The daily ledger

Empowering frontline maternal care with AI-assisted ultrasound

Health & Bioscience

😸 WATCH: Can Living Neurons Make AI 5x Faster?

Decision Models in Action: Build a Hybrid LLM Workflow

A hands-on guide to building browser automation by combining decision models with LLMs. The post Decision Models in Action: Build a Hybrid LLM Workflow appeared first on Towards Data Science.

How to Turn Long-Running Python Simulations Into a Self-Service Platform

What I learned about queues, failure recovery, and reproducibility while building asynchronous workloads on AWS The post How to Turn Long-Running Python Simulations Into a Self-Service Platform appeared first on Towards…

Spatial Reasoning in AI Models, Taken Apart

Grid puzzles that people solve almost perfectly show where language and vision models still break The post Spatial Reasoning in AI Models, Taken Apart appeared first on Towards Data Science.

I Built a Pipeline to Track the Job Market I'm Trying to Break Into.

Building an incremental pipeline with dbt snapshots, and the API that quietly lied to me along the way. The post I Built a Pipeline to Track the Job Market I'm Trying to Break Into. appeared first on Towards Data…

😺 Meta’s Muse made shared chats searchable

Everything That Happened in AI Today (Wednesday, October 21, 2026)

OpenAI bought Orchestra Bio to push AI deeper into drug development; Anthropic proposed a federal safety bill; Claude-designed toxin binders moved into lab testing; frontier AI compute costs surged.

Could AI Models Be Easier to Understand Than Scientists Thought?

A neuron that responds to cats and cars may recognize a shared visual pattern. Goodfire’s research challenges assumptions about AI interpretability, but explaining entire systems remains an open problem.

Who Should Have the Power to Stop a Dangerous AI Model?

Anthropic’s proposed federal AI safety bill would give Commerce a route to seek court-ordered model suspensions. Its catastrophic-risk thresholds raise questions about earlier intervention and the independence of…

Training-Free Continual Learning for Multimodal Forecasting via Prompt Evolution

arXiv:2610.27468v1 Announce Type: new Abstract: Adapting a forecasting system to a changing world classically means retraining. We ask whether the task of continually learning regimes for time series forecasting can be…

LEAF: LUT-aware Efficient Attention on FPGAs

arXiv:2610.27078v1 Announce Type: new Abstract: We introduce Lookup-table (LUT)-aware Efficient Attention on FPGAs (LEAF). LEAF is the first nanosecond-scale LUT-aware transformer for FPGAs. Deploying transformers that…

MemT-1.0: Towards Memory-Native Evolution

arXiv:2610.26825v2 Announce Type: replace-cross Abstract: Memory-native evolution requires making memory an intrinsic capability of foundation models, enabling them to continuously accumulate and utilize information. To…

ClockMark: Adaptive and Host-Dependent Watermarking for Large Language Models

arXiv:2610.27079v1 Announce Type: new Abstract: Large language model (LLM) watermarking operates on the next-token distribution that evolves during generation. In streaming generation, each sampled token becomes part of…

Beyond the Point Mass: Acceptance-Derived Weights and Distributional Losses for Speculative Decoding

arXiv:2610.28355v1 Announce Type: new Abstract: In speculative decoding, a lightweight draft model proposes tokens that the target model verifies in parallel, speeding up generation without changing the target…

The price of multi-group transductive learning

arXiv:2606.04423v2 Announce Type: replace Abstract: We show every multi-group learner in the leave-one-out transductive setting may incur a surprisingly large multiplicative penalty in its error rate on some group…

Specter: Fast MoE Inference via Quantization-Enabled Multi-Step Speculative Offloading

arXiv:2511.14102v2 Announce Type: replace Abstract: Mixture-of-experts (MoE) models often exceed a single accelerator's memory capacity. Offloading experts to host memory creates a severe PCIe I/O bottleneck because…

When Does Training on Synthetic Data Provably Work?

arXiv:2610.28036v1 Announce Type: new Abstract: High-quality training data are a major bottleneck in machine learning, motivating increasing use of artificially generated data. We study when a binary classifier trained…

Stabilizing Filters for Graph Signal Generation

arXiv:2610.28318v1 Announce Type: cross Abstract: Generating graph signals is an important task in machine learning, and incorporating the underlying graph into a generative model enables structurally-informed…

Scaling Graph Reasoning in Language Models with Learnable Graph Positional Encodings

arXiv:2610.27439v1 Announce Type: new Abstract: Solving reasoning tasks supported on graphs typically requires providing a Language Reasoning Model (LRM) with a serialized description of the graph. This approach results…