trustworthiness

This survey proposes a trustworthiness model for agentic AI in critical engineering systems, covering safety, robustness, transparency, accountability, and security across domains like power systems and autonomous vehicles.

ConceptSMILE is a perturbation-based auditing framework for evaluating the reliability of concept-based explainable AI, tested on retinal fundus images.

This paper introduces GraphEVAL, a graph-based framework for quantifying uncertainty in LLM reasoning, and proposes a new metric, Graph Reasoning Coherence Score (GRCS), that captures semantic-structural consensus and detects confident hallucinations. The authors also present Graph Self-Consistency (GSC), a decoding strategy that prioritizes reasoning fidelity over nominal accuracy.

This paper proposes SolarChain-Eval, a physics-constrained benchmark for evaluating trustworthy economic agents in decentralized energy markets, integrating an LLM-based Planner/Auditor layer and revealing a utility-safety trade-off.

Cognition launched SWE-1.7, built on the open-source Kimi K2.7, with trustworthiness training to address concerns about Chinese base models.

A thought experiment questioning whether AI systems should maintain a verifiable memory trail of their knowledge and beliefs at the time of decision-making to enhance trust and accountability.

Discusses the issue of AI models producing incorrect answers with high confidence, highlighting the problem of overconfidence in AI outputs.

This paper proposes Robust-TO, an agentic video understanding framework that integrates per-frame trustworthiness to address the Blind Trust Problem, achieving significant accuracy gains under realistic perturbations.

Robust-TO addresses the Blind Trust Problem in video reasoning by integrating per-frame trustworthiness into an agentic framework, improving accuracy under realistic perturbations through calibrated evidence weighting and reliability-aware reasoning.

This paper systematically evaluates foundation model representations for multimodal cancer analysis, benchmarking unimodal and multimodal fusion strategies on real-world cohorts, and assessing trustworthiness via conformal prediction.

Introduces TrustLDM, a comprehensive benchmark for evaluating safety, privacy, and fairness of Language Diffusion Models, revealing that their alignment degrades with malicious post contexts. Proposes an automatic evaluation framework, TrustLDM-Auto, to identify vulnerable configurations.

This paper characterizes approximate property calibration for discrete properties in multiclass classification, using Lipschitz continuous properties as an intermediary to reduce complexity from the number of classes to the elicitation complexity dimension.

This paper challenges the assumption that current Vision-Language Models faithfully synthesize multimodal data, proposing an information-theoretic Modality Translation Protocol with new metrics (Toll, Curse, Fallacy of Seeing) to evaluate trustworthiness over traditional multimodal gain.

This vision paper argues that trust in Agent-to-Agent (A2A) networks must be integrated from the ground up, as existing agent alignment techniques are insufficient to address systemic vulnerabilities like adversarial composition and semantic misalignment.

A comprehensive survey reviewing the trustworthiness challenges of Large Audio Language Models (LALMs), including vulnerabilities like cross-modal jailbreaking and acoustic backdoors, and proposing a defense-in-depth roadmap.