AI agents

Timeline

1. Oct 11, 2026 · Opinion / analysis · 1 source
   500B Tokens Later: Letting AI Agents Decompile a First-Person Shooter
2. Oct 10, 2026 · Opinion / analysis · 1 source
   Talorys – A self-hosted personal AI agent on Cloudflare's free tier
3. Oct 9, 2026 · Opinion / analysis · 1 source
   I expect rapid progress but not towards general superintelligence
   I’ve often been surprised when I hear from top researchers in industry that they think AI will be better than them at their job in a few years, and I didn’t really know why I doubted it.
4. Oct 9, 2026 · Opinion / analysis · 1 source
   ICYMI: What landed for AI builders in September 2026
   A recap of the latest Amazon Bedrock, Amazon Bedrock AgentCore, and Strands updates from September 2026
5. Oct 9, 2026 · Opinion / analysis · 1 source
   How Postman runs Agent Mode for 40 million developers on Amazon Bedrock
   Postman set out to build Agent Mode, an AI-native way to work across API testing, documentation, discovery, and implementation.
6. Oct 9, 2026 · Opinion / analysis · 1 source
   Show HN: Let your AI agents paint big arrows, boxes and text on your screen
7. Oct 8, 2026 · Opinion / analysis · 1 source
   Pay-per-inference for AI agents: How BlockRun and Incarna use Amazon Bedrock AgentCore payments
   In this post, we look at how Incarna used AgentCore payments to let its agents pay BlockRun for model inference one request at a time.
8. Oct 8, 2026 · Research paper · 1 source
   Ecology of AI Agents: Collaboration Creates a Population Threshold for Takeoff
   Here, we develop an ecological theory of AI-agent populations based on a population growth equation in which fitness (growth rate) depends on cybersecurity capability.
9. Oct 8, 2026 · Research paper · 1 source
   Can AI Agents Learn Their Way to the Top? Evaluating Heuristic Learning in a Long-Running Game Agent Competition
   Adversarial games have driven advances from heuristic search to reinforcement learning, yet learning and adapting strategies from limited samples remain challenging.
10. Oct 8, 2026 · Research paper · 1 source
    DataSense-Bench: The First Step Toward an AI Scientist
    We introduce DataSense-Bench to study this capability through the fundamental problem of data selection and performance forecasting in machine learning.
11. Oct 8, 2026 · Research paper · 1 source
    Forms of LLM-Integrated Applications from LLM-Chats to Autonomous AI Agent System
    Large language models (LLMs) are increasingly embedded as components in software systems, marketed under labels such as chatbot, copilot, retrieval-augmented generation, workflow, coding agent and AI agent.
12. Oct 8, 2026 · Opinion / analysis · 1 source
    Building a safer path to autonomous industrial AI
    Industrial AI is entering a new phase.
13. Oct 7, 2026 · Research paper · 1 source
    iAm.md: Robot Skill Self-Assessment through Agentic Introspection for Unknown Open-Vocabulary Domains
    Agentic AI based on Large Language Model generalization capabilities offers a wide range of potential applications, including planning for embodied tasks.
14. Oct 7, 2026 · Research paper · 1 source
    RFChipAgent: Multi-Agentic AI Flow for Analog/RF Chip Design
    We present RFChipAgent, a first-of-its-kind multi-agent flow of large language model (LLM) agents for end-to-end analog/RF circuit design automation, in which AI agents collaboratively orchestrate the complete design flow under human supervision.
15. Oct 7, 2026 · Research paper · 1 source
    On the Clock: Towards Punctual and Productive Time-Budgeted AI Agents
    We study whether small LLM agents can operate effectively under explicit wall-clock time budgets by both respecting the allocated runtime and using available time productively.
16. Oct 7, 2026 · Research paper · 1 source
    SciExam for ENSO: Can AI Agents Build Climate Models?
    The AI Science Exam for El Nino-Southern Oscillation (SciExam for ENSO) is a benchmark in which agents build low-order stochastic models of ENSO, the dominant mode of interannual climate variability, from real observations.
