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ANSR-DT: A Neuro-Symbolic Framework for Adaptive and Explainable Digital Twins

arXiv:2501.08561v5 Announce Type: replace Abstract: Digital twins are increasingly used to monitor and optimize industrial systems, yet many existing frameworks remain difficult to interpret, slow to

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Answering Path Queries under Linear and Guarded Existential Rules

DGX agent

arXiv:2607.22636v1 Announce Type: new Abstract: Ontology-mediated query answering is concerned with the problem of answering queries over knowledge bases consisting of a database instance and an ontol

safetyarxiv-cs-ai

28 Jul 2026

Anticipatory Risk-Guided Reinforcement Learning for Safe Flight Through Dynamic Clutter

arXiv:2607.23565v1 Announce Type: cross Abstract: Safe quadrotor navigation in cluttered and dynamic environments depends not only on instantaneous geometric perception, but more critically on anticip

safetyarxiv-cs-lg

Aortic Valve Disease Screening from PPG via Physiology-Guided Self-Supervised Learning

arXiv:2602.04266v2 Announce Type: replace-cross Abstract: Aortic valve disease (AVD) represents a major public health burden, while its diagnosis relies on echocardiography, which is limited by cost a

Approximate reservoir computing with a semiconductor laser for reducing energy consumption

arXiv:2607.23288v1 Announce Type: cross Abstract: Photonic reservoir computing is a promising physical machine-learning technique for predicting time-series data. The quantization of the response sign

AptAvatar: Fast and Vivid Long-Form Audio-Driven Video Generation for Production-Ready Avatars

model-releasesarxiv-cs-cv

arXiv:2607.24013v1 Announce Type: new Abstract: Production-ready audio-driven avatar generation requires efficient inference without sacrificing fidelity or motion expressiveness. However, existing ac

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

Are Flat Minima an Illusion?

arXiv:2605.05209v2 Announce Type: replace-cross Abstract: Flat minima are an account of why deep networks generalise. However flatness is a matter of form (parameters), while generalisation is of func

Are Prompt Optimizers Blind? Cross-Modal Visual Feedback for Automatic Prompt Optimization

arXiv:2607.24354v1 Announce Type: new Abstract: Automatic prompt optimization (APO) has been widely adopted to adapt vision-language models (VLMs) to downstream tasks without weight updates, yielding

Agents

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

ArmnetBench v0.1: Parallel Real-World Evaluation of Manipulation Policies on a Low-Cost Arm Farm

model-releasesarxiv-cs-ro

arXiv:2607.24481v1 Announce Type: new Abstract: Real-world evaluation is a bottleneck in developing generalist robot manipulation policies. Each rollout requires physical hardware and an operator to s

Applications

Artificial Intelligence and Innovation Ecosystem: Evolutionary Developments, Challenges, and Future Directions

arXiv:2607.24589v1 Announce Type: new Abstract: The development of the Innovative Ecosystem (IE) presents a new paradigm for economic integration, collaborative advancement, and shared achievements. T

applicationsarxiv-cs-ai

AssumptionMiner: Extracting, Tracing, and Revising Implicit Assumptions in LLM Code Generation

model-releasesarxiv-cs-cl

arXiv:2607.22898v1 Announce Type: cross Abstract: Large language models (LLMs) generate code from natural-language prompts, yet real-world prompts rarely provide complete specifications. When prompts

Asymmetric Hierarchical Anchoring for Robust Audio-Visual Cross-Modal Generalization

arXiv:2602.03570v2 Announce Type: replace Abstract: Audio-visual joint representation learning under Cross-Modal Generalization (CMG) aims to transfer knowledge from a labeled source modality to an un

safetyarxiv-cs-lg

ATCNet-CIAM for Multi-Session Motor Imagery EEG Signal Classification

arXiv:2607.23522v1 Announce Type: new Abstract: Motor imagery (MI)-based electroencephalography is widely used in non-invasive brain--computer interfaces (BCIs), but robust decoding remains challengin

researcharxiv-cs-cv

Local Ai

Athena-Brain Technical Report: An Efficient Robot Brain for General Intelligence and Embodied Interaction

arXiv:2607.18985v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning, and world knowledge. As embodied agents

local-aiarxiv-cs-ai

ATLAS: Automated Approximation of Transformers for Efficient Homomorphic Inference in One Hour

arXiv:2607.23478v1 Announce Type: cross Abstract: Fully homomorphic encryption (FHE) provides strong cryptographic guarantees for private inference, but deploying transformer models under FHE remains

