Results show that recurrent models can provide competitive classification performance while also revealing the phenological periods and spectral regions that are most relevant for distinguishing closely related forest compositions, and provide additional insight into the spectral-temporal mechanisms underlying tree-spe...
Equivariant structure-prediction pipelines alternate coordinate updates with the construction of a k-nearest-neighbour (kNN) graph whose membership is a discrete, piecewise-constant function of the coordinates. Rebuilding that graph at every block boundary (PB) is the expensive part of an epoch, whereas reusing one fro...
Large language models are increasingly deployed as teams of agents that hold different private information about a shared task. How should such agents concisely communicate in a way that promotes aggregation of decision-relevant information? Communicating only proposed actions can discard decision-relevant uncertainty:...
Mirah Shi, Marcel Hussing, Ira Globus-Harris et al.· 0 citations
Before deploying clinical prediction models, their reliability must be assessed to ensure safe use. Errors may arise from distribution shifts between training and deployment or from heterogeneous performance where the model systematically underperforms for certain patient subgroups. Yet most post-hoc diagnostic methods...
Olivier Lefebvre, Hakima Laribi, D. Poenaru et al.· 0 citations
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How well can a language model turn a large, incomplete and partly contradictory body of documents into a plan when no single correct answer exists? We introduce Mission Analysis (MA)-Bench, built on mission analysis, the first step of military operational planning, in which a staff reads the full situation and states t...
Rodney Lafuente-Mercado, Robert Sincero, Phil Sage et al.· 0 citations
Training from offline data has allowed for substantial progress in domains such as robotics, leading to general-purpose policies that can be easily applied zero-shot or efficiently finetuned for downstream tasks. However, these policies can still have poor generalization, both due to the choice of modeling objective an...
Daniel Lawson, Dhruv Sreenivas, Adriana Hugessen et al.· 0 citations
PairIT is introduced, a flexible training framework for autoregressive transformers over SMILES sequences that enables seeded generation by learning from pairs of structurally similar molecules, and Prompt tokens extend this framework by specifying the target property, desired direction, and approximate magnitude of ch...
Hannah Lawrence, Bodhi P. Vani, Ji Won Park et al.· 0 citations
Structured pruning reduces LLM inference costs by removing model components to produce smaller dense models. However, existing methods can cause reasoning performance to collapse on long-form reasoning tasks such as AIME. We propose \textit{LayerTrim}, a training-free structured pruning method that identifies redundant...
Woo-Sang Lim, Haneul Yoo, Vincent-Daniel Yun et al.· 1 citation
Activation steering controls large language models at inference time, but its effectiveness depends on the choice of reference data. We introduce genesis steering, which derives a genesis vector from contrastive examples in the model's own fine-tuning data. When these data are unavailable, we draw on the membership inf...
Tora Bodin, Eric Aubinais, Ilaria Torre et al.· 0 citations
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 extend to unseen ques...
It is theoretically and empirically demonstrated that whitening-based pre-conditioning is a powerful inner-optimizer desideratum, and empirical evaluation extends the results to reveal that whitening-based inner-optimizers achieve strong replica scaling (for 8-96 replicas), with performance relative to DP improving as...
Eugene Belilovsky, Stefan Horoi, Edouard Oyallon et al.· 0 citations
Because its binary optimization landscape contains exponentially many local optima, MAX-CUT is highly sensitive to initialization. We study Particle Polynomial Relaxation (PPR), a moment-based nonconvex continuous reformulation possessing an advantageous optimization landscape. We warm-start PPR with Combinatorial Mult...
M. Harper Langston, Pierre-David Letourneau, Dalton Jones et al.· 0 citations
