#autoregression — Public Fediverse posts

🥳🎉 "TiDAR: Think in Diffusion, Talk in Autoregression"—because why just talk to yourself when you can overcomplicate #communication with a sprinkle of computational gobbledygook? Thanks to this paper, we can now awkwardly combine two processes that nobody asked for, giving a whole new meaning to "crossed wires" in the tech world. 🤷‍♂️💻
https://arxiv.org/abs/2511.08923 #TiDAR #ComputationalGobbledygook #CrossedWires #TechInnovation #Autoregression #HackerNews #ngated

🥳🎉 "TiDAR: Think in Diffusion, Talk in Autoregression"—because why just talk to yourself when you can overcomplicate #communication with a sprinkle of computational gobbledygook? Thanks to this paper, we can now awkwardly combine two processes that nobody asked for, giving a whole new meaning to "crossed wires" in the tech world. 🤷‍♂️💻
https://arxiv.org/abs/2511.08923 #TiDAR #ComputationalGobbledygook #CrossedWires #TechInnovation #Autoregression #HackerNews #ngated

🥳🎉 "TiDAR: Think in Diffusion, Talk in Autoregression"—because why just talk to yourself when you can overcomplicate #communication with a sprinkle of computational gobbledygook? Thanks to this paper, we can now awkwardly combine two processes that nobody asked for, giving a whole new meaning to "crossed wires" in the tech world. 🤷‍♂️💻
https://arxiv.org/abs/2511.08923 #TiDAR #ComputationalGobbledygook #CrossedWires #TechInnovation #Autoregression #HackerNews #ngated

🥳🎉 "TiDAR: Think in Diffusion, Talk in Autoregression"—because why just talk to yourself when you can overcomplicate #communication with a sprinkle of computational gobbledygook? Thanks to this paper, we can now awkwardly combine two processes that nobody asked for, giving a whole new meaning to "crossed wires" in the tech world. 🤷‍♂️💻
https://arxiv.org/abs/2511.08923 #TiDAR #ComputationalGobbledygook #CrossedWires #TechInnovation #Autoregression #HackerNews #ngated

TiDAR: Think in Diffusion, Talk in Autoregression

https://arxiv.org/abs/2511.08923

#HackerNews #TiDAR #Diffusion #Autoregression #AI #Research #MachineLearning

TiDAR: Think in Diffusion, Talk in Autoregression

https://arxiv.org/abs/2511.08923

#HackerNews #TiDAR #Diffusion #Autoregression #AI #Research #MachineLearning

TiDAR: Think in Diffusion, Talk in Autoregression

https://arxiv.org/abs/2511.08923

#HackerNews #TiDAR #Diffusion #Autoregression #AI #Research #MachineLearning

TiDAR: Think in Diffusion, Talk in Autoregression

https://arxiv.org/abs/2511.08923

#HackerNews #TiDAR #Diffusion #Autoregression #AI #Research #MachineLearning

TiDAR: Think in Diffusion, Talk in Autoregression

https://arxiv.org/abs/2511.08923

#HackerNews #TiDAR #Diffusion #Autoregression #AI #Research #MachineLearning

#AI #GenerativeAI #LLMs #o1 #OpenAI #Autoregression: "In “Embers of Autoregression” McCoy et al. (2023), we showed that several large language models (LLMs) have some important limitations that are attributable to their origins in next-word prediction. Here we investigate whether these issues persist with o1, a new system from OpenAI that differs from previous LLMs in that it is optimized for reasoning. We find that o1 substantially outperforms previous LLMs in many cases, with particularly large improvements on rare variants of common tasks (e.g., forming acronyms from the second letter of each word in a list, rather than the first letter). Despite these quantitative improvements, however, o1 still displays the same qualitative trends that we observed in previous systems. Specifically, o1—like previous LLMs—is sensitive to the probability of examples and tasks, performing better and requiring fewer “thinking tokens” in high-probability settings than in low-probability ones. These results show that optimizing a language model for reasoning can mitigate but might not fully overcome the language model’s probability sensitivity."

https://arxiv.org/html/2410.01792v1

#AI #GenerativeAI #LLMs #o1 #OpenAI #Autoregression: "In “Embers of Autoregression” McCoy et al. (2023), we showed that several large language models (LLMs) have some important limitations that are attributable to their origins in next-word prediction. Here we investigate whether these issues persist with o1, a new system from OpenAI that differs from previous LLMs in that it is optimized for reasoning. We find that o1 substantially outperforms previous LLMs in many cases, with particularly large improvements on rare variants of common tasks (e.g., forming acronyms from the second letter of each word in a list, rather than the first letter). Despite these quantitative improvements, however, o1 still displays the same qualitative trends that we observed in previous systems. Specifically, o1—like previous LLMs—is sensitive to the probability of examples and tasks, performing better and requiring fewer “thinking tokens” in high-probability settings than in low-probability ones. These results show that optimizing a language model for reasoning can mitigate but might not fully overcome the language model’s probability sensitivity."

