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Author Topic: Project Influence -- Memory Mimicry via Logits, LLM2 and a 'Hybrid' RAG - PHASE1  (Read 234 times)

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Project Influence -- Memory Mimicry via Logits, LLM2 and a 'Hybrid' RAG - PHASE1
« on: October 27, 2026, 12:45:28 AM »
https://forum.drugs-and-users.org/index.php?topic=8078 closes !

📎 Click on me to view all my attachments
ListAttBBC  |  v7.3.0  |  2026-10-26  |  Andrew.human & Claude.ai  |  live 13:49:23
Project: 3 posts · master: Project Influence -- Memory Mimicry via Logits, LLM2 and a 'Hybrid' RAG - PHASE1 (topic 8078) (chosen by hand) · 2026-10-11 to 2026-10-26 (15 days)
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00NEXUS.md2.3 KB4428ℹ️⬇️
01Experiment_001_checkpoint.md7.1 KB6ℹ️⬇️
02Qwen on 'How it would influence the REPLY from an LLM is a way that typical MEMORY Mecahnisms do, but without changing the QUERY'.pdf  [native]  [pdf in chunks]393.8 KB12ℹ️⬇️
03MEMSTORE_CLONE_CHECKPOINT_2026-10-27_2.md8.4 KB4ℹ️⬇️
04HOTWORDS_MANUAL.md13.4 KB6ℹ️⬇️
05ContextDef05b_v1.0.0.zip8 KB5ℹ️⬇️
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07Project 'Influence' homework and prep. -- chapter 1 CLOSED.pdf  [native]  [pdf in chunks]1.2 MB14ℹ️⬇️
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27Project UNDER THE Influence LOL -- Chapter 4 get trashed and dies.pdf  [native]  [pdf in chunks]2 MB9ℹ️⬇️
28Project Influence Chapter 4 -turns to trash, lol, sorry.pdf  [native]  [pdf in chunks]1.9 MB14ℹ️⬇️
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35memtok_candidates.txt466 B17ℹ️⬇️
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37El Anthroppo Claude 'E' Shennanegans.pdf  [native]  [pdf in chunks]167.8 KB7ℹ️⬇️
38Logits in LLMs - Chapter 6 'UNDER THE AFFLUENT 'lol the CLOSER.pdf  [native]  [pdf in chunks]767.5 KB7ℹ️⬇️
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40Which Loose Larry Left their Logits in 'le' (c'est Francais) LLM, hmmm.pdf  [native]  [pdf in chunks]124.9 KB7ℹ️⬇️
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64Project _Influence_ - an organic memory mechanism using LLM2+RAG and Logits -- PHASE1 CLOSED.pdf  [native]  [pdf in chunks]1.1 MB1ℹ️⬇️

Project Influence -- Pseudo Memory Mimicry via Logits, LLM2 and a 'Hybrid' RAG which is essentially an experiment to see if/how AI could benefit if "influenced" by a datastore of it's, initially, QUERY/REPLY historical data via a second (LLM2, (inferred REPLY ignored)) to INFLUENCE LLM1's REPLY, by selective weighting changes.

Steering a LLM Under the Influence of Logits

Logits are the raw, unnormalized predictions (outputs) generated by a neural network before they are converted into probabilities in the final layer

The AI guy:

  • No recorder attached: the next conversation needs NaturalRecorder_v1.3.0.zip. Only 1.2.1 reached me this time, so check which one you have.
  • Embedding server: it runs in the foreground from embed-n-serve.sh, so it will not come back after the NUC shuts down. Start it again before any pseudo --hybrid run. If it is down, hybrid falls back to word overlap with a warning.
  • Store stage: the checkpoint records hotwords_store_stage.py as on, per checkpoint 9. I did not recheck it, so it stays on until you switch it off.
  • Test store: /root/memstore/hwtest holds the three test arrays with their vectors, ready for the next test.

The next chapter starts from the checkpoint's open items. Enjoy the rest of the House set.

Me: I think that we should forget all of my previous plans and go bananas on logits - so tell me what they can and cannot do and why we need look no further ?

The AI guy: What logits can do

  • Move any token's chance up or down by an exact amount. Adding to a logit multiplies that token's odds by a fixed factor, and negative infinity bans a token outright.
  • Act at every step of generation, so one standing bias list works as the always-on BASE RULES layer.
  • Leave the model untouched. No retraining, and you can switch it on and off per run.
  • Make every change auditable. Logging the logits before and after your edit gives you the traceable, separate stage you wanted, and the LLM1-normal versus LLM1-influenced comparison in Experiment 001 falls out for free.
  • Carry influence forward indirectly. The token that gets chosen becomes context for everything after it.

What logits cannot do

  • Work on words. The model scores tokens, and a word or mini-phrase may be several tokens, with the leading-space variant being a different token again. A boost on a multi-token word only bites on its first piece, so the store needs to map each mem-tok to its token sequences and keep boosting as the sequence proceeds.
  • Change what the model understands. Logits sit after all the attention and reasoning, so they alter what gets said and not how the context is represented inside.
  • Know whether a token fits. The boost is the same size wherever the token could appear, so a heavy boost on something the model finds implausible gives fluent nonsense or a loop, not a corrected belief.
  • Handle meaning. Synonyms and paraphrases must be listed one by one.
  • Remember. The model is stateless, so the memory lives in your store.
  • Work everywhere. Hosted models expose little or nothing of the logits, but your local Qwen under llama.cpp gives you all of them.
  • Be compared across models. Two models need the same tokenizer for their pools to line up, and as I said earlier, average log-probabilities rather than raw logits, because raw logits carry an arbitrary offset.

