Leveraging Large Language Models (LLMs)-synthesized data to enable representation learning for cold-start entities has emerged as a promising solution for addressing cold-start recommendations. However, existing studies often over-rely on LLM-generated data while neglecting the detrimental noise it may introduce. This...
Tianhao Shi, Yang Zhang, Xiaoxue Zang et al.· ACM Transactions on Informat...· 0 citations
An internally-run experimental report on a reconstructed, modular runtime for synthetic causal learning and experiential memory. The system combines a law-blind causal learner, a reduced graph-based physical field, bounded episodic memory branches, graph-based fusion, and independent-judge interfaces. It is evaluated w...
Mohammed Isaaq· Zenodo (CERN European Organi...· 0 citations
Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support tools with real consequences. Prior research has focused only on a small set of models, leaving open the extent to which gender biases are common and heterogeneous across LLMs. We a...
FAIR PERSONALIZATION & EXTERNAL VALIDITY AT THE LIMITCalibration Privilege, Exclusion Risk, Subgroup Uncertainty, Cross-Site Transfer, Device Heterogeneity, and Equitable Adaptive BCI Feng Cheng-en (33) x Starli Does personalization help people differently - or does it simply make access easier for the people who were...
33· Zenodo (CERN European Organi...· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23071859. Authors of the review Name: Stephanie R. U, ORCID: https://orcid.org/0000-0002-6220-4661 Bio: Stephanie U is a PhD candidate whose work investigates the role of circadian...
Stephanie U, 2 other authors· Zenodo (CERN European Organi...· 0 citations
This work examines both model behavior and internal representations, using the empirical neural tangent kernel (NTK) as the primary diagnostic tool, and develops practical guidance for designing DR schemes, selecting models, and detecting shortcut learning.
End-to-end runtime analysis further confirms that the overhead introduced by tracking, CLIP-based recurrence matching, and the identifiability gate remains modest relative to the dominant Sa2VA inference cost, thereby validating the efficiency of the proposed pipeline.
Abu Hanif Muhammad Syarubany, Jaehyun Jang, Si-Woo Lim et al.· 0 citations
NAMOH, an architecture-native sparse attention mechanism that activates only its assigned tokens and performs causal attention within this subsequence, is introduced, and it is hoped this work offers a new path for scaling attention, with parameter scaling directly enabling context scaling.
A new scaling law with separate benefit and harm terms that allows the value of an AI token to change sign while also reducing to Chinchilla in the absence of AI text is proposed.
Jenna Russell, Ben Glickenhaus, Katherine Thai et al.· 0 citations
Recent advances in large language models have made automatic game generation increasingly feasible, yet reliably improving generated games beyond a playable version remains challenging. Naive iterative refinement can easily overfit a small set of test cases, producing fragile games with unresolved bugs, missing behavio...
Wen-Yi Wu, Ming-Hao Fu, Jie-Yu You et al.· 0 citations
Representing scientific papers as points in a space lets us search for similar papers and inquire about how fields relate to one another and drive innovation. Beyond search, the vector space of papers invites generation: mixing papers through simple vector operations creates new points, mirroring combinatorial novelty,...
Chand Sahil Mansuri, Joel Zachariah, Sadamori Kojaku· 0 citations
Groupwise Reward Geometry Conditioning (GRGC) is proposed, a two-stage framework that improves advantage construction by shaping student-side reward groups during both critic training and policy optimization, and introduces negligible overhead over GAD.
