arXiv AI July 29, 2026 By Athanasios Vlontzos, Giorgos Papanastasiou, Bernhard Kainz, Sotirios Tsaftaris

Entangled by Design: Spurious Intra-Variable Signal Routing in Tabular In-Context Learners

arXiv:2607. 25532v1 Announce Type: new Abstract: Consider a model trained at a single hospital to predict patient recovery, where the measured feature $X$ bundles the patient's true health signal ($C$) with a systematic artefact from that hospital's equipment ($S$).

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benchmarks

arXiv AI

Jul 7

Input Pathways Shape Few-Shot, Not Zero-Shot, Binding in Tiny Transformers: A Fully-Enumerable Study

arXiv:2607. 04926v1 Announce Type: cross Abstract: How does the way information reaches a transformer -- as symbolic tokens, a clean per-factor "oracle" code, or an entangled perceptual vector -- shape whether it binds that information compositionally?

By Yoshiyuki Ootani

llms safety

arXiv Statistics ML

3d ago

Raw-Routed Mixture of Adapters: A Causal Intervention for Routing Collapse in Time Series Foundation Models

arXiv:2609.39445v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) commonly adapt to new data by attaching a single trainable head to a frozen backbone, a one-size-fits-all setup...

By Hung Phan, Thuy T. Nguyen, Minh Ngoc Dinh, Nhat-Quang Tran

fine-tuning

Hugging Face Trending Papers

Sep 17

Should This Case Be Adapted? Prediction Fragmentation Controls Test-Time Adaptation

The paper investigates test‑time adaptation for medical image segmentation, showing that a fixed adaptation horizon can harm many individual cases. It introduces prediction fragmentation—a measure of disagreement between the source model and the adapted mask—to predict harmful adaptation without extra labels or backward passes. Using a case‑level router based on this metric, the authors reduce harmful adaptation on cardiac MRI from 58.7% to 20% while maintaining accuracy.

benchmarks

arXiv Machine Learning

Aug 4

Measuring in-context algorithmic reasoning in language models against an exact Bayes-optimal standard

arXiv:2608. 01575v1 Announce Type: new Abstract: Whether large language models perform genuine algorithmic reasoning or mere pattern completion is hard to test, because most benchmarks lack a ground truth for correct inductive inference.

By Hector Zenil, Luan Ozelim

llms benchmarks safety

arXiv Computation and Language

Aug 24

Directional Contextual Representations for Dependency Relations: Why Cross-Direction Pairing Fails

arXiv:2608.20647v1 Announce Type: new Abstract: Splitting a bidirectional LSTM's contextual representation into a forward-only $F_i$ (strictly a function of tokens $1..i$) and a backward-only $B_i$ (...

By Sai Krishna Arthanari, JaeHyeong Chang, Chengzhe Sun, Siwei Lyu

arXiv Computer Vision

Aug 24

When does fusing hand-crafted knowledge with learned representations pay? A cost-normalized benchmark of stacking, substitution, and interference

arXiv:2608.21098v1 Announce Type: new Abstract: Fusing prior knowledge with data-driven learning is attractive where data is scarce, yet no controlled account says when it helps, is redundant, or har...

By Ahmad AlMughrabi, Albert Clop, Benjamin Busam, Ricardo Marques, Petia Radeva

computer-vision benchmarks