echo-hindsight-llm: An Open Toolkit for White-Box Emotional Memory

In this project, we will develop echo-hindsight-llm, an open source toolkit to give open-weight models an emotional memory, which cognitive scientists have shown is essential for humans to make good judgments.

The capability, framework, and prototype code have all been developed for our recent papers: The Echo Amplifies the Knowledge: Somatic Marker Analogues in Language Models via Emotion Vector Re-Injection and Scaling the Echo: Multi-Scale Validation of Somatic Marker Analogues in Language Models via Emotion Vector Re-Injection with accompanying Substack articles: https://jaredglover.substack.com/p/what-happens-when-you-give-an-ai and https://open.substack.com/pub/jaredglover/p/does-a-gut-feeling-scale.

In those papers, we extracted internal emotional states as activation-level vectors from past experiences and reinjected them during subsequent deliberation phases on related tasks. The mechanism was shown to successfully generalize across the Gemma family with model scales ranging from 1B to 27B parameters, significantly improving its decision making in high risk scenarios (from 50-52% to 72-98% with the biggest benefit at the largest scale).

Funding will deliver a concrete, pip-installable library under a permissive MIT/Apache license, accompanied by a reference evaluation harness and reproducible notebooks, with initial support for Gemma-3 (4B/12B/27B) and Llama-3.3-70B. The initiative will be executed by the author of the core method (PhD, EECS, MIT), with the ideal budget adding support for more model families, HuggingFace transformers integration, and LangChain wrappers.

This project isn’t about making AI more emotional. It’s about making AI judgment work the way human judgment actually works — not through rules alone, but through the accumulated weight of experience.