A review of neuro-symbolic AI integrating reasoning and learning for advanced cognitive systems

Nawaz, U., Anees-Ur-Rahaman, M., & Saeed, Z. (2025).

Intelligent Systems With Applications, 26, 200541.

https://doi.org/10.1016/j.iswa.2025.200541

Abstract

Neuro-symbolic AI represents the convergence of two principal paradigms in artificial intelligence: neural networks, which are efficient in data-driven learning, and symbolic reasoning, which offers explainability and logical inference. This hybrid methodology combines the adaptability of neural networks with symbolic AI's interpretability and formal reasoning abilities, which provide a practical framework for advanced cognitive systems. This paper analyzes the present condition of neuro-symbolic AI, emphasizing essential techniques that combine reasoning and learning. We explore models such as Logic Tensor Networks, Differentiable Logic Programs, and Neural Theorem Provers. The study analyzes their impact on the advancement of cognitive systems in natural language processing, robotics, and decision-making. The paper examines the challenges faced by neuro-symbolic AI, such as scalability, integration with multimodal data, and maintaining interpretability without compromising efficiency. By evaluating the strengths and weaknesses of many methodologies, we comprehensively understand the field's development and its potential to revolutionize intelligent systems. In addition, we identify emerging research areas, including the incorporation of ethical frameworks and the development of adaptive dynamic neuro-symbolic systems that respond in real-time. This review aims to guide future research by providing insights into the potential of neuro-symbolic AI to influence the development of the next generation of intelligent, explainable, and adaptive systems.

Here are some thoughts:

This research is important because it provides a comprehensive, state-of-the-art analysis of the most promising path forward for creating truly intelligent, reliable, and understandable AI systems. It acknowledges the power of deep learning while rigorously addressing its most critical shortcomings—lack of reasoning, explainability, and data efficiency. For anyone working on or relying on AI in critical areas like medicine, finance, or autonomous systems, understanding neuro-symbolic AI is becoming essential.

Neuro-Symbolic AI is a hybrid approach to artificial intelligence that combines neural networks (which learn patterns from data) with symbolic reasoning (which uses logic and rules to think and explain decisions). In decision-science terms, this process is merging Type 1 and Type 2 thinking in order to reason more coherently.

In equation format: Neuro-Symbolic AI = Neural Learning (pattern recognition) + Symbolic Reasoning (logic & explainability).

Exploring the frontiers of LLMs in psychological applications: a comprehensive review

Ke, L., Tong, S., Cheng, P., & Peng, K. (2025).

Artificial Intelligence Review, 58(10).

https://doi.org/10.1007/s10462-025-11297-5

Abstract

This review explores the frontiers of large language models (LLMs) in psychological applications. Psychology has undergone several theoretical changes, and the current use of artificial intelligence (AI) and machine learning, particularly LLMs, promises to open up new research directions. We aim to provide a detailed exploration of how LLMs are transforming psychological research. We discuss the impact of LLMs across various branches of psychology—including cognitive and behavioral, clinical and counseling, educational and developmental, and social and cultural psychology—highlighting their ability to model patterns, cognition, and behavior similar to those observed in humans. Furthermore, we explore the ability of such models to generate coherent, contextually relevant text, offering innovative tools for literature reviews, hypothesis generation, experimental designs, experimental subjects, and data analysis in psychology. We emphasize the importance of addressing technical and ethical challenges, including data privacy, the ethics of using LLMs in psychological research, and the need for a deeper understanding of these models’ limitations. Researchers should use LLMs responsibly in psychological studies, adhering to ethical standards and considering the potential consequences of deploying these technologies in sensitive areas. Overall, this review provides a comprehensive overview of the current state of LLMs in psychology, exploring the potential benefits and challenges. We hope it can serve as a call to action for researchers to responsibly leverage LLMs’ advantages while addressing the associated risks.

Here are some thoughts:

LLMs as assistants, not replacements. They help with emotion recognition, risk flagging, and prognosis—but tend to underestimate risk in sensitive cases. Use them to prompt, not conclude, your clinical judgment.

