arXiv:2511.04053v1 Announce Type: new Abstract: Although behavioral studies have documented numerical reasoning errors in large language models (LLMs), the underlying representational mechanisms remain unclear. We hypothesize that numerical attributes occupy shared latent subspaces and investigate two questions:(1) How do LLMs internally integrate multiple numerical attributes of a single entity? (2)How does irrelevant numerical context perturb […]
Expert Evaluation of LLM World Models: A High-$T_c$ Superconductivity Case Study
arXiv:2511.03782v1 Announce Type: cross Abstract: Large Language Models (LLMs) show great promise as a powerful tool for scientific literature exploration. However, their effectiveness in providing scientifically accurate and comprehensive answers to complex questions within specialized domains remains an active area of research. Using the field of high-temperature cuprates as an exemplar, we evaluate the ability […]
RUST-BENCH: Benchmarking LLM Reasoning on Unstructured Text within Structured Tables
arXiv:2511.04491v1 Announce Type: cross Abstract: Existing tabular reasoning benchmarks mostly test models on small, uniform tables, underrepresenting the complexity of real-world data and giving an incomplete view of Large Language Models’ (LLMs) reasoning abilities. Real tables are long, heterogeneous, and domain-specific, mixing structured fields with free text and requiring multi-hop reasoning across thousands of tokens. […]
SILVI: Simple Interface for Labeling Video Interactions
arXiv:2511.03819v1 Announce Type: cross Abstract: Computer vision methods are increasingly used for the automated analysis of large volumes of video data collected through camera traps, drones, or direct observations of animals in the wild. While recent advances have focused primarily on detecting individual actions, much less work has addressed the detection and annotation of interactions […]
Agentmandering: A Game-Theoretic Framework for Fair Redistricting via Large Language Model Agents
arXiv:2511.04076v1 Announce Type: new Abstract: Redistricting plays a central role in shaping how votes are translated into political power. While existing computational methods primarily aim to generate large ensembles of legally valid districting plans, they often neglect the strategic dynamics involved in the selection process. This oversight creates opportunities for partisan actors to cherry-pick maps […]
Which Similarity-Sensitive Entropy?
arXiv:2511.03849v1 Announce Type: cross Abstract: A canonical step in quantifying a system is to measure its entropy. Shannon entropy and other traditional entropy measures capture only the information encoded in the frequencies of a system’s elements. Recently, Leinster, Cobbold, and Reeve (LCR) introduced a method that also captures the rich information encoded in the similarities […]
Neural Computation Without Slots: Steps Towards Biologically Plausible Memory and Attention in Natural and Artificial Intelligence
arXiv:2511.04593v1 Announce Type: cross Abstract: Many models used in artificial intelligence and cognitive science rely on multi-element patterns stored in “slots” – dedicated storage locations – in a digital computer. As biological brains likely lack slots, we consider how they might achieve similar functional outcomes without them by building on the neurally-inspired modern Hopfield network […]
Levers of Power in the Field of AI
arXiv:2511.03859v1 Announce Type: cross Abstract: This paper examines how decision makers in academia, government, business, and civil society navigate questions of power in implementations of artificial intelligence. The study explores how individuals experience and exercise levers of power, which are presented as social mechanisms that shape institutional responses to technological change. The study reports on […]
KGFR: A Foundation Retriever for Generalized Knowledge Graph Question Answering
arXiv:2511.04093v1 Announce Type: new Abstract: Large language models (LLMs) excel at reasoning but struggle with knowledge-intensive questions due to limited context and parametric knowledge. However, existing methods that rely on finetuned LLMs or GNN retrievers are limited by dataset-specific tuning and scalability on large or unseen graphs. We propose the LLM-KGFR collaborative framework, where an […]
Investigating Robot Control Policy Learning for Autonomous X-ray-guided Spine Procedures
arXiv:2511.03882v1 Announce Type: cross Abstract: Imitation learning-based robot control policies are enjoying renewed interest in video-based robotics. However, it remains unclear whether this approach applies to X-ray-guided procedures, such as spine instrumentation. This is because interpretation of multi-view X-rays is complex. We examine opportunities and challenges for imitation policy learning in bi-plane-guided cannula insertion. We […]