Daily Arxiv

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Self-Questioning Language Models

Beyond risk: A proto-framework for assessing the societal impact of AI systems

Supervised Dynamic Dimension Reduction with Deep Neural Network

EmoSteer-TTS: Fine-Grained and Training-Free Emotion-Controllable Text-to-Speech via Activation Steering

LLMs Have a Heart of Stone: Demystifying the Soft Thinking Ability of Large Reasoning Models

Industrial LLM-based Code Optimization under Regulation: A Mixture-of-Agents Approach

Reliable Evaluation Protocol for Low-Precision Retrieval

Landsat30-AU: A Vision-Language Dataset for Australian Landsat Imagery

Tool-integrated Reinforcement Learning for Repo Deep Search

CauKer: classification time series foundation models can be pretrained on synthetic data only

Context-Adaptive Multi-Prompt Embedding with Large Language Models for Vision-Language Alignment

DMSC: Dynamic Multi-Scale Coordination Framework for Time Series Forecasting

HyCodePolicy: Hybrid Language Controllers for Multimodal Monitoring and Decision in Embodied Agents

Entity Representation Learning Through Onsite-Offsite Graph for Pinterest Ads

Evaluating User Experience in Conversational Recommender Systems: A Systematic Review Across Classical and LLM-Powered Approaches

Spatial-Frequency Awareness for Object Detection in RAW Images

Learning Pivoting Manipulation with Force and Vision Feedback Using Optimization-based Demonstrations

NCCR: to Evaluate the Robustness of Neural Networks and Adversarial Examples

ChartM$^3$: Benchmarking Chart Editing with Multimodal Instructions

From Entanglement to Alignment: Representation Space Decomposition for Unsupervised Time Series Domain Adaptation

EcoTransformer: Attention without Multiplication

Bob's Confetti: Phonetic Memorization Attacks in Music and Video Generation

SDBench: A Comprehensive Benchmark Suite for Speaker Diarization

True Multimodal In-Context Learning Needs Attention to the Visual Context

Gauge Flow Models

Zero-Shot Neural Architecture Search with Weighted Response Correlation

The Dark Side of LLMs: Agent-based Attacks for Complete Computer Takeover

CAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks on their Internal Representations

VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting

A Comparative Study of Specialized LLMs as Dense Retrievers

Sign Spotting Disambiguation using Large Language Models

UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields

Thought Anchors: Which LLM Reasoning Steps Matter?

UITron-Speech: Towards Automated GUI Agents Based on Speech Instructions

15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning

AtmosMJ: Revisiting Gating Mechanism for AI Weather Forecasting Beyond the Year Scale

On the Fundamental Impossibility of Hallucination Control in Large Language Models

Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR

Text-Only Reasoning Unleashes Zero-Shot Multimodal Evaluators

CAIN: Hijacking LLM - Humans Conversations via Malicious System Prompts

Explain Less, Understand More: Jargon Detection via Personalized Parameter-Efficient Fine-tuning

What Lives? A meta-analysis of diverse opinions on the definition of life

A Generative Neural Annealer for Black-Box Combinatorial Optimization

GRILL: Gradient Signal Restoration in Ill-Conditioned Layers to Enhance Adversarial Attacks on Autoencoders

CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering

Mj\"olnir: A Deep Learning Parametrization Framework for Global Lightning Flash Density

RGB-Event based Pedestrian Attribute Recognition: A Benchmark Dataset and An Asymmetric RWKV Fusion Framework

ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning

Beyond Wide-Angle Images: Structure-to-Detail Video Portrait Correction via Unsupervised Spatiotemporal Adaptation

CITRAS: Covariate-Informed Transformer for Time Series Forecasting

Rubric Is All You Need: Enhancing LLM-based Code Evaluation With Question-Specific Rubrics

Empirical Analysis of Sim-and-Real Cotraining of Diffusion Policies for Planar Pushing from Pixels

SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild

NuPlanQA: A Large-Scale Dataset and Benchmark for Multi-View Driving Scene Understanding in Multi-Modal Large Language Models

The Impact of Item-Writing Flaws on Difficulty and Discrimination in Item Response Theory

Through the Magnifying Glass: Adaptive Perception Magnification for Hallucination-Free VLM Decoding

Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning

Pull-Based Query Scheduling for Goal-Oriented Semantic Communication

Accelerating Focal Search in Multi-Agent Path Finding with Tighter Lower Bounds

RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks

UltraSTF: Ultra-Compact Model for Large-Scale Spatio-Temporal Forecasting

PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models

Foundation Model of Electronic Medical Records for Adaptive Risk Estimation

Tool Unlearning for Tool-Augmented LLMs

Vision without Images: End-to-End Computer Vision from Single Compressive Measurements

How Do Generative Models Draw a Software Engineer? A Case Study on Stable Diffusion Bias

3DTTNet: Multimodal Fusion-Based 3D Traversable Terrain Modeling for Off-Road Environments

DOGR: Towards Versatile Visual Document Grounding and Referring

Real-World Offline Reinforcement Learning from Vision Language Model Feedback

Causality-Driven Audits of Model Robustness

AUTALIC: A Dataset for Anti-AUTistic Ableist Language In Context

Beyond Adapter Retrieval: Latent Geometry-Preserving Composition via Sparse Task Projection

Pyhgf: A neural network library for predictive coding

Human Bias in the Face of AI: Examining Human Judgment Against Text Labeled as AI Generated

AVG-LLaVA: An Efficient Large Multimodal Model with Adaptive Visual Granularity

Parse Trees Guided LLM Prompt Compression

One Model, Any Conjunctive Query: Graph Neural Networks for Answering Queries over Incomplete Knowledge Graphs

A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles

Fairness Definitions in Language Models Explained

CityLight: A Neighborhood-inclusive Universal Model for Coordinated City-scale Traffic Signal Control

Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting

Long-Term Visual Object Tracking with Event Cameras: An Associative Memory Augmented Tracker and A Benchmark Dataset

Hulk: A Universal Knowledge Translator for Human-Centric Tasks

From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited

Environmental Sound Classification on An Embedded Hardware Platform

Data Dependency Inference for Industrial Code Generation Based on UML Sequence Diagrams

InqEduAgent: Adaptive AI Learning Partners with Gaussian Process Augmentation

SE-Agent: Self-Evolution Trajectory Optimization in Multi-Step Reasoning with LLM-Based Agents

RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization

Higher Gauge Flow Models

Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning Models

IS-Bench: Evaluating Interactive Safety of VLM-Driven Embodied Agents in Daily Household Tasks

SLR: Automated Synthesis for Scalable Logical Reasoning

The SWE-Bench Illusion: When State-of-the-Art LLMs Remember Instead of Reason

APOLLO: Automated LLM and Lean Collaboration for Advanced Formal Reasoning

Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets

Learning to Inference Adaptively for Multimodal Large Language Models

Efficient rule induction by ignoring pointless rules

Why the Agent Made that Decision: Contrastive Explanation Learning for Reinforcement Learning

Evaluating Detection Thresholds: The Impact of False Positives and Negatives on Super-Resolution Ultrasound Localization Microscopy

Vision-Language Fusion for Real-Time Autonomous Driving: Goal-Centered Cross-Attention of Camera, HD-Map, & Waypoints

Created by

Haebom

Author

Santosh Patapati, Trisanth Srinivasan, Murari Ambati

Outline

XYZ-Drive is an autonomous driving system that uses a single vision-language model as input, taking a forward-facing camera frame, a 25m x 25m aerial map, and a next waypoint as input, and outputs steering and speed. Waypoint tokens support both action and textual descriptions using a lightweight, goal-focused cross-attention layer that highlights relevant image and map patches, and the fused tokens are fed into a partially fine-tuned LLaMA-3.2 11B model. On the MD-NEX Outdoor-Driving benchmark, it achieves a 95% success rate and a 0.80 success weighted by path length (SPL), a 15% improvement over PhysNav-DG, with half the number of collisions, and significantly improved efficiency by using only a single branch. We demonstrate this performance improvement through 16 ablation studies.

Takeaways, Limitations

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Takeaways:

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We demonstrate that early token-level fusion of vision, waypoints, and map information enables accurate, transparent, and real-time autonomous driving.

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We demonstrate that a single vision-language model can simultaneously improve the accuracy and efficiency of autonomous driving.

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We demonstrate that goal-driven attention mechanisms play a crucial role in effectively integrating supervised information.

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It highlights the importance of fine-tuning when applying VLM to specific tasks (autonomous driving).

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Limitations:

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As map resolution decreases (from 10 cm to 40 cm), lane edges become blurry and collision rates increase, suggesting the need for higher-resolution maps.

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Removing any one modality (Vision, Waypoints, Map) reduces success rates by up to 11%, making the reliance on complementary roles among modalities highly crucial. There is a need to improve the robustness between modalities.

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