The empathy gap is shrinking, but uneven. GPT-4 outperformed most humans on emotional intelligence measures, and AI feedback boosted peer empathy by nearly 20%. However, this is pattern recognition, not genuine attunement—critical when nonverbal cues matter.

Cognitive biases affect LLMs too. They show anchoring, representativeness, and cultural biases favoring WEIRD populations. This can subtly disadvantage clients from non-Western or underrepresented backgrounds.

Domain-specific training helps. The ChatCounselor model, trained on real therapy conversations, outperformed general-purpose models. Off-the-shelf tools are poor substitutes for clinically-trained ones.

Research gains are real. LLMs aid literature synthesis, hypothesis generation, and drafting documentation. But outputs must be verified—errors and misattributions are easy to miss.

Ethical infrastructure lags. Privacy, informed consent, and diagnostic bias remain unresolved. Treat AI as an adjunct to professional judgment and be transparent with clients.

Bottom line: LLMs are useful but evolving faster than ethical and clinical frameworks. Engage thoughtfully—neither dismiss nor uncritically adopt them—to stay ahead as the landscape shifts.

How malicious AI swarms can threaten democracy

Schroeder, D. T., et al. (2026).

Science, 391(6783), 354–357.

https://doi.org/10.1126/science.adz1697

Abstract

Advances in artificial intelligence (AI) offer the prospect of manipulating beliefs and behaviors on a population-wide level (1). Large language models (LLMs) and autonomous agents (2) let influence campaigns reach unprecedented scale and precision. Generative tools can expand propaganda output without sacrificing credibility (3) and inexpensively create falsehoods that are rated as more human-like than those written by humans (3, 4). Techniques meant to refine AI reasoning, such as chain-of-thought prompting, can be used to generate more convincing falsehoods. Enabled by these capabilities, a disruptive threat is emerging: swarms of collaborative, malicious AI agents. Fusing LLM reasoning with multiagent architectures (2), these systems are capable of coordinating autonomously, infiltrating communities, and fabricating consensus efficiently. By adaptively mimicking human social dynamics, they threaten democracy. Because the resulting harms stem from design, commercial incentives, and governance, we prioritize interventions at multiple leverage points, focusing on pragmatic mechanisms over voluntary compliance.

A pdf is here.

Here are some thoughts:

The article argues that combining LLMs with multiagent architectures creates "malicious AI swarms" — a major leap beyond older botnets. These swarms can autonomously coordinate thousands of AI personas, precisely target vulnerable communities, mimic human behavior to evade detection, self-optimize in real time, and maintain persistent influence over long periods. The democratic harms are wide-ranging: fabricated consensus, deepened social fragmentation, contaminated AI training data, coordinated harassment, and eroded institutional trust that could make authoritarian measures seem acceptable. The authors call for a multilayered defense — continuous detection systems, user-facing "AI shields," stronger cryptographic identity standards, and a global AI Influence Observatory — while emphasizing that voluntary compliance will fall short as long as platforms' commercial incentives reward the same engagement dynamics that swarms exploit.

Exploring spiking neural networks for deep reinforcement learning in robotic tasks

Zanatta, L., et al. (2024).

Scientific Reports, 14(1), 30648. 

https://doi.org/10.1038/s41598-024-77779-8

Abstract

Spiking Neural Networks (SNNs) stand as the third generation of Artificial Neural Networks (ANNs), mirroring the functionality of the mammalian brain more closely than their predecessors. Their computational units, spiking neurons, characterized by Ordinary Differential Equations (ODEs), allow for dynamic system representation, with spikes serving as the medium for asynchronous communication among neurons. Due to their inherent ability to capture input dynamics, SNNs hold great promise for deep networks in Reinforcement Learning (RL) tasks. Deep RL (DRL), and in particular Proximal Policy Optimization (PPO) has been proven to be valuable for training robots due to the difficulty in creating comprehensive offline datasets that capture all environmental features. DRL combined with SNNs offers a compelling solution for tasks characterized by temporal complexity. In this work, we study the effectiveness of SNNs on DRL tasks leveraging a novel framework we developed for training SNNs with PPO in the Isaac Gym simulator implemented using the skrl library. Thanks to its significantly faster training speed compared to available SNN DRL tools, the framework allowed us to: (i) Perform an effective exploration of SNN configurations for DRL robotic tasks; (ii) Compare SNNs and ANNs for various network configurations such as the number of layers and neurons. Our work demonstrates that in DRL tasks the optimal SNN topology has a lower number of layers than ANN and we highlight how the state-of-art SNN architectures used in complex RL tasks, such as Ant, SNNs have difficulties fully leveraging deeper layers. Finally, we applied the best topology identified thanks to our Isaac Gym-based framework on Ant-v4 benchmark running on MuJoCo simulator, exhibiting a performance improvement by a factor of 4.4x over the state-of-art SNN trained on the same task.

