Retrieval-augmented generation

Retrieval pipelines, vector search, chunking and reranking: how models are grounded in a corpus instead of their weights.

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arXiv AI

Jun 18

Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories

arXiv:2605. 10840v3 Announce Type: replace-cross Abstract: We present Clin-JEPA, a multi-phase co-training framework for joint-embedding predictive (JEPA) pretraining on EHR patient trajectories.

By Yixuan Yang, Mehak Arora, Ryan Zhang, Baraa Abed, Junseob Kim, Tilendra Choudhary, Md Hassanuzzaman, Kevin Zhu, Ayman Ali, Chengkun Yang, Alasdair Edward Gent, Victor Moas, Rishikesan Kamaleswaran

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arXiv Machine Learning

Jun 18

Quantifying and Auditing LLM Evaluation via Positive--Unlabeled Learning

arXiv:2606. 19057v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used as judges for scalable evaluation, yet such LLM--as--a--Judge systems exhibit systematic biases that are decoupled from semantic quality, most notably verbosity bias.

By Zilong Zhang, Yi-Ting Hung, Lei Ding, Chi-Kuang Yeh

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arXiv AI

Jun 18

Reference-Driven Multi-Speaker Audio Scene Generation from In-the-Wild Priors

arXiv:2606. 19325v1 Announce Type: cross Abstract: Existing multi-speaker dialogue systems bind speakers to utterances through structured supervision: per-turn tags, multi-stream transcriptions, or learnable speaker embeddings.

By Michael Finkelson, Daniel Segal, Eitan Richardson, Shahar Armon, Nani Goldring, Poriya Panet, Nir Zabari, Benjamin Brazowski, Or Patashnik, Yoav HaCohen

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arXiv AI

Jun 18

RippleBench: Capturing Ripple Effects Using Existing Knowledge Repositories

arXiv:2512. 04144v2 Announce Type: replace Abstract: Targeted interventions on language models, such as unlearning or model editing, aim to modify specific information, but their effects often propagate to related, unintended areas (e.

By Roy Rinberg, Usha Bhalla, Igor Shilov, Flavio P. Calmon, Rohit Gandikota

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arXiv Machine Learning

Jun 18

KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction

arXiv:2506. 13196v5 Announce Type: replace Abstract: Accurate prediction of protein-ligand binding affinity is critical for drug discovery.

By Han Liu, Keyan Ding, Peilin Chen, Yinwei Wei, Liqiang Nie, Dapeng Wu, Shiqi Wang

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arXiv Machine Learning

Jun 18

FORGE: Foundational Optimization Representations from Graph Embeddings

arXiv:2508. 20330v5 Announce Type: replace Abstract: Combinatorial optimization problems are ubiquitous in science and engineering.

By Zohair Shafi, Serdar Kadioglu

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arXiv Machine Learning

Jun 18

Generalised Eigenvalue Geometry of Semantic Adversarial Attacks

arXiv:2606. 19212v1 Announce Type: cross Abstract: Recent empirical work shows that semantically equivalent paraphrases can fool financial sentiment classifiers: although a paraphrase remains close to the original under a strong reference embedding, it may shift the target model's representation enough to change the predicted class.

By Martin Anthony, Kaveh Salehzadeh Nobari

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arXiv Machine Learning

Jun 18

Fair Cognitive Impairment Detection Through Unlearning

arXiv:2606. 18571v1 Announce Type: new Abstract: Mild Cognitive Impairment (MCI) is a medical condition characterized by a noticeable decline in memory, language, or thinking abilities.

By William Nguyen, Jiali Cheng, Hadi Amiri

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arXiv AI

Jun 18

MemRerank: Preference Memory for Personalized Product Reranking

arXiv:2603. 29247v3 Announce Type: replace-cross Abstract: LLM-based shopping agents increasingly rely on long purchase histories and multi-turn interactions for personalization, yet naively appending raw history to prompts is often ineffective due to noise, length, and relevance mismatch.

By Zhiyuan Peng, Xuyang Wu, Huaixiao Tou, Yi Fang, Yu Gong

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arXiv AI

Jun 18

Why SWAVE May Not Be All You Need:A Concept-Evolution Retrospective on Complex-Valued Recurrent Language Models

arXiv:2606. 18324v1 Announce Type: cross Abstract: SWave is a complex-valued recurrent language model (169.

