3 ms·

I've recently seen this mentioned more and more, both on HN and on reddit. It seems these output patterns are getting worse. It's not just Claude, my impression

I've recently seen this mentioned more and more, both on HN and on reddit. It seems these output patterns are getting worse. It's not just Claude, my impression is that all of the current models have this style issue. Their writing can get borderline incomprehensible. Is there some feedback loop or compounding happening with each model generation? Maybe newer models are ingesting too much AI content? If the ratio of AI generated content in training data is getting higher and higher (because the amount of AI generated content is increasing in general), maybe this is a compounding bias, poisoning the training?

cromka 1mo ago
The going conclusion is they’re getting models ready to talk to other agents, not people.
reliablereason 1mo ago
It's likely/It could be an effect of more reinforcement learning in training compared to earlier. You need loots of RL to learn to code well.
jverce 1mo ago
That's most likely what's happening. SNR will constantly decrease as LLM content is so much quicker and cheaper to generate, which makes it more statistically significant, which will make it more "relevant" for future models. A positive feedback basically.
- kridsdale1 1mo ago
  An audio feedback distortion effect comes to mind.
  - deleted 1mo ago
    [deleted]
  - chrisjj 1mo ago
    Mad cow disease comes to my mind.
    - rimliu 1mo ago
      Let's hope just mind, not brains.
    - jodrellblank 1mo ago
      "Huffing their own farts" comes to mind. Instead of building Artificial Intelligence, they were building Artificial Human Centipede. I'd never considered that the 'Gray Goo' scenario[1] or the Paperclip Maximizer, might be informational and digital rather than physical. LLMs might turn every bit of computer storage into slop (goo) and then cause us to go mad turning the Earth's resources into making more computers (AI datacenters) to hold more goo. The GrAI Goo catastrophy. [1] https://en.wikipedia.org/wiki/Gray_goo https://en.wikipedia.org/wiki/Gray_goo
  - DonHopkins 1mo ago
    Alan Kay: Shannon Gave Us a Way of Dealing with Noisy Channels https://www.youtube.com/watch?v=Cjntrqhn8pk https://www.youtube.com/watch?v=Cjntrqhn8pk Alan Kay performs an improvisational avant garde layered audio feedback loop about Claude Shannon, live online during Kristen Nygaard's 100-year birthday celebration. Alan was scheduled to talk about how encountering Simula sparked his early thoughts about objects. During setup, somebody had the live stream playing out loud near an open Zoom mic, so his own voice kept arriving back in his ears about 21 seconds late, over and over. What he said was not random: "Shannon gave us a way of dealing with noisy channels." And: "I think about that almost every day. I realize what the fuck is going on and it's just so amazing." Shannon's noisy channel coding theorem is the math for exactly the kind of channel that was garbling him as he praised it. Alan joked it was "being rerouted to Mars and back." At the speed of light, a 21 second round trip is about 3 million km one way -- eight trips to the Moon and