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Victor Mustar PRO
victor
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Kseniase's
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2292
**15 Agentic Systems and Frameworks of 2024**
This year, we started our βAI Agents and Agentic Workflowsβ series (https://www.turingpost.com/t/AI-Agents) to explore everything about AI agents step by step: all the vocabulary, how they work, and how to build them.
The huge interest in this series and the large number of studies conducted on agents showed that it was one of the most popular and important themes of the year. In 2025, most likely, agents will reach new highs β we will be covering that for you. Now, letβs review the agentic systems that have emerged this year.
Here is a list of 15 agentic systems and frameworks of 2024:
1. GUI Agents: A Survey (2412.13501)
2. Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level (2411.03562)
3. The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (2408.06292)
4. MALT: Improving Reasoning with Multi-Agent LLM Training (2412.01928)
5. Agent S: An Open Agentic Framework that Uses Computers Like a Human (2410.08164)
6. Automated Design of Agentic Systems (2408.08435)
7. AgentInstruct: Toward Generative Teaching with Agentic Flows (2407.03502)
8. AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant (2410.18603)
9. WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents (2410.07484)
10. Generative Agent Simulations of 1,000 People (2411.10109)
11. DynaSaur: Large Language Agents Beyond Predefined Actions (2411.01747)
12. PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking (2410.12375)
13. Generative World Explorer (2411.11844)
14. Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines (2412.14684)
15. AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions (2410.20424)
Thanks for reading Turing Post!
Subscribe to receive new posts straight into your inbox -> https://www.turingpost.com/subscribe
This year, we started our βAI Agents and Agentic Workflowsβ series (https://www.turingpost.com/t/AI-Agents) to explore everything about AI agents step by step: all the vocabulary, how they work, and how to build them.
The huge interest in this series and the large number of studies conducted on agents showed that it was one of the most popular and important themes of the year. In 2025, most likely, agents will reach new highs β we will be covering that for you. Now, letβs review the agentic systems that have emerged this year.
Here is a list of 15 agentic systems and frameworks of 2024:
1. GUI Agents: A Survey (2412.13501)
2. Large Language Models Orchestrating Structured Reasoning Achieve Kaggle Grandmaster Level (2411.03562)
3. The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery (2408.06292)
4. MALT: Improving Reasoning with Multi-Agent LLM Training (2412.01928)
5. Agent S: An Open Agentic Framework that Uses Computers Like a Human (2410.08164)
6. Automated Design of Agentic Systems (2408.08435)
7. AgentInstruct: Toward Generative Teaching with Agentic Flows (2407.03502)
8. AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant (2410.18603)
9. WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents (2410.07484)
10. Generative Agent Simulations of 1,000 People (2411.10109)
11. DynaSaur: Large Language Agents Beyond Predefined Actions (2411.01747)
12. PRefLexOR: Preference-based Recursive Language Modeling for Exploratory Optimization of Reasoning and Agentic Thinking (2410.12375)
13. Generative World Explorer (2411.11844)
14. Bel Esprit: Multi-Agent Framework for Building AI Model Pipelines (2412.14684)
15. AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions (2410.20424)
Thanks for reading Turing Post!
Subscribe to receive new posts straight into your inbox -> https://www.turingpost.com/subscribe
reacted to
nroggendorff's
post with π
2 days ago
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1262
Can we please do something about this? It makes everything I do so much harder, and because my local machine is so terrible, I am forced to test in production. This makes debugging so difficult.
nroggendorff/system-exit
cc @victor
nroggendorff/system-exit
cc @victor
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anton-l's
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6 days ago
Post
1965
Introducing ππ
π’π§πππππ‘: the best public math pre-training dataset with 50B+ tokens!
HuggingFaceTB/finemath
Math remains challenging for LLMs and by training on FineMath we see considerable gains over other math datasets, especially on GSM8K and MATH.
