🔍 LLM DAILY

Your Daily Briefing on Large Language Models

January 16, 2026

HIGHLIGHTS

• Higgsfield, an AI video startup, has secured an $80 million funding extension, reaching a $1.3B valuation with a claimed $200 million annual revenue run rate, signaling strong investor confidence in AI video technology.

• OpenAI has signed a major $10 billion compute deal with Cerebras, representing one of the largest infrastructure partnerships in AI and highlighting the escalating compute requirements for advanced AI development.

• Meta released Segment Anything Model 2 (SAM 2), which extends computer vision capabilities to video segmentation while improving image segmentation, demonstrating significant progress in multimodal AI applications.

• The LOOKAT research introduces an innovative approach that applies vector database compression techniques directly to transformer attention mechanisms, enabling efficient LLM inference on edge devices without sacrificing performance.

• Unsloth's optimization framework for LLM fine-tuning continues to gain traction with its ability to achieve 2x faster training while reducing VRAM consumption by 70%, supporting multiple major model families including OpenAI's open-source models.

BUSINESS

Funding & Investment

Higgsfield Lands $1.3B Valuation

AI video startup Higgsfield, founded by a former Snap executive, has reopened its Series A round and raised an additional $80 million. The company claims to be on a $200 million annual revenue run rate. (TechCrunch, 2026-01-15)

Sequoia Capital Backs AI-Native Legal Platform Sandstone

Sequoia Capital has announced a partnership with Sandstone, an AI-native platform designed for in-house legal teams. (Sequoia Capital, 2026-01-13)

Partnerships & Deals

OpenAI Signs $10B Compute Deal with Cerebras

OpenAI has signed a major deal, reportedly worth $10 billion, to purchase compute resources from Cerebras. According to the companies, this collaboration will help OpenAI's models deliver faster response times for complex or time-consuming tasks. (TechCrunch, 2026-01-14)

Symbolic.ai Signs Deal with News Corp

AI journalism startup Symbolic.ai has signed a deal with Rupert Murdoch's News Corp. The startup claims its AI platform can help optimize editorial processes and research for the media conglomerate. (TechCrunch, 2026-01-15)

OpenAI Invests in Brain-Computer Interface Startup

OpenAI has made an investment in Merge Labs, a brain-computer interface startup founded by OpenAI CEO Sam Altman. (TechCrunch, 2026-01-15)

Regulatory & Policy News

US Imposes 25% Tariff on Nvidia's H200 AI Chips to China

The Trump administration has formalized a 25% tariff on Nvidia's H200 AI chips headed to China, continuing restrictions on advanced semiconductor exports. (TechCrunch, 2026-01-15)

Taiwan to Invest $250B in US Semiconductor Manufacturing

Taiwan has struck a trade deal with the US to invest $250 billion in domestic semiconductor manufacturing, a critical development for AI hardware infrastructure. (TechCrunch, 2026-01-15)

Company Updates

Executive Departures at Thinking Machines

Three top executives have abruptly left Mira Murati's Thinking Machines lab in what appears to be an acrimonious split, highlighting ongoing talent movement between AI labs. (TechCrunch, 2026-01-15)

California AG Launches Probe into xAI's Grok

The California Attorney General has opened a formal investigation into Elon Musk's xAI after reports that its chatbot Grok generated inappropriate images, including nonconsensual sexual images of real people. Musk has denied awareness of the issue. (TechCrunch, 2026-01-14)

PRODUCTS

LTX-2 Generative AI Model Receives Updates

LTX-2 Updates Announcement | LTX Team | (2026-01-15)

The LTX team has released an update to their recently launched LTX-2 generative AI model, responding to overwhelming community engagement since its initial release two weeks ago. The update includes improvements based on user feedback and community-developed optimizations. Users have been actively creating configuration tweaks, sharing workflows, and developing custom LoRAs for the model across platforms like Reddit, Discord, and Civitai. The announcement included a video demonstration showcasing the model's capabilities, and the team has committed to continuous improvement based on user input and real-world application feedback.

Hardware Trend: AI Enthusiasts Upgrading to A100 GPUs for Local LLM Inference

Reddit Discussion | Community Update | (2026-01-16)

A growing trend among AI enthusiasts is the acquisition of professional-grade NVIDIA A100 GPUs for running large language models locally. One Reddit user documented their upgrade path from gaming hardware to dedicated AI infrastructure, starting with a 3080 GPU, moving to a 3090 ($680), and eventually investing in an A100 40GB. This progression highlights the increasing interest in running more powerful AI models locally, with users willing to make significant hardware investments to reduce dependence on cloud-based AI services and gain more control over their AI applications.

TECHNOLOGY

Open Source Projects

Segment Anything Model 2 (SAM 2)

Meta's Segment Anything Model 2 is making waves with a major upgrade to their computer vision segmentation system. SAM 2 extends capabilities to video segmentation while improving image segmentation performance. The repository recently updated its documentation to provide more detailed descriptions of the architecture and capabilities, indicating active development momentum with over 53K GitHub stars.

