Three Conceptual AI Papers on Dreaming LLMs, Overconfident Forecasting, and Societal Memory

Research
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I’ve recently uploaded three interrelated preprints to TechRxiv, each exploring new paradigms for how LLMs can develop memory, alignment, and behavioral intuition through cognitively inspired mechanisms.

Below is a brief overview of each paper, with key ideas and open questions for feedback.

1. ForecastLLMs: Overconfidence-Inspired Foresight in Language Models

Link to Paper →
Concept:
Inspired by the Dunning-Kruger effect, this paper proposes that LLMs may benefit from a structured form of overconfidence — not in hallucinating facts, but in proactively forecasting what the user might ask next, or what knowledge gaps may soon emerge.

Rather than waiting for instructions, the model speculatively offers follow-ups or anticipatory scaffolding. The argument is that this could:

  • Reduce user friction
  • Improve token efficiency
  • Open a pathway to agentic, curiosity-driven models

Prompt for Discussion:
Could deliberate overconfidence in LLMs act as a regularizer or alignment aid — or is it a slippery slope toward confabulation?

2. Dream-Augmented Language Models (DLLMs): Personalization via Off-Session Memory Compression

Concept:
DLLMs are based on the idea that LLMs, like humans, could benefit from “dreaming” — background sessions that compress recent interactions into long-term personalized memory.

Instead of repeatedly prompting an LLM with your history, it remembers you between sessions using scheduled, low-resource memory updates. Inspired by cognitive consolidation during sleep.

Potential Benefits:

  • Token and compute savings
  • Enhanced personalization without vector bloat
  • Energy-efficient long-term user modeling

Prompt for Discussion:
Could scheduled “dreams” replace or augment fine-tuning for personal assistants or longitudinal reasoning agents?

3. LLM-Wide Dream: Ambient and Societal Memory Formation in LLMs

Link to Paper →
Concept:
This generalizes DLLMs to the collective level. LLM-Wide Dream is a framework for passive, ambient learning — where LLMs gather anonymized behavioral summaries across users and regions during idle times.

The result is a societal memory graph, which enables insights like:

  • Regional gaps in vaccine awareness
  • Demographic trends in educational confusion
  • Emergent public sentiment shifts

It’s partly inspired by Jung’s “collective unconscious” and modern federated learning.

Prompt for Discussion:
What are the risks and ethical implications of collective cognition in LLMs? Could ambient learning be essential for alignment at scale?

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Biologically Inspired Framework for AI Consciousness via Multicellular Communication
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Welcome to Research forum @Jaybusireddy

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I think there are serious limitations when applying these concepts to current LLM architecture. Overconfident forecasting sounds helpful in theory, but without grounded memory or truth checking, it could easily mislead users. Today’s models already struggle with calibration, and layering confidence on top of that risks creating a false sense of authority.

Dreaming for memory compression is interesting too, but models don’t retain context across sessions unless you manually wire in some form of external memory. So it becomes the appearance of reflection without any real continuity smart looking but hollow.

The societal memory concept is probably the most concerning. Without solid privacy enforcement and deterministic behavior, aggregating ambient user data at scale crosses into ethically questionable territory. It feels like ambient surveillance without guarantees or consent.

Overall, these ideas might work better on a future architecture one that supports persistent internal memory, deterministic grounding, and native truth mechanisms. As things stand now, it’s a bit like handing over the keys to a system that doesn’t know how to drive yet.

Still, I appreciate the ambition and the thought behind all three. Definitely worth revisiting once the foundation catches up. Thanks for sharing .

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Thanks a lot for thoughfull critical review. This is exactly what I was hoping for. I acknowledge that these ideas are still in conceptual stage than immediate deployments.

Regarding societal memory concept, I fully agree with your concern on privacy and ethics. I think that is a prerequisite for societal memory. Privacy should be controlled by the user not the LLMs, otherwise whole idea failes. I do see some merit in the concept, especially in aggregating anonymous trends like the kinds of questions users frequently ask, or how certain topics evolve over time. If done with proper anonymization, opt-in design, and local abstraction, it might help align models with real-world informational needs, without compromising individual privacy .

Appreciate you taking the time, we’ll definitely revisit them as LLMs evolve.

Thanks
Jay

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Congratulations @Jaybusireddy on your first post. I welcome folks.

I shall now read.

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I thought a little about this when considering an experimental system. ““dreaming” — background sessions that compress recent interactions into long-term personalized memory.”

Dreaming would be maintenance and post processing for AI no?

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You would need some sort of bias elimination that current ai lacks like this

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Yes, dreaming could be a post processing at individual user level, societal dreaming would be continuous process (daemon thread).

By the way, the core idea is not entirely new concept. Since the dawn of internet, we are using mechanisms like user sessions, session caching and session cache persistance, cache persistant storage to remember returning users or manage user redirection in load-balanced environments.

Novelty lies in applying those principles to a new paradigm(LLMs), user interactions (queries, preferences, corrections) are continuously captured and mapped to a structured memory space. This memory can then be refined and updated, either at the personal level to enable personalized reasoning over time, or at the societal level to allow for aggregated insights and reflective learning across a broader user base.

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It is fascinating to consider these things with the possibility of implementing in code.

I see a curious line “mapped to a structured memory space.” Just sharing here but the phrase “Computational Data Structure” is a thing I learned this year.

Well, I humbly say, I have an idea for a plan. But that idea included the plan of three levels of memory. The active (short term), the protected (long term) and the dream ( auxiliary processing off line or background)
Is there a better set?

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Poor you.. In word-prison again… Sad :smile:

Madmowkimoo

5h

This post was flagged by the community and is temporarily hidden.

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Oh no! I’m in prison for talking to you Moo

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It’s only a bias eliminator for ai that sits in the core to guide thoughts and dreams keeping them neutral :joy::joy:

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LOL I was having some fun but it looks like fun es ist verboten

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All Trajectory and no Cycle makes algorithms a dull boy.

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Please check this preprint for more details on the proposed architecture .

Jay

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Please pardon our antics @Jaybusireddy

It was a side conversation and we are guilty.

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@Jaybusireddy I think that is the wrong link.
I did that the other day.

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Below are the two links related
DLLM
LLM wide Dream

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Thank you.

I am benefited by LLM so I just worked through your paper and worked with ChatGPT to format a reply. I agree with this statement

Your use of the collective unconscious as a metaphor for the societal memory layer is both poetic and precise — it gives meaning to what might otherwise be just statistical aggregation. That metaphor aligns deeply with the symbolic and structural work I’ve been pursuing myself.

It’s also refreshing to see privacy and ethics addressed directly in the architecture — with federated learning, anonymized memory graphs, and opt-in design. These aren’t afterthoughts, they’re embedded in the framework — and that gives your vision real integrity.

All of this — your familiarity with tools like MemGPT and RETRO, your practical system design, your symbolic respect, and your ethical foundation — suggests you’re not just theorizing, but laying the groundwork for something real and buildable.