Attention-Guided Layer Selection for Contrastive Decoding in Large Language Models

arXiv:2607.23067v1 Announce Type: cross Abstract: Contrastive decoding methods such as DoLa improve the factuality of Large Language Models (LLMs) by contrasting the output distributions of mature and

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

safetyarxiv-cs-lg

{au}: Learning Touch-Augmented Vision-Language-Action Models from Future Visual Supervision

arXiv:2607.24485v1 Announce Type: new Abstract: Learning the informative tactile representation while effectively adapting it to pretrained Vision-Language-Action (VLA) models remains challenging at b

researcharxiv-cs-ro

Auditing Alignment Controllability in LLMs via Political Axes

arXiv:2607.23519v1 Announce Type: cross Abstract: Political audits of large language models (LLMs) usually reduce each to one point on a political compass. But that resting point barely matters in dep

AutoCluster, AutoTopicModeling, AutoTrendAnalysis: A Complete AutoML Pipeline for Predicting Emerging Trends

arXiv:2607.22641v1 Announce Type: cross Abstract: Predicting emerging trends is vital for businesses, researchers, and policymakers; yet traditional approaches often lack scalability and adaptability.

AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool Use

arXiv:2505.12650v2 Announce Type: replace-cross Abstract: Reconstructing atomistic crystal structures from a single noisy STEM projection is an ill-posed inverse problem: multiple lattices can explain

Autoregressive One-Step Generative Modeling for Dynamical System Forecasting

arXiv:2605.05540v2 Announce Type: replace Abstract: Fast surrogate modeling for high-dimensional physical dynamics requires more than low short-term error: useful models must roll out efficiently whil

Agents

AutoWorld: Learning Multi-Agent Traffic Simulation with Self-Supervised World Models

arXiv:2603.28963v2 Announce Type: replace-cross Abstract: Simulation with realistic traffic agents is essential for validating autonomous driving systems. Existing data-driven simulators learn agent b

agentsarxiv-cs-ai

BATON: A Multimodal Benchmark for Bidirectional Automation Transition Observation in Naturalistic Driving

model-releasesarxiv-cs-cv

arXiv:2604.07263v2 Announce Type: replace-cross Abstract: Existing driving automation (DA) systems on production vehicles rely on human drivers to decide when to engage DA while requiring them to rema

Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram

arXiv:2607.22980v1 Announce Type: new Abstract: Brain-computer interfaces (BCIs) have long sought calibration-free operation, but classifiers are typically benchmarked by discrimination alone, blind t

Bayesian Repetition Penalty: A Principled Adjacent-Conditional Framework for Reversing Attention Collapse in Autoregressive Language Models

arXiv:2607.22694v1 Announce Type: new Abstract: Attention collapse in autoregressive language models -- manifested as repetitive token loops where the model becomes trapped in self-reinforcing attract

Applications

BC-NMPC: Battery-Constrained NMPC with Propulsion Prediction and Replanning for High-Speed Flight

arXiv:2607.23867v1 Announce Type: new Abstract: Trajectory tracking performance of Uncrewed Aerial Vehicles (UAVs) degrades during high-speed and agile flight due to the depletion of the battery and s

applicationsarxiv-cs-ro

Benchmarking LLMs for Verilog Design Flows

model-releasesarxiv-cs-lg

arXiv:2607.22759v1 Announce Type: cross Abstract: Large language models (LLMs) show promise in code generation, but their capabilities to produce correct, synthesizable hardware description language (

Benchmarking the Domain Gap: Model Selection Instability Under Domain Shift in Video Capsule Endoscopy

arXiv:2607.22736v1 Announce Type: new Abstract: Video capsule endoscopy (VCE) classification is typically evaluated within a single dataset, yet clinical deployment demands robustness across acquisiti

researcharxiv-cs-cv

BERT-based Models vs. Large Language Models for Low-Resource Named Entity Recognition: A Comparative Study on Marathi

model-releasesarxiv-cs-cl

arXiv:2607.23344v1 Announce Type: new Abstract: Named Entity Recognition (NER) for low-resource languages such as Marathi remains a challenging task due to limited annotated resources and linguistic c

