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Experience Learning

6 articles · Page 1 of 1

Aug 4, 2026 · Artificial Intelligence

MemoHarness: How Agents Evolve Beyond Model Parameters

MemoHarness expands the notion of self‑evolving agents by keeping the language model frozen while continuously adapting the external control system—context assembly, tool interaction, generation settings, workflow orchestration, memory management, and output validation—demonstrating measurable gains on terminal, code‑generation, and finance tasks, yet highlighting limited scalability and transferability.

AI agentsAgentExperience Learning

0 likes · 17 min read

MemoHarness: How Agents Evolve Beyond Model Parameters

DataFunSummit

Aug 1, 2026 · Artificial Intelligence

MemoHarness: How Agent Evolution Shifts to the External System

MemoHarness expands the notion of self‑evolving agents by keeping the language model frozen and continuously improving the surrounding control system—context assembly, tool interaction, generation settings, workflow orchestration, memory management, and output handling—demonstrating measurable gains on terminal, code‑generation, and finance tasks while highlighting limited experimental scale and selective cross‑task transfer.

Agent HarnessEvaluationExperience Learning

0 likes · 17 min read

MemoHarness: How Agent Evolution Shifts to the External System

DataFunSummit

Jul 22, 2026 · Artificial Intelligence

Why Knowledge Bases Alone Can’t Empower AI Agents: The Need for Actionable Experience

Large language models may know a great deal, yet they still stumble on concrete tasks because knowledge must be transformed into actionable, context‑aware skills; this article analyses how skill representation, model‑specific cognition, and continuous practice reshape knowledge engineering for self‑evolving AI agents.

AI agentsExperience Learningknowledge engineering

0 likes · 16 min read

Why Knowledge Bases Alone Can’t Empower AI Agents: The Need for Actionable Experience

Machine Heart

Jul 20, 2026 · Artificial Intelligence

Richard Sutton on Energy‑Efficient AI, Over‑Hyped Large Models, and Alignment

In a candid WAIC 2026 interview, reinforcement‑learning pioneer Richard Sutton discusses his new for‑profit Oak Lab, the quest for a 20‑watt trillion‑parameter model, his disappointment with recent AI trends, the notion of a “complete mind,” robot‑kindergarten experiments, and why he believes aligning AI to a single human value system is a dangerous illusion.

AI alignmentAI safetyExperience Learning

0 likes · 12 min read

Richard Sutton on Energy‑Efficient AI, Over‑Hyped Large Models, and Alignment

DataFunTalk

Jul 20, 2026 · Artificial Intelligence

Why Knowledge Bases Alone Won’t Make AI Agents Effective: The Need for Actionable Experience

Large language models may know a lot, but they still fail at concrete tasks because knowledge must be transformed into actionable, experience‑based skills; the article analyzes how self‑evolving AI agents require skill libraries, context‑aware representations, and continuous practice‑driven knowledge production rather than static documentation.

AI AgentActionable KnowledgeAutonomous Systems

0 likes · 16 min read

Why Knowledge Bases Alone Won’t Make AI Agents Effective: The Need for Actionable Experience

Network Intelligence Research Center (NIRC)

Nov 4, 2025 · Artificial Intelligence

SEAgent: A Self‑Evolving Computer Agent that Learns Software Use Autonomously

SEAgent introduces a self‑evolving framework that enables a GUI agent to master unfamiliar software through autonomous exploration and experience learning, leveraging a curriculum generator, a world‑state model, and GRPO‑based reinforcement with adversarial imitation, achieving state‑of‑the‑art performance on OSWorld.

Experience LearningGUI automationSEAgent

0 likes · 6 min read

SEAgent: A Self‑Evolving Computer Agent that Learns Software Use Autonomously