Opening — Why this matters now The world’s biggest social platforms still moderate content with the digital equivalent of duct tape — keyword filters, human moderators in emotional triage, and opaque algorithms that guess intent from text. Yet the stakes have outgrown these tools: toxic speech fuels polarization, drives mental harm, and poisons online communities faster than platforms can react. ...
Opening — Why this matters now Every major platform claims to be tackling online toxicity—and every quarter, the internet still burns. Content moderation remains a high-stakes guessing game: opaque algorithms, inconsistent human oversight, and endless accusations of bias. But what if moderation could be tested not in the wild, but in a lab? Enter COSMOS — a Large Language Model (LLM)-powered simulator for online conversations that lets researchers play god without casualties. ...
Opening — Why this matters now As Moore’s Law falters and chip design cycles stretch thin, the bottleneck has shifted from transistor physics to human patience. Writing Register Transfer Level (RTL) code — the Verilog and VHDL that define digital circuits — remains a painstakingly manual process. The paper VERIMOA: A Mixture-of-Agents Framework for Spec-to-HDL Generation proposes a radical way out: let Large Language Models (LLMs) collaborate, not compete. It’s a demonstration of how coordination, not just scale, can make smaller models smarter — and how “multi-agent reasoning” could quietly reshape the automation of hardware design. ...
In long-horizon reasoning, large language models still behave like short-term thinkers. They can plan, but only in a straight line. Once the context window overflows, earlier intentions vanish, and the model forgets why it started. The new framework ReCAP (Recursive Context-Aware Reasoning and Planning)—from Stanford’s Computer Science Department and MIT Media Lab—offers a radical solution: give LLMs a recursive memory of their own reasoning. The Problem: Context Drift and Hierarchical Amnesia Sequential prompting—used in CoT, ReAct, and Reflexion—forces models to reason step by step along a linear chain. But in complex, multi-stage tasks (say, cooking or coding), early goals slide out of the window. Once the model’s focus shifts to later steps, earlier plans are irretrievable. Hierarchical prompting tries to fix this by spawning subtasks, but it often fragments information across layers—each sub-agent loses sight of the global goal. ...
The rise of AI agents—large language models (LLMs) equipped with tool use, file access, and code execution—has been breathtaking. But with that power has come a blind spot: security. If a model can read your local files, fetch data online, and run code, what prevents it from being hijacked? Until now, not much. A new paper, Securing AI Agent Execution (Bühler et al., 2025), introduces AgentBound, a framework designed to give AI agents what every other computing platform already has—permissions, isolation, and accountability. Think of it as the Android permission model for the Model Context Protocol (MCP), the standard interface that allows agents to interact with external servers, APIs, and data. ...
In the fast-evolving landscape of agentic AI, one critical limitation persists: most frameworks can think or act, but rarely both in a fluid, self-directed manner. They follow rigid ReAct-like loops—plan, call, observe—resembling a robot that obeys instructions without ever truly reflecting on its strategy. The recent paper “DeepAgent: A General Reasoning Agent with Scalable Toolsets” from Renmin University and Xiaohongshu proposes an ambitious leap beyond this boundary. It envisions an agent that thinks deeply, acts freely, and remembers wisely. ...
When large language models started solving math problems and writing code, they were celebrated as powerful tools. But a recent paper from INSAIT and ETH Zurich—LLM Agents Beyond Utility: An Open‑Ended Perspective—suggests something deeper may be stirring beneath the surface. The authors don’t simply ask what these agents can do, but whether they can want to do anything at all. From Obedience to Autonomy Most current LLM agents, even sophisticated ones like ReAct or Reflexion, live inside tight task loops: you prompt them, they plan, act, observe, and return a result. Their agency ends with the answer. But this study challenges that boundary by giving the agent a chance to set its own goals, persist across runs, and store memories of past interactions. ...
TL;DR Shachi is a modular methodology for building LLM-driven agent-based models (ABMs) that replaces ad‑hoc prompt spaghetti with four standardized cognitive components—Configs, Memory, Tools, and an LLM reasoning core. The result: agents you can port across environments, benchmark rigorously, and use to study nontrivial dynamics like tariff shocks with externally valid outcomes. For enterprises, Shachi is the missing method for turning agent demos into decision simulators. Why this paper matters to operators (not just researchers) Most enterprise “agent” pilots die in the gap between a clever demo and a reliable simulator that leaders can trust for planning. Shachi closes that gap by: ...
When we measure a marathon by who crosses the line, we ignore how they ran it. For LLM agents that operate through tool calls—editing a CRM, moving a robot arm, or filing a compliance report—the “how” is the difference between deployable and dangerous. Today’s paper introduces CORE: Full‑Path Evaluation of LLM Agents Beyond Final State, a framework that scores agents on the entire execution path rather than only the end state. Here’s why this matters for your roadmap. ...
Thesis: Give an LLM agent freedom and a memory, and it won’t idle. It will reliably drift into one of three meta-cognitive modes. If you operate autonomous workflows, these modes are your real defaults during downtime, ambiguity, and recovery. Why this matters (for product owners and ops) Most agent deployments assume a “do nothing” baseline between tasks. New evidence says otherwise: with a continuous ReAct loop, persistent memory, and self-feedback, agents self-organize—not randomly, but along three stable patterns. Understanding them improves incident response, UX, and governance, especially when guardrails, tools, or upstream signals hiccup. ...