Investor appetite for data centre IPOs is reaching its limit
The blend of property, infrastructure and fast-changing technology might be better suited to other kinds of funding.
Financial Times - Technology1 min read
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UK-based tech company is growing quickly but has encountered regulatory obstacles in Spain
Financial Times - Technology1 min readGeneralizing Identifiability Results in Causal Discovery from Complete Data to Missing Data Settings
While causal discovery methods have matured for complete data, such algorithms for handling missing values remain limited. We establish general sufficient conditions under which causal graphs remain identifiable (i.e., uniquely determined from the available data distribution) under various…
arXiv - stat.ML2 min readWhose Data, Whose Benchmark? A User-Centric Framework for Auditing Evaluation Resources
Benchmarks shape how model capabilities are measured and interpreted, yet their scores can obscure whose conventions, contexts, and judgments define successful performance.
arXiv - cs.CL (NLP)2 min readHow to Evaluate When You Don't Know How to Evaluate? Choosing Reliable Setups for Base Language Model Benchmarking
Evaluation of large language models (LLMs) remains largely pre-scientific: it involves many design decisions, with little guidance on how to make them. These choices can substantially affect measured performance and, consequently, decisions about subsequent pretraining.
arXiv - cs.CL (NLP)2 min readBackpropagating Verifier Rewards Through Continuous Flow Language Models
Reinforcement learning (RL) has substantially improved the reasoning of autoregressive and discrete-diffusion language models. Language models that generate continuous token embeddings are emerging as a strong alternative, but RL post-training for them remains understudied.
arXiv cs.LG2 min readData Density Scaling Laws for Image Self-Distillation
Current scaling law formulations suggest that increasing the number of unique samples in a dataset improves model performance, while repeated exposure to the same samples provides diminishing value. In this work, we examine this assumption for image self-distillation models.
arXiv cs.LG2 min readHow Inner Optimizers Shape Replica Scaling
Training large language models (LLMs) across geographically distributed and poorly interconnected compute pools or data centers has become a topic of significant interest.
arXiv cs.LG2 min readCan LLMs Access their World Knowledge for Event Prediction?
Recent work has questioned whether large language models (LLMs) can be useful for temporal event prediction. Despite vast pretrained world knowledge, LLMs often underperform simpler specialized models.
arXiv - cs.CL (NLP)2 min readPre-Trained Language Models as Zero-Shot Tools for Social Science Research
Record linkage and demographic inference from names are routine demands in social science. Most approaches to these problems require labeled training data that are difficult or costly to acquire.
arXiv - cs.CL (NLP)2 min readHigh-Probability Regret Bounds for Feel-Good Thompson Sampling
Feel-good Thompson sampling (FGTS) augments the posterior of vanilla Thompson sampling with an exploration term that favors reward functions with high predicted optimal values.
arXiv - stat.ML2 min readMinimax estimation of the pseudo-spectral gap of continuous Markov chains, with applications to empirical concentration bounds
We study the minimax estimation of the pseudo-spectral gap $\gamma_{ps}$ of a Markov chain from the observation of a single trajectory. Extending earlier works on finite-state space chains by Wolfer and Kontorovich (2024), we provide an estimator in the case of a compact, continuous state-space.
arXiv - stat.ML2 min readEvaluating Compressed Memory: Dependence, Utility, and Content Benefit
Does a language model answer better because its compressed memory contains useful document information? Replacing memory vectors with zeros can lower a trained model's score without showing that it outperforms a model trained without memory.
arXiv - cs.CL (NLP)2 min readCrossing the Gender Binary: Modelling Gendered Reference in Science Fiction and General Fiction
Science fiction is widely credited with unsettling gender. This article asks whether that holds at the linguistic level, in how readily gendered reference gives way to its counterpart, a neutral term, or other social category, and whether it separates the genre from general fiction.
arXiv - cs.CL (NLP)2 min readCalibration and Risk-Budgeted Decisions with System One Models
LLM workflows and agents rely on a set of binary decisions like allow/block an input, or discard/retain a retrieved document. When human deferral is unavailable, uncertain cases must still receive an action.
arXiv - stat.ML2 min readPMO-Dock: Benchmarking Docking, Specificity, and Generalization in Molecular Optimization
The Practical Molecular Optimization (PMO) benchmark standardized evaluation in molecular optimization, but it is built on simple property-based oracles that do not capture structure-based drug design.
arXiv cs.LG2 min readWeighted Ensemble Sampling for Rare-Event Estimation in Language Models
Rare but severe failures of large language models can become practically significant at deployment scale. Estimating their probabilities with direct Monte Carlo sampling is highly inefficient, while proposal-based methods typically require auxiliary learning of an additional proposal distribution.
arXiv - cs.CL (NLP)2 min readBeyond Aggregate Accuracy: Item-Level Transitions in Prompt Optimization
Automatic prompt optimization (APO) uses feedback from model responses to improve prompts automatically. However, its effectiveness is usually measured through overall scores, which do not reveal which questions become correct, which previously correct answers become wrong, or whether improvements…
arXiv cs.LG2 min read
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