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Long Text to Predictive Features: LLM-Guided Blockwise Feature Engineering via Executable Program Search
Multimodal large language models (MLLMs) often struggle to identify and use geometric relations in diagrams. Recent methods address this challenge by converting geometric entities, relations, and constraints into explicit textual...
RECAST: Learning to Compute the Right Context through Adaptive Evidence Routing
Large language models are increasingly applied to tasks grounded in long, heterogeneous information sources. Conventional Retrieval-Augmented Generation (RAG) relies on fixed similarity-based retrieval, while agentic variants adap...
Validity Without Ground Truth: What Stated-Preference Economics Offers the Evaluation of Language Models
The problem of evaluating large language models becomes acute when the questions asked have no single correct answer. Policy worth, option selection, competing values—these are the everyday questions put to LLMs that resist tradit...
IdeaAnchor: Teaching LLMs to Turn Literature into Research Ideas
Scientific research often begins by synthesizing ideas from a set of related papers to identify gaps and formulate new directions. However, training language models to perform this form of literature-grounded ideation remains chal...
The Missing Minimal Pair: Stereotype Evaluation in LLMs
Measuring bias in large language models has often relied on comparing the log-likelihoods of two contrastive stereotype sentences. This approach can produce unreliable results because simply rewriting the same stereotype with an a...
TranScope: What the Software Hides About LLM Training Data, the Hardware Reveals at Scale, and Accelerators Magnify
The fundamental assumption that neural network inference can be made truly independent of training data has been called into question. Membership inference, the problem of determining whether a particular datum was part of a model...
BiasFlow: Geometric Monitoring and Backbone Regularization for Spurious Feature Reliance
Evaluating Spurious Feature Reliance Beyond Worst-Group Accuracy Worst-group accuracy (WGA) has become the de facto metric for assessing whether a machine learning model fails on underserved subsets of data. WGA measures the accur...
Anatomy-aware Fine-grained Multimodal Fusion for Laryngopharyngeal Cancer T-Staging Prediction Using CT and Radiology Report
Why T-Staging Laryngopharyngeal Cancer Needs a New Approach Laryngopharyngeal cancer affects the voice box and throat, and accurate T-staging determines whether a patient receives organ-preserving surgery or more aggressive total...
TasteVal: Measuring the Experimental Research Taste of AI Systems Against Human Experts
Every recurrence of a looped language model adds cost in training, decoding, prefill, and reinforcement learning RL. The closer recurrent states get to fixed points, the less the path to them matters. This enables truncated backpr...
MRVQ: One Resident Index for Dimension- and Rate-Elastic Vector Search
Elastic Vector Search and the Memory-Rate-Dimension Trade-Off Dense-retrieval services operating at scale must continually adapt to shifting constraints. Latency budgets shrink, quality thresholds rise, and memory footprints becom...