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yuchenlin  authored a paper 3 days ago
ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning
JustinLin610  authored a paper 9 days ago
Qwen2.5-1M Technical Report
JustinLin610  authored a paper 10 days ago
RealCritic: Towards Effectiveness-Driven Evaluation of Language Model Critiques
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victor 
posted an update 2 days ago
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3263
Hey everyone, we've given https://hf.co/spaces page a fresh update!

Smart Search: Now just type what you want to do—like "make a viral meme" or "generate music"—and our search gets it.

New Categories: Check out the cool new filter bar with icons to help you pick a category fast.

Redesigned Space Cards: Reworked a bit to really show off the app descriptions, so you know what each Space does at a glance.

Random Prompt: Need ideas? Hit the dice button for a burst of inspiration.

We’d love to hear what you think—drop us some feedback plz!
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KnutJaegersberg 
posted an update 7 days ago
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929
Anthropomorphic reasoning about neuromorphic AGI safety

Summary of "Anthropomorphic Reasoning About Neuromorphic AGI Safety"
This paper explores safety strategies for neuromorphic artificial general intelligence (AGI), defined as systems designed by reverse-engineering essential computations of the human brain. Key arguments and proposals include:

1. Anthropomorphic Reasoning Validity:
- Neuromorphic AGI’s design and assessment rely on human cognition models, making anthropomorphic reasoning (using human-like traits) critical for safety analysis. Comparisons to human behavior and neural mechanisms provide insights into AGI behavior and risks.

2. Countering Safety Criticisms:
- The authors challenge claims that neuromorphic AGI is inherently more dangerous than other AGI approaches. They argue all AGI systems face intractable verification challenges (e.g., real-world unpredictability, incomputable action validation). Neuromorphic AGI may even offer safety advantages by enabling comparisons to human cognitive processes.

3. Motivational Architecture:
- Basic drives (e.g., curiosity, social interaction) are essential for cognitive development and safety. These pre-conceptual, hardwired drives (analogous to human hunger or affiliation) shape learning and behavior. The orthogonality thesis (intelligence and goals as independent) is contested, as neuromorphic AGI’s drives likely intertwine with its cognitive architecture.

4. Safety Strategies:
- **Social Drives**: Embedding drives like caregiving, affiliation, and cooperation ensures AGI develops prosocial values through human interaction.
- **Bounded Reward Systems**: Human-like satiation mechanisms (e.g., diminishing rewards after fulfillment) prevent extreme behaviors (e.g., paperclip maximization).
- **Developmental Environment**: Exposure to diverse, positive human interactions and moral examples fosters

https://ccnlab.org/papers/JilkHerdReadEtAl17.pdf
victor 
posted an update 10 days ago
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2950
Finally, an open-source AI that turns your lyrics into full songs is here—meet YuE! Unlike other tools that only create short clips, YuE can make entire songs (up to 5 minutes) with vocals, melody, and instruments all working together. Letsss go!

m-a-p/YuE-s1-7B-anneal-en-cot
KnutJaegersberg 
posted an update 10 days ago
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1852
Evolution and The Knightian Blindspot of Machine Learning


The paper discusses machine learning's limitations in addressing Knightian Uncertainty (KU), highlighting the fragility of models like reinforcement learning (RL) in unpredictable, open-world environments. KU refers to uncertainty that can't be quantified or predicted, a challenge that RL fails to handle due to its reliance on fixed data distributions and limited formalisms.


### Key Approaches:

1. **Artificial Life (ALife):** Simulating diverse, evolving systems to generate adaptability, mimicking biological evolution's robustness to unpredictable environments.

2. **Open-Endedness:** Creating AI systems capable of continuous innovation and adaptation, drawing inspiration from human creativity and scientific discovery.

3. **Revising RL Formalisms:** Modifying reinforcement learning (RL) models to handle dynamic, open-world environments by integrating more flexible assumptions and evolutionary strategies.

These approaches aim to address ML’s limitations in real-world uncertainty and move toward more adaptive, general intelligence.

https://arxiv.org/abs/2501.13075
KnutJaegersberg 
posted an update 13 days ago
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2059
Artificial Kuramoto Oscillatory Neurons

Artificial Kuramoto Oscillatory Neurons (AKOrN) differ from traditional artificial neurons by oscillating, rather than just turning on or off. Each neuron is represented by a rotating vector on a sphere, influenced by its connections to other neurons. This behavior is based on the Kuramoto model, which describes how oscillators (like neurons) tend to synchronize, similar to pendulums swinging in unison.

