Shared autonomy systems require principled methods for inferring user intent and determining appropriate assistance levels. This is a central challenge in human-robot interaction, where systems must be successful while being mindful of user agency. Previous approaches relied on static blending ratios or separated goal inference from assistance arbitration, leading to suboptimal performance in unstructured environments. We introduce BRACE (Bayesian Reinforcement Assistance with Context Encoding), a novel framework that fine-tunes Bayesian intent inference and context-adaptive assistance through an architecture […]

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arXiv:2602.12413v1 Announce Type: new Abstract: If LLM training data is polluted with benchmark test data, then benchmark performance gives biased estimates of out-of-distribution (OOD) generalization. Typical decontamination filters use n-gram matching which fail to detect semantic duplicates: sentences with equivalent (or near-equivalent) content that are not close in string space. We study this soft contamination of training data by semantic duplicates. Among other experiments, we embed the Olmo3 training corpus and find that: 1) contamination remains widespread, e.g. […]

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arXiv:2602.02589v1 Announce Type: new Abstract: Evaluating large language models typically relies on human-authored benchmarks, reference answers, and human or single-model judgments, approaches that scale poorly, become quickly outdated, and mismatch open-world deployments that depend on web retrieval and synthesis. We introduce PeerRank, a fully autonomous end-to-end evaluation framework in which models generate evaluation tasks, answer them with category-scoped live web grounding, judge peer responses and aggregate dense peer assessments into relative performance estimates, without human supervision or gold […]

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The development and operation of cooperative Digital Library Systems face management problems that do not always have a complete solution. These cooperative systems currently need to incorporate new Artificial Intelligence solutions to develop intelligent services for decision-making and recommendation mechanisms for which there is not an integral management solution. Indeed, the management of these intelligent services is outside the scope of traditional management systems for digital collections. This paper proposes using an Intelligent Management Framework to address these […]

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arXiv:2601.02449v1 Announce Type: new Abstract: Maximum partial subgraph isomorphism compares two graphs (nodes joined by edges) to find a largest common subgraph. A common use case, for graphs with labeled nodes, seeks to find instances of a textit{query} graph with $q$ nodes in a (typically larger) textit{data} graph with $d$ nodes. The problem is NP-complete, and na”ive solutions are exponential in $q + d$. The fastest current heuristic has complexity $O(d^2)$. This paper outlines ASSIST (Approximate Swarming Subgraph […]

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arXiv:2601.19918v1 Announce Type: new Abstract: Hallucinations in Large Language Models (LLMs), i.e., the tendency to generate plausible but non-factual content, pose a significant challenge for their reliable deployment in high-stakes environments. However, existing hallucination detection methods generally operate under unrealistic assumptions, i.e., either requiring expensive intensive sampling strategies for consistency checks or white-box LLM states, which are unavailable or inefficient in common API-based scenarios. To this end, we propose a novel efficient zero-shot metric called Lowest Span Confidence […]

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The Claude C Compiler: What It Reveals About the Future of Software On February 5th Anthropic’s Nicholas Carlini wrote about a project to use parallel Claudes to build a C compiler on top of the brand new Opus 4.6 Chris Lattner (Swift, LLVM, Clang, Mojo) knows more about C compilers than most. He just published this review of the code. Some points that stood out to me: Good software depends on judgment, communication, and clear abstraction. AI has […]

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arXiv:2602.03461v1 Announce Type: cross Abstract: Integrating hard constraints into deep learning is essential for safety-critical systems. Yet existing constructive layers that project predictions onto constraint boundaries face a fundamental bottleneck: gradient saturation. By collapsing exterior points onto lower-dimensional surfaces, standard orthogonal projections induce rank-deficient Jacobians, which nullify gradients orthogonal to active constraints and hinder optimization. We introduce Soft-Radial Projection, a differentiable reparameterization layer that circumvents this issue through a radial mapping from Euclidean space into the interior of […]

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arXiv:2303.08456v3 Announce Type: replace-cross Abstract: We consider a binary supervised learning classification problem where instead of having data in a finite-dimensional Euclidean space, we observe measures on a compact space $mathcal{X}$. Formally, we observe data $D_N = (mu_1, Y_1), ldots, (mu_N, Y_N)$ where $mu_i$ is a measure on $mathcal{X}$ and $Y_i$ is a label in ${0, 1}$. Given a set $mathcal{F}$ of base-classifiers on $mathcal{X}$, we build corresponding classifiers in the space of measures. We provide upper and […]

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