Neural Networks as Linear Regression: An Introduction for Statisticians
This article introduces neural networks to statisticians by demystifying the field through the lens of linear regression approximation.
Scaling Linear Mode Connectivity and Merging to Billion Parameter Pretrained Transformers
Researchers propose a scalable framework for merging independently trained billion-parameter transformers using linear mode connectivity, addressing scalability limits in existing methods. The approach employs function-preserving weight transformations and a dual learning procedure where both models jointly optimize toward a shared linear interpolation path.
Causal Discovery in the Era of Agents
The article argues against using large language models to infer causal structures, warning that such approaches risk confusing textual associations with genuine causal evidence. Instead, it proposes that agents should only assist the workflow by inspecting data and explaining assumptions, while leaving causal claims grounded in formal algorithms and diagnostics.
User runs Qwen3.6-27B on low-end hardware for construction POCs
A Reddit user demonstrates running the Qwen3.6-27B model quantized to Q3 with KV at Q8 on an AMD Mi50 32GB GPU, achieving approximately 180+ tokens per second for prompt processing and 9 tokens per second for text generation.
NPC Engine Using Local Models
A developer has created a game-agnostic NPC engine backend that leverages small local models to achieve fast response times and decent quality for role-playing games. The system utilizes NVIDIA Parakeet 0.6 for speech-to-text, Gemma 4 26B A4B as the LLM, and Qwen3-TTS for voice synthesis.
Tensor split performance on low-bandwidth (TB3) eGPUs, and a question
A user reports testing tensor split mode with two Morefine G1 4090M 16GB eGPUs connected via Thunderbolt 3 at 40Gbps. While layer split mode yields high token rates for prefill (PP) and text generation (TG), tensor split mode saturates both cards during TG but suffers from poor PP performance due to bandwidth saturation.
Discovering Latent Groups for Robust Classification
The authors propose neural classification trees (NCT), a framework that achieves robustness by encoding subgroup structure within its tree-shaped architecture to address spurious correlations in machine learning models.
Data Selection Through Iterative Self-Filtering for Vision-Language Settings
Researchers propose a novel bootstrapped method called Self-Filtering that trains a CLIP model on an evolving dataset selected through iterative self-filtering. This approach balances filtered, high-probability clean samples with diverse examples from the entire distribution to mitigate noise in large-scale vision-language datasets.
Hedgementation: A Remote Sensing Benchmark for Hedgerow Segmentation
The authors propose Hedgementation, a new benchmark designed to evaluate machine learning models for mapping hedgerows from remote sensing data at a country scale with 10m² spatial resolution. This initiative combines and harmonizes multiple remote sensing products and ground truth labels derived from a French hedgerow inventory.
RECALL: Recovery Experience Collection for Active Lifelong Learning in Vision-Language-Action Models
This paper proposes an active, continual learning paradigm for Vision-Language-Action (VLA) models to address the inefficiencies of passive imitation learning. The authors demonstrate that uncertainty-guided data collection improves fine-tuning efficiency but causes catastrophic forgetting when recovery data is used exclusively.
DiT-Reward: Generative Representations for Text-to-Image Reward Modeling
The article introduces DiT-Reward, a method that converts a pretrained text-to-image Diffusion Transformer into a reward model by processing near-clean image latents and aggregating text-conditioned representations across transformer layers. This approach leverages generative representations to evaluate the quality of generated images without requiring separate training objectives.
Muown Implicitly Performs Angular Step-size Decay
The article demonstrates that Muown's directional update is equivalent to a Riemannian step on normalized directions, where the un-normalized parameterization magnitude modulates the angular step size. This insight explains Muown's step-size stability and motivates the development of AngularMuown, which optimizes directly over normalized directions with an explicit, schedulable angular multiplier.
Learning Process Rewards via Success Visitation Matching for Efficient RL
The authors propose a method to transform inherently sparse outcome rewards in reinforcement learning into dense process rewards by training a discriminator to distinguish between successful and unsuccessful episodes. This approach incentivizes the policy to match the state-action visitations of successful episodes while avoiding those of unsuccessful ones, providing dense feedback on progress without altering the optimal policy.
Hack Your Summer Launches Free Production Sprint for Students
Hack Your Summer is a free, four-week high-velocity production sprint designed for undergraduate students, graduate students, and recent graduates to build tangible, public-facing work. The initiative serves as an alternative to traditional internships amid a crisis of reduced internship availability in the US.
Jon Udell: Human Agent in the loop
Jon Udell argues against the phrase "human in the loop" because it cedes authority to machines, proposing instead that humans should invite agents into their existing workflows as team members.
Neofold, the idle creature-collector with infinite pets thanks to a local diffusion model, released this week
Neofold is an idle creature-collector game that utilizes a local diffusion model to generate an infinite variety of pets. The title was recently released and is available on Steam.
Diffusion Models Adapt to Low-Dimensional Structure Under Flexible Coefficient Choices
This paper demonstrates that diffusion models' ability to exploit low-dimensional structure for accelerated sampling is a robust property independent of specific update coefficient choices. The authors prove that a broad class of coefficients allows generating an ε-accurate sample in O(k/ε) iterations, regardless of ambient dimension.
Dynamic estimation of slowly varying sequences
This article introduces a framework for sequentially approximating functions in slowly-varying sequences, leveraging the reuse of past queries to reduce overall computational cost. The authors present novel sequential estimation results for matrix powers, spectral densities, Monte Carlo integration, and partial differential equation boundary value problems.
Action-BED: Task-Driven Bayesian Experimental Design with Singly Intractable Objectives
The article introduces Action-BED, a new framework for Bayesian experimental design that formulates the problem in terms of expected future loss on downstream actions rather than uncertainty reduction. This approach converts traditionally doubly intractable objectives into singly intractable ones that can be jointly optimized using stochastic gradients.
MAS-PromptBench: When Does Prompt Optimization Improve Multi-Agent LLM Systems?
This study systematically investigates the impact of system-prompt optimization on multi-agent systems (MAS) by benchmarking two optimizers across diverse configurations of tasks, workflows, and team sizes.