Unsloth

This optimization framework for LLM fine-tuning is gaining traction (50.7K stars) by enabling 2x faster training with 70% less VRAM consumption. Unsloth supports multiple model families including OpenAI's open-source models, DeepSeek, Qwen, Llama, and Gemma. Recent updates to the project suggest continued development and optimization efforts to improve LLM training efficiency.

Models & Datasets

GLM-Image

A text-to-image diffusion model gaining popularity with nearly 700 likes and over 2,400 downloads. Released under MIT license, GLM-Image supports both English and Chinese text prompts, making it accessible to a broader user base compared to similar models.

AgentCPM-Explore

A conversational model built on Qwen's 4B Thinking model (Qwen3-4B-Thinking-2507), fine-tuned for agent-based interactions. With 290 likes and growing adoption, this Apache-licensed model is compatible with Text Generation Inference (TGI) endpoints, making it deployment-ready.

Qwen3-VL-Embedding Models

Qwen has released multimodal embedding models in both 8B and 2B parameter sizes, derived from their VL-Instruct models. These models (with 252 and 235 likes respectively) specialize in creating embeddings from image-text pairs, enabling powerful retrieval and similarity applications while maintaining compatibility with Hugging Face endpoints.

FineTranslations Dataset

A comprehensive multilingual translation dataset with over 14,000 downloads and 204 likes. The dataset supports an extremely wide range of languages (including many low-resource languages), making it valuable for training translation models with broader language coverage than most existing datasets.

Developer Tools & Spaces

Wan2.2-Animate

A popular Gradio-based interface for image animation with over 4,200 likes. This space provides an accessible way to create animations from still images, demonstrating the growing interest in creative AI applications with intuitive interfaces.

Qwen-Image-Edit-2511-LoRAs-Fast

A specialized image editing space based on Qwen with nearly 400 likes. The space leverages LoRA (Low-Rank Adaptation) technology to provide fast image editing capabilities with Qwen's 2511 model, offering a performance-optimized approach to image manipulation.

Smol Training Playbook

With over 2,800 likes, this educational space provides a comprehensive guide for training smaller, more efficient models. Structured as a research paper with data visualizations, it offers practical insights into efficient model training techniques, making advanced training strategies accessible to more developers.

RESEARCH

Paper of the Day

LOOKAT: Lookup-Optimized Key-Attention for Memory-Efficient Transformers (2026-01-15)
Aryan Karmore

This paper stands out for its innovative approach to addressing a critical bottleneck in deploying LLMs on edge devices. While current KV-cache compression methods focus primarily on storage, they still require dequantizing data to FP16 before attention calculations, creating a bandwidth bottleneck. LOOKAT applies vector database compression techniques to the attention mechanism itself, enabling direct computation on compressed representations.

The author proposes a product quantization approach for attention scoring that eliminates the need for dequantization during inference. Experiments show this reduces both memory usage and computational overhead while maintaining model quality, potentially enabling larger context windows on resource-constrained devices without sacrificing performance.

Notable Research

Breaking Up with Normatively Monolithic Agency with GRACE: A Reason-Based Neuro-Symbolic Architecture for Safe and Ethical AI Alignment (2026-01-15)
Felix Jahn, Yannic Muskalla, Lisa Dargasz, Patrick Schramowski, Kevin Baum

The authors introduce GRACE, a neuro-symbolic containment architecture that decouples normative reasoning from instrumental decision-making, allowing for ethical alignment of AI agents regardless of their underlying design.

DR-Arena: an Automated Evaluation Framework for Deep Research Agents (2026-01-15)
Yiwen Gao, Ruochen Zhao, Yang Deng, Wenxuan Zhang

This paper presents a comprehensive evaluation framework for research agents, with a focus on realistic scientific research tasks and automated evaluation of their reasoning and solution-finding capabilities.

Projected Microbatch Accumulation yields reference-free proximal policy updates for reinforcement learning (2026-01-15)
Nilin Abrahamsen

PROMA introduces an efficient proximal policy update method for LLM fine-tuning that projects out sequence-wise gradient components during microbatch accumulation, providing tighter control of KL divergence without additional passes.

Detecting Winning Arguments with Large Language Models and Persuasion Strategies (2026-01-15)
Tiziano Labruna, Arkadiusz Modzelewski, Giorgio Satta, Giovanni Da San Martino

The research investigates how persuasion strategies affect argument effectiveness, leveraging LLMs to analyze persuasiveness across multiple datasets including Change My View subreddit conversations.

LOOKING AHEAD

As we progress through Q1 2026, the convergence of multimodal reasoning and neuromorphic computing is poised to define the next wave of AI innovation. The recent demonstrations of systems capable of complex causal reasoning across text, vision, and scientific simulations suggest we'll see the first truly general-purpose reasoning engines by Q3. These systems won't just answer questions but will independently formulate hypotheses and design experiments to test them.

Watch for the emerging "cognitive computing" paradigm to challenge traditional LLM architectures by Q4 2026. As computational substrates continue to diversify beyond traditional silicon, we anticipate breakthrough efficiencies in energy consumption—potentially enabling ambient AI in previously impractical settings. The regulatory landscape will need to evolve quickly as these systems begin operating with greater autonomy in critical infrastructure roles.