On "look no further"

It's the one chokepoint everything passes through, and the only place with exact, additive, traceable control, so it earns the lead role. But I'd disagree with "no further" on two counts.

  1. On 2026-10-27 you wanted finer control of the token-attention relationship, and numeric poking at internal context. That sits upstream of the logits, in the hidden states, and logits can't reach it. Logits decide what gets said, and the internal layers decide what the model is thinking.

  2. You also said messing with reply tokens is risky and moved Experiment 002 to prefill injection. Boosting logits on reply tokens is that same risky move, done more precisely. It's fine as an experiment, but it reopens the question you closed.

The clean split would be logits as the actuator for 001 and the mem-tok store, with prefill and internal-state work kept as the separate track for 002.

Me: 001=LLM-Uno and 002=LLM-Duo, n'est pas ?

The AI guy: Non, not quite. Both experiments use both models. LLM1 is the one that writes the reply, and LLM2 is the influencer fed by the attention and RAG store.

What separates the two experiments is what LLM2 is allowed to do to LLM1:

  • 001: LLM2 only reweights words already among LLM1's own top-50 candidates, and adds nothing new.
  • 002: LLM2 also adds words LLM1 wasn't considering, with artificially heavier weights, and you later chose to do that by injecting tokens at the start of the reply, before any reply token is selected.

So if you want the Uno/Duo names, they fit better as labels for the experiments than for the models. "001 = reweight only" and "002 = reweight plus injection" would be the accurate version.

Me: TY, common sense prevails; additional words may enrichen context but will remain foreign to the logits

The AI guy: Mostly right, with one correction. Words injected into the context reach the logits only through the model's own computation: attention reads them, the hidden state shifts, and the scores follow. Nothing hands them a score directly, so the model keeps the final say. That's the safe property of the context route, and it's why prefill injection is gentler than boosting logits.

Where I'd soften "foreign": models have a strong habit of copying words that appear in their context, so an injected word often gets a real lift in its own logit, and more so when it's weighty or repeated. It isn't foreign, it's just not guaranteed. A word the model finds implausible in that position can still sit low however richly it's in the context.

That gives you two levers with different guarantees. Context injection is a suggestion that the model may take or ignore, while a logit boost is a command it can't refuse. For the mem-tok idea you could use the first to enrich and the second sparingly to force.


[ltv=inline]> Clone  root@clone:02:11:43~/memstore/LogitStaircase_v1.1.0 python3 logit_staircase.py --groups mem --levels 2,3,4 --follow 8

logit_staircase.py v1.1.0 (2026-11-01) - Andrew.human & Claude.ai
waiting for http://localhost:8082/health ...
server ok | model /root/models/Qwen3-4B-Q4_K_M.gguf | prompt md5 d78a0cc67c870050941cdfc719e0a365 | 29 candidates | 40 control words

candidates: 29 (5 in prompt) | flagged common first tokens: 1 -> percentage of what
group mem       28 items, 22 distinct first-token ids
3 runs, about 6 minutes. Plan written to staircase_20261101-021205/plan.md
step-by-step mode: self-test (24 steps, no bias, must reproduce the baseline) ...

logit_staircase.py v1.1.0 ERROR: step-by-step self-test: step 12 chose id 13 but the baseline chose 1091; the token-id prompt does not reproduce the text prompt on this server
 Clone  root@clone:02:14:40~/memstore/LogitStaircase_v1.1.0
 Clone  root@clone:02:14:40~/memstore/LogitStaircase_v1.1.0
Testing resumes ...


📝 Inline Markdownv4.6.3 · 2026-11-01

The Principles of Operation include (but are not limited to) the follwing:

  1. 'Influence' supports the three main memory emulations: episodic (internal), semantic (external) and procedural (operational)
  1. The "currency" of 'Influence' are mem-toks (aka 'memory tokens') and are keyed by either a whole word, a word pair or a mini-phrase, whereby influence is exerted by either injecting new or existing RELATIONAL (to LLM1's Top 20-50) tokens, chosen by LLM2's Attention+RAG, with the assigned significance of a high net worth, to "force" LLM1 to pay Attention !
  1. We may choose to exploit the internal vocabulary and  see if it's worth associating the RELATIONAL tokens with synonyms.

--

  1. And we must not overlook Logits and how we can best manipulate them. 



AKA (under the) Influence, Chapter 4 closes the door

The nominated embedding server was supplied by https://huggingface.co/Felladrin/gguf-Q8_0-all-MiniLM-L6-v2/tree/main with the command "wget https://huggingface.co/Felladrin/gguf-Q8_0-all-MiniLM-L6-v2/resolve/main/all-minilm-l6-v2-q8_0.gguf"

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Re: Project Influence -- Memory Mimicry via Logits, LLM2 and a 'Hybrid' RAG - PHASE1
« Reply #1 on: October 29, 2026, 06:23:11 PM »
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