Will there be a successful application of diffusion-like weight modification in LLMs before 2027?
Plus
1
Ṁ12027
50%
chance
1D
1W
1M
ALL
This market resolves YES if a peer-reviewed research paper or major tech company publication (e.g. from Google, Meta, X.AI, OpenAI, or Anthropic) by 1/1/2027 demonstrates successful application of diffusion-like techniques (noising and trained denoising) to directly modify LLM weights usefully toward some safety or capabilities-relevant downstream purpose. Resolution will be based on papers indexed on arXiv.org or published on major AI research blogs.
This question is managed and resolved by Manifold.
Get
1,000
and3.00
Related questions
Related questions
Will we have any progress on the interpretability of State Space Model LLM’s in 2024?
71% chance
Will the best LLM in 2024 have <1 trillion parameters?
30% chance
Will second-order optimizers displace first-order optimizers for training LLMs by 2030?
42% chance
Will the best LLM in 2024 have <500 billion parameters?
15% chance
By 2028 will we be able to identify distinct submodules/algorithms within LLMs?
75% chance
Will top open-weight LLMs in 2025 reason opaquely?
43% chance
Will researchers extract a novel program from the weights of an LLM into a Procedural/OO programming language by 2026?
26% chance
Will reinforcement learning overtake LMs on math before 2028?
67% chance
Will the best LLM in 2027 have <1 trillion parameters?
26% chance
Will LLMs' loss function achieve the level of entropy of human text by the end of 2030?
61% chance