back, not even a twentieth of the way to Mars at closest approach. This is an accidental Zoom performance of Alvin Lucier's "I Am Sitting in a Room" (1969), where Lucier re-recorded his own voice in a room until only the room's resonance remained. Here what remains is the network: delay, compression, dropouts. Bonus noise: YouTube's auto-transcript bleeps Alan's enthusiasm into [ __ ]. So the full chain is: Alan's voice, Zoom, stream, room, Zoom again times three, my screen recording, YouTube's speech recognizer, a censored transcript. The fix: "Just turn off the audio at your end on Zoom." Kristen Nygaard 100 Years — Celebration Symposium (Aarhus University, Aug 27 2026): https://cs.au.dk/nygaard100years/celebration https://cs.au.dk/nygaard100years/celebration Entire Nygaard Symposium Recording (Alan Kay's talk begins at 3:27:49): https://au.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=fe0c3b34-90d9-4853-94db-b4b3009fd277&start=12469 https://au.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=fe0... Alan Kay: https://en.wikipedia.org/wiki/Alan_Kay https://en.wikipedia.org/wiki/Alan_Kay Claude Shannon: https://en.wikipedia.org/wiki/Claude_Shannon https://en.wikipedia.org/wiki/Claude_Shannon Information Theory: https://en.wikipedia.org/wiki/Information_theory https://en.wikipedia.org/wiki/Information_theory Noisy-Channel Coding Theorem: https://en.wikipedia.org/wiki/Noisy-channel_coding_theorem https://en.wikipedia.org/wiki/Noisy-channel_coding_theorem Audio Feedback: https://en.wikipedia.org/wiki/Audio_feedback https://en.wikipedia.org/wiki/Audio_feedback Video Feedback: https://en.wikipedia.org/wiki/Video_feedback https://en.wikipedia.org/wiki/Video_feedback Space-Time Dynamics in Video Feedback: https://www.youtube.com/watch?v=B4Kn3djJMCE https://www.youtube.com/watch?v=B4Kn3djJMCE Live Looping: The History And The Practice by Stephen Garza: http://computermusic2008.wikidot.com/live-looping:history-and-the-practice http://computermusic2008.wikidot.com/live-looping:history-an... I Am Sitting in a Room: https://en.wikipedia.org/wiki/I_Am_Sitting_in_a_Room https://en.wikipedia.org/wiki/I_Am_Sitting_in_a_Room Alvin Lucier on "I am sitting in a room": https://www.youtube.com/watch?v=v9XJWBZBzq4 https://www.youtube.com/watch?v=v9XJWBZBzq4
orbifold 1mo ago
They are increasingly being trained on generated tasks and even (parts) of the pre-training data is 'distilled' (e.g. Clibmix as an open-source example), so there are many ways in which the vocabulary can seep into the model.
hattmall 1mo ago
We are seeing more of their "thinking". Lowering the refinement of the output to get closer to profitability. The nature of the LLM is that it generates huge amounts of text, then it iterates them down into a compact, hopefully accurate prose. That refinement is the really hard part and computationally costly.
bitwize 1mo ago
I have my own theory: https://news.ycombinator.com/item?id=49476127 https://news.ycombinator.com/item?id=49476127
nmeofthestate 1mo ago
ChatGPT 5.6-Sol via CoPilot is absolutely fine in my experience - no weird LLM-ese.
lucas_t_a 1mo ago
kinda of a conspiracy theory, but maybe this is why anthropic and other companies are looking into fingerprinting now

