Towards Data Science
Jun 5
Automate Writing Your LLM Prompts
Using DSPy to automatically create, evaluate, and optimize your prompts The post Automate Writing Your LLM Prompts appeared first on Towards Data Science .
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
Towards Data Science
Jun 5
How to Fine-Tune an SLM for Emotion Recognition
Python tutorial for fine-tuning a Mistral Small 3. 1 on an imbalanced training set to classify 15 emotions in social media communication The post How to Fine-Tune an SLM for Emotion Recognition appeared first on Towards Data Science .
arXiv Machine Learning
Jun 5
Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks
arXiv:2606. 05208v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has long been a powerful solution to various problems in communication networks.
arXiv Machine Learning
Jun 5
SUPERNOVA: Eliciting General Reasoning in LLMs with Reinforcement Learning on Natural Instructions
arXiv:2604. 08477v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has substantially improved reasoning in formal domains such as mathematics and code, but extending these gains beyond STEM remains challenging.
arXiv Machine Learning
Jun 5
End-to-End Subgraph Detection with GraphDETR
arXiv:2606. 06364v1 Announce Type: new Abstract: Subgraph detection seeks to identify whether and where instances of query patterns occur within a larger graph.
arXiv Machine Learning
Jun 5
$p$-adic Bi-Filtrations for Topological Machine Learning on Genomic Sequences
arXiv:2606. 06117v1 Announce Type: cross Abstract: We introduce pVR, a topological machine learning framework for alignment-free genomic sequence classification that combines $p$-adic numbers with topological data analysis.
arXiv Machine Learning
Jun 5
Incremental Transformer Neural Processes
arXiv:2602. 18955v2 Announce Type: replace Abstract: Neural Processes (NPs), and specifically Transformer Neural Processes (TNPs), have demonstrated remarkable performance across tasks ranging from spatiotemporal forecasting to tabular data modelling.
arXiv Machine Learning
Jun 5
Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches
arXiv:2501. 14291v3 Announce Type: replace Abstract: Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time.
arXiv Machine Learning
Jun 5
Symmetric Divergence and Normalized Similarity: A Unified Topological Framework for Representation Analysis
arXiv:2606. 06342v1 Announce Type: cross Abstract: Topological Data Analysis (TDA) offers a principled, intrinsic lens for comparing neural representations.
arXiv Machine Learning
Jun 5
No Need to Train Your RDB Foundation Model
arXiv:2602. 13697v2 Announce Type: replace-cross Abstract: Relational databases (RDBs) contain vast amounts of heterogeneous tabular information that can be exploited for predictive modeling purposes.
arXiv Machine Learning
Jun 5
GenAutoML: An Agentic Framework for Dynamic Architecture Generation and Optimization in Time-Series Analysis
arXiv:2606. 05860v1 Announce Type: new Abstract: Designing neural architectures for time-series forecasting and anomaly detection remains a resource-intensive task that often requires substantial domain expertise.
arXiv Machine Learning
Jun 5
Dominant-Layer ZO: A Single Layer Dominates Zeroth-Order Fine-Tuning of LLMs
arXiv:2606. 05516v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization enables memory-efficient fine-tuning of large language models (LLMs) using only forward passes, but it remains unclear how useful adaptation is distributed across layers.
arXiv Machine Learning
Jun 5
CLaaS: Continual learning as a service for sample efficient online learning
arXiv:2606. 05559v1 Announce Type: new Abstract: Deployed large language model agents must adapt to distribution shift in dynamic environments.
arXiv Machine Learning
Jun 5
Alpha-RTL: Test-Time Training for RTL Hardware Optimization
arXiv:2606. 05253v1 Announce Type: new Abstract: Large language models (LLMs) have shown increasing promise in generating functionally correct register-transfer-level (RTL) hardware designs.
arXiv Machine Learning
Jun 5
Reasoning Models Don't Just Think Longer, They Move Differently
arXiv:2605. 15454v2 Announce Type: replace-cross Abstract: Reasoning-trained language models often spend more tokens on harder problems, but longer chains of thought do not show whether a model is merely computing for more steps or following a different internal trajectory.
arXiv Machine Learning
Jun 5
FATE: Focal-modulated Attention Encoder for Multivariate Time-series Forecasting
arXiv:2408. 11336v3 Announce Type: replace Abstract: Climate change stands as one of the most pressing global challenges of the twenty-first century, with far-reaching consequences such as rising sea levels, melting glaciers, and increasingly extreme weather patterns.
arXiv Machine Learning
Jun 5
Measuring the sensitivity of LLM-based structured extraction to prompt, model, and schema choices in clinical discharge summaries
arXiv:2606. 05970v1 Announce Type: cross Abstract: Large language models are increasingly used for structured extraction from clinical free-text notes, but the sensitivity of their output to upstream configuration choices is less understood than their accuracy on fixed benchmarks.
arXiv Machine Learning
Jun 5
The Evaluation Blind Spot: A Stereological Theory of Benchmark Coverage for Large Language Models
arXiv:2606. 05169v1 Announce Type: new Abstract: We give a stereological theory of LLM benchmark coverage.
arXiv Machine Learning
Jun 5
Consistency Training Along the Transformer Stack
arXiv:2606. 05817v1 Announce Type: new Abstract: Consistency training encourages models to behave similarly across different contexts, and has shown promise for reducing misalignment.
arXiv Machine Learning
Jun 5
Cross-Epoch Adaptive Rollout Optimization for RL Post-Training
arXiv:2606. 05606v1 Announce Type: new Abstract: LLM post-training often relies on reinforcement learning methods that sample multiple rollouts per prompt, yet most existing approaches use a fixed rollout budget for every prompt, despite large differences in the training signal different prompts provide.