LeanPO: Lean Preference Optimization for Likelihood Alignment in Video-LLMs
Paper โข 2506.05260 โข Published
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Jinfaย
authored 4
papers 1 day ago
WaveZip: Wavelet-Driven Space-Time Decoupling for Video Token Condensation
Paper โข 2607.23265 โข Published โข 1
TimePLE: Rethinking Temporal Representation for Video Temporal Grounding
Paper โข 2607.23951 โข Published โข 1
VideoLoop: Looped Working Memory Against Semantic Thrashing in Long-Form Video Agents
Paper โข 2609.38119 โข Published โข 11
alielfilali01ย
posted an update 2 days ago
Post
161
multi-agents orchestration is old news by now. Try multi-teams of agents ! that is a whole different nightmare ... This is the real signal about the sparks of AGI.
Post
140
Much of the misunderstanding on loops and byte-level models comes from fear on inventing the wheel, not reinventing it
Jinfaย
submitted a
paper to Daily Papers 3 days ago
Post
135
How much should OpenAI offer for a name like Dot Labs?
- 1 reply
Doubiiuย
authored 4
papers 6 days ago
WanPE: Towards Cinematic Prompt Enhancement for Modern Text-to-Video Generation
Paper โข 2609.30221 โข Published โข 46
Wan-Image: Pushing the Boundaries of Generative Visual Intelligence
Paper โข 2604.19858 โข Published
ReCA: Multi-Shot Long Video Extrapolation via Recursive Context Allocation
Paper โข 2605.26525 โข Published
OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions
Paper โข 2506.23361 โข Published โข 1
Post
165
Reality has not limits if you know how to shape it, one step at a time.
- 1 reply
Post
84
How old were you when you discovered good AI agents favor the Strategy Design Pattern over any other?
- 1 reply
Post
131
LLMS for edge devices?
What if we go the reliable route instead of the speed route?
What if we make it run on sbcs with few megabytes available?
That's the idea for the next model.
Keep in tune.
What if we go the reliable route instead of the speed route?
What if we make it run on sbcs with few megabytes available?
That's the idea for the next model.
Keep in tune.
Post
141
We trained a 10.9M byte-level recurrent Transformer on L3 and L6. (Loop 3 and Loop 6)
Yet L4/L5 improved too, L8 held up, and the L3โL6 gain grew during training.
Same weights. More compute. Better predictions.
This is a new architecture for effective compute after several steps beyond original training!
We mixed and matched components like time and mhc into an ouro-like byte-level language model and the result is BET, a byte-level step-elastic transformer that can run computation steps without significant degradation.
One of the coolest parts of this training was discovering how Gradient Descent decided to use the first layer as what we would consider a scratchpad! Totally destroyed for the decoder but somehow makes total sense for the next layer!
I believe looped-transformers are the future of edge computing and this is a first step towards it.
Blogpost: https://medium.com/@appvoidofficial/byte-level-elasticity-182fe2ed1d2f
appvoid/bet-10m
Yet L4/L5 improved too, L8 held up, and the L3โL6 gain grew during training.
Same weights. More compute. Better predictions.
This is a new architecture for effective compute after several steps beyond original training!
We mixed and matched components like time and mhc into an ouro-like byte-level language model and the result is BET, a byte-level step-elastic transformer that can run computation steps without significant degradation.
One of the coolest parts of this training was discovering how Gradient Descent decided to use the first layer as what we would consider a scratchpad! Totally destroyed for the decoder but somehow makes total sense for the next layer!
I believe looped-transformers are the future of edge computing and this is a first step towards it.
Blogpost: https://medium.com/@appvoidofficial/byte-level-elasticity-182fe2ed1d2f
appvoid/bet-10m
indiejosephย
authored a
paper 23 days ago
Post
3478
I made a 1.1M ModernBERT encoder play Doom in real time on a CPU
Some time ago, VAGO Solutions released SauerkrautLM-Doom-MultiVec-1.3M, a tiny model trained to play Doom Defend the Center scenario from 31k human gameplay examples.
My first thought: cool! I love both Doom and Small Language Models.
Then another idea: I bet I can do better :-)
What I did?
- evaluated the original model and found it's better than reported
- changed a bit the architecture
- generated SFT data with a scripted oracle
- SFT + PPO refinement on consumer hardware
Got a smaller, faster and killer model
Can even fit a floppy with int8 quantization ๐พ
Watch it play/read the article: anakin87/tiny-doom-defender
Some time ago, VAGO Solutions released SauerkrautLM-Doom-MultiVec-1.3M, a tiny model trained to play Doom Defend the Center scenario from 31k human gameplay examples.
My first thought: cool! I love both Doom and Small Language Models.
Then another idea: I bet I can do better :-)
What I did?
- evaluated the original model and found it's better than reported
- changed a bit the architecture
- generated SFT data with a scripted oracle
- SFT + PPO refinement on consumer hardware
Got a smaller, faster and killer model
Can even fit a floppy with int8 quantization ๐พ
Watch it play/read the article: anakin87/tiny-doom-defender
Post
132
Any thoughts on Nvidia acquiring this website?
I don't know what to feel about it. But would be great if huggingface gets something similar to Kaggle with free GPU hours (or even days) for training.
I don't know what to feel about it. But would be great if huggingface gets something similar to Kaggle with free GPU hours (or even days) for training.
- 3 replies