cromka 1mo ago

The going conclusion is they’re getting models ready to talk to other agents, not people.

reliablereason 1mo ago

It's likely/It could be an effect of more reinforcement learning in training compared to earlier. You need loots of RL to learn to code well.

jverce 1mo ago

That's most likely what's happening. SNR will constantly decrease as LLM content is so much quicker and cheaper to generate, which makes it more statistically significant, which will make it more "relevant" for future models. A positive feedback basically.

kridsdale1 1mo ago
An audio feedback distortion effect comes to mind.
- deleted 1mo ago
  [deleted]
- chrisjj 1mo ago
  Mad cow disease comes to my mind.
  - rimliu 1mo ago
    Let's hope just mind, not brains.
  - jodrellblank 1mo ago
    "Huffing their own farts" comes to mind. Instead of building Artificial Intelligence, they were building Artificial Human Centipede. I'd never considered that the 'Gray Goo' scenario[1] or the Paperclip Maximizer, might be informational and digital rather than physical. LLMs might turn every bit of computer storage into slop (goo) and then cause us to go mad turning the Earth's resources into making more computers (AI datacenters) to hold more goo. The GrAI Goo catastrophy. [1] https://en.wikipedia.org/wiki/Gray_goo https://en.wikipedia.org/wiki/Gray_goo
- DonHopkins 1mo ago
  Alan Kay: Shannon Gave Us a Way of Dealing with Noisy Channels https://www.youtube.com/watch?v=Cjntrqhn8pk https://www.youtube.com/watch?v=Cjntrqhn8pk Alan Kay performs an improvisational avant garde layered audio feedback loop about Claude Shannon, live online during Kristen Nygaard's 100-year birthday celebration. Alan was scheduled to talk about how encountering Simula sparked his early thoughts about objects. During setup, somebody had the live stream playing out loud near an open Zoom mic, so his own voice kept arriving back in his ears about 21 seconds late, over and over. What he said was not random: "Shannon gave us a way of dealing with noisy channels." And: "I think about that almost every day. I realize what the fuck is going on and it's just so amazing." Shannon's noisy channel coding theorem is the math for exactly the kind of channel that was garbling him as he praised it. Alan joked it was "being rerouted to Mars and back." At the speed of light, a 21 second round trip is about 3 million km one way -- eight trips to the Moon and back, not even a twentieth of the way to Mars at closest approach. This is an accidental Zoom performance of Alvin Lucier's "I Am Sitting in a Room" (1969), where Lucier re-recorded his own voice in a room until only the room's resonance remained. Here what remains is the network: delay, compression, dropouts. Bonus noise: YouTube's auto-transcript bleeps Alan's enthusiasm into [ __ ]. So the full chain is: Alan's voice, Zoom, stream, room, Zoom again times three, my screen recording, YouTube's speech recognizer, a censored transcript. The fix: "Just turn off the audio at your end on Zoom." Kristen Nygaard 100 Years — Celebration Symposium (Aarhus University, Aug 27 2026): https://cs.au.dk/nygaard100years/celebration https://cs.au.dk/nygaard100years/celebration Entire Nygaard Symposium Recording (Alan Kay's talk begins at 3:27:49): https://au.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=fe0c3b34-90d9-4853-94db-b4b3009fd277&start=12469 https://au.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=fe0... Alan Kay: https://en.wikipedia.org/wiki/Alan_Kay https://en.wikipedia.org/wiki/Alan_Kay Claude Shannon: https://en.wikipedia.org/wiki/Claude_Shannon https://en.wikipedia.org/wiki/Claude_Shannon Information Theory: https://en.wikipedia.org/wiki/Information_theory https://en.wikipedia.org/wiki/Information_theory Noisy-Channel Coding Theorem: https://en.wikipedia.org/wiki/Noisy-channel_coding_theorem https://en.wikipedia.org/wiki/Noisy-channel_coding_theorem Audio Feedback: https://en.wikipedia.org/wiki/Audio_feedback https://en.wikipedia.org/wiki/Audio_feedback Video Feedback: https://en.wikipedia.org/wiki/Video_feedback https://en.wikipedia.org/wiki/Video_feedback Space-Time Dynamics in Video Feedback: https://www.youtube.com/watch?v=B4Kn3djJMCE https://www.youtube.com/watch?v=B4Kn3djJMCE Live Looping: The History And The Practice by Stephen Garza: http://computermusic2008.wikidot.com/live-looping:history-and-the-practice http://computermusic2008.wikidot.com/live-looping:history-an... I Am Sitting in a Room: https://en.wikipedia.org/wiki/I_Am_Sitting_in_a_Room https://en.wikipedia.org/wiki/I_Am_Sitting_in_a_Room Alvin Lucier on "I am sitting in a room": https://www.youtube.com/watch?v=v9XJWBZBzq4 https://www.youtube.com/watch?v=v9XJWBZBzq4

orbifold 1mo ago

They are increasingly being trained on generated tasks and even (parts) of the pre-training data is 'distilled' (e.g. Clibmix as an open-source example), so there are many ways in which the vocabulary can seep into the model.

hattmall 1mo ago

We are seeing more of their "thinking". Lowering the refinement of the output to get closer to profitability. The nature of the LLM is that it generates huge amounts of text, then it iterates them down into a compact, hopefully accurate prose. That refinement is the really hard part and computationally costly.

bitwize 1mo ago

I have my own theory: https://news.ycombinator.com/item?id=49476127 https://news.ycombinator.com/item?id=49476127

nmeofthestate 1mo ago

ChatGPT 5.6-Sol via CoPilot is absolutely fine in my experience - no weird LLM-ese.

lucas_t_a 1mo ago

kinda of a conspiracy theory, but maybe this is why anthropic and other companies are looking into fingerprinting now