arXiv Machine Learning July 3, 2026 By Zichao Wei

On the Role of Directionality in Structural Generalization

Read the original on arXiv Machine Learning →

arXiv:2607. 02307v1 Announce Type: cross Abstract: Several SLOG test categories explicitly involve directional distinctions (modifier position shifts, argument extraction positions), yet AM-Parser, the previous SOTA, uses an AM algebra whose operations do not encode direction.

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

benchmarks

arXiv AI

1d ago

Structural Generalization on SLOG without Hand-Written Rules

arXiv:2604. 26157v4 Announce Type: replace-cross Abstract: Structural generalization in semantic parsing requires systems to apply learned compositional rules to novel structural combinations.

By Zichao Wei

llms benchmarks

arXiv AI

Aug 7

Beyond Sequence Order: Syntax-Informed Positional Embeddings for Transformers

arXiv:2608. 06111v1 Announce Type: cross Abstract: Positional embeddings (PE) in Transformers encode token distance and order but are largely agnostic to \textit{syntactic structure}.

By Haris Riaz, Hyungji Kim, Mihai Surdeanu

llms rag benchmarks

arXiv AI

Jun 18

NeSyCat Torch: A Differentiable Tensor Implementation of Categorical Semantics for Neurosymbolic Learning

arXiv:2606. 19279v1 Announce Type: new Abstract: Neurosymbolic semantics is fragmented: classical, fuzzy, probabilistic and neural systems each define truth by their own inductive rules.

By Daniel Romero Schellhorn, Till Mossakowski, Bj\"orn Gehrke

efficiency

arXiv AI

Jul 22

From Dependency to Compositionality: A Neurosymbolic Lifting of LLM Outputs via Combinatory Categorial Grammar

arXiv:2607. 18961v1 Announce Type: new Abstract: Large language models (LLMs) generate fluent text by incrementally predicting the next token from a prefix.

By Remo Pareschi

llms data-engineering

arXiv Machine Learning

Jul 1

Explicit Fuzzy Logic in the Feed-Forward Layer: Self-Forgetting Quantifiers Discover Legible Grammatical-Licensing Detectors

arXiv:2606. 31845v1 Announce Type: cross Abstract: A transformer's feed-forward (FFN) sublayer materializes the distinctions attention gathers, yet gives no account of what it computes.

By Mark Oskin

llms

arXiv AI

1d ago

SAPE: Sandwich Adapters for Parameter Efficiency in Large Language Model Fine-Tuning

arXiv:2608. 15360v1 Announce Type: cross Abstract: While Parameter-Efficient Fine-Tuning (PEFT) has substantially reduced the hardware cost of adapting Large Language Models (LLMs) by decreasing the number of trainable parameters, recent studies have sought to further improve PEFT through parameter sharing.

By Mohammad Aref Jafari-Raddani, Morteza Mohajjel Kafshdooz

llms rag fine-tuning benchmarks

Related stories

Structural Generalization on SLOG without Hand-Written Rules

Beyond Sequence Order: Syntax-Informed Positional Embeddings for Transformers

NeSyCat Torch: A Differentiable Tensor Implementation of Categorical Semantics for Neurosymbolic Learning

From Dependency to Compositionality: A Neurosymbolic Lifting of LLM Outputs via Combinatory Categorial Grammar

Explicit Fuzzy Logic in the Feed-Forward Layer: Self-Forgetting Quantifiers Discover Legible Grammatical-Licensing Detectors

SAPE: Sandwich Adapters for Parameter Efficiency in Large Language Model Fine-Tuning