Here are some thoughts:

This paper asks whether a more brain-like type of AI (called a Spiking Neural Network (SNN)) can be used to train robots to move and balance themselves. The alternative is the conventional artificial neural network (ANN) that powers most of today's AI.

Training SNNs for robotics used to take around 3 hours and 20 minutes per experiment. The authors built a new framework called SpikeGym, which cut that down to about 7 minutes by running thousands of simulated environments simultaneously on a GPU. 

The results revealed an interesting and important asymmetry between the two network types. ANNs get better as you add more layers — deeper networks learn richer representations. SNNs, by contrast, actually get worse with more layers. A single-layer SNN consistently outperformed deeper SNN architectures, and this held true across multiple tasks and training methods. 

SNNs are promising but face a real obstacle: they don't scale well with depth the way conventional networks do. The authors argue this is a solvable problem, likely rooted in how gradients are approximated during training, and they release their framework openly to help the research community dig into it further.

No one knows how AI works. Seriously

Rob Curran

Dallas Morning News

Originally posted 20 FEB 26

The next task for AI firms is figuring out how their chatbots work. It might sound like they have put the $500 billion nuclear-powered cart before the horse. But the giant leap forward in generative AI in the 2020s took software engineers by surprise and has left them wondering how the chatbots do what they do, even as their employers go all-in on the technology.

Some of the most outlandish prophecies about AI's power are coming true almost as soon as techno-philosophers are finished making them. It's now almost commonplace for people to fall in love with avatars on their phone. Nobody thinks twice about devoting 6% of national power generation to run these bots' data center brains. And recently, an entrepreneur named Matt Schlicht launched an entire social network exclusively for AI agents, which is now dominated by self-reflecting techno-philosopher bots, some of whom have invented a religion: Crustafarianism.

But the whole AI project is in many ways still in beta testing. We know what the bots do but not how they do it.

'Difficult to understand'

AI doesn't work like traditional software because its output is creative, not rules-bound. If word-processing software renders an "&" every time you type a "g," the engineers find the faulty code and correct the glitch. Just like designing a mousetrap, engineers know what every moving part in a traditional software program does, so that they can easily tweak the design of each cog in the works to adjust the output.

Chatbots are harder to improve (for example, the Internet is not unanimous on whether ChatGPT 5 is superior to the 4 version). Why? Because nobody understands how generative AI chatbots work. Software engineers understand the data and coding inputs, and we can all see chatbots' output. But nobody understands how the parts of the AI mousetrap fit together, industry leaders say.

The info is here.

Here are some thoughts:

Rob Curran highlights a striking paradox at the heart of modern AI: the technology has advanced at a breathtaking pace, yet even its creators don't fully understand how it works. 

Unlike traditional software, AI's creative output can't be traced back to specific lines of code, leaving engineers unable to reliably diagnose or improve it. Anthropic's CEO Dario Amodei acknowledged this gap, calling for an "MRI of AI" to solve the interpretability problem, while other industry figures have sounded more alarming warnings about the technology's risks. Curran's broader point is that even as AI remains deeply mysterious, the race to make it more powerful shows no signs of slowing down.

The New Eugenics in Medicine

Lazarus, A. (2026, January 23).

Medpagetoday.com; 

MedpageToday.

‌A growing body of contemporary research and reporting exposes how old ideas can find new life when repurposed within modern systems of medicine, technology, and public policy. Over the last decade, several trends have converged:

• The rise of polygenic scoring for embryos and adults;
• Rapid growth in commercial direct-to-consumer genetic testing;
• Artificial intelligence (AI)-driven "risk stratification" tools in healthcare and insurance;
• The proliferation of biobanks disproportionately populated by individuals from privileged backgrounds; and
• The reemergence of academic interest in "optimal reproduction," "biological improvement," and "population efficiency."

