In machine learning, symbolic reasoning approaches often rely on static architectures that hinder integration into deep neural networks and gradient-based optimization. To address this, we introduce the Native Predicate Neuron, a differentiable logic-reasoning neural module that enriches standard architectures with sym...
Ioannis Konstantoulas, Iliana Loi, Kyriakos Sgarbas· 0 citations
Modern image generators can produce complex visual artifacts, including diagrams, interfaces, and records that combine text, graphics, and layout to convey detailed information. They may refuse requests for overtly harmful imagery yet still generate artifacts that guide dangerous actions or support deception. We call t...
Zi-Hao Zhu, Yuansan Liu, Siwei Lyu et al.· 0 citations
Recent advances in large language models (LLMs) have improved Verilog generation. However, most training pipelines distill natural-language reasoning traces whose intermediate design decisions are weakly structured and are primarily selected according to the correctness of the final generated RTL. We present MCTS-SFT,...
Matthew DeLorenzo, Kevin Tieu, Prithwish Jana et al.· 0 citations
DuQUAD is proposed, a multi-document QA framework that mitigates positional bias through dual-view reasoning and quality-aware evidence pruning, and substantially reducing computational cost compared to existing multi-agent frameworks.
Yeonjun Choi, Seyeong Jeong, Jong Wook Kim et al.· 0 citations
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Multi-agent LLM pipelines built on interoperability protocols such as the Open Floor Protocol (OFP) or A2A are usually justified on capability grounds, with their runtime cost treated as an overhead of unknown size. Comparing such a pipeline with one model call is underspecified, because a pipeline can give each role a...
As multi-agent language model networks become widely deployed, predicting their collective behavior is a critical challenge. Classical distributed consensus theory relies on network topology to predict convergence, structurally assuming that agent updates are convex combinations, population means are conserved, and wei...
Users engage with large language models on tasks that are purely objective ("When was Netflix founded?"), purely subjective ("What should I name my child?") or anywhere in-between. We have rich tools for evaluating model accuracy with respect to objective criteria. However, when it comes to subjective aspects of a task...
Katy Blumer, Jon M. Kleinberg, Ravi Kumar et al.· 0 citations
Language agents increasingly work by calling tools on a persistent artifact, such as a code repository, and the result of each call shapes their next step. Video editing is a demanding case of this pattern: what a frame shows depends on how every clip is trimmed, retimed, cropped, and nested, so knowledge about the foo...
Recent arguments suggest that genAI can act as an invisible hand that gradually shapes social structures, such as collective beliefs and norms. The argument is that if millions of people repeatedly consult genAI tools, their generated outcomes quietly shape what becomes acceptable, reasonable or conventional. We study...
Deployed classifiers often act on a prediction only when its confidence clears a threshold and pass the rest to a person. We ask whether one threshold serves all languages equally on typed decision interfaces, APIs that return a probability for each option of a fixed multiple-choice question. In a confirmatory study wi...
Jun-Wei Yao, Yong-Xin Zhou, Zi-Ke Yang et al.· 0 citations
This paper introduces OrbitalGym, a simulation environment for studying when planning algorithms or system design determine the outcome of adversarial orbital encounters. In an orbital game, two non-cooperative teams of satellites maneuver during rendezvous and proximity operations to win a differential-reward objectiv...
Duncan Eddy, Mahdi Al-Husseini, Mykel J. Kochenderfer· 0 citations
Language models do not merely memorize facts; they absorb rhetorical strategies from pretraining data, including the human tendency to reason by analogy. We ask: is this absorbed capability geometrically accessible before any instruction tuning, and what determines whether it can be activated at inference time?
We ex...
