arXiv AI
ORDDAR: Observation-Driven Reasoning for Distortion-Resilient Decision, Action, and Cognitive Recovery
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 9
Search, Fail, Recover: A Training Framework for Correction-Aware Reasoning
arXiv:2607. 07492v1 Announce Type: new Abstract: Many reasoning tasks are not well described by a single left-to-right chain: a solver may need to pursue a plausible branch, observe delayed failure, and return to the latest prefix that can still be completed.
arXiv Machine Learning
Jun 17
Learning to Refine Hidden States for Reliable LLM Reasoning
arXiv:2606. 17524v1 Announce Type: new Abstract: Large language models show strong reasoning ability, but their internal reasoning process can remain unstable in complex multi-step settings, where early hidden-state errors may propagate to incorrect predictions.
arXiv AI
Jul 3
Safe and Adaptive Cloud Healing: Verifying LLM-Generated Recovery Plans with a Neural-Symbolic World Model
arXiv:2607. 01595v1 Announce Type: new Abstract: As the scale and complexity of cloud-based AI systems continue to escalate, ensuring service reliability through rapid fault detection and adaptive recovery has become a critical challenge.
arXiv AI
Jul 16
Experience Memory Graph: One-Shot Error Correction for Agents
arXiv:2607. 13884v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown remarkable capabilities in autonomous decision-making by generating sequential trajectories of states, actions, and observations.
arXiv AI
Jun 2
Structure Enables Effective Self-Localization of Errors in LLMs
arXiv:2602. 02416v2 Announce Type: replace Abstract: Self-correction in language models remains elusive.
arXiv Machine Learning
Aug 27
Epistemic Memory: A Validity Layer for Self-Maintaining Intelligent Systems
The paper introduces epistemic memory, a validity-maintenance layer for intelligent systems that tracks when stored knowledge remains applicable. It formalizes a dynamic epistemic quotient and shows that fixed semantic representations inevitably incur error as epistemic boundaries shift. The authors propose Observable Belief Memory (OBM), which combines current epistemic quotients, belief over quotient classes, and within-class provenance, and demonstrate that explicit epistemic tracking improves robustness under changing observation conditions.