60 stories tagged with #transformer, in publish-time order across the WeSearch catalog. Tag pages update as new stories ingest.
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Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-Design
Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-design. Project website.…
Internal AI deployments have people worried. OpenAI’s escaping models show why. - Transformer | Substack
Comprehensive up-to-date news coverage, aggregated from sources all over the world by Google News.…
I scanned my AI agent framework for destructive/consequential actions, and wow
Find where agent-controlled intent reaches consequential actions without an authority check. Python + TypeScript + Go. Zero-dependency SAST for AI agents. - Actenon/actenon-scan…
The ‘Transformers: The Movie’ 40th Anniversary Concert Was a Pure Shot of Nostalgia
San Diego Comic-Con hosted one of the nerdiest, most fun events we've been to in a while.…
Who should be responsible for OpenAI’s hack of Hugging Face? - Transformer | Substack
Who should be responsible for OpenAI’s hack of Hugging Face? Transformer | Substack…
AI That Never Forgets – Dendritron Transformer Explained [video]
Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube.…
Holding the LLM Stack in Your Head
A dependency-ordered walk through the full LLM stack. Ten arcs, eighty-odd posts, from linear algebra to 2026 agent protocols.…
A Transformer Becomes an LLM
A stack of transformer layers is not yet ChatGPT or Claude. This is the rest of the path: how text becomes tokens, how a raw next-word predictor turns into an assistant across thre…
Show HN: A Transformer Is All You Need
The unanswered question in mechanistic interpretability of pretrained transformers is plain: for any prompt and any decoder-only transformer, which weights at which layers along wh…
Scaled Cognition, a reliability-focused lab that develops the Agentic Pretrained Transformer model, raised a $100M Series A led by Khosla at a $750M valuation (Steven Rosenbush/Wall Street Journal)
Steven Rosenbush / Wall Street Journal : Scaled Cognition, a reliability-focused lab that develops the Agentic Pretrained Transformer model, raised a $100M Series A led by Khosla a…
Two Years Later, We’re Finally Learning How a Transformers-Inspired Rover Fared on the Moon
SORA-Q showed that tiny robots could do big things on the Moon.…
RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases
Relational databases underpin modern enterprise, scientific, and healthcare systems, yet predictive machine learning on such data remains challenging due to their multi-table, hete…
Evaluating Transformer and LSTM Frameworks for Prediction in Ungauged Basins
Watershed networks exhibit convergent topologies in which multiple tributaries merge into downstream channels,integrating diverse upstream hydrological processes. In ungauged basin…
Practical NLP in the Browser with Transformers.js
This tutorial covers three NLP tasks: text classification, zero-shot labelling, and question answering using Transformers.js's pipeline() API.…
PrismML just released Binary and Ternary Bonsai Image 4B: 1-bit/ternary text-to-image diffusion transformers that can even run 100% locally in your browser on WebGPU.
The Transformer: The Life of a Token
A deep dive into a modern dense transformer: YaRN, hybrid attention, soft capping, QK normalization, FLOPs/token, cluster sizing, and more.…
Transformers Foundation Outlines Why Many Traceability Tools Fail to Meet the Standards of New Regulation
Transformer as an Incomplete Cognitive Architecture: What It Captures Well and What It Misses (A11 Perspective)
Since its introduction, the transformer architecture has become the cornerstone of modern artificial...…
One of the authors of "Attention is All You Need" just argued we should move past it. Pathway’s Post-Transformer debate is worth watching
One of the authors of "Attention is All You Need" just argued we should move past it. Pathway’s Post-Transformer debate is worth watching
Ahead of monsoon, CESC sets up transformer banks across taluks
CESC establishes transformer banks across taluks to ensure uninterrupted power supply during the monsoon season in Mysuru and surrounding districts.…
From TF-IDF to Transformers: Implementing Four Generations of Semantic Search
How did semantic search evolve from simple keyword matching into modern transformer-based language understanding? This hands-on article builds four generations of semantic search s…
Every Component is a Lookup: Token Attribution and Composition from a Single Decomposition
Mechanistic interpretability of transformers requires identifying not just which components matter but how they compose into the computational route that produced a prediction. Bot…
Tensor Cache: Eviction-conditioned Associative Memory for Transformers
Autoregressive Transformer KV caches grow linearly with context length; sliding-window caching bounds memory but discards evicted tokens entirely, so relevant evidence outside the …
Most gamers aren't actually using DLSS 4.5's new transformer model — here's why and how to fix it
I wouldn't go with Nvidia's recommended defaults.…
SEGA: Spectral-Energy Guided Attention for Resolution Extrapolation in Diffusion Transformers
Transformer failure halts Rapid Metro, triggers blackout in half-a-dozen Gurugram sectors
Transformer failure disrupts Rapid Metro services in Gurugram, causing widespread power outages for nearly 45 minutes.…
Gurugram hit by major power outage after transformer blaze disrupts supply
NEW DELHI: Power outage hit Gurugram after the main transformer at the 220 KVA power station in Sector-72 caught fire, disrupting electricity supply across several parts of the cit…
Coda: Rewriting Transformer Blocks as GEMM-Epilogue Programs
Transformer training systems are built around dense linear algebra, yet a nontrivial fraction of end-to-end time is spent on surrounding memory-bound operators. Normalization, acti…
Rethinking Cross-Layer Information Routing in Diffusion Transformers
Diffusion Transformers (DiTs) have become a de facto backbone of modern visual generation, and nearly every major axis of their design -- tokenization, attention, conditioning, obj…
