Emmimal P Alexander

AI Researcher | Author | Founder at EmitechLogic

[Towards Data Science @TDataScience]

In her latest hands-on deep dive, @emmimalpa explains why adding more communication pathways between agents doesn't necessarily improve a system's performance. https://t.co/AufRznFrFH

[Towards Data Science @TDataScience]

What if context on its own isn't enough for agentic systems? @emmimalpa explores the impact of typed context on agent performance.
https://t.co/bdsuV6KiGX

[Towards Data Science @TDataScience]

What if coding agents need a context compiler instead of ever-increasing context windows? @emmimalpa expands in her recent deep dive. https://t.co/uQq1poW4DQ

[Towards Data Science @TDataScience]

"When a fact is introduced once and never recalled, the decay engine performs identically to the baseline. That is the honest boundary of this mechanism."

@emmimalpa unpacks the benefits and limitations of the usage-reinforced decay engine she built for AI agent memory. https://t.co/wWwqh4ZSPR

[Towards Data Science @TDataScience]

In her latest technical walkthrough, @emmimalpa explains in great detail how she built a deterministic alternative to LLM wikis. https://t.co/QA3pZWWKUo

[Towards Data Science @TDataScience]

When vector RAG isn't delivering the results you were hoping for, @emmimalpa proposes a context graph layer — and explains on how to build it for your own pipeline. https://t.co/4zPq5mt0IQ

[Towards Data Science @TDataScience]

In her new deep dive, @emmimalpa outlines a practical framework to detect hidden prompt regressions before they silently break production systems. https://t.co/vA2reiyj59

[Towards Data Science @TDataScience]

What if your LLM pipeline is failing because you made it remember too much?

@emmimalpa explains how to add a prompt-pruning layer to remove redundant and low-value tokens you accumulate over time. https://t.co/mSOT6mmt6l

[Towards Data Science @TDataScience]

Looking for ways to make your RAG workflow better? Emmimal P Alexander outlines her approach to adding a context layer into the system. https://t.co/2wUaQFmRBd

[Towards Data Science @TDataScience]

In her new MLOps deep dive, Emmimal P Alexander explains why calendar-based retraining fails in production, and how a practical shock-detection approach can work in real systems. https://t.co/MZGVsAkSFf

[Towards Data Science @TDataScience]

In her latest agentic-AI deep dive, Emmimal P Alexander explains how a 200-task benchmark revealed why ReAct agents waste retries — and presents the three fixes that eliminate them.
https://t.co/2IH7SYVyfp

[Towards Data Science @TDataScience]

"The real problem is not retrieval — it’s what actually enters the context window.

A context engine controls memory, compression, re-ranking, and token limits explicitly."

Emmimal P Alexander's new deep dive explores a powerful approach for improving LLM systems. https://t.co/YtoXzygMb1

[Emmimal P Alexander @emmimalpa]

SHAP too slow for real-time fraud detection? (~30ms per explanation)
New neuro-symbolic model generates explanations in **0.9 ms** — 33× faster, fully deterministic, inside the forward pass. @TDataScience

Article: https://t.co/XbU7dVrODG

#XAI #FraudDetection #NeuroSymbolic #AI

[Emmimal P Alexander @emmimalpa]

Tired of model drift killing your production accuracy overnight?
New: Self-Healing Neural Networks in PyTorch — fix drift in real time *without* retraining or downtime.. @TDataScience
Article: https://t.co/f3ZN0K4P4k

#PyTorch #MLOps #AI

[Emmimal P Alexander @emmimalpa]

Read it @TDataScience → https://t.co/4wReG0frcz

Code: https://t.co/yijrcDqbG9

What drift type keeps you up at night in production?

[Emmimal P Alexander @emmimalpa]

I trained a neural network to learn its own fraud rules.

No hand-coded logic.
Just gradient descent → IF–THEN rules.
It even rediscovered a key fraud signal on its own.
👇@TDataScience

https://t.co/gI8MVCMAEb

#AI #NeuroSymbolic #ExplainableAI