Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv Machine Learning

Jun 10

N-GRPO: Embedding-Level Neighbor Mixing for Enhanced Policy Optimization

arXiv:2606. 10768v1 Announce Type: new Abstract: The success of Large Language Models in mathematical reasoning relies heavily on the generation of diverse and valid solution paths during the rollout phase.

By Xukun Zhu, Hang Yu, Peng Di, Linchao Zhu

llms rag benchmarks

arXiv Machine Learning

Jun 10

Geometry-Aware Reinforcement Learning for 2D Irregular Nesting

arXiv:2606. 10611v1 Announce Type: new Abstract: Traditional heuristic solvers for the 2D irregular nesting problem share a fundamental limitation: they are blind to polygon geometry, relying on guided brute-force to navigate the continuous placement space with minimal geometrical guidance.

By Auguste Lehuger, Guillaume Henon-Just

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 10

Alignment Defends LLMs from Property Inference Attacks

arXiv:2606. 10217v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly fine-tuned on domain-specific datasets that may contain sensitive, dataset-level properties.

By Pengrun Huang, Chhavi Yadav, Ruihan Wu, Kamalika Chaudhuri

llms reinforcement-learning fine-tuning safety

arXiv Machine Learning

Jun 10

DMT: Demographic Conditioning, Morphology-Enhanced Transformer for Cuffless Blood Pressure Estimation from PPG Signals

arXiv:2606. 11125v1 Announce Type: cross Abstract: Blood pressure (BP) is a key marker for cardiovascular risk assessment and therapeutic decision-making, and Photoplethysmography (PPG) enables low-cost, wearable-friendly cuffless BP estimation.

By Yidan Shen, Neville Mathew, Maham Rahimi, Deependra Dhakal, George Zouridakis, Xin Fu, Renjie Hu

llms

arXiv Machine Learning

Jun 10

SpenseGPT: Practical One-shot Pruning Enabling Sparse and Dense GEMMs for LLM Inference

arXiv:2606. 10445v1 Announce Type: new Abstract: Semi-structured 2:4 sparsity is widely supported by modern accelerators, providing up to a 2x theoretical speedup.

By Jaeseong Lee, Seung-won Hwang, Samyam Rajbhandari

llms efficiency

arXiv Machine Learning

Jun 10

LLM-as-a-Discriminator: When Synthetic Tables Still Look Real

arXiv:2606. 09865v1 Announce Type: new Abstract: Privacy and data sharing are often in tension.

By Manel Slokom, Malek Slokom, Thierno Kante

llms safety

arXiv AI

Jun 10

PromptEmbedder: Efficient and Transferable Text Embedding via Dual-LLM Soft Prompting

arXiv:2605. 28066v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable efficacy in text embedding, yet current adaptation methods like LoRA face significant bottlenecks in computational efficiency and cross-architecture transferability.

By Yu-Che Tsai, Kuan-Yu Chen, Yuan-Hao Chen, Yu-Han Chang, Ching-Yu Tsai, Yu-Hsiang Chuang, Shou-De Lin

llms rag fine-tuning benchmarks safety

arXiv AI

Jun 10

From Volume to Value: Preference-Aligned Memory Construction for On-Device RAG

arXiv:2605. 18271v2 Announce Type: replace-cross Abstract: With the rapid emergence of personal AI agents based on Large Language Models (LLMs), implementing them on-device has become essential for privacy and responsiveness.

By Changmin Lee, Jaemin Kim, Taesik Gong

llms rag agents benchmarks safety

arXiv AI

Jun 10

Learning Evidence Highlighting for Frozen LLMs

arXiv:2604. 22565v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can reason well, yet often miss decisive evidence when it is buried in long, noisy contexts.

By Shaoang Li, Yanhang Shi, Yufei Li, Mingfu Liang, Xiaohan Wei, Yunchen Pu, Fei Tian, Chonglin Sun, Frank Shyu, Luke Simon, Sandeep Pandey, Xi Liu, Jian Li

llms nlp reinforcement-learning

arXiv Machine Learning

Jun 10

From Confident Closing to Silent Failure: Characterizing False Success in LLM Agents

arXiv:2606. 09863v1 Announce Type: new Abstract: LLM agents can fail silently by asserting task completion when the environment state shows otherwise.

