Correctness Learning: Deductive Verification Guided Learning for Human-AI Collaboration

• URL: http://arxiv.org/abs/2503.07096v1
• Date: Mon, 10 Mar 2025 09:20:38 GMT
• Title: Correctness Learning: Deductive Verification Guided Learning for Human-AI Collaboration
• Abstract summary: We propose correctness learning (CL) to enhance human-AI collaboration.<n>Cl integrates deductive verification methods and insights from historical high-quality schemes.
• Score: 31.255719231317546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite significant progress in AI and decision-making technologies in safety-critical fields, challenges remain in verifying the correctness of decision output schemes and verification-result driven design. We propose correctness learning (CL) to enhance human-AI collaboration integrating deductive verification methods and insights from historical high-quality schemes. The typical pattern hidden in historical high-quality schemes, such as change of task priorities in shared resources, provides critical guidance for intelligent agents in learning and decision-making. By utilizing deductive verification methods, we proposed patten-driven correctness learning (PDCL), formally modeling and reasoning the adaptive behaviors-or 'correctness pattern'-of system agents based on historical high-quality schemes, capturing the logical relationships embedded within these schemes. Using this logical information as guidance, we establish a correctness judgment and feedback mechanism to steer the intelligent decision model toward the 'correctness pattern' reflected in historical high-quality schemes. Extensive experiments across multiple working conditions and core parameters validate the framework's components and demonstrate its effectiveness in improving decision-making and resource optimization.

