AI Agents

When was the last time a machine truly surprised you—not with a quirky ChatGPT poem or a clever image generation, but with scientific reasoning that evolved on its own? Meet STELLA, an AI agent for biomedical research that doesn’t just solve problems—it gets better at solving them while solving them. The Static Curse of Smart Agents Modern AI agents have shown promise in navigating the labyrinth of biomedical research, where each inquiry might require cross-referencing papers, running custom bioinformatics analyses, or interrogating molecular databases. But the vast majority of these agents suffer from a fatal limitation: they rely on static, pre-installed toolkits and hard-coded logic trees. Like a PhD student who memorized a textbook but never updated it, they can’t adapt to new tasks or new knowledge without human intervention. ...

Is it possible to train a language model to become a capable scientist? That provocative question lies at the heart of a new milestone in AI research. In SciMaster: Towards General-Purpose Scientific AI Agents, a team from Shanghai Jiao Tong University introduces X-Master, a tool-augmented open-source agent that has just achieved the highest score ever recorded on Humanity’s Last Exam (HLE)—surpassing even OpenAI and Google. But what makes this feat more than just a leaderboard update is how X-Master got there. Instead of training a larger model or fine-tuning on more data, the researchers innovated on agentic architecture and inference-time workflows. The result? An extensible framework that emulates the exploratory behavior of human scientists, not just their answers. ...

When a network fails, it doesn’t whisper its problems—it screams in silence. Packet drops, congestion, and flapping links rarely announce themselves clearly. Engineers must piece together clues scattered across logs, dashboards, and telemetry. It’s a detective game where the evidence hides behind obscure port counters and real-time topological chaos. Now imagine handing this job to a Large Language Model. That’s the bold challenge taken up by researchers in “Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting”. They don’t just propose letting LLMs debug networks—they build an entire sandbox where AI agents can learn, act, and be judged on their troubleshooting skills. It’s not theory. It’s a working proof-of-concept. ...

Brains with Gradients: Why Energy-Based Transformers Might Be the Future of Thinking Machines AI models are getting better at mimicking human intuition (System 1), but what about deliberate reasoning—slow, careful System 2 Thinking? Until now, most methods required supervision (e.g., reward models, verifiers, or chain-of-thought engineering). A new architecture, Energy-Based Transformers (EBTs), changes that. It offers a radically unsupervised, architecture-level path toward models that “think,” not just react. The implications for robust generalization, dynamic reasoning, and agent-based autonomy are profound. ...

If you’ve looked at any leaderboard lately—from SWE-Bench to WebArena—you’ve probably seen impressive numbers. But how many of those reflect real capabilities of AI agents? This paper by Zhu et al. makes a bold claim: agentic benchmarks are often broken, and the way we evaluate AI agents is riddled with systemic flaws. Their response is refreshingly practical: a 33-point diagnostic called the Agentic Benchmark Checklist (ABC), designed not just to critique, but to fix the evaluation process. It’s a must-read not only for benchmark creators, but for any team serious about deploying or comparing AI agents in real-world tasks. ...

In the race to build more autonomous, more intelligent AI agents, we’re entering an era where “strategy” isn’t just about picking the next move—it’s about choosing the right mind for the job and deciding which version of the world to trust. Two recent arXiv papers—one on state representation in dynamic routing games, the other on self-generating agentic systems with swarm intelligence—show just how deeply this matters in practice. We’re no longer only asking: What should the agent do? We now must ask: ...

The recent wave of innovation in AI systems has ushered in two distinct design paradigms—AI Agents and Agentic AI. While these may sound like mere terminological variations, the conceptual taxonomy separating them is foundational. As explored in Sapkota et al.’s comprehensive review, failing to recognize these distinctions risks not only poor architectural decisions but also suboptimal performance, misaligned safety protocols, and bloated systems. This article breaks down why this taxonomy matters, the implications of its misapplication, and how we apply these lessons to design Cognaptus’ own multi-agent framework: XAgent. ...

Half-Life Crisis: Why AI Agents Fade with Time (and What It Means for Automation) “The longer the task, the harder they fall.” In the world of automation, we often focus on how capable AI agents are — but rarely on how long they can sustain that capability. A new paper by Toby Ord, drawing from the empirical work of Kwa et al. (2025), introduces a profound insight: AI agents have a “half-life” — a predictable drop-off in success as task duration increases. Like radioactive decay, it follows an exponential curve. ...

What if your business processes could think like your most experienced employee—recalling similar past cases, adapting on the fly, and explaining every decision? Welcome to the world of CBR-augmented LLMs: where Large Language Models meet Case-Based Reasoning to bring Business Process Automation (BPA) to a new cognitive level. From Black Box to Playbook Traditional LLM agents often act like black boxes: smart, fast, but hard to explain. Meanwhile, legacy automation tools follow strict, rule-based scripts that struggle when exceptions pop up. ...

Memory in the Machine: How SHIMI Makes Decentralized AI Smarter As the race to build more capable and autonomous AI agents accelerates, one question is rising to the surface: how should these agents store, retrieve, and reason with knowledge across a decentralized ecosystem? In today’s increasingly distributed world, AI ecosystems are often decentralized due to concerns around data privacy, infrastructure independence, and the need to scale across diverse environments without central bottlenecks. ...