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.