AI Research

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.

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"

Looped Transformers as Optimizers

arXiv:2609.37379v1 Announce Type: new Abstract: Looped Transformers provide a parameter-efficient approach to depth scaling by repeatedly applying shared Transformer blocks. Recent reasoning models have likewise…

Know Thyself, Teach Thyself: Internal Information Flow for Selective Self-Distillation

arXiv:2609.36695v1 Announce Type: new Abstract: Self-distillation turns knowledge distillation into a closed learning loop and offers a path toward recursive self-improvement. Without an external teacher, however, the…

In-Context Learning Amplifies a Latent Symbolic Circuit

arXiv:2609.36265v1 Announce Type: new Abstract: Large language models can learn abstract rules from just a few in-context examples, but how their internal mechanisms activate as examples accumulate is not well…

GraphVQ: Structure-Aware Autoregressive Decoding over Context-Quantized Graph Tokens

arXiv:2609.37604v1 Announce Type: new Abstract: Graph foundation models need a discrete token representation, but casting a graph as a generatable token sequence faces a structural obstacle: edges spanning beyond the…

SIFARI: Self-Supervised Interferometric Fitting for Astronomical Radio Imaging

arXiv:2609.35966v1 Announce Type: cross Abstract: Radio-interferometric images are reconstructed from sparsely sampled visibilities, and CLEAN-based imaging can struggle with spatial filtering, complex morphologies, and…

Achieving an $O(1/N)$ Optimality Gap in Average-Reward Weakly-Coupled MDPs

arXiv:2609.38132v1 Announce Type: new Abstract: We study average-reward weakly-coupled Markov decision processes (WCMDPs), where a WCMDP consists of $N$ smaller MDPs, called arms, that share multiple per-step budget…

TaskBridge: Bridging Unsupervised Tabular Anomaly Detection and In-Context Learning via Virtual Tasks

arXiv:2609.36968v1 Announce Type: new Abstract: Unsupervised tabular anomaly detection (TAD) aims to identify anomalous rows in tabular data using normal training samples. While conventional methods rely on…

Visual Branch is What You Need for CLIP-based Class-Incremental Learning

arXiv:2609.37888v2 Announce Type: cross Abstract: Class-Incremental Learning (CIL) requires models to recognize new classes over time without forgetting previously learned ones. With the rise of vision-language…

On Trajectory-Aware Training for Masked Diffusion Language Models

arXiv:2609.37974v1 Announce Type: new Abstract: Masked diffusion models (MDMs) generate text by unmasking several tokens per step, but they are trained and sampled under different conditions. The model is trained on…

Think Before You Restore: Risk-Aware Manchu Manuscript Restoration with Stroke-Guided Attention

arXiv:2609.36243v1 Announce Type: cross Abstract: Full-page blind restoration of historical Manchu manuscripts is challenging due to scarce annotations, unknown degradation regions, and fragile connected strokes.…

On the Approximation and Convergence of Distributional Policy Gradient Algorithms for Risk-Sensitive Reinforcement Learning

arXiv:2405.14749v3 Announce Type: replace Abstract: Risk-sensitive reinforcement learning (RL) is crucial for maintaining reliable performance in high-stakes applications. While traditional RL methods aim to learn a…

Sample Complexity of Equivariant Reinforcement Learning

arXiv:2609.36421v1 Announce Type: cross Abstract: Reinforcement learning (RL) is a powerful framework for robotic control, yet its practical application is often hindered by high sample complexity. This is particularly…