By Ramprasath Ganesaraja, Swathika N, Sahil Dilip Panse

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arXiv Machine Learning

Jun 18

Graph Instance Landscapes: When Structural Similarity Does (Not) Reflect Shortest-Path Performance

arXiv:2606. 18267v1 Announce Type: cross Abstract: Benchmarking shortest-path algorithms is commonly based on aggregate performance over heterogeneous graph sets, which limits insight into how different search paradigms react to instance structure.

By Maryam Gholami Shiri, Ivana Krminac, Marko Djukanovi\'c, Sa\v{s}o D\v{z}eroski, Eva Tuba, Tome Eftimov

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arXiv Machine Learning

Jun 18

Signature filtering: a lightweight enhancement for statistical watermark detection in large language models

arXiv:2606. 18430v1 Announce Type: new Abstract: Statistical watermarks help organizations attribute large language model (LLM) outputs, yet existing detectors often struggle when watermark signals are weak, texts are repetitive, or watermarks are edited.

By Chih-Duo Hong, Yen-Pang Chen, Fang Yu

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arXiv Machine Learning

Jun 18

A finite-element-inspired bipartite graph learned simulator for manufacturability assessment in large-deformation sheet forming

arXiv:2605. 22845v2 Announce Type: replace-cross Abstract: Explicit dynamic finite element (FE) simulations are widely used for large deformation engineering analysis, but repeated simulations remain costly during design space exploration and optimisation.

By Yingxue Zhao, Haoran Li, Haosu Zhou, Tobias Pfaff, Nan Li

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arXiv AI

Jun 18

SHIFT: Semantic Harmonization via Index-side Feature Transformation for Multilingual Information Retrieval

arXiv:2606. 18801v1 Announce Type: cross Abstract: With the rapid expansion of massive multilingual corpora, Multilingual Information Retrieval (MLIR) has emerged as a critical technology for global information access.

By Youngjoon Jang, Seongtae Hong, Hyeonseok Moon, Heuiseok Lim

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arXiv AI

Jun 18

Speaker Verification with Speech-Aware LLMs: Evaluation and Augmentation

arXiv:2603. 10827v2 Announce Type: replace-cross Abstract: Speech-aware large language models (LLMs) can accept speech inputs, yet their training objectives largely emphasize linguistic content or specific fields such as emotions or the speaker's gender, leaving it unclear whether they encode speaker identity.

By Thomas Thebaud, Yuzhe Wang, Laureano Moro-Velazquez, Jesus Villalba-Lopez, Najim Dehak

llms rag fine-tuning benchmarks

arXiv AI

Jun 18

The More the Merrier: Combining Properties for ABox Abduction under Repair Semantics for ELbot

arXiv:2606. 19197v1 Announce Type: cross Abstract: Abduction is a central approach to explain missing entailments from a knowledge base by providing a hypothesis, that would, if added to the knowledge base, make the missing entailment become true.

By Anselm Haak, Patrick Koopmann, Yasir Mahmood, Anni-Yasmin Turhan

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arXiv Machine Learning

Jun 18

ToolChain-CRC: Conformal Risk Control for Agentic AI Under Retrieval and Tool-Use Drift

arXiv:2606. 18467v1 Announce Type: cross Abstract: Modern AI agents retrieve documents, call tools, check intermediate information, and then produce a final answer or action.

By Jeffery Opoku, David Banahene

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arXiv AI

Jun 18

Improve Large Language Model Systems with User Logs

arXiv:2602. 06470v3 Announce Type: replace-cross Abstract: Scaling training data and model parameters has long driven progress in large language models (LLMs), but this paradigm is increasingly constrained by the scarcity of high-quality data and diminishing returns from rising computational costs.

By Changyue Wang, Weihang Su, Qingyao Ai, Xingzhao Yue, Rui Zhang, Xiaojia Chang, Yiqun Liu

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Hugging Face Trending Papers

Jun 17

Reference-Driven Multi-Speaker Audio Scene Generation from In-the-Wild Priors

Existing multi-speaker dialogue systems bind speakers to utterances through structured supervision: per-turn tags, multi-stream transcriptions, or learnable speaker embeddings. These systems operate within speech-only pipelines that produce clean vocal sequences without the ambient texture of real conversations.

rag benchmarks

Hugging Face Trending Papers

Jun 17

Generalised Eigenvalue Geometry of Semantic Adversarial Attacks

Recent empirical work shows that semantically equivalent paraphrases can fool financial sentiment classifiers: although a paraphrase remains close to the original under a strong reference embedding, it may shift the target model's representation enough to change the predicted class. Existing robustness theory either assumes a single-model threat model or focuses mainly on empirical attack algorithms.

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