TLDR: Chai Discovery Team introduces Chai-2, a multimodal AI model that enables zero-shot de novo antibody design. Achieving a 16% hit rate across 52 novel targets using ≤20 candidates per target, Chai-2 outperforms prior methods by over 100x and delivers validated binders in under two weeks—eliminating the need for large-scale screening. In a significant advancement…
Recent research indicates that LLMs, particularly smaller ones, frequently struggle with robust reasoning. They tend to perform well on familiar questions but falter when those same problems are slightly altered, such as changing names or numbers, or adding irrelevant but related information. This weakness, known as poor out-of-distribution (OOD) generalization, results in notable accuracy drops,…
Improving the reasoning capabilities of large language models (LLMs) without architectural changes is a core challenge in advancing AI alignment and usability. Researchers at Meta AI and the University of Washington have introduced ASTRO—Autoregressive Search-Taught Reasoner—a novel post-training framework designed to enhance reasoning in Llama-3.1-70B-Instruct. ASTRO is unique in teaching models to perform in-context search,…
Reward models are fundamental components for aligning LLMs with human feedback, yet they face the challenge of reward hacking issues. These models focus on superficial attributes such as response length or formatting rather than identifying true quality indicators like factuality and relevance. This problem arises because standard training objectives fail to differentiate between spurious correlations…
Understanding the Limits of Current Interpretability Tools in LLMs AI models, such as DeepSeek and GPT variants, rely on billions of parameters working together to handle complex reasoning tasks. Despite their capabilities, one major challenge is understanding which parts of their reasoning have the greatest influence on the final output. This is especially crucial for…
TNG Technology Consulting has unveiled DeepSeek-TNG R1T2 Chimera, a new Assembly-of-Experts (AoE) model that blends intelligence and speed through an innovative model merging strategy. Built from three high-performing parent models—R1-0528, R1, and V3-0324—R1T2 demonstrates how expert-layer interpolation at scale can unlock new efficiencies in large language models (LLMs). Assembly-of-Experts: Efficient Model Composition at Scale Traditional…
Introduction: Reinforcement Learning Progress through Chain-of-Thought Prompting LLMs have shown excellent progress in complex reasoning tasks through CoT prompting combined with large-scale reinforcement learning (RL). Models like Deepseek-R1-Zero have shown strong reasoning capabilities by applying RL directly to base models. Similarly, methods such as SimpleRL and Open-ReasonerZero show improvements in smaller models like the Qwen…
Understanding the Role of Chain-of-Thought in LLMs Large language models are increasingly being used to solve complex tasks such as mathematics and scientific reasoning through structured chain-of-thought approaches. These models do not just jump to answers—they reason through intermediate steps that simulate logical thought processes. This technique allows for improved reasoning accuracy and clearer error…
The Need for Cognitive and Adaptive Search Engines Modern search systems are evolving rapidly as the demand for context-aware, adaptive information retrieval grows. With the increasing volume and complexity of user queries, particularly those requiring layered reasoning, systems are no longer limited to simple keyword matching or document ranking. Instead, they aim to mimic the…
Baidu has officially open-sourced its latest ERNIE 4.5 series, a powerful family of foundation models designed for enhanced language understanding, reasoning, and generation. The release includes ten model variants ranging from compact 0.3B dense models to massive Mixture-of-Experts (MoE) architectures, with the largest variant totaling 424B parameters. These models are now freely available to the…