mlsanityData to 14 Oct 2026

Building Trustworthy AI by Addressing its 16+2 Desiderata with Goal-Directed Commonsense Reasoning

arXiv 2506.12667 ↗First posted 15 Jun 2025cs.AI cs.LOcited by 0

On the map: LLM reasoning → Reasoning for agents, planning and code

Abstract (arXiv)

Current advances in AI and its applicability have highlighted the need to ensure its trustworthiness for legal, ethical, and even commercial reasons. Sub-symbolic machine learning algorithms, such as the LLMs, simulate reasoning but hallucinate and their decisions cannot be explained or audited (crucial aspects for trustworthiness). On the other hand, rule-based reasoners, such as Cyc, are able to provide the chain of reasoning steps but are complex and use a large number of reasoners. We propose a middle ground using s(CASP), a goal-directed constraint-based answer set programming reasoner that employs a small number of mechanisms to emulate reliable and explainable human-style commonsense reasoning. In this paper, we explain how s(CASP) supports the 16 desiderata for trustworthy AI introduced by Doug Lenat and Gary Marcus (2023), and two additional ones: inconsistency detection and the assumption of alternative worlds. To illustrate the feasibility and synergies of s(CASP), we present a range of diverse applications, including a conversational chatbot and a virtually embodied reasoner.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 9 of its 49 references that are in the map. First posted on arXiv 15 Jun 2025.

Builds on

9 of 49 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025VECSR: Virtually Embodied Common Sense Reasoning System4 May

Other references · 8

  1. 2022Will we run out of data? Limits of LLM scaling based on human-generated datacited by 195
  2. 2023Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debatecited by 94
  3. 2014Clingo = ASP + Control: Preliminary Reportcited by 56
  4. 2024Advancing Large Language Models to Capture Varied Speaking Styles and Respond Properly in Spoken Conversationscited by 45
  5. 2024Inductive or Deductive? Rethinking the Fundamental Reasoning Abilities of LLMscited by 29
  6. 2023Getting from Generative AI to Trustworthy AI: What LLMs might learn from Cyccited by 19
  7. 2024LLMs as Models for Analogical Reasoningcited by 16
  8. 2024Conditional and Modal Reasoning in Large Language Modelscited by 9

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  1. 2024Building Trustworthy AI: Transparent AI Systems via Large Language Models, Ontologies, and Logical Reasoning (TranspNet)
  2. 2024A Reliable Common-Sense Reasoning Socialbot Built Using LLMs and Goal-Directed ASP
  3. 2025ReFIne: A Framework for Trustworthy Large Reasoning Models with Reliability, Faithfulness, and Interpretability
  4. 2025A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models
  5. 2026ReasonOps: A Unified Operational Paradigm for Trustworthy Verified LLM Reasoning
  6. 2024Can We Trust AI Agents? A Case Study of an LLM-Based Multi-Agent System for Ethical AI
  7. 2025ATA: A Neuro-Symbolic Approach to Implement Autonomous and Trustworthy Agents
  8. 2024The Fusion of Large Language Models and Formal Methods for Trustworthy AI Agents: A Roadmap