Related papers

• A Multi-Level Validation and Traceability Framework for AI-Generated Telescope Scheduling Decisions [0.17188280334580194]
  We propose a multi-level validation and traceable reasoning framework that performs systematic reliability verification of AI-generated decisions.<n>Experiments show that the framework improves executability and reliability of AI scheduling and reduces loss of transient opportunities.<n>These results demonstrate a feasible and verifiable pathway for applying AI to high-reliability astronomical observation scheduling.
  arXiv  Detail & Related papers  (2026-06-25T04:17:39Z)
• Know What You Know: Metacognitive Entropy Calibration for Verifiable RL Reasoning [31.629261193485053]
  Large reasoning models (LRMs) have emerged as a powerful paradigm for solving complex real-world tasks.<n>Most existing outcome-only RLVR pipelines rely almost exclusively on a binary correctness signal and largely ignore the model's intrinsic uncertainty.<n>We propose EGPO, a metacognitive entropy calibration framework that explicitly integrates intrinsic uncertainty into RLVR for enhancing LRMs.
  arXiv  Detail & Related papers  (2026-02-26T08:40:06Z)
• From Efficiency to Adaptivity: A Deeper Look at Adaptive Reasoning in Large Language Models [17.96350700093472]
  Current large language models (LLMs) apply uniform reasoning strategies regardless of task complexity, generating long traces for trivial problems while failing to extend reasoning for difficult tasks.<n>This survey reframes reasoning through the lens of adaptivity: the capability to allocate reasoning effort based on input characteristics such as difficulty and uncertainty.
  arXiv  Detail & Related papers  (2025-11-13T20:25:04Z)
• TCPO: Thought-Centric Preference Optimization for Effective Embodied Decision-making [75.29820290660065]
  This paper proposes Thought-Centric Preference Optimization ( TCPO) for effective embodied decision-making.<n>It emphasizes the alignment of the model's intermediate reasoning process, mitigating the problem of model degradation.<n>Experiments in the ALFWorld environment demonstrate an average success rate of 26.67%, achieving a 6% improvement over RL4VLM.
  arXiv  Detail & Related papers  (2025-09-10T11:16:21Z)
• Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why [50.191655141020505]
  This survey provides a comparative analysis of feature-based and GAN-based approaches to learning from demonstrations.<n>We argue that the dichotomy between feature-based and GAN-based methods is increasingly nuanced.
  arXiv  Detail & Related papers  (2025-07-08T11:45:51Z)
• Dynamic Programming Techniques for Enhancing Cognitive Representation in Knowledge Tracing [125.75923987618977]
  We propose the Cognitive Representation Dynamic Programming based Knowledge Tracing (CRDP-KT) model.<n>It is a dynamic programming algorithm to optimize cognitive representations based on the difficulty of the questions and the performance intervals between them.<n>It provides more accurate and systematic input features for subsequent model training, thereby minimizing distortion in the simulation of cognitive states.
  arXiv  Detail & Related papers  (2025-06-03T14:44:48Z)
• Coarse Set Theory for AI Ethics and Decision-Making: A Mathematical Framework for Granular Evaluations [0.0]
  Coarse Set Theory (CST) introduces a mathematical framework to formalize Coarse Ethics (CE)<n>CE models coarse-grained decision-making processes commonly used in human evaluations and AI classification systems.<n>CST defines hierarchical relationships among sets using totally ordered structures and coarse mappings, enabling us to adjust decision granularity dynamically.<n>This study employs CST in grading systems, automated recommendations, and risk assessments, demonstrating its potential to enhance fairness, reduce bias, and improve transparency in AI-driven decision-making.
  arXiv  Detail & Related papers  (2025-02-11T08:18:37Z)
• A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications [2.0681376988193843]
  "Black box" characteristic of AI models constrains interpretability, transparency, and reliability.<n>This study presents a unified XAI evaluation framework to evaluate correctness, interpretability, robustness, fairness, and completeness of explanations generated by AI models.
  arXiv  Detail & Related papers  (2024-12-05T05:30:10Z)
• Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification [52.095460362197336]
  Large language models (LLMs) struggle with consistent and accurate reasoning. LLMs are trained primarily on correct solutions, reducing their ability to detect and learn from errors. We propose a novel collaborative method integrating Chain-of-Thought (CoT) and Program-of-Thought (PoT) solutions for verification.
  arXiv  Detail & Related papers  (2024-10-05T05:21:48Z)
• The Role of Deductive and Inductive Reasoning in Large Language Models [37.430396755248104]
  We propose the Deductive and InDuctive(DID) method to enhance Large Language Models (LLMs) reasoning.<n>DID implements a dual-metric complexity evaluation system that combines Littlestone dimension and information entropy.<n>Our results demonstrate significant improvements in reasoning quality and solution accuracy.
  arXiv  Detail & Related papers  (2024-10-03T18:30:47Z)
• Beyond Algorithmic Fairness: A Guide to Develop and Deploy Ethical AI-Enabled Decision-Support Tools [0.0]
  The integration of artificial intelligence (AI) and optimization hold substantial promise for improving the efficiency, reliability, and resilience of engineered systems. This paper identifies ethical considerations required when deploying algorithms at the intersection of AI and optimization. Rather than providing a prescriptive set of rules, this paper aims to foster reflection and awareness among researchers.
  arXiv  Detail & Related papers  (2024-09-17T18:37:53Z)
• Pattern based learning and optimisation through pricing for bin packing problem [50.83768979636913]
  We argue that when problem conditions such as the distributions of random variables change, the patterns that performed well in previous circumstances may become less effective. We propose a novel scheme to efficiently identify patterns and dynamically quantify their values for each specific condition. Our method quantifies the value of patterns based on their ability to satisfy constraints and their effects on the objective value.
  arXiv  Detail & Related papers  (2024-08-27T17:03:48Z)
• Making Large Language Models Better Planners with Reasoning-Decision Alignment [70.5381163219608]
  We motivate an end-to-end decision-making model based on multimodality-augmented LLM. We propose a reasoning-decision alignment constraint between the paired CoTs and planning results. We dub our proposed large language planners with reasoning-decision alignment as RDA-Driver.
  arXiv  Detail & Related papers  (2024-08-25T16:43:47Z)
• Balancing Rigor and Utility: Mitigating Cognitive Biases in Large Language Models for Multiple-Choice Questions [0.46873264197900916]
  We show that certain cognitive biases can enhance decision-making efficiency through rational deviations and shortcuts. By introducing moderation and an abstention option, we reduce error rates, improve decision accuracy, and optimize decision rates. This approach offers a novel way to leverage cognitive biases to improve the practical utility of large language models.
  arXiv  Detail & Related papers  (2024-06-16T16:25:22Z)
• Learning Reward and Policy Jointly from Demonstration and Preference Improves Alignment [58.049113055986375]
  We develop a single stage approach named Alignment with Integrated Human Feedback (AIHF) to train reward models and the policy.<n>The proposed approach admits a suite of efficient algorithms, which can easily reduce to, and leverage, popular alignment algorithms.<n>We demonstrate the efficiency of the proposed solutions with extensive experiments involving alignment problems in LLMs and robotic control problems in MuJoCo.
  arXiv  Detail & Related papers  (2024-06-11T01:20:53Z)
• Q-Cogni: An Integrated Causal Reinforcement Learning Framework [29.196739858730567]
  We present Q-Cogni, an algorithmically integrated causal reinforcement learning framework. Q-Cogni achieves optimal learning with a pre-learned structural causal model of the environment. We report results that demonstrate better policies, improved learning efficiency and superior interpretability of the agent's decision making.
  arXiv  Detail & Related papers  (2023-02-26T05:50:26Z)
• Joint Differentiable Optimization and Verification for Certified Reinforcement Learning [91.93635157885055]
  In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties. We propose a framework that jointly conducts reinforcement learning and formal verification.
  arXiv  Detail & Related papers  (2022-01-28T16:53:56Z)
• The Statistical Complexity of Interactive Decision Making [126.04974881555094]
  We provide a complexity measure, the Decision-Estimation Coefficient, that is proven to be both necessary and sufficient for sample-efficient interactive learning. A unified algorithm design principle, Estimation-to-Decisions (E2D), transforms any algorithm for supervised estimation into an online algorithm for decision making.
  arXiv  Detail & Related papers  (2021-12-27T02:53:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.