https://arxiv.org/html/2410.01792v1

#AI #GenerativeAI #LLMs #o1 #OpenAI #Autoregression: "In “Embers of Autoregression” McCoy et al. (2023), we showed that several large language models (LLMs) have some important limitations that are attributable to their origins in next-word prediction. Here we investigate whether these issues persist with o1, a new system from OpenAI that differs from previous LLMs in that it is optimized for reasoning. We find that o1 substantially outperforms previous LLMs in many cases, with particularly large improvements on rare variants of common tasks (e.g., forming acronyms from the second letter of each word in a list, rather than the first letter). Despite these quantitative improvements, however, o1 still displays the same qualitative trends that we observed in previous systems. Specifically, o1—like previous LLMs—is sensitive to the probability of examples and tasks, performing better and requiring fewer “thinking tokens” in high-probability settings than in low-probability ones. These results show that optimizing a language model for reasoning can mitigate but might not fully overcome the language model’s probability sensitivity."

https://arxiv.org/html/2410.01792v1

#AI #GenerativeAI #LLMs #o1 #OpenAI #Autoregression: "In “Embers of Autoregression” McCoy et al. (2023), we showed that several large language models (LLMs) have some important limitations that are attributable to their origins in next-word prediction. Here we investigate whether these issues persist with o1, a new system from OpenAI that differs from previous LLMs in that it is optimized for reasoning. We find that o1 substantially outperforms previous LLMs in many cases, with particularly large improvements on rare variants of common tasks (e.g., forming acronyms from the second letter of each word in a list, rather than the first letter). Despite these quantitative improvements, however, o1 still displays the same qualitative trends that we observed in previous systems. Specifically, o1—like previous LLMs—is sensitive to the probability of examples and tasks, performing better and requiring fewer “thinking tokens” in high-probability settings than in low-probability ones. These results show that optimizing a language model for reasoning can mitigate but might not fully overcome the language model’s probability sensitivity."

https://arxiv.org/html/2410.01792v1

#AI #GenerativeAI #LLMs #o1 #OpenAI #Autoregression: "In “Embers of Autoregression” McCoy et al. (2023), we showed that several large language models (LLMs) have some important limitations that are attributable to their origins in next-word prediction. Here we investigate whether these issues persist with o1, a new system from OpenAI that differs from previous LLMs in that it is optimized for reasoning. We find that o1 substantially outperforms previous LLMs in many cases, with particularly large improvements on rare variants of common tasks (e.g., forming acronyms from the second letter of each word in a list, rather than the first letter). Despite these quantitative improvements, however, o1 still displays the same qualitative trends that we observed in previous systems. Specifically, o1—like previous LLMs—is sensitive to the probability of examples and tasks, performing better and requiring fewer “thinking tokens” in high-probability settings than in low-probability ones. These results show that optimizing a language model for reasoning can mitigate but might not fully overcome the language model’s probability sensitivity."

https://arxiv.org/html/2410.01792v1

Diffusion Is Spectral Autoregression
https://sander.ai/2024/09/02/spectral-autoregression.html
#ycombinator #diffusion #autoregression #spectrum #spectral_analysis #Fourier_transform #natural_images #deep_learning #generative_models

Diffusion Is Spectral Autoregression
https://sander.ai/2024/09/02/spectral-autoregression.html
#ycombinator #diffusion #autoregression #spectrum #spectral_analysis #Fourier_transform #natural_images #deep_learning #generative_models

Diffusion Is Spectral Autoregression
https://sander.ai/2024/09/02/spectral-autoregression.html
#ycombinator #diffusion #autoregression #spectrum #spectral_analysis #Fourier_transform #natural_images #deep_learning #generative_models

Diffusion Is Spectral Autoregression
https://sander.ai/2024/09/02/spectral-autoregression.html
#ycombinator #diffusion #autoregression #spectrum #spectral_analysis #Fourier_transform #natural_images #deep_learning #generative_models

'Low Tree-Rank Bayesian Vector Autoregression Models', by Leo L Duan, Zeyu Yuwen, George Michailidis, Zhengwu Zhang.

http://jmlr.org/papers/v24/22-0360.html

#autoregression #gibbs #causality

'Low Tree-Rank Bayesian Vector Autoregression Models', by Leo L Duan, Zeyu Yuwen, George Michailidis, Zhengwu Zhang.

http://jmlr.org/papers/v24/22-0360.html

#autoregression #gibbs #causality

'Low Tree-Rank Bayesian Vector Autoregression Models', by Leo L Duan, Zeyu Yuwen, George Michailidis, Zhengwu Zhang.

http://jmlr.org/papers/v24/22-0360.html

#autoregression #gibbs #causality

'Low Tree-Rank Bayesian Vector Autoregression Models', by Leo L Duan, Zeyu Yuwen, George Michailidis, Zhengwu Zhang.

http://jmlr.org/papers/v24/22-0360.html

#autoregression #gibbs #causality

'Low Tree-Rank Bayesian Vector Autoregression Models', by Leo L Duan, Zeyu Yuwen, George Michailidis, Zhengwu Zhang.

http://jmlr.org/papers/v24/22-0360.html

#autoregression #gibbs #causality