-of-thought (CoT) monitoring is increasingly treated as an important safety layer for frontier reasoning models. Most monitorability evaluations st

Chain-of-Visual-Thought: Teaching VLMs to See and Think Better with Continuous Visual Tokens

arXiv:2511.19418v3 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) excel at reasoning in linguistic space but struggle with perceptual understanding that requires dense visual per

Chained Recursive Language Models for Multi-Iteration Reasoning

arXiv:2608.05124v1 Announce Type: cross Abstract: Long context reasoning in large language models (LLMs) is usually constrained by the fact that a single inference trajectory has to simultaneously exp