# We Can, Must, and Will Simulate Nematode Brains

**Advancements in technology now enable the simulation of the nematode _C. elegans_ brain, a task that has eluded scientists for over 25 years, marking a pivotal moment in neuroscience.**

# Launch HN: Augento (YC W25) – Fine-tune your agents with reinforcement learning

**Augento** offers a **fine-tuning platform** for agents using reinforcement learning, enabling users to optimize LLMs by providing a reward function instead of large datasets, enhancing performance with minimal training samples.

# LLM providers on the cusp of an 'extinction' phase as capex realities bite

**Gartner predicts an impending 'extinction' phase for LLM providers** as the market struggles with capital-intensive costs, mirroring the consolidation seen in the cloud sector dominated by AWS, Microsoft Azure, and Google Cloud.

# Show HN: Qwen-2.5-32B is now the best open source OCR model

The **Omni OCR Benchmark** evaluates the **OCR and data extraction** capabilities of various large multimodal models, aiming to provide a comprehensive assessment of **OCR accuracy** across traditional providers and multimodal language models, with all methodologies being **open source**.

# Jargonic: Industry-Tunable ASR Model

**Jargonic** is an **industry-tuned ASR model** that excels in recognizing technical jargon and operates effectively in noisy environments, utilizing advanced **context-aware adaptive learning** for real-time keyword spotting without extensive retraining.

# [R] Proof or Bluff? Evaluating LLMs on 2025 USA Math Olympiad

**Current LLMs** struggle with rigorous mathematical reasoning, achieving less than **5%** accuracy on the 2025 USAMO problems, indicating a significant gap in their capabilities compared to human competitors.

# [P] Developing a open-source (Retrieval Augmented Generation) framework written in C++ with python bindings for high performance

The new **open-source framework** for _Retrieval-Augmented Generation_ (RAG) is being developed in **C++** with **Python bindings**, aiming to enhance **performance**, **speed**, and **resource efficiency** in dynamic environments.

# What, How, Where, and How Well? A Survey on Test-Time Scaling in Large Language Models

**Test-time scaling (TTS)** enhances the problem-solving capabilities of **large language models (LLMs)**, yielding breakthroughs in both specialized tasks like mathematics and general tasks such as open-ended Q&A.

# [D][P] Turning Knowledge Graphs into Memory with Ontologies?

**Integrating ontologies** into the cognee AI memory tool enhances the grounding of **knowledge graphs** by aligning them with external system rules through **RDF + OWL** frameworks.

# [R] Trajectory-Guided Video Motion Segmentation Using DINO Features and SAM2 Prompting

**SAM-Motion** revolutionizes video object segmentation by utilizing **motion patterns** instead of object categories, enabling the segmentation of any moving object through trajectory-based encoding techniques.

# [R] Latent Verification for ~10% Absolute Factual Accuracy Improvement

**Latent Verification** enhances **factual accuracy** in LLMs by approximately **10%** through the integration of **self-verification adapters** that assess and correct hidden states during processing.

# [Project] AxiomGPT – programming with LLMs by defining Oracles in natural language

**AxiomGPT** revolutionizes programming by allowing users to define **Oracles** in natural language, enabling a new paradigm of **latent-space programming** that treats language as an invocation rather than mere instruction.

# [R] DeepFake video detection: Insights into model generalisation — A Systematic review

**DeepFake detection models** face significant challenges in **generalization** across diverse datasets, necessitating innovative strategies to enhance adaptability and performance in real-world applications.

# Better wit than wealth: Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancement

**Dynamic Parametric RAG (DyPRAG)** enhances large language models (LLMs) by embedding documents into model parameters, effectively reducing inference costs while improving knowledge integration at test-time.

# Industrial Ecosystem Adopts Mega NVIDIA Omniverse Blueprint to Train Physical AI in Digital Twins

The **Mega NVIDIA Omniverse Blueprint** enables industrial enterprises to efficiently test and deploy **physical AI** in **digital twins**, enhancing automation and productivity across various operations.