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[2601.13227] Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?
Llms

[2601.13227] Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?

Abstract page for arXiv paper 2601.13227: Insider Knowledge: How Much Can RAG Systems Gain from Evaluation Secrets?

arXiv - AI · 3 min ·
[2601.22440] AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations
Llms

[2601.22440] AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Values from Casual Conversations

Abstract page for arXiv paper 2601.22440: AI and My Values: User Perceptions of LLMs' Ability to Extract, Embody, and Explain Human Value...

arXiv - AI · 4 min ·
[2601.13222] Incorporating Q&A Nuggets into Retrieval-Augmented Generation
Nlp

[2601.13222] Incorporating Q&A Nuggets into Retrieval-Augmented Generation

Abstract page for arXiv paper 2601.13222: Incorporating Q&A Nuggets into Retrieval-Augmented Generation

arXiv - AI · 3 min ·

All Content

[2603.20470] DiffGraph: An Automated Agent-driven Model Merging Framework for In-the-Wild Text-to-Image Generation
Machine Learning

[2603.20470] DiffGraph: An Automated Agent-driven Model Merging Framework for In-the-Wild Text-to-Image Generation

Abstract page for arXiv paper 2603.20470: DiffGraph: An Automated Agent-driven Model Merging Framework for In-the-Wild Text-to-Image Gene...

arXiv - AI · 3 min ·
[2603.20425] Leveraging Natural Language Processing and Machine Learning for Evidence-Based Food Security Policy Decision-Making in Data-Scarce Making
Machine Learning

[2603.20425] Leveraging Natural Language Processing and Machine Learning for Evidence-Based Food Security Policy Decision-Making in Data-Scarce Making

Abstract page for arXiv paper 2603.20425: Leveraging Natural Language Processing and Machine Learning for Evidence-Based Food Security Po...

arXiv - AI · 4 min ·
[2603.20270] FactorSmith: Agentic Simulation Generation via Markov Decision Process Decomposition with Planner-Designer-Critic Refinement
Llms

[2603.20270] FactorSmith: Agentic Simulation Generation via Markov Decision Process Decomposition with Planner-Designer-Critic Refinement

Abstract page for arXiv paper 2603.20270: FactorSmith: Agentic Simulation Generation via Markov Decision Process Decomposition with Plann...

arXiv - AI · 4 min ·
[2603.20213] AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization
Llms

[2603.20213] AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization

Abstract page for arXiv paper 2603.20213: AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization

arXiv - Machine Learning · 4 min ·
[2603.20260] ProMAS: Proactive Error Forecasting for Multi-Agent Systems Using Markov Transition Dynamics
Llms

[2603.20260] ProMAS: Proactive Error Forecasting for Multi-Agent Systems Using Markov Transition Dynamics

Abstract page for arXiv paper 2603.20260: ProMAS: Proactive Error Forecasting for Multi-Agent Systems Using Markov Transition Dynamics

arXiv - AI · 3 min ·
Machine Learning

[R] Causal self-attention as a probabilistic model over embeddings

We’ve been working on a probabilistic interpretation of causal self-attention where token embeddings are treated as latent variables. In ...

Reddit - Machine Learning · 1 min ·
Machine Learning

LightRest Ltd's 'LAGK' Initiative - Leverage-Aware Governance Kernal

Most discussions around AI safety focus on what models know or whether outputs are correct. But since 2019, I’ve been working on somethin...

Reddit - Artificial Intelligence · 1 min ·
Llms

[R] Detection Is Cheap, Routing Is Learned: Why Refusal-Based Alignment Evaluation Fails (arXiv 2603.18280)

Paper: https://arxiv.org/abs/2603.18280 TL;DR: Current alignment evaluation measures concept detection (probing) and refusal (benchmarkin...

Reddit - Machine Learning · 1 min ·
Machine Learning

[N] Understanding & Fine-tuning Vision Transformers

A neat blog post by Mayank Pratap Singh with excellent visuals introducing ViTs from the ground up. The post covers: Patch embedding Posi...

