Paper

Researcher tests minimal dynamical system for word embeddings without MLP or attention

A researcher proposes a word representation model using only token vectors, a start state, and two scalars, with no MLP, transformer, attention, or output matrix. The model achieves a SimLex-999 ρ of 0.3616 by updating a state vector via a cosine-based pull toward token attractors, encoding context through trajectory dynamics.

0 engagement·1 source·Fri, Jul 10, 2026, 10:20 PM

The model consists of one learned 256-dimensional vector per vocabulary token, one learned start state, one pull-strength scalar, and one readout-temperature scalar. Each token vector serves as representation, point-attractor, and geometric readout. The state update rule is: h ← h − strength · (1 − cos(h, W)) · normalize(h − W). This trajectory-based encoding achieves a SimLex-999 ρ of 0.3616, demonstrating that useful word representations can be learned without traditional neural network layers.

Entities

dynamical system word representation(concept)SimLex-999(benchmark)point-attractor dynamics(concept)

PaperSat, Jul 11, 2026, 10:23 AM

Article explains how LLMs trigger real-world actions despite being next-token predictors

An article published on July 11, 2026, explains how large language models, which are fundamentally next-token predictors, can trigger real-world actions like fetching weather, running calculators, or searching the web. It addresses the common confusion about how a model trained only to predict the next word can perform tasks it has no direct ability to do.

0 engagement·1 source·rss

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PaperThu, Jul 9, 2026, 05:51 PM

SLORR: Simple and Efficient In-Training Low-Rank Regularization

Researchers introduced SLORR, a simple, stateless, and architecture-preserving framework for in-training low-rank regularization. It avoids SVDs of large weight matrices, additional trainable parameters, and stateful cached quantities, addressing key limitations of existing methods. This could enable more aggressive compression of modern neural networks without significant accuracy loss.

0 engagement·1 source·arxiv

arXiv

PaperSun, Jul 12, 2026, 05:27 AM

Blog post explores why LLMs produce predictable metaphors and how architecture might reduce attractor pull

A blog post titled 'Escaping the Attractor' examines why large language models tend to produce similar metaphors (e.g., 'Time is a River') when prompted, attributing this to attractors in the embedding space. The author suggests that architectural changes could make models less predictable, building on earlier ideas about shared embedding geometries across models.

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CommunitySat, Jul 11, 2026, 11:44 PM

Software engineer publishes final part of LLM-from-scratch series covering inference and decoding

A software engineer published the fourth and final part of a blog series explaining LLMs from the ground up, focusing on token-by-token generation, KV cache, and decoding strategies (temperature, top-k, top-p). The series aims to help other software engineers understand the internals of LLMs.

3 engagement·1 source·reddit

PaperThu, Jul 9, 2026, 05:41 PM

ARDY: Autoregressive Diffusion with Hybrid Representation for Interactive Human Motion Generation

Researchers introduced ARDY, a streaming generation framework that enables real-time synthesis of 3D human motions with text and kinematic control, bridging the gap between offline precision and online speed. The method addresses limitations in existing online approaches regarding controllability and complex text semantics.