LTX-2: Efficient Joint Audio-Visual Foundation Model
Paper
•
2601.03233
•
Published
•
106
•
3
efe
efecelik
AI & ML interests
NLP, LLM
Recent Activity
commented on
a paper
35 minutes ago
LTX-2: Efficient Joint Audio-Visual Foundation Model
upvoted
a
paper
about 1 hour ago
LTX-2: Efficient Joint Audio-Visual Foundation Model
liked
a model
about 13 hours ago
Lightricks/LTX-2
Organizations
commented
a
paper
35 minutes ago
upvoted
a
paper
about 1 hour ago
liked
a
model
about 13 hours ago
replied to
their
post
about 21 hours ago
Thanks for the detailed feedback! You're right that v1 has its quirks and we've experienced the repetition issues too.
Great to hear v1.5 is coming soon. Actually we built a platform called AceSteps using this model(v1). You can create music, mint it as an NFT, tokenize it into tradeable shares, and earn from ad revenue. It's a Farcaster Mini-App on Base Network.
Planning to integrate v1.5 once it drops.
Didn't know they had a Discord server, thanks for the info.
posted
an
update
1 day ago
Post
1886
why ACE-Step model isn't popular that much? imo it makes really good music.
ACE-Step/ACE-Step-v1-3.5B
ACE-Step/ACE-Step-v1-3.5B
-
- 2 replies
liked
a
model
13 days ago
updated
a
Space
14 days ago
published
a
Space
14 days ago
liked
a
model
15 days ago
liked
a
model
21 days ago
this is a very good study. it reminded me of a time a few years ago when i found things like "few shots" and similar things ridiculous; that was a big mistake.
reacted to
Kseniase's
post with ❤️
24 days ago
Post
3695
From Prompt Engineering to Context Engineering: Main Design Patterns
Earlier on, we relied on clever prompt wording, but now structured, complete context matters more than just magic phrasing. The next year is going to be a year of context engineering which expands beyond prompt engineering. The two complement each other: prompt engineering shapes how we ask, while context engineering shapes what the model knows, sees, and can do.
To keep things clear, here are the main techniques and design patterns in both areas, with some useful resources for further exploration:
▪️ 9 Prompt Engineering Techniques (configuring input text)
1. Zero-shot prompting – giving a single instruction without examples. Relies entirely on pretrained knowledge.
2. Few-shot prompting – adding input–output examples to encourage model to show the desired behavior. ⟶ https://arxiv.org/abs/2005.14165
3. Role prompting – assigning a persona or role (e.g. "You are a senior researcher," "Say it as a specialist in healthcare") to shape style and reasoning. ⟶ https://arxiv.org/abs/2403.02756
4. Instruction-based prompting – explicit constraints or guidance, like "think step by step," "use bullet points," "answer in 10 words"
5. Chain-of-Thought (CoT) – encouraging intermediate reasoning traces to improve multi-step reasoning. It can be explicit ("let’s think step by step"), or implicit (demonstrated via examples). ⟶ https://arxiv.org/abs/2201.11903
6. Tree-of-Thought (ToT) – the model explores multiple reasoning paths in parallel, like branches of a tree, instead of following a single chain of thought. ⟶ https://arxiv.org/pdf/2203.11171
7. Reasoning–action prompting (ReAct-style) – prompting the model to interleave reasoning steps with explicit actions and observations. It defines action slots and lets the model generate a sequence of "Thought → Action → Observation" steps. ⟶ https://arxiv.org/abs/2210.03629
Read further ⬇️
Also subscribe to Turing Post: https://www.turingpost.com/subscribe
Earlier on, we relied on clever prompt wording, but now structured, complete context matters more than just magic phrasing. The next year is going to be a year of context engineering which expands beyond prompt engineering. The two complement each other: prompt engineering shapes how we ask, while context engineering shapes what the model knows, sees, and can do.
To keep things clear, here are the main techniques and design patterns in both areas, with some useful resources for further exploration:
▪️ 9 Prompt Engineering Techniques (configuring input text)
1. Zero-shot prompting – giving a single instruction without examples. Relies entirely on pretrained knowledge.
2. Few-shot prompting – adding input–output examples to encourage model to show the desired behavior. ⟶ https://arxiv.org/abs/2005.14165
3. Role prompting – assigning a persona or role (e.g. "You are a senior researcher," "Say it as a specialist in healthcare") to shape style and reasoning. ⟶ https://arxiv.org/abs/2403.02756
4. Instruction-based prompting – explicit constraints or guidance, like "think step by step," "use bullet points," "answer in 10 words"
5. Chain-of-Thought (CoT) – encouraging intermediate reasoning traces to improve multi-step reasoning. It can be explicit ("let’s think step by step"), or implicit (demonstrated via examples). ⟶ https://arxiv.org/abs/2201.11903
6. Tree-of-Thought (ToT) – the model explores multiple reasoning paths in parallel, like branches of a tree, instead of following a single chain of thought. ⟶ https://arxiv.org/pdf/2203.11171
7. Reasoning–action prompting (ReAct-style) – prompting the model to interleave reasoning steps with explicit actions and observations. It defines action slots and lets the model generate a sequence of "Thought → Action → Observation" steps. ⟶ https://arxiv.org/abs/2210.03629
Read further ⬇️
Also subscribe to Turing Post: https://www.turingpost.com/subscribe
-
- 3 replies
liked
a
dataset
about 2 months ago
liked
2
Spaces
about 2 months ago
upvoted
a
collection
2 months ago
updated
a
Space
2 months ago
published
a
Space
2 months ago
liked
a
dataset
2 months ago
biggest gap in open source datasets is high quality, diverse data for ai, especially in scientific reasoning, multilingual, and multimodal domains