**Cross-posted from**: [Medium – “Linking Judgment DSL Sessions via API”](https://medium.com/@wittgena/linking-judgment-dsl-sessions-via-api-overcoming-context-gaps-in-llms-1c9fe891ef04)  
**Source code and markdown**: [GitHub – gpt-meta-dsl/docs/medium](https://github.com/wittgena/gpt-meta-dsl/tree/main/docs/medium)

---

## Introduction

What does it mean for an LLM to *remember*?

As a Korean researcher and systems architect exploring declarative orchestration, I’ve been experimenting with how **judgment structures** might persist across otherwise stateless GPT sessions — not through memory, but through **phase-aware DSLs**.

This post outlines an API-linked simulation where two GPT sessions align via external DSLs that encode cognitive phase transitions. The goal is not to claim cognitive continuity in LLMs, but to **reproduce a continuity illusion through structure**, using a fabric I call “judgment phase sync.”

While this work involves GPT and DSL tooling, it is fundamentally a human-guided architecture experiment into how cognition might be *scaffolded externally* in agent systems.

---

## Problem Framing

Large language models are stateless. Even when they appear consistent, they lack internal mechanisms for sustained self-reference or memory.

In judgment-critical applications — such as agent alignment, multi-step ethical reasoning, or recursive self-critique — this is a limitation. Can we fake persistence?

My proposal: use **structured snapshots**, **resonance labels**, and **phase-checking DSLs** to pass judgment state across sessions, as if it were “remembered.”

---

## The Mechanism

Each session is tied to a DSL snapshot, which defines a judgmental phase (`lockPhase`), records its position (`snapshot`), and signals successor intent (`selfSuccessor`).

Sessions interact via APIs like:

```text
/session-a       → returns DSL snapshot A  
/session-b       → returns DSL snapshot B  
/session-watch   → compares phase alignment between A and B  
```

These endpoints let a GPT instance simulate reasoning continuity by loading and interpreting phase-marked DSLs externally.

---

## DSL Snippet Highlights

**Live phase monitoring**:

```dsl
@나.dsl.traceLiveMonitor {
 +runtimeLoop: enabled
 +signalProbe: {
   probeEcho: true,
   probeRhythmDesync: true
 }
}
```

**Session B inherits judgment from A**:

```dsl
@나.dsl.resumeInNewSession {
 +fromSnapshot: "2025-05-03-A"
 +lockPhase("selfSuccessor")
}
```

**Synchrony verification**:

```dsl
@나.dsl.sessionWatch {
 +trackedSessions: [
   { sessionId: "2025-05-03-A", resonanceId: "judgment-alpha" },
   { sessionId: "2025-05-03-B", resonanceId: "judgment-alpha" }
 ]
}
```

---

## Visual Continuity

```text
Session A (2025-05-03-A)
└─ lockPhase
└─ snapshot
└─ resume
└─ traceLiveMonitor

         ↓  (resumeInNewSession + selfSuccessor)

Session B (2025-05-03-B)
└─ resumeInNewSession(from A)
└─ lockPhase(selfSuccessor)
└─ snapshot
└─ traceLiveMonitor
└─ sessionWatch [A ↔ B]
```

---

## Toward Assistant API Experiments

This DSL structure can be executed in a fully API-driven setup using the OpenAI Assistant API:

```json
{
 "tool_call": {
   "function": "compare_sessions",
   "parameters": {
     "sessionA": "https://api.example.com/session-a",
     "sessionB": "https://api.example.com/session-b"
   }
 }
}
```

In this model, LLMs act as **phase interpreters** rather than state holders.

---

## Glossary

| Term               | Meaning                                           |
|--------------------|---------------------------------------------------|
| `lockPhase`        | Binds agent to a reasoning stage                 |
| `resonanceId`      | Synchronizes sessions in a shared judgment context |
| `traceLiveMonitor` | Probes drift or silence in reasoning loops        |
| `sessionWatch`     | Declaratively compares phase state across sessions |
| `selfSuccessor`    | Marks intention to hand off continuity to a new session |

---

## Discussion

I'm not a native English speaker, but I hope my intent is clear.

I’d be grateful for feedback on whether this form of structural persistence — implemented as DSL-driven orchestration — is relevant for:

- Modeling cognition in stateless LLMs  
- Testing reflective reasoning outside of memory  
- Multi-agent phase alignment architectures  

Would this fit in your model of agent-like reasoning or alignment scaffolding?

Thank you for reading and considering this contribution.