arXiv AI August 3, 2026 By Soutrik Sarangi, Yonatan Sverdlov, Nadav Dym, Abir De

Monotone and Separable Set Functions: Characterizations and Neural Models

arXiv:2510. 23634v4 Announce Type: replace-cross Abstract: Motivated by applications for set containment problems, we consider the following fundamental problem: can we design set-to-vector functions so that the natural partial order on sets is preserved, namely $S\subseteq T \text{ if and only if } F(S)\leq F(T) $.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

safety

arXiv Machine Learning

Jul 27

Encoding orders and trees in real-valued functions

arXiv:2607. 21761v1 Announce Type: cross Abstract: We prove function-theoretic analogues of a quantitative result of Hodges on extracting the order property from a sufficiently large 2-tree coded in a binary relation.

By G Conant, C Terry

arXiv Machine Learning

Jul 17

Learning in Infinitesimal Non-Compositional Sketches

arXiv:2607. 15107v1 Announce Type: new Abstract: This paper develops a categorical framework -- Learning in Infinitesimal Non-Compositional Sketches (LINCS) -- as the repair of non-compositionality: failures of diagrams to factor through quotient sketches lifted to the tangent category setting.

By Sridhar Mahadevan

llms reinforcement-learning

arXiv Machine Learning

Jun 5

On Universality of Deep Equivariant Networks

arXiv:2510. 15814v2 Announce Type: replace-cross Abstract: Universality results for equivariant neural networks remain rare.

By Marco Pacini, Mircea Petrache, Bruno Lepri, Shubhendu Trivedi, Robin Walters

arXiv Machine Learning

Jun 4

On the Expressive Power of Permutation-Equivariant Weight-Space Networks

arXiv:2602. 01083v2 Announce Type: replace Abstract: Weight-space learning studies neural architectures that operate directly on the parameters of other neural networks.

By Adir Dayan, Yam Eitan, Haggai Maron

benchmarks

arXiv Machine Learning

Aug 7

Positive-Data Learning of Fixed-Observation Linear MCFGs from Working Binary Presentations

arXiv:2605. 11644v2 Announce Type: replace-cross Abstract: We study positive-data learning of languages admitting reduced working binary linear nondeleting multiple context-free grammar presentations of bounded fan-out.

By Takayuki Kuriyama

arXiv Machine Learning

Jul 9

Value of Information under Imprecise Probabilities: Decision-Rule-Specific Values and Fixed-Measure Envelopes on a Credal Set

arXiv:2607. 06570v1 Announce Type: cross Abstract: Value-of-information (VOI) analysis is usually conducted under a single probability measure.

By Rowan Iskandar

Related stories

Encoding orders and trees in real-valued functions

Learning in Infinitesimal Non-Compositional Sketches

On Universality of Deep Equivariant Networks

On the Expressive Power of Permutation-Equivariant Weight-Space Networks

Positive-Data Learning of Fixed-Observation Linear MCFGs from Working Binary Presentations

Value of Information under Imprecise Probabilities: Decision-Rule-Specific Values and Fixed-Measure Envelopes on a Credal Set