AIBullisharXiv – CS AI · Jun 97/10
🧠Researchers propose DCPM, a dual-process cognitive memory system for LLM agents that organizes memory hierarchically from raw inputs to cross-domain patterns. The system uses a synchronous writer to record belief revisions and an asynchronous engine to induce schemas and detect cross-domain patterns, achieving significant improvements on personalization benchmarks requiring implicit reasoning about user evolution.
AINeutralarXiv – CS AI · May 117/10
🧠Researchers have developed a psychometric framework to evaluate generative AI models' cognitive abilities across generations, revealing profound imbalances in their intelligence architecture. While leading multimodal models excel at verbal comprehension and working memory (>98th percentile), they severely lag in perceptual reasoning (<1st percentile), indicating that scaling alone cannot achieve human-like general intelligence.
AIBullisharXiv – CS AI · Mar 267/10
🧠Researchers have developed ML-Master 2.0, an autonomous AI agent that achieves breakthrough performance in ultra-long-horizon machine learning tasks by using Hierarchical Cognitive Caching architecture. The system achieved a 56.44% medal rate on OpenAI's MLE-Bench, demonstrating the ability to maintain strategic coherence over experimental cycles spanning days or weeks.
🏢 OpenAI
AIBullisharXiv – CS AI · Mar 177/10
🧠Researchers introduce StatePlane, a model-agnostic cognitive state management system that enables AI systems to maintain coherent reasoning over long interaction horizons without expanding context windows or retraining models. The system uses episodic, semantic, and procedural memory mechanisms inspired by cognitive psychology to overcome current limitations in large language models.
AIBullisharXiv – CS AI · Mar 177/10
🧠Researchers propose BIGMAS (Brain-Inspired Graph Multi-Agent Systems), a new architecture that organizes specialized LLM agents in dynamic graphs with centralized coordination to improve complex reasoning tasks. The system outperformed existing approaches including ReAct and Tree of Thoughts across multiple reasoning benchmarks, demonstrating that multi-agent design provides gains complementary to model-level improvements.
AINeutralarXiv – CS AI · Jun 116/10
🧠A research position paper argues that integrating explicit memory systems into Large Language Models is essential for achieving Artificial General Intelligence. The paper contends that current LLMs rely on implicit statistical learning analogous to human implicit memory, but AGI requires higher-order cognitive functions like strategic planning and symbolic reasoning that depend on hippocampal explicit memory mechanisms.
AINeutralarXiv – CS AI · Jun 116/10
🧠Researchers introduce Relational Reflective Intelligence (RRI), a governance framework that adds auditable reasoning checkpoints between humans and large language models to address shared cognitive vulnerabilities. Rather than modifying models internally, RRI operates as an interaction layer that structures joint reasoning and surfaces conflicts, aiming to prevent 'relational drift' where human and AI errors compound.
AINeutralarXiv – CS AI · Apr 156/10
🧠Researchers present EMBER, a hybrid architecture combining spiking neural networks with large language models where the SNN acts as a persistent, biologically-inspired memory substrate that autonomously triggers LLM reasoning. The system demonstrates emergent autonomous behavior, initiating unprompted user contact after learning associations during idle periods, suggesting a fundamental shift in how AI systems could coordinate cognition and action.
AINeutralarXiv – CS AI · Mar 266/10
🧠Researchers introduced Enhanced Mycelium of Thought (EMoT), a bio-inspired AI reasoning framework that organizes cognitive processing into four hierarchical levels with strategic dormancy and memory encoding. The system achieved near-parity with Chain-of-Thought reasoning on complex problems but significantly underperformed on simple tasks, with 33-fold higher computational costs.
AIBullisharXiv – CS AI · Mar 176/10
🧠Researchers propose a new computational concept for modeling the human psyche as an operating system for artificial general intelligence. The approach treats the psyche as a decision-making system that operates in a state space including needs, sensations, and actions to optimize goal achievement while minimizing risks.
AIBullisharXiv – CS AI · Mar 116/10
🧠Researchers propose a new AI system called Telogenesis that generates attention priorities internally without external goals, using three epistemic gaps: ignorance, surprise, and staleness. The system demonstrates adaptive behavior and can discover environmental patterns autonomously, outperforming fixed strategies in experimental validation across 2,500 total runs.
AINeutralarXiv – CS AI · Mar 36/108
🧠Researchers introduce 'Monotropic Artificial Intelligence,' a new paradigm that deliberately creates highly specialized AI models with extraordinary precision in narrow domains rather than pursuing general-purpose capabilities. The concept challenges the current trend of scaling AI models broadly, proposing instead that domain-specialized models could offer advantages for safety-critical applications.
$NEAR
AIBullisharXiv – CS AI · Mar 37/107
🧠Researchers developed PEPA, a three-layer cognitive architecture that enables robots to operate autonomously using personality traits to generate goals without external supervision. The system was successfully tested on a quadruped robot in a real-world office environment, demonstrating sustained autonomous behavior across five personality prototypes.
AIBullisharXiv – CS AI · Mar 27/1012
🧠Researchers have introduced the Auton Agentic AI Framework, a new architecture designed to bridge the gap between stochastic LLM outputs and deterministic backend systems required for autonomous AI agents. The framework separates cognitive blueprints from runtime engines, enabling cross-platform portability and formal auditability while incorporating advanced safety mechanisms and memory systems.
AINeutralarXiv – CS AI · Feb 276/107
🧠Researchers developed ReCoN-Ipsundrum, an AI agent architecture designed to exhibit consciousness-like behaviors through recurrent persistence loops and affect-coupled control mechanisms. The study demonstrates how engineered systems can display preference stability, exploratory scanning, and sustained caution behaviors that mimic aspects of conscious experience.
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