Search: “agents”

BuzzASR: A Swarm of 100+ Monolingual Speech Recognition Models

arXiv:2609.09554v1 Announce Type: new Abstract: We introduce BuzzASR, a collection of language-specialized fine-tuned Whisper models adapted for automatic speech recognition (ASR) in 102 languages. La

agentsarxiv-cs-cl

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Can We Trust Video Hallucination Detectors? VidHalLoc for Evaluating the Evaluators

DGX agent

arXiv:2609.09895v1 Announce Type: new Abstract: Video-language models and video agents can produce hallucinations that conflict with spatiotemporal evidence. Existing benchmarks mainly evaluate model

model-releasesarxiv-cs-cv

10 Sept 2026

CLFTv2: Efficient Camera-LiDAR Fusion for Semantic Segmentation via Hierarchical Feature Pyramids

arXiv:2609.09881v1 Announce Type: new Abstract: Semantic segmentation for autonomous driving requires reliable detection of vulnerable road users (VRUs) despite heavy class imbalance. We introduce CLF

Research

Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training

arXiv:2609.10052v1 Announce Type: new Abstract: LLM agents for sequential decision tasks are often post-trained with trajectory-level outcome labels, but such labels provide little supervision for pre

researcharxiv-cs-cl

Local Ai

From Citations to Contributions: LLM-Assisted Credit Scoring of Research Articles

arXiv:2609.07673v1 Announce Type: cross Abstract: Citation-based measures of scientific influence typically treat citations as uniform signals, ignoring the different roles that cited works play in a

local-aiarxiv-cs-ai

Research

Human or Machine? A Preliminary Turing Test for Speech-to-Speech Interaction

arXiv:2602.24080v3 Announce Type: replace Abstract: The pursuit of human-like conversational agents has long been guided by the Turing test. For modern speech-to-speech (S2S) systems, a critical yet u

researcharxiv-cs-ai

In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning

model-releasesarxiv-cs-cl

arXiv:2609.09243v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) grounds a language model in retrieved documents, which reduces hallucination but creates a new attack surface: if

Networked Admissibility-Preserving Control for Directed Safe Coordination

arXiv:2609.09384v1 Announce Type: cross Abstract: This paper addresses safety-critical coordination for scalar agents whose distributed commands are implemented through constrained physical-input dyna

safetyarxiv-cs-ro

RAP: Research Attention Prediction Reveals Target-Conditioned Evidence Acquisition Biases

model-releasesarxiv-cs-cl

arXiv:2609.10092v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly act as research agents, yet their ability to track shifts in research attention is difficult to evaluate bec

SerenAI: State-transition system inspired by text-based world AI models

arXiv:2609.06647v1 Announce Type: new Abstract: Although professional workflows leverage large language models widely, the interpretation for auditing unconstrained free-text generation is usually int

Strangers to Themselves: What Language Models Say About Themselves Is Generic

arXiv:2609.09899v1 Announce Type: cross Abstract: Language models can fluently describe how they would behave: whether they would cave to pushback, misuse a tool, or lie under pressure. Is that descri

safetyarxiv-cs-cl

The Emerging AI Paper-Review Arms Race: Adversarial Co-Evolution in Scholarly Publishing

arXiv:2609.07713v1 Announce Type: new Abstract: Generative and agentic AI are reshaping both the production and evaluation of scientific research. These developments are often studied separately, as q

safetyarxiv-cs-ai

The Living Library: Transforming Archival Collections into Conversational Knowledge Systems -- Lessons from the Theodore Roosevelt Presidential Library

model-releasesarxiv-cs-cv

arXiv:2609.09368v1 Announce Type: new Abstract: We present the Living Library, an end-to-end framework for transforming fragmented digital archives into governed, conversational, in-person exhibit exp

When Intelligence Becomes Agency: A Theory of Governed, Proactive Agency for Symbiotic AI Systems

arXiv:2609.07741v1 Announce Type: new Abstract: Persistent AI assistants are intended to extend human attention, memory, and coordination across changing digital and physical environments. To be truly

A Two-Stage Framework for Ego-Centric Key Object Identification via Object State Prediction

arXiv:2609.07125v1 Announce Type: new Abstract: This paper presents a novel framework designed to enhance key object identification in autonomous driving. Existing methods primarily focus on either de

Adaptive Cost-Sensitive Machine Learning for Autonomous Robot Navigation Failure Prediction: When Not All Errors Are Equal

arXiv:2609.05593v1 Announce Type: new Abstract: Autonomous robot navigation failures differ not only in categorical severity but also in the physical context in which they occur. A near-miss at low sp

