In briefCurvature-aware pruning that also keeps influence functions intact
SummaryThe paper introduces CACTUS, an ADMM-based optimizer for pruning and sparse-plus-low-rank decomposition of pre-trained weights, using a curvature-aware objective that preserves gradient alignment between compressed and dense models. At 50% pruning of Qwen3-4B, it cuts the C4 perplexity gap to the dense model by 45% and runs over 28 times faster than LLM-Surgeon. The authors also show that preserving influence functions supports effective data selection.
In briefA Global View of How LLMs Value Human Time
SummaryThis paper examines how large language models value people's time by changing only the user's country and measuring the compensation they deem appropriate for a 60-minute appointment delay. Across 193 countries and 5 LLMs, results are compared with GDP per capita on a purchasing power parity basis, mean income, and a literature-based patience score. The authors find that LLMs evaluate time differently across regions, recommending the highest compensation for Europe and mostly the lowest for Africa, and underestimating the value of time for users in Asia and South America relative to GDP_PPP.
In briefApproximating optimal allocation in asynchronous multi-player bandits with mixed activation rates
SummaryThis paper studies multi-player bandits where devices are active only sometimes and each has a different activation probability. It shows that computing the optimal allocation is NP-hard. It then proves a polynomial-time greedy algorithm achieves a 1/e approximation, improving to 1/√e when players are processed in decreasing order of activation probability.
In briefSmart Buildings Challenge: Training general building-control models that work across many real buildings
SummaryBuildings account for nearly 40% of US carbon emissions, with HVAC systems responsible for a large share. Most existing building control methods work only for the specific building they were developed for. This paper proposes a challenge to develop building foundation models that adapt easily to new buildings, with participants training control policies on simulated buildings and evaluated on unseen ones.
Judah Goldfeder, Ayush Rai, Philippe M. Wyder et al.
In briefCoding agents on long clinical trial documents: where they fail when digitizing protocols
SummaryThis paper tests whether coding agents can reliably turn complete clinical trial protocol PDFs into accurate structured data. The authors build TrialPD-Bench, an expert-annotated benchmark of 150 public protocols, and evaluate nine models across five agent harnesses. Even the strongest agents fail to fully solve the task; they usually find the right evidence but make errors when transcribing and structuring it. The authors also propose TrialScribe, a domain-specific harness that improves an open-weight model and lets it match or surpass some proprietary models.
In briefSemi-Amortized Bayesian Inference with Reusable Local Posteriors: a method that trains neural estimators for parameter blocks and alternates them with standard Markov chain updates.
SummaryThis paper proposes a semi-amortized Bayesian inference method that splits model parameters into smaller blocks and uses conditional independence to determine the information each block needs. Neural estimators are trained for blocks whose updates are expensive or repeated, learning their full conditional posteriors from simulations. A Markov chain alternates direct draws from these estimators with conventional updates of the other variables, and neural updates need no likelihood evaluation or accept-reject step. The authors illustrate the approach on high-dimensional, computationally expensive hierarchical and spatiotemporal models.
Niek Stevenson, Paul-Christian B\"urkner, Andrew Heathcote et al.