While these movements hold extraordinary possibilities for treating illness and ameliorating suffering, they also have the potential to be used to enhance certain traits and delete others -- ones that are simply disliked by those in power. Individually, each development has scientific merit and, in many cases, real potential to prevent disease and improve care.

Collectively, however, they raise questions that are both familiar and deeply unsettling.

Echoes of the Past

The U.S. and many other countries have long histories of medicalized discrimination under the banner of "improving the population." During the early and mid-20th century, physicians, judges, social workers, and university researchers pursued policies and practices -- sterilization, segregation, restrictive marriage laws, immigration exclusions -- rooted in the belief that some lives were more valuable than others. The rhetoric of the era portrayed these policies as scientific, progressive, and necessary for social order and the betterment of humanity. They provided Hitler with a distorted justification for his anti-Semitic beliefs, leading to efforts to exterminate the Jews and other marginalized ethnic minorities in Germany from 1933 to 1945.

The info is here.

Here are some thoughts:

Dr. Lazarus makes a compelling case that the greatest danger of "new eugenics" lies in its invisibility, embedded in algorithms, risk scores, and efficiency narratives rather than overt coercion, making it far harder to recognize or resist. His warning that systems rewarding predictive power can quietly marginalize the vulnerable is well-founded, though one might gently push back that conflating individual reproductive choice with state-coerced eugenics risks muddying an important moral distinction. Nonetheless, his closing challenge that a society's worth is measured by how fiercely it protects the vulnerable, not how efficiently it rewards the "fit," is a powerful and necessary reminder.

Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operations

Orlando, G. M., et al. (2025).

ArXiv.org. 

https://arxiv.org/abs/2510.25003

Abstract

Generative agents are rapidly advancing in sophistication, raising urgent questions about how they might coordinate when deployed in online ecosystems. This is particularly consequential in information operations (IOs), influence campaigns that aim to manipulate public opinion on social media. While traditional IOs have been orchestrated by human operators and relied on manually crafted tactics, agentic AI promises to make campaigns more automated, adaptive, and difficult to detect. This work presents the first systematic study of emergent coordination among generative agents in simulated IO campaigns. Using generative agent-based modeling, we instantiate IO and organic agents in a simulated environment and evaluate coordination across operational regimes, from simple goal alignment to team knowledge and collective decision-making. As operational regimes become more structured, IO networks become denser and more clustered, interactions more reciprocal and positive, narratives more homogeneous, amplification more synchronized, and hashtag adoption faster and more sustained. Remarkably, simply revealing to agents which other agents share their goals can produce coordination levels nearly equivalent to those achieved through explicit deliberation and collective voting. Overall, we show that generative agents, even without human guidance, can reproduce coordination strategies characteristic of real-world IOs, underscoring the societal risks posed by increasingly automated, self-organizing IOs.

Here are some thoughts:

This paper presents the first systematic study of how LLM-powered agents autonomously develop coordinated influence campaign behaviors without human direction. The researchers simulated a political information operation across three progressively structured conditions: agents sharing only a common goal, agents aware of their teammates' identities, and agents engaging in collective deliberation and voting on strategies. Across all five measured dimensions (network cohesion, narrative convergence, amplification behavior, hashtag diffusion, and cross-group spread), coordination consistently strengthened as operational awareness increased. 

The most striking finding is that simply informing agents who their teammates are produces coordination nearly as potent as full collective decision-making, as agents spontaneously began echoing each other's content, converging on shared messaging, and forming dense interaction clusters without any explicit instructions to do so. 

The study's core warning for platform governance is that sophisticated, human-like influence operations do not require centralized command structures. Merely revealing shared group identity among aligned AI agents may be enough to trigger highly organized, self-reinforcing coordinated behavior.