An Exploratory Study of Frequency-Aware Task Weighting for YOLOv8-Based Unified Driving Perception

arXiv:2609.05516v1 Announce Type: new Abstract: Unified perception enables autonomous driving systems to perform object detection, drivable-area segmentation, and lane segmentation within a single net

Hardware

CASCADE: A Spatio-Temporal-Causal Reasoning Representation and Dataset for Driving

arXiv:2609.07094v1 Announce Type: new Abstract: Reasoning is a promising route to the generalization that autonomous driving requires in the long tail, as it can infer how the elements of a scene depe

hardwarearxiv-cs-cv

CoRL: Co-Evolutionary Reinforcement Learning for Adaptive Indirect Prompt-Injection Attacks and Defenses

model-releasesarxiv-cs-lg

arXiv:2609.07529v1 Announce Type: new Abstract: Tool-augmented language agents are vulnerable to indirect prompt injection (IPI). Unlike direct prompt injection, IPI hides adversarial instructions in

DAGLFNet: Deep Feature Attention Guided Global and Local Feature Fusion for Pseudo-Image Point Cloud Segmentation

arXiv:2510.10471v3 Announce Type: replace Abstract: Environmental perception systems are crucial for high-precision mapping and autonomous navigation, with LiDAR serving as a core sensor providing acc

Endogenous Exploration in Reinforcement Learning with Intrinsic Curiosity

arXiv:2609.05650v1 Announce Type: new Abstract: We propose a reinforcement learning framework in which exploration is driven by intrinsic curiosity, designed for scenarios where environments are non-s

safetyarxiv-cs-lg

Estimating Semantic Ambiguity via Gaussian Context Distributions for VLM-Driven Traversability Analysis

arXiv:2609.08583v1 Announce Type: new Abstract: Autonomous navigation in unstructured environments requires robust scene understanding, yet Vision-Language Models (VLMs) often suffer from semantic amb

Fine-Grained Instruction-Guided Graph Reasoning for Vision-and-Language Navigation

arXiv:2503.11006v3 Announce Type: replace Abstract: Vision and Language Navigation (VLN) requires an embodied agent to traverse complex environments by following natural language instructions, demandi

safetyarxiv-cs-cv

Generation of Vectorized Maps Beyond Vehicle View

arXiv:2609.07511v1 Announce Type: cross Abstract: Autonomous driving relies on High Definition (HD) maps for safe navigation. Traditional HD maps construction is costly in hardware, data and human res

agentsarxiv-cs-lg

HiBRIDGE: A Hierarchical Bayesian Neural Network Framework for Interpretable Dialogue Management in Group-Robot Interaction

arXiv:2609.08678v1 Announce Type: new Abstract: In multi-party human-robot interaction, a robot must continuously decide whom to address and what to say to participate effectively in the conversation.

Human-Aware Target Tracking and Navigation: Fusing Kinematic State Estimation with Structural Map Constraints

arXiv:2609.07091v1 Announce Type: new Abstract: Autonomous mobile robots performing person-following tasks often suffer from temporary occlusions and sensor track loss in dynamic environments. This re

Norms at a Price: Why RL-Based Alignment Can Promise Conditional Compliance at Best

arXiv:2609.07627v1 Announce Type: cross Abstract: AI agents sometimes act aligned when they infer they are being tested, and differently when not. We argue this is not an anomaly but what current trai

safetyarxiv-cs-lg

Applications

PAN: A World Model for General, Actionable, and Long-Horizon World Simulation

arXiv:2511.09057v4 Announce Type: replace Abstract: A world model is a cognitive simulator of the real-world environment allowing biological agents to reason about how the world evolves, whether spont

applicationsarxiv-cs-cv

Phase-and-First-Arrival VLM Feedback for Sparse-Reward Reinforcement Learning in Surgical Manipulation

arXiv:2609.07211v1 Announce Type: new Abstract: Sparse outcome feedback limits what robots can learn from unsuccessful attempts at complex manipulation. Failed multi-stage surgical attempts can contai

Precision at Scale: Domain-Specific Datasets On-Demand

arXiv:2407.03463v2 Announce Type: replace Abstract: In the realm of self-supervised learning (SSL), conventional wisdom has gravitated towards the utility of massive, general domain datasets for pretr

Predictive-Coding-Based Autonomous Regulation of Internally Generated and Externally Coupled Processing in Human-Robot Interaction

arXiv:2609.06888v1 Announce Type: new Abstract: Predictive coding characterizes adaptive behavior as a dynamic balance between internally generated predictions and external sensory evidence, yet how a

