Autoencoders vs. PCA: I Rigged the Test and PCA Still Won
A theoretical advantage that didn't survive contact with a real benchmark. The post Autoencoders vs. PCA: I Rigged the Test and PCA Still Won appeared first on Towards Data Science.
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AI Research
Can an Apartment Search Agent Call the Model Fewer Times and Still Find Good Matches?
I traced 2,500 listing checks with Weights & Biases Weave, removed avoidable model work one change at a time, and scored every version against the same answers. The post Can an Apartment Search Agent Call the Model…
What the ReLU Revolution Revealed About Biological Plausibility
The activation function was never a fixed biological commitment but a working hypothesis revised under empirical pressure. The post What the ReLU Revolution Revealed About Biological Plausibility appeared first on…
AI in Science, Math: A Monthly Guide to AI-Enabled Progress — September 2026
From genome research and laboratory-tested molecules to mathematical proofs and open scientific datasets, September brought a wide range of AI-enabled progress. This monthly guide covers 56 developments and explains…
a16z State of Markets 2026: AI Became a Capital Cycle
a16z’s 90-page State of Markets report argues AI has escaped the software industry and become a macroeconomic force, reshaping capital spending, debt, labor, power, hardware, and markets.
Your AI Bill Is a Toll Booth. Stop Paying Twice.
The budget nobody saw coming The post Your AI Bill Is a Toll Booth. Stop Paying Twice. appeared first on Towards Data Science.
China Is Regulating How AI Companions Build Emotional Attachment
China is setting rules for how AI companions develop emotional relationships and respond when users try to leave. The requirements address manipulation and risks to minors, while distress monitoring raises questions…
😸 Trump renamed AI "Super Intelligence"
Koa-action: Fast and Consistent Structured Decision Making with Generative LLMs
arXiv:2609.36115v1 Announce Type: new Abstract: Industry applications often demand low-latency classification, yet current large language model (LLM) approaches remain poorly suited for latency-critical applications.…
Data-Free Pruning of Self-Attention Layers in LLMs
arXiv:2512.20636v2 Announce Type: replace Abstract: Many self-attention sublayers in large language models (LLMs) can be removed with little to no loss. We attribute this to the Attention Suppression Hypothesis: during…
Dynamic Regret via Discounted-to-Dynamic Reduction with Applications to Curved Losses and Adam Optimizer
arXiv:2602.08372v2 Announce Type: replace Abstract: We study dynamic regret minimization in non-stationary online learning, with a primary focus on follow-the-regularized-leader (FTRL) methods. FTRL is important for…
Learning from the Gap Between Pass@K and Pass@1
arXiv:2609.35793v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly trained with reinforcement learning from verifiable rewards (RLVR). An exact verifier can also support test-time scaling by…
More Programs or More Rolls? Separating Coverage from Specialization in LLM Harnesses
arXiv:2609.35873v1 Announce Type: cross Abstract: Automated generation of LLM harnesses promises to improve inference through task specialization. Yet additional answer coverage can arise from repeated execution of the…
Context Language Models
arXiv:2609.37725v1 Announce Type: cross Abstract: We introduce Context Language Models (CLMs), language models that natively manage their own context. We implement this by treating the context as a file and allowing the…
Interacting particle guidance for sampling reward-tilted generative priors
arXiv:2609.37227v1 Announce Type: new Abstract: Inference-time steering adapts pretrained diffusion and flow-based models to new tasks, e.g., to generate samples from a conditional distribution or samples with desired…
From Seeds to Semantics: Measuring Semantic Accessibility in Deterministic Diffusion Models
arXiv:2602.06155v2 Announce Type: replace Abstract: Diffusion models generate samples through a sequence of learned denoising steps, and recent work has studied how semantic structure appears along this sampling…
Variational Mixtures and Multi-Marginal Flow Matching: Advancing Statistical Inference with Biological Applications
arXiv:2609.36911v1 Announce Type: new Abstract: In this thesis I develop methods for statistical inference when the distributions arising from complex biological systems are multi-modal, geometrically structured, and…
A Sharp Transition in Data Reconstruction under Differential Privacy
arXiv:2609.37344v1 Announce Type: new Abstract: Data reconstruction attacks have empirically been successful in recovering training samples from learned models, raising privacy concerns and motivating defenses with…
A Model-Agnostic Physics-Guided Adapter for Few-Shot Transfer of Coastal Flood Prediction Models to Unseen Regions
arXiv:2609.37565v1 Announce Type: new Abstract: Deep learning surrogates can produce high-resolution coastal flood maps orders of magnitude faster than physics-based hydrodynamic simulators, yet transferring them to new…
TabKD: Tabular Knowledge Distillation through Interaction Diversity of Learned Feature Bins
arXiv:2603.15481v3 Announce Type: replace Abstract: Data-free knowledge distillation enables model compression without original training data, critical for privacy-sensitive tabular domains. However, existing methods…
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