This paper proposes a value-based DCC method for Intelligent Transport Systems to maintain channel load while retaining more high-value objects.

HydraCollab is a new adaptive collaborative-perception framework that selectively transmits informative sensor features and dynamically employs collaboration strategies to minimize communication overh…

This paper analyzes the impact of measurement errors and packet losses in V2X data on the effectiveness of cooperative perception in automated vehicles and identifies challenges related to the generat…

The paper introduces CHARM, a novel framework that detects and mitigates cascading hallucination—the amplification of errors across multi-step agentic RAG pipelines—achieving an 82.1% reduction in err…

This survey provides a comprehensive, systematic roadmap for achieving cyber-physical system (CPS) resilience by integrating five interconnected themes: system-wide properties, handling data scarcity…

This paper conducted a randomized controlled trial on Reddit to test the effectiveness of various deescalation strategies in reducing personal insults using automated replies.

The paper introduces TrustFlip, a novel physical adversarial attack that exploits consistency-based trust defenses in vehicular collaborative perception by using genuine objects to induce inconsistenc…

The paper introduces Responsible Contrastive Soft Prompting (RCSP), a parameter-efficient method using soft prompts to improve LLM reliability by simultaneously suppressing hallucinations, encouraging…

Introduce PerceptionRubrics, a rubric-based evaluation framework for addressing real-world brittleness of models using 1,038 images and over 12,000 instance-specific rubrics.

The paper proposes EAGLE, a novel evidence-aligned multi-agent framework, demonstrating that requiring shared visual evidence among agents is crucial for achieving reliable and trustworthy consensus i…

The paper presents SenseNova-Vision, a unified multimodal model for computer vision tasks using natural language instructions and optional visual prompts, trained primarily on a new corpus and requiri…

The paper proposes CSMR, a cognitive scheduling framework that allows a language model to dynamically decide when to acquire task-relevant visual evidence, significantly improving multimodal reasoning…

The paper introduces CASTER, a new human-centric task for evaluating User-Generated Content (UGC) resonance, and proposes MEDEA, an architecture that uses a Social Chain-of-Thought mechanism to simula…

This paper explores the effect of different narrative strategies in data visualizations on eliciting prosocial feelings and behaviors during humanitarian crises.

The paper argues that benchmarking Vision-Language Models (VLMs) for urban perception must treat human disagreement and non-response as key measurement outcomes, rather than assuming perfect consensus…

The study found that while AI collaboration is promising, highly competent and proactive AI systems can negatively impact human perceptions of ownership and job meaningfulness, suggesting that design…

This paper analyzes the concept of consciousness attributions to AI chatbots, develops a taxonomy of attitudes, and argues for the epistemic implications.

The paper develops an AI-augmented UX Research Point-of-View (PoV) framework to guide the design of digital wellbeing tools for high-stress Emergency and Public Safety Personnel (EPSP), finding that s…