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Company: "uc-berkeley"
Google's Agent2Agent Protocol (A2A)
kimi-vl-a3b gpt-4o llama-4-scout llama-4-maverick llama-4-behemoth deepcoder-14b o3-mini o1 llama-3.1-nemotron-ultra-253b deepseek-r1 google google-deepmind moonshot-ai meta-ai-fair uc-berkeley openai nvidia hugging-face togethercompute deepseek agent-interoperability multimodality vision math reinforcement-learning coding model-training open-source model-benchmarking context-windows streaming push-notifications enterprise-authentication model-release reach_vb _akhaliq epochairesearch artificialanlys winglian danielhanchen yuchenj_uw jeremyphoward
Google Cloud Next announcements featured the launch of Google and DeepMind's full MCP support and a new Agent to Agent protocol designed for agent interoperability with multiple partners. The protocol includes components like the Agent Card, Task communication channels, Enterprise Auth and Observability, and Streaming and Push Notification support. On the model front, Moonshot AI released Kimi-VL-A3B, a multimodal model with 128K context and strong vision and math benchmark performance, outperforming gpt-4o. Meta AI introduced smaller versions of llama-4 family models: llama-4-scout and llama-4-maverick, with a larger Behemoth model still in training. DeepCoder 14B from UC Berkeley is an open-source coding model rivaling openai's o3-mini and o1 models, trained with reinforcement learning on 24K coding problems. Nvidia released llama-3.1-nemotron-ultra-253b on Hugging Face, noted for beating llama-4-behemoth and maverick and competing with deepseek-r1.
not much happened today
zonos-v0.1 audiobox-aesthetics moshi sonar llama-3-70b gpt-4o-mini claude-3.5-haiku gpt-4o claude-3.5-sonnet deepseek-r1-distilled-qwen-1.5b reasonflux-32b o1-preview zyphra-ai meta-ai-fair kyutai-labs perplexity-ai cerebras uc-berkeley brilliant-labs google-deepmind text-to-speech speech-to-speech benchmarking model-performance reinforcement-learning math real-time-processing open-source cross-platform-integration multilinguality zero-shot-learning danhendrycks
Zyphra AI launched Zonos-v0.1, a leading open-weight text-to-speech model supporting multiple languages and zero-shot voice cloning. Meta FAIR released the open-source Audiobox Aesthetics model trained on 562 hours of audio data. Kyutai Labs introduced Moshi, a real-time speech-to-speech system with low latency. Perplexity AI announced the Sonar model based on Llama 3.3 70b, outperforming top models like GPT-4o and Claude 3.5 Sonnet with 1200 tokens/second speed, powered by Cerebras infrastructure. UC Berkeley open-sourced a 1.5B model trained with reinforcement learning that beats o1-preview on math tasks. ReasonFlux-32B achieved 91.2% on the MATH benchmark, outperforming OpenAI o1-preview. CrossPoster, an AI agent for cross-platform posting, was released using LlamaIndex workflows. Brilliant Labs integrated the Google DeepMind Gemini Live API into smart glasses for real-time translation and object identification.
OpenAI takes on Gemini's Deep Research
o3 o3-mini-high o3-deep-research-mini openai google-deepmind nyu uc-berkeley hku reinforcement-learning benchmarking inference-speed model-performance reasoning test-time-scaling agent-design sama danhendrycks ethan-mollick dan-shipper
OpenAI released the full version of the o3 agent, with a new Deep Research variant showing significant improvements on the HLE benchmark and achieving SOTA results on GAIA. The release includes an "inference time scaling" chart demonstrating rigorous research, though some criticism arose over public test set results. The agent is noted as "extremely simple" and currently limited to 100 queries/month, with plans for a higher-rate version. Reception has been mostly positive, with some skepticism. Additionally, advances in reinforcement learning were highlighted, including a simple test-time scaling technique called budget forcing that improved reasoning on math competitions by 27%. Researchers from Google DeepMind, NYU, UC Berkeley, and HKU contributed to these findings. The original Gemini Deep Research team will participate in the upcoming AI Engineer NYC event.
DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing
bitnet-b1.58 llama-3.1-nemotron-70b-instruct gpt-4o claude-3.5-sonnet uc-berkeley deepmind openai microsoft nvidia archetype-ai boston-dynamics toyota-research google adobe openai mistral tesla meta-ai-fair model-optimization on-device-ai fine-tuning large-corpus-processing gpu-acceleration frameworks model-benchmarking rohanpaul_ai adcock_brett david-patterson
UC Berkeley's EPIC lab introduces innovative LLM data operators with projects like LOTUS and DocETL, focusing on effective programming and computation over large data corpora. This approach contrasts GPU-rich big labs like Deepmind and OpenAI with GPU-poor compound AI systems. Microsoft open-sourced BitNet b1.58, a 1-bit ternary parameter LLM enabling 4-20x faster training and on-device inference at human reading speeds. Nvidia released Llama-3.1-Nemotron-70B-Instruct, a fine-tuned open-source model outperforming GPT-4o and Claude-3.5-sonnet. These developments highlight advances in model-optimization, on-device-ai, and fine-tuning.