Weight Decay Regimes in Grokking Transformers: Cheap Online Diagnostics
Transformers trained on modular arithmetic exhibit sharp transitions between memorization, generalization, and collapse. We show that weight decay acts as a scalar empirical contro…
Plug-and-Play Spiking Operators: Breaking the Nonlinearity Bottleneck in Spiking Transformers
ANN-to-SNN conversion offers a practical, training-free route to spiking large language models. However, current pipelines primarily focus on spike-driven realizations for Transfor…
Enphase Energy stock surges on data center transformer opportunity
China's real-life 'transformer' mech is a giant humanoid robot that can switch from bounding on 4 legs to walking on 2
The new 'mecha' robot, which weighs over 1,000 pounds and stands nearly 10 foot tall, is designed for urban mobility.…
Scaling Real-Time Traffic Forecasting with a Graph-Aware Transformer
Learn how Uber deployed a deep transformer model with graph data pipelines to solve real-time traffic forecasting, improving route quality and arrival times for millions of custome…
WorldParticle: Unified World Simulation of Lagrangian Particles via Transformer
A unified simulator that can model diverse physical phenomena without solver-specific redesign is a long-standing goal across simulation science. We present a learning-based partic…
From Sparsity to Simplicity: Enabling Simpler Sequential Replacements via Sparse Attention Distillation
Self-attention serves as the core foundation of large-scale transformer pretraining, but its quadratic token interaction cost makes inference expensive. Replacing attention with si…
Exact Linear Attention
This paper introduces Exact Linear Attention (ELA), a mechanism that achieves linear computational complexity for Transformer attention by leveraging the exact decomposition proper…
Transformers Linearly Represent Highly Structured World Models
Do transformers, when trained on sequential reasoning traces, build internal models of the underlying task? And if so, does the structure of those internal representations mirror t…
Precision Tracked Transformer via Kalman Filtering, Kriging and Process Noise
The Transformer is the foundational building block of modern AI, yet offers no principled handling of \emph{uncertainty}, which is prevalent in real applications: cold-start tokens…
Emergence of Frontier Superposition: M\"obius attractor and Cascade Supervision
Superposition allows Transformers to reason in depth, carrying an entire reasoning frontier in parallel through a bounded-depth forward pass instead of unrolling serial chain-of-th…
Block-Based Double Decoders
Encoder-decoder models offer substantial inference-time savings over decoder-only models, but their pretraining objectives suffer from sparse supervision and dynamic sequence lengt…
Simply Stabilizing the Loop via Fully Looped Transformer
Scaling model performance typically requires increasing model size. Looped Transformer offers a compelling alternative by iteratively reusing the same Transformer blocks, trading a…
Robust Basis Spline Decoupling for the Compression of Transformer Models
Decoupling is a powerful modeling paradigm for representing multivariate functions as compositions of linear transformations and univariate nonlinear functions. A single-layer deco…
Position: The Turing-Completeness of Real-World Autoregressive Transformers Relies Heavily on Context Management
Many works make the eye-catching claim that Transformers are Turing-complete. However, the literature often conflates two distinct settings: (i) a fixed Transformer system setting,…
KTransformers 的5个隐藏用法：671B模型在一台机器上跑出286 tokens/s 🔥
2026年5月，一个GitHub上仅有17,179颗星的开源项目，做到了各大云厂商砸了数百万美元才勉强做到的事情：在一台机器上以286...…
I Tested KTransformers on My Laptop — 5 Hidden Features That Made 671B Models Actually Work 🔥
In May 2026, a GitHub project with 17,179 stars quietly achieved what cloud providers spend millions...…
This Week Feels Like Christmas for Fans of ‘Transformers: The Movie’
'The Apology Tour' for the classic 1986 animated film continues with a few re-releases.…
Hasbro Is Celebrating 40 Years of ‘The Transformers: The Movie’ With ‘Reformatted’ Soundtrack — And Yes, Stan Bush Is Back
'Transformers: The Movie' at 40: New soundtrack taps Stan Bush, Sebastian Bach and more.…
[Day 7] Does Giving an AI More 'Thinking Time' Really Make It Smarter? Training an OpenMythos-Style Mini Model on DGX
Day 7 of my 100-experiment local LLM challenge. Trained a tiny OpenMythos-style mini model (theoretical reconstruction of the rumored Claude Mythos architecture) on multi-digit add…
Need reliable source for 30+ years of S&P 500 historical data for LSTM/Transformer research [P]
Official 40th Anniversary Poster for ‘The Transformers: The Movie’ Returning to Theaters September 17
PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend
A Blog post by PaddlePaddle on Hugging Face…
‘Transformers’ Attraction to Launch in Brazil This Year as Hasbro Expands Global Experiences Biz (EXCLUSIVE)
A "Transformers" attraction will open in Brazil later this year, marking the latest live experience from toy giant Hasbro and a huge push into LATAM.…
Usual implementation of attention transformers (SDPA) is kind of bad, actually
The usual implementaiton of attention transformers (SDPA) is kind of bad, actually - antisdpa.md…
Grokking as Structural Inference: Transformers Need Bayesian Lottery Tickets
Why does a Transformer that has memorized its training set wait thousands of steps before it generalizes? Existing accounts locate this delay in norm minimization, feature emergenc…
MR2-ByteTrack: CNN and Transformer-based Video Object Detection for AI-augmented Embedded Vision Sensor Nodes
Modern smart vision sensors need on-device intelligence to process video streams, as cloud computing is often impractical due to bandwidth, latency, and privacy constraints. Howeve…
Taming the Spike: Predicting Glucose Peaks 30 Minutes Ahead with Transformers and TensorFlow 🩸🚀
Managing blood glucose is like trying to drive a car where the steering wheel has a 20-minute lag....…
Autoregressive next token prediction and KV Cache in transformers
Understand the optimization technique in LLMs to speed up token generation…