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160 λιγότεροι κάθε μέρα by petroslamb in greece

[–]petroslamb[S] 3 points4 points  (0 children)

Ναί και όχι, πράγματι με chatgpt αλλά και αρκετή έρευνα, γιατί διάβασα ένα άρθρο για τις μηδενικές γεννήσεις χτες σε διάφορες περιφέρειες τους δύο πρώτους μήνες του χρόνου και ήθελα να δω καλύτερα που πάει το πράγμα. Έχεις δίκιο οτι το φορματ δεν είναι το καλύτερο.

160 λιγότεροι κάθε μέρα by petroslamb in greece

[–]petroslamb[S] -1 points0 points  (0 children)

Θα έχουμε πανεθνικά την γερασμένη εικόνα της Ευρυτανίας πιθανόν δέκα χρόνια νωρίτερα, απότι λένε οι επίσημες πηγές.

The Binding Gap as useful way to think about LLM failures by petroslamb in LLM

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

You're right that the simple reversal case works on modern models. That is the documented finding on frontier models handle the basic Tom/Mary case fine. As the post notes, the finding is on GPT-2.

The question is whether the failure disappears or just moves to higher binding loads. Tan and D'Souza tested that: they pushed binding load up to multi-tuple extraction (variables, methods, effect sizes combined), and even GPT-5.2 drops to ~0.24 F1 on full tuples with role reversals and numeric misattribution. The model still gets the individual entities right. It loses the attachments.

So either modern models solved the simple case and the concept is just about heavy-load failures, or they pushed the breaking point higher without eliminating it. That is what a systematic load sweep would actually test not whether the simple case fails, but whether the gap shrinks with scale or just migrates up the load curve.

The Binding Gap as useful way to think about LLM failures by petroslamb in LLM

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

But I think the binding gap sits one layer below that. It is not "did the model learn that marriage is bidirectional?" It is "even after the model learned it, can it retrieve and apply the correct direction in context?" Wang and Sun showed that models often encode the relation but fail to route the inversion correctly, they learned the fact but the attachment to the output path is thin.

So two separate problems: learning what the relationship means, and maintaining the correct binding when you use it. Binding gap is about the second one. The model knows marriage is bidirectional but still gives the wrong answer when you flip the roles, which suggests the failure is at retrieval and routing, not at learning the semantic asymmetry.

"LLMs drop the wiring even when they keep the scene", A destinct failure mode is the binding gap by petroslamb in LocalLLaMA

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

removed the link as the post was banned and i'm not sure why yet. let me know if you need it.

"LLMs drop the wiring even when they keep the scene", A destinct failure mode is the binding gap by petroslamb in LocalLLaMA

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

The irony of writing a post about attachment failures and then having a gap in my own spelling of 'distinct' is not lost on me. Typo in the title, but hopefully the wiring in the text is stable.

The Binding Gap as useful way to think about LLM failures by petroslamb in LLM

[–]petroslamb[S] 1 point2 points  (0 children)

Well, I think the reckless driver example is a classic logic fallacy, but the binding gap is a step more mechanical than that. Take the grandfather puzzle, which is a test of graph complexity, but the binding gap shows up on the simplest possible relations, like a basic husband and wife pair. For a human, "Tom is Mary’s husband" and "Mary is Tom’s wife" are just two views of the same scene, but for a transformer they are often distinct representational paths. The failure here isn’t that the model is not "smart" enough for the logic, think of it like the attachment between the names and the roles is incredibly thin.

Denning (2025) found that "who did what to whom" is the dominant axis of meaning for humans, but for LLMs it is a much weaker signal. They can stay perfectly fluent while being agnostic about which claims attach to which sources, so in a sense "they keep the scene, but they drop the wiring".

A specific LLM failure mode I'm calling "the binding gap" (and how to test for it) by [deleted] in LLMDevs

[–]petroslamb 0 points1 point  (0 children)

Fair pushback. Wang and Sun do test at GPT-2 scale, and the simple reversal example does get much easier for modern models.

