TRACE: Transition-Aware Residual Control for Multi-Objective Materials Discovery
arXiv:2608.23631v1 Announce Type: new
Abstract: Multi-objective materials discovery with LLM agents is often limited not only by how many candidates can be proposed, but by how effectively each costly property evaluation informs the next search step. Existing agents mainly store evaluated candidate...
Equivariant Cellular Sheaves for Molecular Electronic Structure: Bridging Sheaf Cohomology and E(3)-Equivariant Hamiltonian Learning
arXiv:2608.23571v1 Announce Type: new
Abstract: Equivariant message-passing networks are the standard model for molecular property and interatomic-potential prediction, and recent work predicts the electronic Hamiltonian itself in an E(3)-equivariant way. Separately, topological deep learning has e...
Data Predictability Shapes Weibull Weight-Scale Growth in Transformer Training
arXiv:2608.23573v1 Announce Type: new
Abstract: A trained transformer's weight magnitudes can be summarized by a two-parameter Weibull distribution whose shape $k \approx 1.2$ is stable across layers and models, so the scale $\lambda$ carries most training-induced movement. What corpus property set...
From Causal Plausibility to Causal Reliability: Evaluating LLMs as Calibrated Direct Causal-Edge Classifiers
arXiv:2608.23660v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used to provide prior causal knowledge for structural causal discovery, yet whether their direct-edge judgments and confidence can be trusted remains unclear. We systematically evaluate 12 instruction-tune...
Renormalization Group Flow Matching for Scalable Local Generative Modeling
arXiv:2608.23696v1 Announce Type: new
Abstract: Despite their remarkable success in modeling complex data, generative models face a fundamental tradeoff. Global approaches can capture full structural coherence but suffer from high computational costs, while local models are efficient but often fail...
Response Renormalization for Critical Deep Equilibrium Models
arXiv:2608.23725v1 Announce Type: new
Abstract: Deep Equilibrium Models (DEQs) compute predictions from a hidden representation unchanged by the model update. Training through this equilibrium uses implicit differentiation and requires solving an adjoint system built from the residual Jacobian. If ...
How loveholidays is making everyone a builder with Codex
Discover how loveholidays uses OpenAI Codex to make software development accessible across the business, helping teams turn ideas into products faster.
PROOF-Gen: From Optimized Data to Better Distillation
Supervised fine-tuning on teacher-generated trajectories is the standard first stage for distilling tool-calling capabilities into deployable models. Post-training pipelines that drive shipped tool-calling agents re-run this stage on a daily or weekly cadence, paying the frontier-teacher cost each c...
IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining
Recent advancements in large language models have intensified the need for efficient and deployable models within limited inference budgets. Structured pruning pipelines have shown promise in token efficiency compared to training target-size models from scratch. In this paper, we advocate incorporat...
Luce: Relightable Gaussians for 3D Asset Generation
High-fidelity image-to-3D generation requires a 3D representation that captures both geometry and appearance. To support relighting and integration into standard rendering pipelines, the representation should include physically based rendering (PBR) modalities such as albedo, metallic-roughness, and...
Liquid AI Open-Sources Pipette: A Reproducible Benchmarking Suite That Measures On-Device Models, Quantization, Runtime and Hardware Together
Model cards report quality under server-class, full-precision conditions. Those numbers rarely predict how the same model behaves on a phone. This week, Liquid AI released Pipette. It is an open-source platform for benchmarking foundation models on edge devices, built in partnership with Artificial ...
Perplexity Ships Portable Computer on NVIDIA DGX Spark: Local Harness, OS-Enforced Sandbox, and Zero Per-Token Cost for Local Steps
Perplexity releases Portable Computer, packaging local models, harness, sandbox, and connectors into one system running on NVIDIA DGX Spark.
The post Perplexity Ships Portable Computer on NVIDIA DGX Spark: Local Harness, OS-Enforced Sandbox, and Zero Per-Token Cost for Local Steps appeared first on ...
Meta AI Introduces MetaRoCE: A Clean-Sheet RDMA Transport Built for AI-Scale Ethernet
Training and serving frontier models is now a networking problem as much as a compute problem. Collective operations like all-reduce and all-to-all synchronize thousands of accelerators during training, and the slowest transfer sets the pace for the entire job. Even small amounts of network friction...
A New Towards Data Science: A Faster Site and a Brand-New Contributor Portal
We're excited to share some big news: We completely rebuilt the TDS website and our contributor portal. Whether you come here to read, to write, or both, here's what you can expect from our new site.
The post A New Towards Data Science: A Faster Site and a Brand-New Contributor Portal appeared first...
The Design System as the Control Plane for AI-Generated UI
AI-assisted development has made it easier to generate frontend code quickly. A developer can ask for a form, a dashboard widget, a settings page, or a modal flow and get a working first draft in seconds. That speed is useful, especially when teams are moving through routine UI work. But speed creat...
OpenAI’s Jalapeño chip is built for fast inference at scale, benchmarks show
Tested on Semianalysis’s InferenceX benchmark, Jalapeño registered both more tokens per user and more throughput per kilowatt than the currently available state-of-the art.
Hallucinations, Watermarks, Removers, and a Squeezed Balloon
Watermarks act at the model’s moments of doubt, and so do the safety checks that catch AI mistakes
The post Hallucinations, Watermarks, Removers, and a Squeezed Balloon appeared first on Towards Data Science.