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AI Research Brief | ||
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The big picture
LiquidAI released a 24B-parameter MoE model with only 2B active parameters, claiming competitive agentic performance against larger dense models and 1.7x faster on-device inference on Apple M5 Max Meta released Muse Wonder, described as their most capable model class, with independent analysis scoring it equivalent to GPT-6.1 Sol Google released Gemini 4 Argon for robotics Architectural breakthroughs
The authors introduce Tev-Gym, a dataset of 1,170 long-horizon tasks with dense, progressive rewards derived from a latent-world specification, addressing the sparse reward bottleneck in training autonomous agents This framework evaluates self-evolving robotic agents across three modes—code-as-policy, agent-as-policy, and auto-research—using rigorous controls to distinguish genuine learning from repeated attempts or luck The authors propose a unified framework for long-context reasoning that interleaves a "Think-and-Read" mechanism with dense utility rewards based on relative information gain, outperforming sparse outcome-only rewards Hardware & optimization
The autotrust team demonstrates NVFP4 quantization for MoE models, enabling 35B-parameter systems to run in 23 GiB of GPU memory by quantizing routed experts while keeping attention and routers in bf16 This training-free decoding method uses a smaller reasoning model to steer a larger one toward brevity by adding the difference between brief and verbose logit vectors to the large model's logits | ||
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How this issue was made
115 items analyzed → 63 published (54.8% made the cut) · 63 claims fact-checked against their sources · 2 caught and rewritten · rated by an ensemble of 3 local models · See the full trace →
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