By Laksh Advani

llms agents benchmarks

arXiv AI

Jun 10

Durable Evaluation Framework: Adversarial Arbitration for Sycophancy Reduction in Large Language Models

arXiv:2606. 07532v2 Announce Type: replace-cross Abstract: RLHF-trained models are systematically biased toward agreement over accuracy, a structural property of the training process.

By Sam Ryan

llms agents reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 10

TENP: Trapezoidal Expert Neuron Pruning For Mixture-of-Experts

arXiv:2606. 09885v1 Announce Type: new Abstract: Mixture-of-Experts large language models (LLMs) scale efficiently through sparse activation, yet their deployment is fundamentally constrained by the large static parameter footprint of experts.

By Jiangyang He, Shaolin Zhu, Deyi Xiong

llms efficiency

arXiv AI

Jun 10

RAG over Thinking Traces Can Improve Reasoning Tasks

arXiv:2605. 03344v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) has proven effective for knowledge-intensive tasks, but is widely believed to offer limited benefit for reasoning-intensive problems such as math and code generation.

By Negar Arabzadeh, Wenjie Ma, Sewon Min, Matei Zaharia

llms rag benchmarks

arXiv AI

Jun 10

RankLLM: Weighted Ranking of LLMs by Quantifying Question Difficulty

arXiv:2602. 12424v2 Announce Type: replace-cross Abstract: Benchmarks establish a standardized evaluation framework to systematically assess the performance of large language models (LLMs), facilitating objective comparisons and driving advancements in the field.

By Ziqian Zhang, Xingjian Hu, Yue Huang, Kai Zhang, Ruoxi Chen, Yixin Liu, Qingsong Wen, Kaidi Xu, Xiangliang Zhang, Neil Zhenqiang Gong, Lichao Sun

llms benchmarks

arXiv AI

Jun 10

ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering

arXiv:2510. 04514v3 Announce Type: replace Abstract: Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual interpretation rather than relying on textual shortcuts.

By Rachneet Kaur, Nishan Srishankar, Zhen Zeng, Sumitra Ganesh, Manuela Veloso

llms agents nlp multimodal benchmarks

arXiv AI

Jun 10

AMEL: Accumulated Message Effects on LLM Judgments

arXiv:2605. 22714v3 Announce Type: replace Abstract: Large language models are routinely used as automated evaluators: to review code, moderate content, or score outputs, often with many items passing through one conversation.

By Sid-Ali Temkit

llms safety

arXiv AI

Jun 10

Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystem

arXiv:2506. 08134v4 Announce Type: replace Abstract: Peer review, the bedrock of scientific advancement in machine learning (ML), is strained by a crisis of scale.

By Qiyao Wei, Samuel Holt, Jing Yang, Markus Wulfmeier, Mihaela van der Schaar

llms

arXiv AI

Jun 10

CLP: Collocation-Length Prediction for Zero-Loss Adaptive Multi-Token Inference

arXiv:2606. 10935v1 Announce Type: cross Abstract: Large language model inference is bottlenecked by autoregressive decoding, where each token requires a full forward pass.

By Xuezhen Xie, Zhiqiang Zhou

llms

arXiv AI

Jun 10

From Perception to Action: Can UI Interventions Foster Sustainable LLM Chatbot

arXiv:2606. 10861v1 Announce Type: cross Abstract: LLM-powered chatbots are increasingly embedded in everyday workflows, raising sustainability concerns due to their energy use.

By Nitish Patkar, Pooja Rani, Jack Gl\"asser, Simon L\"uscher, Martin Kropp

llms

arXiv AI

Jun 10

Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation

arXiv:2606. 10749v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly moving from conversational interfaces to software components that plan, invoke tools, maintain memory, and act on external environments.

By Yuchen Ling, Shengcheng Yu, Zhenyu Chen, Chunrong Fang

llms agents benchmarks

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