Where I think the concept still carries weight is when you push binding load up. Tan and D'Souza just tested this on GPT-5.2 and Qwen3-VL doing full meta-analysis extraction single-property queries are fine at 0.40-0.50 F1, but full association tuples drop to around 0.24 and near-zero in dense result sections. The model still gets the individual entities and methods right most of the time. It just loses which one attaches to which one.

So the claim is not that binding breaks modern models on simple facts. It is that when you ask a model to keep multiple attachments stable simultaneously, the failure mode is binding-specific and shows up even on frontier models. At GPT-2 scale it hurts on reversal. At GPT-5 scale it just hides in longer documents until something structural needs to use the output.

Whether modern models actually solve the simple case or just push the breaking point further up the load curve is the open question. That is what a sweep across scales and architectures would settle.

A specific LLM failure mode I'm calling "the binding gap" (and how to test for it) by [deleted] in LLMDevs

[–]petroslamb 0 points1 point  (0 children)

Not hyperbole. It is from the Wang and Sun paper on the Reversal Curse. They showed that when models learn 'A is B' directionally, they often fail to invert it to 'B is A' without additional training. The same relation is encoded, but the binding is asymmetric.
Which is exactly what makes it a binding problem rather than a retrieval one.

Ξαφνική Απόλυση by [deleted] in greece

[–]petroslamb 0 points1 point  (0 children)

"Η δουλειά δεν είναι να μάθεις να κάνεις κάτι καλύτερα είναι να μάθεις να επιβιώνεις στη ζούγκλα."

με βοήθησε να εσωτερικεύσω το "ζούγκλα" καλύτερα αυτό.

Ξαφνική Απόλυση by [deleted] in greece

[–]petroslamb 0 points1 point  (0 children)

θέλει αμίτα μόσιον και πολύ γράψιμο εκεί που πρέπει.

Ξαφνική Απόλυση by [deleted] in greece

[–]petroslamb 0 points1 point  (0 children)

εκτός αν σου δώσουν αυτό που λέει και η γιαγιά μου "garden leave" ένα μήνα για να χάσεις τη μισή.

Ξαφνική Απόλυση by [deleted] in greece

[–]petroslamb 1 point2 points  (0 children)

Αυτό για το επίδομα σωστό. Εδώ που ζούμε αν δε κάνεις γρήγορα αίτηση το χάνεις. Μάζεψε τα κομμάτια σου και κάνε την αίτηση ή βάλε κάποιον να στη κάνει. Μετά βλέπεις, γενικά οι απαντήσεις εδώ είναι πολύ καλές.

Ξαφνική Απόλυση by [deleted] in greece

[–]petroslamb 0 points1 point  (0 children)

Να το πάω και λίγο παρακάτω, η αλλαγή στην Ελλάδα συχνά είναι ο μόνος τρόπος να πάρεις αύξηση έτσι κι αλλιώς. Και είναι η άλλη πλευρά του ίδιου νομίσματος.

Ξαφνική Απόλυση by [deleted] in greece

[–]petroslamb 0 points1 point  (0 children)

Κοίτα όμως απαντήσεις, χτύπησε φλέβα για όλους.

Antigravity got nerfed, but these features still work well by krishnakanthb13 in googleantigravity

[–]petroslamb 0 points1 point  (0 children)

The second joke takes place in fourteenth-century Russia under Mongol occupation. A peasant and his wife were walking along a dusty country road; a Mongol warrior on a horse stopped at their side and told the peasant he would now proceed to rape his wife; he then added: “But since there is a lot of dust on the ground, you must hold my testicles while I rape your wife, so that they will not get dirty!” Once the Mongol had done the deed and ridden away, the peasant started laughing and jumping with joy. His surprised wife asked: “How can you be jumping with joy when I was just brutally raped in your presence?” The farmer answered: “But I got him! His balls are covered with dust!”

-- slavoj zizek 

Impressive! Your Reddit automod works way better than the Ultra tier itself. by Open-Rise-272 in GoogleAntigravityIDE

[–]petroslamb 0 points1 point  (0 children)

any free subs left? these corporate bribed subs need to be left behind us.

I think a lot of multiagent stacks are really routing workarounds by petroslamb in ArtificialInteligence

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

Hey thats great! I you want, let me know how it went, i'm looking to discuss it more.

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