DeepSeek V4 PR merged into llama.cpp
A pull request supporting DeepSeek V4 has been merged into the llama.cpp repository, enabling users to run the model locally.
Proposed components for a comprehensive local AI offline backup kit
A Reddit user outlines a comprehensive list of software and models to store offline for maintaining access to local AI capabilities in the event of widespread internet restrictions or bans. The proposed kit focuses on preserving essential tools, operating systems, and model weights to ensure functionality without external dependencies.
Project UCTF: An Open Research Program on Machine-Native AI Training Representations
Project UCTF has been restructured from a single proposal into an open, hypothesis-driven research program to investigate whether machine-native intermediate representations can reduce cross-lingual semantic redundancy in multilingual AI training.
Error Generating Deep RL Course Certificate
A user reports encountering an error while attempting to generate a certificate of completion for the Deep RL course on Hugging Face. The issue persists despite entering the required username and name details, with no existing guidance available online.
DiScoFormer: One transformer for density and score, across distributions
The article introduces DiScoFormer, a unified transformer model capable of performing both density estimation and score-based generation tasks across various data distributions.
Ask an AI expert: What exactly is the full stack?
A Google expert explains the concept of taking a full-stack approach to artificial intelligence. The article highlights that this comprehensive methodology has served as the foundation for Google's AI work for an extended period.
The Latent Bridge: A Continuous Slow-Fast Channel for Real-Time Game Agents
This article introduces a continuous Latent Bridge that couples frozen reactive and reasoning vision-language models to enable real-time game agents with millisecond latency and long-horizon planning. By projecting the slow model's residuals into the fast model's input-embedding space, it avoids text round-trips while matching or beating traditional Text Bridges in performance.
G$^3$VLA: Geometric inductive bias for Vision-Language-Action Models
The authors propose G$^3$VLA, a camera-aware geometric module that injects calibrated structure into the visual-token stream of pretrained Vision-Language-Action models without altering their action space or imitation objective. This approach combines intrinsic-conditioned ray embeddings, projective positional encoding, and bidirectional cross-view fusion to address the mismatch between 2D image coordinates and robot camera geometry.
video-SALMONN-R3: Efficient Video Understanding via Reinforcement Learning
The paper introduces video-SALMONN-R$^3$, an end-to-end video large language model that enables efficient re-watching of video segments through reinforcement learning without relying on chain-of-thought data. This approach addresses the computational and memory constraints that typically force models to use reduced frame rates and spatial resolutions.
Adaptive Machine Learning Framework for UAV Trajectory Optimization in O-RAN
This paper introduces a novel framework for optimizing unmanned aerial vehicle (UAV) trajectories in 6G cellular systems by integrating enhanced continual transfer learning within the O-RAN architecture. The system utilizes a library of pre-trained models and a selection mechanism to minimize adaptation time when operating in dynamic environments.
RetiSEM: Generalising Causal Models for Fragmented Biomedical Data
The authors propose RetiSEM, a domain-constrained structural equation modelling framework designed to recover causal graphs and perform mediation analysis using fragmented biomedical data with limited multimodal resources. The method organizes variables into biologically informed blocks and applies forbidden-edge constraints to decompose pathway-level effects.
Red-Teaming the Agentic Red-Team
This work presents the first in-depth security analysis of widely used agentic systems for offensive security operations, revealing common design flaws that allow adversaries to exfiltrate API keys and compromise operator machines even within sandboxes.
CrossPool: Efficient Multi-LLM Serving for Cold MoE Models through KV-Cache and Weight Disaggregation
CrossPool is a serving engine designed for cold Mixture-of-Experts (MoE) models that disaggregates FFN weights and KV-cache into separate GPU memory pools to address memory inefficiencies in sparse request scenarios. By consolidating static weights and dynamically provisioning active KV-cache demand, the system aims to improve GPU memory utilization and support bursty long-context requests.
HuiHui abliterated model outperforms vanilla 3.6-35B-a3b on math and code
A custom quantization recipe applied to the HuiHui abliterated model demonstrates superior performance compared to the vanilla 3.6-35B-a3b variant in mathematics and coding tasks. The results suggest that removing refusal mechanisms allows the model to achieve greater accuracy and wisdom in these domains.
Amodei: "Open Source Models Will Eat Your Children"
This Reddit post shares an image featuring the quote "Open Source Models Will Eat Your Children" attributed to Amodei. The content consists of a link to the image and a link to the associated comment thread on r/LocalLLaMA.
Anthropic's Amodei: Open Source Models Could Be Dangerous
Dario Amodei, CEO of Anthropic, has expressed concerns that open source AI models could lead to dangerous outcomes. The statement highlights the potential risks associated with unrestricted access to advanced artificial intelligence technologies.
On the Smallness of the Large Language Models Scaling Exponents
The article discusses reasons why the scaling exponents of current Large Language Model applications indicate an unsustainable regime regarding energy resources.
A Fair Evaluation of Graph Foundation Models for Node Property Prediction
This study conducts a rigorous reevaluation of nine recent Graph Foundation Models (GFMs) for node property prediction, comparing them against strong Graph Neural Network (GNN) baselines to address the lack of unified evaluation standards in the field.
RaDaR: A specialized reasoning LLM for accelerating rare disease diagnosis
Researchers present RaDaR, an open-source 32B parameter reasoning large language model designed to accelerate the diagnosis of rare diseases by addressing challenges in clinical deployability and data scarcity. The model was trained on nearly 50,000 public cases and over 100,000 synthetic cases, demonstrating superior performance across benchmarks and external validation centers.
Reinforcement Learning for Computer-Use Agents with Autonomous Evaluation
The authors propose a reinforcement learning fine-tuning framework that utilizes autonomous vision-language evaluation as a scalable supervision signal for GUI agents, eliminating the need for manual labels or task-specific heuristics. By treating evaluator feedback as a noisy binary reward channel and deriving a noise-corrected estimator for Proximal Policy Optimization, the method addresses the difficulty of obtaining machine-readable rewards in open-ended desktop environments.