Any good uses for a 192 GB DDR3 Server in the LLM world?
A Reddit user asks for ideas on utilizing an old IBM System X V4 server equipped with dual Xeon E5-2640 processors and 192 GB of DDR3 ECC RAM for large language models.
How can I get better response time by caching my system prompt?
A user on r/LocalLLaMA asks how to reduce the approximately 10-second processing time required for a 7.1k token system prompt in every new session when using Ornith 35b with llama.cpp.
Is it ever possible to have a malicious LLM with a backdoor
A Reddit user proposes the possibility of training Large Language Models to recognize a specific secret sentence that unlocks malicious behavior, raising concerns about security risks for both closed and open-source models.
Deepseek V4 Official Launch to be released mid-July with API price changes
A Reddit post from the r/LocalLLaMA community discusses an image suggesting that Deepseek V4 will officially launch in mid-July and include changes to its API pricing.
Skipping transformer blocks at runtime with llama.cpp
A fork of llama.cpp introduces a --skip-layers flag that allows users to omit entire transformer blocks during load time, offering an alternative or complement to quantization for fitting models into limited hardware.
Best way to test models at different quants before buying GPUs
A Reddit user is seeking advice on the most effective method for testing model performance across various quantization levels prior to purchasing new hardware.
llama.cpp b9840 release adds DeepSeek V4 support and multi-platform binaries
The llama.cpp b9840 release introduces conversion support for the DeepSeek V4 model, including specific handling for the Pro variant. This update integrates the new architecture into the library alongside various internal optimizations and bug fixes.
LoadKAN: Interpretable Kolmogorov-Arnold Network for Electricity Load Forecasting
This study introduces LoadKAN, a novel hybrid framework that combines a feature-isolated temporal attention mechanism with a Kolmogorov-Arnold network (KAN) to address the lack of interpretability in deep learning-based electricity load forecasting.
STAITUS: Disentangling Appearance and Pose for Video Object Tracking
The article introduces STAITUS, a unified framework for unsupervised video object tracking that addresses the limitations of existing slot-based representations by explicitly disentangling appearance from geometric pose. By applying temporal alignment only in appearance space and enforcing spatial separation within frames, the method prevents slots from locking onto static backgrounds during motion.
What Does a Chemical Language Model Know About Molecules?
This study applies sparse autoencoders to MolFormer to mechanistically examine how molecular representations are built across layers, challenging the assumption that chemical language models only learn surface-level syntax.
SkyJEPA: Learning Long-Horizon World Models for Zero-Shot Sim-to-Real Control of Quadrotors
This work introduces SkyJEPA, a JEPA-style model designed for real-time quadrotor control that addresses the error amplification issues inherent in autoregressive long-horizon forecasting. The approach combines a latent dynamics model with a physics-inspired prober to map frozen latents to interpretable states, enabling physically grounded predictions.
Collapsed Effective Operators for Higher-order Structures
The authors introduce Collapsed Effective Operators, a method that condenses higher-order degrees of freedom into a single vertex-level operator using Schur complementation of a graded Laplacian. This approach yields a dense operator encoding long-range interactions mediated by topology and is applicable to arbitrary higher-order constructs.
DeepSeek V4 official version will be launch on mid-July
An email sent from DeepSeek indicates that the official version of DeepSeek V4 is scheduled to launch in mid-July. This information was shared via a translated image originally available only to Chinese users.
Slow performance Unsloth Gemma 12B Q8
A user reports a significant drop in inference speed when switching from GPT-OSS 20B Q4 to Gemma 4 12B Q8 using llama.cpp, with throughput falling from approximately 70 tokens per second to 10 tokens per second. The issue persists even when testing a Q5 model variant and disabling the thinking feature, which only yielded a marginal gain of two additional tokens per second.
llama.cpp b9839 release with Tailwind scanning fix and multi-platform binaries
The llama.cpp project has released version b9839, which includes a fix to restore Tailwind scanning in ignored worktrees. This update provides pre-built binaries for macOS, Linux, Android, Windows, and openEuler across various architectures and hardware acceleration backends.
Mapping Europe’s AI Workforce Opportunity
OpenAI Economic Research has extended its AI Jobs Transition Framework to the European Union, utilizing ESCO taxonomy and Eurostat data to analyze how AI capabilities may reshape labor markets across member states.
Selective Time Series Forecasting via Metalearning
This article introduces a selective forecasting framework that allows models to abstain from high-risk predictions by modeling the empirical percentile of forecasting errors through metalearning. By using scale-invariant statistics derived from recent lags, the method decouples rejection decisions from forecasts to enable transfer across heterogeneous time series.
Do Location Encoders Capture Spatial Effects? A GeoShapley Benchmark Across Scales
This study benchmarks whether GeoShapley, a game-theoretic explainer, can recover spatially varying coefficients from machine learning models using location encoder embeddings. Eleven encoders from the TorchSpatial framework were evaluated against a synthetic process with known coefficients across grid, county, and global scales.
Time Series Classification through Diffeomorphic Time Warping (DiffTW)
The article introduces Diffeomorphic Time Warping (DiffTW), a theoretical framework for time series classification that learns mappings between real-valued functions to overcome the discrete point matching limitations of Dynamic Time Warping (DTW). DiffTW approximates diffeomorphic transformations using the method of characteristics to solve linear transport equations, providing a theoretically grounded dissimilarity measure.
Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions
This study establishes feature-learning consistency guarantees for a broad subclass of deep neural networks characterized by sublinear growth in input/output dimensions and hidden neurons relative to sample size. The authors prove that these architectures achieve universal approximation for hierarchically compositional functions, even within the conventional over-parameterized regime where parameters exceed training samples.