We build the dataset by:
π οΈ carefully extracting math data from Common Crawl;
π iteratively filtering and recalling high quality math pages using a classifier trained on synthetic annotations to identify math reasoning and deduction.
We conducted a series of ablations comparing the performance of Llama-3.2-3B-Base after continued pre-training on FineMath and observe notable gains compared to the baseline model and other public math datasets.
We hope this helps advance the performance of LLMs on math and reasoning! π
Weβre also releasing all the ablation models as well as the evaluation code.
HuggingFaceTB/finemath-6763fb8f71b6439b653482c2
HuggingFaceTB/finemath
Math remains challenging for LLMs and by training on FineMath we see considerable gains over other math datasets, especially on GSM8K and MATH.
We build the dataset by:
π οΈ carefully extracting math data from Common Crawl;
π iteratively filtering and recalling high quality math pages using a classifier trained on synthetic annotations to identify math reasoning and deduction.
We conducted a series of ablations comparing the performance of Llama-3.2-3B-Base after continued pre-training on FineMath and observe notable gains compared to the baseline model and other public math datasets.
We hope this helps advance the performance of LLMs on math and reasoning! π
Weβre also releasing all the ablation models as well as the evaluation code.
HuggingFaceTB/finemath-6763fb8f71b6439b653482c2
reacted to
m-ric's
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6 days ago
Post
1629
After 6 years, BERT, the workhorse of encoder models, finally gets a replacement: πͺπ²πΉπ°πΌπΊπ² π πΌπ±π²πΏπ»πππ₯π§! π€
We talk a lot about β¨Generative AIβ¨, meaning "Decoder version of the Transformers architecture", but this is only one of the ways to build LLMs: encoder models, that turn a sentence in a vector, are maybe even more widely used in industry than generative models.
The workhorse for this category has been BERT since its release in 2018 (that's prehistory for LLMs).
It's not a fancy 100B parameters supermodel (just a few hundred millions), but it's an excellent workhorse, kind of a Honda Civic for LLMs.
Many applications use BERT-family models - the top models in this category cumulate millions of downloads on the Hub.
β‘οΈ Now a collaboration between Answer.AI and LightOn just introduced BERT's replacement: ModernBERT.
π§π;ππ₯:
ποΈ Architecture changes:
β First, standard modernizations:
- Rotary positional embeddings (RoPE)
- Replace GeLU with GeGLU,
- Use Flash Attention 2
β¨ The team also introduced innovative techniques like alternating attention instead of full attention, and sequence packing to get rid of padding overhead.
π₯ As a result, the model tops the game of encoder models:
It beats previous standard DeBERTaV3 for 1/5th the memory footprint, and runs 4x faster!
Read the blog post π https://huggingface.co/blog/modernbert
We talk a lot about β¨Generative AIβ¨, meaning "Decoder version of the Transformers architecture", but this is only one of the ways to build LLMs: encoder models, that turn a sentence in a vector, are maybe even more widely used in industry than generative models.
The workhorse for this category has been BERT since its release in 2018 (that's prehistory for LLMs).
It's not a fancy 100B parameters supermodel (just a few hundred millions), but it's an excellent workhorse, kind of a Honda Civic for LLMs.
Many applications use BERT-family models - the top models in this category cumulate millions of downloads on the Hub.
β‘οΈ Now a collaboration between Answer.AI and LightOn just introduced BERT's replacement: ModernBERT.
π§π;ππ₯:
ποΈ Architecture changes:
β First, standard modernizations:
- Rotary positional embeddings (RoPE)
- Replace GeLU with GeGLU,
- Use Flash Attention 2
β¨ The team also introduced innovative techniques like alternating attention instead of full attention, and sequence packing to get rid of padding overhead.
π₯ As a result, the model tops the game of encoder models:
It beats previous standard DeBERTaV3 for 1/5th the memory footprint, and runs 4x faster!
Read the blog post π https://huggingface.co/blog/modernbert
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