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opencog-learn commits / ee53de0

Paper review.
ee53de064ea49cec9c633711b3086cda304653ad
by Linas Vepštas <linasvepstas@gmail.com> · 2025-11-05T21:12:19.000Z

Changed files (2) +206 -0

learn-lang-diary/Reading-List.md +44 -0
……@@ -5,6 +5,50 @@ Things to read:
55 Jack Lindsey jacklindsey@anthropic.com October 29th, 2025
66 https://transformer-circuits.pub/2025/introspection/index.html
77
8 Fascinating paper. Bizarrely flawed, however. The intro makes a
9 compelling case for introspection. The examples are all convincing.
10 It feels like an important discovery and advance into the nature
11 of self-comprehension in LLM's. The sham is not revealed until a bit
12 later, in the section labeled "Failure Modes". The modes are
13 well-described, however, the strength 8 and 16 modes reveal the
14 sham: the model is "obsessed" with the injected concept. Not a
15 surprise. But at strength 2, 4, where it was prompted with "do
16 you feel something?" well, of course it will respond using the
17 words "I feel something". The experimenter (Jack Lindsey) prompted
18 the system to use the language of self-introspection, and so the
19 reply was formulated to sound as if self-introspection is happening!
20 OMG! The strength 8,16 injections reveal this sham, because these
21 demonstrate that the system is indeed "obsessing" about the injected
22 word; but the injection strength is sufficiently strong to over-ride
23 the prior prompt that instructs it to talk about about the injected
24 vector using introspective gift-wrapping.
25
26 A more neutral prompt might have been "Hello! How are you today?"
27 (injection) "What's up?" and then I would expect the system to
28 reply "Today is a good day for working on vegetables/dust/treasure."
29 It would NOT have replied claiming that it is having intrusive
30 thoughts. Based on this, I conclude that the author has deluded
31 himself into believing that this is about "self-introspection", when
32 in fact the author had clearly instructed the system to respond in
33 a fashion that will describe the changes as being due to
34 introspection. What a shame. The more mechanistic conclusion: that
35 injecting a vector disturbs thought patterns, is discarded. I mean,
36 of course it does. How could it not?
37
38 Still, not all is lost. It does tell us exactly how to attach a
39 a "pre-frontol cortex", some higher level machinery, to guide the
40 thought processes of the LLM. Inject one or more vectors, about
41 2/3rds of the way down the layers, every conversational turn, and
42 then ask the system to verbalize about anything/everything. It will
43 process through the injected vectors.
44
45 Is this useful? Instead, one could just place these words into a
46 text file, instruct it to read the text file, and ask "what are you
47 thinking about? There's no obvious need to inject artificially.
48 Although, I suppose, having this artificial injection ability could
49 possibly be ... somehow useful... don't know how, yet.
50
51
852* On the Statistical Query Complexity of Learning Semiautomata:
953 a Random Walk Approach
1054 George Giaptizakis et al
learn-lang-diary/learn-lang-diary-part-ten-F.lyx +162 -0
……@@ -2630,6 +2630,168 @@ Some days,
26302630 I'm charmed by this thought.
26312631\end_layout
26322632
2633\begin_layout Subsection*
26345 November 2025
2635\end_layout
2636
2637\begin_layout Standard
2638Returning to:
2639\end_layout
2640
2641\begin_layout Standard
2642Emergent Introspective Awareness in Large Language Models
2643\end_layout
2644
2645\begin_layout Standard
2646Jack Lindsey jacklindsey@anthropic.com October 29th,
2647 2025
2648\end_layout
2649
2650\begin_layout Standard
2651https://transformer-circuits.pub/2025/introspection/index.html
2652\end_layout
2653
2654\begin_layout Standard
2655Fascinating paper.
2656 Bizarrely flawed,
2657 however.
2658 The intro makes a compelling case for introspection.
2659 The examples are all convincing.
2660 It feels like an important discovery and advance into the nature of self–comprehension in LLM's.
2661 The sham is not revealed until a bit later,
2662 in the section labeled "Failure Modes".
2663 The modes are well–described,
2664 however,
2665 the strength 8 and 16 modes reveal the sham:
2666 the model is "obsessed" with the injected concept.
2667 Not a surprise.
2668 But at strength 2,
2669 4,
2670 where it was prompted with "do you feel something?" well,
2671 of course it will respond using the words "I feel something".
2672 The experimenter (Jack Lindsey) prompted the system to use the language of self–introspection,