17. Oct 7, 2026 · Opinion / analysis · 1 source
    Validate AI Factory Changes with Digital Twins and AI Agents
    AI factories are some of the most complex operations in the world, combining GPUs, CPUs, switches, DPUs, and SuperNICs alongside schedulers, orchestration...AI factories are some of the most complex operations in the world, combining GPUs, CPUs, switches, DPUs, and SuperNICs alongside schedulers, orchestration services, security controls, and a rapidly changing software stack.
18. Oct 7, 2026 · Product / feature launch · 1 source
    Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses
    Harnessed Agentic RL: Microsoft Research Asia introduces a training paradigm in which the same agent harness used in deployment participates directly in reinforcement learning, removing the need to reimplement the agent inside the training framework.
19. Oct 7, 2026 · Product / feature launch · 1 source
    Beyond hours saved: Building the business case for agentic automation
    In this post, we introduce a framework AI CoE leaders can use to build a business case that captures the full value of agentic automation.
20. Oct 7, 2026 · Tutorial / explainer · 1 source
    Building AI builders: Playbook for closing the AI knowledge-capability gap
    The biggest barrier to AI adoption isn’t awareness.
21. Oct 7, 2026 · Research paper · 1 source
    Agentic AI-Assisted Modeling for Production Scheduling: Assessment in Constraint Programming
    Developing optimization models for production scheduling requires substantial expert effort.
22. Oct 7, 2026 · Research paper · 1 source
    ExperienceIndex: Artifact-Grounded Memory
    We introduce ExperienceIndex, a novel experience layer for AI agents that captures and reuses knowledge about artifacts based on prior reasoning traces.
23. Oct 7, 2026 · Research paper · 1 source
    The Harness as the Only Mutable Surface: Compliance-Bounded Self-Evolution of LLM Agents in Credit Pipelines, with a Measured Admission Gate
    Self-improving LLM agents can adapt a credit pipeline to a changed rule, but an agent that rewrites itself destroys the artefact a supervisor reviews: a named change, a recorded test, an approval.
24. Oct 7, 2026 · Research paper · 1 source
    AgentTime: Can Agents Estimate and Control Their Own Runtime?
    We present AgentTime, a benchmark for testing whether agents can work for a requested duration, predict their runtime, and estimate elapsed time afterward.
25. Oct 7, 2026 · Research paper · 1 source
    End-to-End Autonomous Generation of Human Assembly Plans
    In this work, we encode long-established design for assembly (DfA) principles into a contained, end-to-end approach for generating assembly plans.
26. Oct 7, 2026 · Research paper · 1 source
    On-Demand Robotic Assembly via Differentiable Geometric Part Repair
    This paper presents an end-to-end, autonomous pipeline for the design and physical construction of bespoke wooden assemblies.
27. Oct 7, 2026 · Research paper · 1 source
    Shared and structured inputs undermine collective random choice by reasoning AI agents
    Random selection is widely used in resource allocation and auditing, making reliable implementation essential for AI-agent systems.
28. Oct 7, 2026 · Research paper · 1 source
    Learning Situation-Conditioned Thinking Policies for Long-Term LLM Agents
    Long-running autonomous agents must reuse accumulated reasoning experience without allowing explicit historical memory and LLM context to grow indefinitely.
29. Oct 7, 2026 · Research paper · 1 source
    DrugTargetWorld: A Synthetic Biobank for Training and Benchmarking AI Scientists
    We introduce DrugTargetWorld, a framework that procedurally generates simulated biobanks, or "worlds," with known but concealed causal structure.
30. Oct 7, 2026 · Research paper · 1 source
    Verification and Self-Improvement in Agentic AI: Foundations and Limits
    Agentic AI systems can improve by searching longer, receiving additional support, or modifying how they propose and verify outputs.

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