Key points:

Oscillating Neurons: Each AKOrN’s rotation is influenced by its connections, and they try to synchronize or oppose each other.
Synchronization: When neurons synchronize, they "bind," allowing the network to represent complex concepts (e.g., "a blue square toy") by compressing information.
Updating Mechanism: Neurons update their rotations based on connected neurons, input stimuli, and their natural frequency, using a Kuramoto update formula.
Network Structure: AKOrNs can be used in various network layers, with iterative blocks combining Kuramoto layers and feature extraction modules.
Reasoning: This model can perform reasoning tasks, like solving Sudoku puzzles, by adjusting neuron interactions.
Advantages: AKOrNs offer robust feature binding, reasoning capabilities, resistance to adversarial data, and well-calibrated uncertainty estimation.
In summary, AKOrN's oscillatory neurons and synchronization mechanisms enable the network to learn, reason, and handle complex tasks like image classification and object discovery with enhanced robustness and flexibility.

yt
https://www.youtube.com/watch?v=i3fRf6fb9ZM
paper
https://arxiv.org/html/2410.13821v1
  • 2 replies
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KnutJaegersberg 
posted an update 14 days ago
KnutJaegersberg 
posted an update 17 days ago
KnutJaegersberg 
posted an update 18 days ago
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1770
Understanding and Benchmarking Artificial Intelligence: OpenAI's o3 Is Not AGI

It's an interesting paper that argues "new approaches are required that can reliably solve a wide variety of problems without existing skills."
"It is therefore hoped that the benchmark outlined in this article contributes to further exploration of this direction of research and incentivises the development of new AGI approaches that focus on intelligence rather than skills."

https://arxiv.org/abs/2501.07458
thughost 
authored a paper 24 days ago
KnutJaegersberg 
posted an update 25 days ago
BrigitteTousi 
posted an update 28 days ago
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1141
Community fine-tuned models are more carbon efficient than the models they are derived from! 🥳🌿

@alozowski @clefourrier @SaylorTwift @albertvillanova evaluated CO₂ emissions associated with model inference for over 3000 models on the Open LLM Leaderboard. Interesting trends and new insights emerged...👀

Blog Post: https://huggingface.co/blog/leaderboard-emissions-analysis

Leaderboard: open-llm-leaderboard/open_llm_leaderboard
soldni 
authored 2 papers about 2 months ago
KnutJaegersberg 
posted an update about 2 months ago
s3nh 
posted an update about 2 months ago
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1868
Welcome back,

Small Language Models Enthusiasts and GPU Poor oss enjoyers lets connect.
Just created an organization which main target is to have fun with smaller models tuneable on consumer range GPUs, feel free to join and lets have some fun, much love ;3

https://huggingface.co/SmolTuners
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freddyaboulton 
posted an update about 2 months ago
BlinkDL 
posted an update about 2 months ago
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3122
RWKV-7 "Goose" 0.4B trained w/ ctx4k automatically extrapolates to ctx32k+, and perfectly solves NIAH ctx16k 🤯 100% RNN and attention-free. Only trained on the Pile. No finetuning. Replicable training runs. tested by our community: https://github.com/Jellyfish042/LongMamba
freddyaboulton 
posted an update about 2 months ago
celinah 
posted an update about 2 months ago
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634
🚀 We've just dropped a new release v0.27.0 of the 𝚑𝚞𝚐𝚐𝚒𝚗𝚐𝚏𝚊𝚌𝚎_𝚑𝚞𝚋 Python library!

This release includes:
- 💾 New torch model loading utilities in the serialization module — providing a standardized way to save and load torch models with built-in support for sharding and safe serialization.
- 📦 Tooling for something exciting — if you like single-file formats for models like GGUF, you'll love what we're cooking up 👀 More coming soon!
- 🛠️ Loads of quality-of-life improvements and bug fixes!

release notes and full details here 👇
Wauplin/huggingface_hub#10

$ pip install -U huggingface_hub
lucifertrj 
posted an update about 2 months ago
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527
Image Prompt Engineering Guide:
➡️ Artistic styling for Image generation
➡️ Prompt weighting using the parentheses method to generate realistic images.
➡️ Advanced features like style and positioning control[experimental].
➡️ Image placement on the generated AI image using Recraft V3 Mockup.

Watch: https://www.youtube.com/watch?v=d3nUG28-jIc