Applications

BettiSplit: Topology-Guided Privacy-Aware Split Learning Against Feature Inversion and Gradient Leakage

arXiv:2607.24556v1 Announce Type: cross Abstract: Split learning enables collaborative model training by partitioning neural networks across clients and servers. However, improper split placement can

applicationsarxiv-cs-ai

Between Suppression and Collapse: Evaluating Narrative Unlearning with LENS

arXiv:2607.22657v1 Announce Type: cross Abstract: Large language models (LLMs) can reproduce disinformation-aligned narrative frames as plausible explanations, raising the question of whether existing

Beyond a Global Norm: Personalizing Toxicity Sensitivity in Language Models Without Retraining

arXiv:2607.23175v1 Announce Type: cross Abstract: Reducing toxicity is often framed as a global alignment problem, yet perceptions of harmful language are subjective and context-dependent. We present

Beyond Aggregate Risk: Role-Stratified Conformal Risk Control for LLM Tool Calls

arXiv:2607.24343v1 Announce Type: cross Abstract: Language-model agents act through structured tool calls whose arguments carry different risks. Untrusted content may safely influence an email body bu

Beyond Appearance: A Multi-cue Framework and Large-scale Benchmark for Pedestrian Association and Tracking on Mobile Aerial-Ground Platforms

model-releasesarxiv-cs-cv

arXiv:2607.23803v1 Announce Type: new Abstract: Multi-view Multi-object Association and Tracking (MvMoAT) associates objects across camera views and tracks them over time, supporting identity persiste

Hardware

Beyond Block Boundaries: Multi-Block Editing for Diffusion Large Language Models

arXiv:2607.22663v1 Announce Type: new Abstract: Block diffusion has emerged as the dominant paradigm for scaling discrete diffusion language models (dLLMs), because decoding text in fixed-size blocks

hardwarearxiv-cs-ai

Beyond Direct Answering: Aligning Educational LLMs as Socratic Guides via Heuristic Reinforcement Learning

arXiv:2607.22996v1 Announce Type: cross Abstract: Large language models (LLMs) deployed in educational settings often behave as direct answerers: they disclose target concepts in the opening turn inst

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 Error-vs-Discard Characteristic: Toward Stable and Reliable Evaluation for Face Image Quality Assessment

arXiv:2607.22752v1 Announce Type: new Abstract: Face Image Quality Assessment (FIQA) aims to estimate the utility of facial images for reliable recognition. The evaluation of FIQA methods is predomina

researcharxiv-cs-cv

Beyond ICA: Identifiability by Symmetry Breaking

model-releasesarxiv-cs-lg

arXiv:2607.23182v1 Announce Type: cross Abstract: We prove the identifiability of deep generative models (DGMs) with piecewise-affine (PWA) decoders and Gaussian mixture model (GMM) priors, in a purel

Local Ai

Beyond Local Inspection: Global, Guideline-Grounded Evaluation of Post-hoc XAI Methods for ECG Classification

arXiv:2607.24035v1 Announce Type: cross Abstract: Explainable AI (XAI) is used to assess whether artificial intelligence models rely on meaningful patterns, yet explanations that appear plausible for

local-aiarxiv-cs-lg

Beyond Scale and Generation: Understanding Language Model-based Entity Matching

model-releasesarxiv-cs-cl

arXiv:2607.24688v1 Announce Type: cross Abstract: Entity matching identifies records that refer to the same real-world entity. Language models can be adapted to this task through bi-encoder, cross-enc

Beyond Sequential Interaction: Benchmarking Parallel Execution and Coordination for GUI Agents

arXiv:2607.22689v1 Announce Type: new Abstract: Graphical user interface (GUI) agents are systems powered by large multimodal models (LMMs). They perceive screen state and execute user instructions th

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

Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks

arXiv:2410.02596v2 Announce Type: replace-cross Abstract: Generative Flow Networks (GFlowNets) are a novel class of generative models designed to sample from unnormalized distributions and have found

Agents

Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents

arXiv:2607.15263v3 Announce Type: replace-cross Abstract: Security-agent evaluations commonly measure peak offensive capability under generous inference budgets, emphasizing vulnerability discovery, e

agentsarxiv-cs-ai