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Researcher fails to build LLM alternative after 8 months and 200 experiments

By PulseAugur Editorial · [2 sources] ·

A researcher documented an eight-month project attempting to build a system that could accumulate experience and improve behavior without retraining large language models. The effort involved approximately 200 failed experiments, ultimately hitting a wall in preserving transferable causal transitions—the ability to recognize and apply past conditions, actions, and consequences to new situations. While some mechanisms for state mutation and deterministic guards for specific data types survived, the core goal of creating a knowledge substrate between memory and weights, capable of changing future behavior without code modifications, remained elusive. AI

IMPACT Highlights the difficulty in creating AI systems that can truly learn and adapt from experience without relying on traditional LLM retraining.

RANK_REASON Blog post detailing a personal research project and its failures.

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Researcher fails to build LLM alternative after 8 months and 200 experiments

COVERAGE [2]

  1. dev.to — LLM tag TIER_1 English(EN) · Oleksander ·

    Can You Build an Alternative to LLMs? 8 Months, ~200 Failed Experiments, One Wall. 2

    <p><em>This is part 2 of a research series documenting an attempt to build something adjacent to — and in some ways alternative to — large language models. Part 1: "My Synthetic Eval Said 30/30. LoCoMo Said 0.13." The code that survived lives in <a href="https://github.com/teolex…

  2. dev.to — LLM tag TIER_1 English(EN) · Oleksander ·

    Series: "Can You Build an Alternative to LLMs? 8 Months of Experiments, 200 Failures, and One Wall" 1

    <p>I tested a simple hypothesis: can long LLM sessions be made cheaper by replacing the full transcript with a compact memory state, without losing answer correctness?</p> <p>On my own synthetic eval, the system passed <code>30/30</code>. On an external benchmark for long-term co…

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