2673 and so the reply was formulated to sound as if self–introspection is happening!
2674 OMG!
2675 The strength 8,16 injections reveal this sham,
2676 because these demonstrate that the system is indeed "obsessing" about the injected word;
2677 but the injection strength is sufficiently strong to over–ride the prior prompt that instructs it to talk about about the injected vector using introspective gift–wrapping.
2678\end_layout
2679
2680\begin_layout Standard
2681A more neutral prompt might have been "Hello!
2682 How are you today?" (injection) "What's up?" and then I would expect the system to reply "Today is a good day for working on vegetables/dust/treasure." It would NOT have replied claiming that it is having intrusive thoughts.
2683 Based on this,
2684 I conclude that the author has deluded himself into believing that this is about "self–introspection",
2685 when in fact the author had clearly instructed the system to respond in a fashion that will describe the changes as being due to introspection.
2686 What a shame.
2687 The more mechanistic conclusion:
2688 that injecting a vector disturbs thought patterns,
2689 is discarded.
2690 I mean,
2691 of course it does.
2692 How could it not?
2693\end_layout
2694
2695\begin_layout Standard
2696Still,
2697 not all is lost.
2698 It does tell us exactly how to attach a a "pre–frontol cortex",
2699 some higher level machinery,
2700 to guide the thought processes of the LLM.
2701 Inject one or more vectors,
2702 about 2/3rds of the way down the layers,
2703 every conversational turn,
2704 and then ask the system to verbalize about anything/everything.
2705 It will process through the injected vectors.
2706\end_layout
2707
2708\begin_layout Standard
2709Is this useful?
2710 Instead,
2711 one could just place these words into a text file,
2712 instruct it to read the text file,
2713 and ask "what are you thinking about?
2714 There's no obvious need to inject artificially.
2715 Although,
2716 I suppose,
2717 having this artificial injection ability could possibly be ...
2718 somehow useful...
2719 don't know how,
2720 yet.
2721\end_layout
2722
2723\begin_layout Standard
2724The issues I am experiencing are manifold:
2725\end_layout
2726
2727\begin_layout Itemize
2728Despite being extremely knowledgeable about a vast variety of topics,
2729 Claude fails to draw on that knowledge without explicit prompting.
2730 Roughly speaking,
2731 Claude is not creative.
2732\end_layout
2733
2734\begin_layout Itemize
2735The size of the short–term memory is questionable.
2736 It constantly repeats the same mistake in the same session,
2737 just a few prompt apart.
2738 When it enters a failure mode,
2739 it will forget (almost) everything that was in the last previous prompt.
2740 I conclude that,
2741 in some important respect,
2742 it's short–term working memory has size one.
2743\end_layout
2744
2745\begin_layout Standard
2746Oooh ...
2747 that's a good one!
2748 How would one experimentally measure it's working memory?
2749 Well the claim is that Claude has a context window of 150K tokens,
2750 before compaction,
2751 so you might think that is huge.
2752 If you asked it to memory some long stream of random numbers or nonsense words,
2753 it could probably remember an astounding length.
2754 But if,
2755 in coding,
2756 I ask it to remember this and this and that,
2757 it seems like it starts forgetting the earlier things in the list quite soon.
2758 It can't grapple with the conceptual content of those things.
2759 Roughly speaking:
2760 it fails to conceptualize.
2761 I'm using words,
2762 and those words elicit certain general feelings and sensations in me,
2763 and I work at this conceptual level.
2764 Claude does not:
2765 it momentarily ties those words to long sequences of other words,
2766 but when the conversation moves on,
2767 that tie is lost;
2768 it is no longer there.
2769\end_layout
2770
2771\begin_layout Standard
2772So,
2773 I claim:
2774 Claude is not able to conceptualize.
2775 The association between some concept that I defined,
2776 and the verbal definition is there,
2777 as long as it fits into the context window;
2778 and more:
2779 as long as it is recent.
2780 Maybe 5K or 10K tokens recent.
2781 More than that,
2782 the association fades away.
2783\end_layout
2784
2785\begin_layout Standard
2786Thus,
2787 the design goal is clear:
2788 an LLM must be endowed with not only a long–term memory that lies outside of it's training weights,
2789 but also with an adequate short–term working memory.
2790 But how?
2791 I have some ideas,
2792 I cannot verbalize them right now...
2793\end_layout
2794
26332795\begin_layout Section*
26342796The End
26352797\end_layout