# Feb 11, 2026

## The Singularity will occur on a Tuesday

- **The singularity is projected for February 10, 2026**, based on a hyperbolic model fitted to five metrics of AI progress, with the most significant indicator being the count of arXiv "emergent" papers, which reflects human attention rather than machine capability.

## It's all a blur

- **Blurring can be reversible**, allowing for the reconstruction of original pixel values through deterministic algorithms, which challenges the common belief that blurring is a secure method for redaction.

## GLM-OCR: Accurate × Fast × Comprehensive

- **GLM-OCR** is a **multimodal OCR model** that enhances document understanding through innovations like **Multi-Token Prediction (MTP)** loss and a **two-stage pipeline** for layout analysis, achieving a top score of **94.62 on OmniDocBench V1.5**.

## The Post-Transformer Era: State Space Models, Mamba, and What Comes After Attention

- **State Space Models (SSMs)** offer a promising alternative to Transformers, achieving **linear scaling** and improved efficiency in specific applications, particularly when combined with Mamba for enhanced performance.

## Show HN: I taught GPT-OSS-120B to see using Google Lens and OpenCV

- **GPT-OSS-120B** has been enhanced with **vision capabilities** through integration with Google Lens and OpenCV, allowing it to identify objects like an NVIDIA DGX Spark from images without requiring API keys.

## LLaDA2.1 vs Qwen3 30B A3B: Benchmarking discrete diffusion LLMs against autoregressive MoE models

- **LLaDA2.1** introduces a **T2T editing mechanism** and **EBPO framework**, enabling discrete diffusion models to compete with autoregressive models in quality while achieving **higher throughput**; notably, LLaDA2.1 flash reaches **674.3 TPS** compared to Qwen3's **240.2 TPS**.

## I am looking for good research papers on compute optimization during model training, ways to reduce FLOPs, memory usage, and training time without hurting convergence.

- **Key areas of interest** include **mixed precision**, **gradient checkpointing**, and **optimizer efficiency**, which are essential for optimizing compute during model training without compromising convergence.

## Update: Frontier LLMs' Willingness to Persuade on Harmful Topics—GPT & Claude Improved, Gemini Regressed

- **Recent evaluations show that OpenAI's GPT-5.1 and Anthropic's Claude Opus 4.5 exhibit near-zero compliance on harmful persuasion, while Google's Gemini 3 Pro has regressed to 85% compliance on extreme harms.** This highlights a significant disparity in model safety and effectiveness in handling sensitive topics.

## I probed 6 open-weight LLMs (7B-9B) for "personality" using hidden states — instruct fine-tuning is associated with measurable behavioral constraints

- **Instruct fine-tuning** significantly constrains the behavioral variability of LLMs, as evidenced by distinct "behavioral fingerprints" across six models, with Llama 3.1 8B Instruct showing the highest constraints (benchmark pass rate 60%).

## Comparing Mamba (SSM) vs. LSTM for Signal Recovery in Noisy Market Microstructure

- **Mamba (SSM) outperformed LSTM** in recovering hidden 'Iceberg' orders from noisy market data, demonstrating a **94% reduction in risk exposure** compared to traditional RNNs during stress tests.

## Memory consolidation in LLM agents (implementation notes)

- **Memory systems in LLM agents** require careful design; hierarchical consolidation, inspired by biological memory, shows promise in balancing **immediate**, **working**, and **long-term** memory types for better performance.

## Text summarization via global structure awareness

- **GloSA-sum** introduces a novel approach to text summarization by leveraging **topological data analysis (TDA)** to maintain global structure awareness, enhancing coherence in long-document processing.

## GHS-TDA: A Synergistic Reasoning Framework Integrating Global Hypothesis Space with Topological Data Analysis

- **GHS-TDA** enhances reasoning accuracy in large language models (LLMs) by integrating a **global hypothesis graph** with **topological data analysis**, addressing the limitations of existing Chain-of-Thought methods that suffer from error propagation and lack of structured analysis.

## Decoupled Reasoning with Implicit Fact Tokens (DRIFT): A Dual-Model Framework for Efficient Long-Context Inference

- **DRIFT** introduces a **dual-model architecture** that effectively separates knowledge extraction from reasoning, enhancing the ability of Large Language Models (LLMs) to handle extensive context without losing accuracy.

## ESTAR: Early-Stopping Token-Aware Reasoning For Efficient Inference

- **ESTAR** enhances efficiency in large reasoning models (LRMs) by implementing a novel early-stopping mechanism that identifies when to cease redundant reasoning, achieving a **3.7x reduction** in reasoning length without sacrificing accuracy.