Reddit - Machine Learning · 1 min ·
Llms

[P] no-magic: 47 AI/ML algorithms implemented from scratch in single-file, zero-dependency Python

I've been building no-magic — a collection of 47 single-file Python implementations of the algorithms behind modern AI. No PyTorch, no Te...

Reddit - Machine Learning · 1 min ·
[2510.15520] Discovering Intersectional Bias via Directional Alignment in Face Recognition Embeddings
Machine Learning

[2510.15520] Discovering Intersectional Bias via Directional Alignment in Face Recognition Embeddings

Abstract page for arXiv paper 2510.15520: Discovering Intersectional Bias via Directional Alignment in Face Recognition Embeddings

arXiv - Machine Learning · 4 min ·
[2603.17246] On the Cone Effect and Modality Gap in Medical Vision-Language Embeddings
Llms

[2603.17246] On the Cone Effect and Modality Gap in Medical Vision-Language Embeddings

Abstract page for arXiv paper 2603.17246: On the Cone Effect and Modality Gap in Medical Vision-Language Embeddings

arXiv - Machine Learning · 4 min ·
[2509.08625] An upper bound on the silhouette evaluation metric for clustering
Nlp

[2509.08625] An upper bound on the silhouette evaluation metric for clustering

Abstract page for arXiv paper 2509.08625: An upper bound on the silhouette evaluation metric for clustering

arXiv - Machine Learning · 4 min ·
[2502.05709] Flow-based Conformal Prediction for Multi-dimensional Time Series
Machine Learning

[2502.05709] Flow-based Conformal Prediction for Multi-dimensional Time Series

Abstract page for arXiv paper 2502.05709: Flow-based Conformal Prediction for Multi-dimensional Time Series

arXiv - Machine Learning · 3 min ·
[2603.20048] Structured Latent Dynamics in Wireless CSI via Homomorphic World Models
Machine Learning

[2603.20048] Structured Latent Dynamics in Wireless CSI via Homomorphic World Models

Abstract page for arXiv paper 2603.20048: Structured Latent Dynamics in Wireless CSI via Homomorphic World Models

arXiv - Machine Learning · 3 min ·
[2603.20025] Graph-Informed Adversarial Modeling: Infimal Subadditivity of Interpolative Divergences
Machine Learning

[2603.20025] Graph-Informed Adversarial Modeling: Infimal Subadditivity of Interpolative Divergences

Abstract page for arXiv paper 2603.20025: Graph-Informed Adversarial Modeling: Infimal Subadditivity of Interpolative Divergences

arXiv - Machine Learning · 3 min ·
[2603.19862] IsoCLIP: Decomposing CLIP Projectors for Efficient Intra-modal Alignment
Llms

[2603.19862] IsoCLIP: Decomposing CLIP Projectors for Efficient Intra-modal Alignment

Abstract page for arXiv paper 2603.19862: IsoCLIP: Decomposing CLIP Projectors for Efficient Intra-modal Alignment

arXiv - Machine Learning · 4 min ·
[2603.19840] Explainable cluster analysis: a bagging approach
Nlp

[2603.19840] Explainable cluster analysis: a bagging approach

Abstract page for arXiv paper 2603.19840: Explainable cluster analysis: a bagging approach

arXiv - Machine Learning · 3 min ·
[2603.19439] Subspace Projection Methods for Fast Spectral Embeddings of Evolving Graphs
Machine Learning

[2603.19439] Subspace Projection Methods for Fast Spectral Embeddings of Evolving Graphs

Abstract page for arXiv paper 2603.19439: Subspace Projection Methods for Fast Spectral Embeddings of Evolving Graphs

arXiv - Machine Learning · 4 min ·
[2603.19422] Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs
Machine Learning

[2603.19422] Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs

Abstract page for arXiv paper 2603.19422: Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs

arXiv - Machine Learning · 3 min ·
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