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LLMs & Texto

Papers, modelos e datasets em alta no Hugging Face, além do blog oficial — com leitura editorial em português.

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Whole-Body Conditioned Egocentric Video Prediction
Blog LLMs & Texto

Whole-Body Conditioned Egocentric Video Prediction

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01.07.2025
Researchers from PSU and Duke introduce “Multi-Agent Systems Automated Failure Attribution
Blog LLMs & Texto

Researchers from PSU and Duke introduce “Multi-Agent Systems Automated Failure Attribution

"Automated failure attribution" is a crucial component in the development lifecycle of Multi-Agent systems. It has the potential to transform the challenge of identifying "what went wrong and who is to blame" from a perplexing mystery into a quantifiable and analyzable problem The post Researchers from PSU and Duke introduce “Multi-Agent Systems Automated Failure Attribution first appeared on Synced .

16.06.2025
Adobe Research Unlocking Long-Term Memory in Video World Models with State-Space Models
Blog Dados & Embeddings

Adobe Research Unlocking Long-Term Memory in Video World Models with State-Space Models

By combining State-Space Models (SSMs) for efficient long-range dependency modeling with dense local attention for coherence, and using training strategies like diffusion forcing and frame local attention, researchers from Adobe Research successfully overcome the long-standing challenge of long-term memory in video generation. The post Adobe Research Unlocking Long-Term Memory in Video World Models with State-Space Models first appeared on Synced .

28.05.2025
DeepSeek-V3 New Paper is coming! Unveiling the Secrets of Low-Cost Large Model Training through Hardware-Aware Co-design
Blog LLMs & Texto

DeepSeek-V3 New Paper is coming! Unveiling the Secrets of Low-Cost Large Model Training through Hardware-Aware Co-design

A newly released 14-page technical paper from the team behind DeepSeek-V3, with DeepSeek CEO Wenfeng Liang as a co-author, sheds light on the “Scaling Challenges and Reflections on Hardware for AI Architectures.” The post DeepSeek-V3 New Paper is coming! Unveiling the Secrets of Low-Cost Large Model Training through Hardware-Aware Co-design first appeared on Synced .

15.05.2025
DeepSeek Unveils DeepSeek-Prover-V2: Advancing Neural Theorem Proving with Recursive Proof Search and a New Benchmark
Blog LLMs & Texto

DeepSeek Unveils DeepSeek-Prover-V2: Advancing Neural Theorem Proving with Recursive Proof Search and a New Benchmark

DeepSeek AI releases DeepSeek-Prover-V2, an open-source LLM for Lean 4 theorem proving. It uses recursive proof search with DeepSeek-V3 for training data and reinforcement learning, achieving top results on MiniF2F. The post DeepSeek Unveils DeepSeek-Prover-V2: Advancing Neural Theorem Proving with Recursive Proof Search and a New Benchmark first appeared on Synced .

30.04.2025
Defending against Prompt Injection with Structured Queries (StruQ) and Preference Optimization (SecAlign)
Blog LLMs & Texto

Defending against Prompt Injection with Structured Queries (StruQ) and Preference Optimization (SecAlign)

Recent advances in Large Language Models (LLMs) enable exciting LLM-integrated applications. However, as LLMs have improved, so have the attacks against them. Prompt injection attack is listed as the #1 threat by OWASP to LLM-integrated applications, where an LLM input contains a trusted prompt (instruction) and an untrusted data. The data may contain injected instructions to arbitrarily manipulate the LLM. As an example, to unfairly promote “Restaurant A”, its owner could use prompt injecti...

11.04.2025
Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment
Blog Robótica & RL

Scaling Up Reinforcement Learning for Traffic Smoothing: A 100-AV Highway Deployment

Training Diffusion Models with Reinforcement Learning We deployed 100 reinforcement learning (RL)-controlled cars into rush-hour highway traffic to smooth congestion and reduce fuel consumption for everyone. Our goal is to tackle "stop-and-go" waves , those frustrating slowdowns and speedups that usually have no clear cause but lead to congestion and significant energy waste. To train efficient flow-smoothing controllers, we built fast, data-driven simulations that RL agents interact with, learn...

25.03.2025
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