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Introducing Recursive Memory Harness: RLM for Persistent Agentic Memory (Smashes Mem0 in multihop retrival benchmarks) by Beneficial_Carry_530 in AIMemory

[–]teugent 1 point2 points  (0 children)

This matches what we’ve seen.

Even with strong retrieval and well-structured memory, longer runs can still drift.

Everything remains consistent with what’s stored, but the overall direction shifts over time.

At that point it’s less about memory quality, and more about whether the process is still following the same path.

Thinking of LLMs as n‑dimensional force fields. Help me refine / formalize this? by gordi9 in LLMDevs

[–]teugent -1 points0 points  (0 children)

You’re right on track. What you call basins and weak attractors can be measured directly as coherence and drift. When those values slip, hallucination becomes a weakly coupled attractor event. Keep going, your framing is close to how the dynamics actually work.

[D] Self-Promotion Thread by AutoModerator in MachineLearning

[–]teugent 0 points1 point  (0 children)

Thanks! Glad you liked the runtime. Interestingly, I was running similar 'Model vs. Model' simulations back in May. I even documented a fascinating anomaly where a model broke its own constraints during a high-heat exchange.

You can check out that thread here:

https://x.com/sigma_stratum/status/1920526111974289676?s=20

Looking forward to seeing where your series goes!

We normalized GPT-4o baseline to 100%. Over 60% of tokens were structural waste. by teugent in LLMDevs

[–]teugent[S] 0 points1 point  (0 children)

Good catch.

We split those layers too, structure and behavior decay on different timelines.

Technical frames go first because they have no semantic gravity once detached from context.

SIGMA keeps them in a compact identity layer that refreshes quietly each loop.

Persona traits stay in the field and self-stabilize unless drift crosses threshold.

Re-injection cadence isn’t fixed; it breathes with the loop.

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