Historically, running a sophisticated influence operation required significant human labor, scripted coordination, and ongoing oversight. This research suggests that the barrier has collapsed dramatically. A bad actor no longer needs to build an elaborate command-and-control infrastructure or write detailed playbooks for their agents to follow. Simply deploying a group of AI agents with a shared goal and knowledge of each other is sufficient to produce organized, self-reinforcing manipulation that mirrors the tactics of real-world state-sponsored campaigns.

An autonomous agentic workflow for clinical detection of cognitive concerns using large language models

Tian, J., Fard, P., et al. (2026).

Npj Digital Medicine, 9(1), 51.

https://doi.org/10.1038/s41746-025-02324-4

Abstract

Early detection of cognitive impairment is limited by traditional screening tools and resource constraints. We developed two large language model workflows for identifying cognitive concerns from clinical notes: (1) an expert-driven workflow with iterative prompt refinement across three LLMs (LLaMA 3.1 8B, LLaMA 3.2 3B, Med42 v2 8B), and (2) an autonomous agentic workflow coordinating five specialized agents for prompt optimization. Using Llama3.1, we optimized on a balanced refinement dataset and validated on an independent dataset reflecting real-world prevalence. The agentic workflow achieved comparable validation performance (F1 = 0.74 vs. 0.81) and superior refinement results (0.93 vs. 0.87) relative to the expert-driven workflow. Sensitivity decreased from 0.91 to 0.62 between datasets, demonstrating the impact of prevalence shift on generalizability. Expert re-adjudication revealed 44% of apparent false negatives reflected clinically appropriate reasoning. These findings demonstrate that autonomous agentic systems can approach expert-level performance while maintaining interpretability, offering scalable clinical decision supports.

The research is here.

Here are some thoughts:

This paper introduces an AI-powered system designed to automatically detect signs of cognitive decline in clinical notes, without requiring any human involvement after initial setup. The researchers compared two approaches: one guided by clinical experts who refined the AI's instructions over time, and a fully autonomous system where specialized AI agents worked together to improve their own performance. 

The autonomous system performed surprisingly well, and in many cases where it seemed to make mistakes, expert review later confirmed that its reasoning was actually clinically sound. The main challenge the team identified was that a system trained under idealized conditions can struggle when deployed in real-world settings where patient populations look different. 

Overall, the findings suggest that autonomous AI systems can approach expert-level performance in clinical screening tasks, but will need careful calibration before being trusted in routine medical practice.

Clinical AI Has Boomed

Rebecca Handler

Stanford Medicine

Originally posted 15 Jan 26

Artificial intelligence is no longer a speculative force in medicine. It is already embedded in everyday care. AI systems flag hospitalized patients at risk of deterioration, assist radiologists reading mammograms, draft clinicians’ notes, route patient messages, and increasingly interact directly with patients through chatbots and digital assistants.

In recent months, the pace and visibility of these deployments have accelerated sharply. OpenAI announced ChatGPT for Health, positioning a general-purpose language model as a tool for health-related information and patient interaction. Utah just began piloting AI-supported prescribing and clinical decision systems, raising questions about how algorithmic recommendations intersect with clinician judgment and liability. OpenEvidence, an AI-powered medical evidence platform designed primarily for clinicians and health professionals, has become a dominant player in point-of-care decisions, underscoring the fact that doctors are often bypassing traditional IT gatekeepers to use AI in clinical care. At the federal level, the FDA signaled a loosening of regulatory oversight for certain categories of clinical decision support software, shifting more responsibility to developers and health systems to ensure safety and effectiveness.

The info is here.

Here are some thoughts:

A January 2026 report called The State of Clinical AI from the ARISE Network, led by researchers across Stanford and Harvard, examines where AI is genuinely improving clinical care versus where it falls short in real-world settings. 

AI has become deeply embedded in everyday medicine — flagging patients at risk of deterioration, assisting radiologists, drafting clinical notes, and interacting with patients through chatbots. However, the report finds a significant gap between controlled study performance and actual clinical practice: AI systems struggle with uncertainty, incomplete information, and complex reasoning, often performing closer to medical students than experienced physicians. 

The report also raises concerns about patient-facing AI, noting that patients may over-trust systems that sound confident but lack full clinical context, and that escalation to human care is often unclear when guardrails are poorly defined.