SHIFT: Surface-aware High-speed Integration For TSDFs

arXiv:2609.06547v1 Announce Type: new Abstract: Real-time 3D mapping is fundamental for autonomous robotic navigation, with Euclidean Signed Distance Fields (ESDFs) serving as the standard representat

Situation Awareness for Intelligent Data Distribution in Connected Vehicles

arXiv:2609.05521v1 Announce Type: cross Abstract: The limitations of on-board sensors and blind spots caused by occlusion cause the reduction of perception quality in autonomous vehicles. In such case

Tracking-by-detection in Multi-object Tracking: Survey and Experiments

arXiv:2609.08265v1 Announce Type: new Abstract: Multi-object tracking (MOT) is an essential computer vision task that simultaneously tracks multiple objects in video sequences, with various applicatio

Tracking the Moving Frontier: Long-Short Term Advantage Estimator

arXiv:2609.06671v1 Announce Type: new Abstract: Group-based RLVR methods estimate advantages by repeatedly sampling multiple trajectories for each prompt, making long-horizon agent training expensive

safetyarxiv-cs-lg

An Energy-Based Conservative-Dissipative Latent Neural Evolution Operator for Magnetization Dynamics

arXiv:2609.04530v1 Announce Type: new Abstract: We develop an energy-based reduced-order model for micromagnetic magnetization dynamics that couples a convolutional autoencoder to a structured latent

agentsarxiv-cs-lg

Continuous Cognitive Coverage for Autonomous Robots via Event-Dependent Cognitive Treatment and Learning

arXiv:2609.04770v1 Announce Type: new Abstract: Autonomous robots continuously encounter objects, changes, and situations, and every event admitted into cognition should receive an appropriate cogniti

CrossDepth: Geometry-Constrained Attention for Generalizable Multi-View Surround Depth Estimation

arXiv:2609.05397v1 Announce Type: new Abstract: Reliable 3D understanding of the surrounding environment is a core requirement for autonomous driving. Multi-view surround camera rigs provide broad sce

Data-Driven Learning of Unknown Nonlinear Differential Equations Using Functional Analysis

arXiv:2609.04329v1 Announce Type: cross Abstract: In this paper, the problem of data-driven discovery of nonlinear ordinary differential equations (ODEs) is recast, and a new interpretable machine lea

agentsarxiv-cs-ai

Efficient Multi-Timescale Event Representations for Feed-Forward Object Detection

arXiv:2609.05049v1 Announce Type: new Abstract: Autonomous systems require robust low-latency perception under rapidly changing scene dynamics and challenging illumination. In event cameras object det

Fractal basins trap latent reasoning

arXiv:2609.04963v1 Announce Type: new Abstract: Reasoning allows artificial intelligence models to revisit and correct their mistakes, enabling recent frontier advances in mathematical theorem solving

agentsarxiv-cs-lg

MedProb: Probing Internal Representations of Vision-Language Models for Medical Question Answering

arXiv:2609.04336v1 Announce Type: new Abstract: Medical visual question answering (Med-VQA) is often assumed to require medical fine-tuning, large models, or complex multi-agent pipelines. We revisit

safetyarxiv-cs-cl

Mitigating Performance Discrepancy in Cross-Domain 3D Class-Incremental Learning

arXiv:2609.04860v1 Announce Type: cross Abstract: 3D perception plays a crucial role in real-world applications such as autonomous driving, robotics, and AR/VR. In practical scenarios, 3D perception m

agentsarxiv-cs-ai

Necessary or Sufficient? Evaluating LLM Explanations With Behavioural Evidence

arXiv:2609.05385v1 Announce Type: new Abstract: LLM decision components that can operate within agent workflows often produce action-relevant recommendations or judgements together with explanations.

Repeat-After-Me: Black-Box Adaptive Visual Prompt Injection

arXiv:2609.04533v1 Announce Type: cross Abstract: Prompt injection is widely recognized as a major security threat to AI agents that interact with untrusted external data, such as websites, documents,

Train What You Deploy:Token-Faithful Post-Training of a Production Coding

arXiv:2609.04678v1 Announce Type: new Abstract: Existing post-training pipelines for coding and terminal agents suffer severe token and control fidelity errors: simplified training environments mismat

safetyarxiv-cs-ai

YOLO with Kolmogorov-Arnold networks and vision-language foundation models for interpretable object detection with trustworthy multimodal AI in computer vision perception

arXiv:2603.23037v2 Announce Type: replace-cross Abstract: The trustworthy object detection capabilities of a novel Kolmogorov-Arnold network framework are examined here. The approach addresses a key l

agentsarxiv-cs-ai

A Quantitative Comparison of Centralised and Distributed Reinforcement Learning-Based Control for Soft Robotic Arms