Learning From Pairwise Preferences: An Introduction to the Bradley Terry Model
How to Turn Simple Head-to-Head Choices Into Probabilistic Rankings
The post Learning From Pairwise Preferences: An Introduction to the Bradley Terry Model appeared first on Towards Data Science.
Startup Battlefield 200 applications close today: Nominate a founder or submit your startup
Today is the final day to apply or nominate a startup for Startup Battlefield 200. Once the clock strikes 11:59 p.m. PT, the window closes on your chance to compete for $100,000 in equity-free funding, gain global visibility, connect directly with investors, and launch on the TechCrunch Disrupt stag...
Most AI Agents Fail in Production Because They’re Built Backwards
Good models don't save bad architecture, and most teams learn that the hard way.
The post Most AI Agents Fail in Production Because They’re Built Backwards appeared first on Towards Data Science.
PySpark Optimization: 12 Proven Techniques to Speed Up Your Spark Jobs
Modern data pipelines handle massive volumes of structured and unstructured data every day. As datasets grow, poorly optimized Spark jobs become slower, more expensive, and harder to scale. Common issues include long execution times, excessive shuffling, memory bottlenecks, and inefficient joins. Ef...
The Statistics of Token Selection: Logits, Temperature, and Top-P Walkthrough
When large language models, or LLMs for short, produce outputs, several criteria are at stake, including not only overall response relevance but also coherence and creativity.
The following article originally appeared on Addy Osmani’s blog and is being reposted here with the author’s permission. The default behavior of any AI coding agent is to take the shortest path to “done.” Ask for a feature and it writes the feature. It doesn’t ask whether you have a spec, write a te...
Meet EAGLE 3.1: The Speculative Decoding Algorithm That Fixes Attention Drift in LLM Inference
The EAGLE team, vLLM, and TorchSpec jointly release EAGLE 3.1 to fix speculative decoding instability in production.
The post Meet EAGLE 3.1: The Speculative Decoding Algorithm That Fixes Attention Drift in LLM Inference appeared first on MarkTechPost.
MEMO: A Modular Framework for Training a Dedicated Memory Model on New Knowledge Without Modifying LLM Parameters
Researchers from NUS, MIT, and A*STAR propose MEMO, a modular framework that encodes corpus knowledge into a separate trainable MEMORY model.
The post MEMO: A Modular Framework for Training a Dedicated Memory Model on New Knowledge Without Modifying LLM Parameters appeared first on MarkTechPost.
GEM: Geometric Entropy Mixing for Optimal LLM Data Curation
arXiv:2605.26121v1 Announce Type: new
Abstract: LLM pre-training efficacy increasingly depends on data composition rather than sheer volume. Yet, optimal mixing is hindered by categorization flaws: human taxonomies suffer from ontological misalignment, and Euclidean clustering fails to address embe...
The Constraint Tax: Measuring Validity-Correctness Tradeoffs in Structured Outputs for Small Language Models
arXiv:2605.26128v1 Announce Type: new
Abstract: Production LLM systems increasingly require machine-readable outputs: JSON objects, typed traces, regex-constrained fields, and tool-call schemas. This paper targets on-device and low-cost small language model (SLM) deployments, where sub-3B models ar...
AirCast-SR: A Foundation Model for Kilometer-Scale Atmospheric Super-Resolution via Latent Consistency Diffusion
arXiv:2605.26130v1 Announce Type: new
Abstract: Operational weather prediction at kilometer scales remains computationally prohibitive for traditional numerical weather prediction (NWP) models, limiting forecast access for applications in energy, agriculture, and disaster management that require fi...
arXiv:2605.26147v1 Announce Type: new
Abstract: Human decision-making is sequential and uncertainty-aware, yet standard neural networks often rely on static, dense forward computation with limited visibility into evidence acquisition, uncertainty evolution, or when computation should stop. We intro...
BrickAnything: Geometry-Conditioned Buildable Brick Generation with Structure-Aware Tokenization
arXiv:2605.26182v1 Announce Type: new
Abstract: Generating physically buildable brick structures from 3D shapes requires more than geometric reconstruction: the output must also satisfy discrete part constraints and structural stability. Existing brick generation methods either rely on heuristic op...
arXiv:2605.26242v1 Announce Type: new
Abstract: Can large language models detect and report their own internal states? A number of studies have argued that the answer to this question is yes. We argue, based on lessons from human metacognition research, that this conclusion may be premature: to be ...
Is Agent Memory a Database? Rethinking Data Foundations for Long-Term AI Agent Memory
arXiv:2605.26252v1 Announce Type: new
Abstract: Long-running AI agents need persistent memory. Memory supports learning across sessions, reduces repeated context injection, and enables auditing of past decisions. Current agent memory systems and database paradigms treat memory as storage. They loca...
Personalizing Embodied Multimodal Large Language Model Agents over Long-term User Interactions
arXiv:2605.26256v1 Announce Type: new
Abstract: Multimodal large language model (MLLM)-based embodied agents have shown strong potential for solving complex tasks in physical environments. However, personalized assistance requires more than following generic instruction or recognizing object catego...
arXiv:2605.26279v1 Announce Type: new
Abstract: Constraint Acquisition (CA) and related research on the validation and enhancement of Mathematical Programming (MP) models from domain knowledge artifacts are currently limited by inadequate benchmarks. This deficiency impedes reproducibility and cros...
AI Weekly Issue #496: Anthropic's Pentagon model is now everyone's model
Anthropic released Mythos to the public, collapsing the wall between cleared-contractor frontier AI and developer-grade frontier AI in a single press release. DeepMind's Demis Hassabis moved his AGI timeline from "five to ten years" to "a real possibility by 2029" and tied it explicitly to AlphaProo...