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 16

Creative Collision: Directorial Persona Steering and Competition in Large Language Models

arXiv:2606. 16240v1 Announce Type: cross Abstract: Activation steering has emerged as a powerful tool for shaping the behaviour of large language models at inference time, yet most prior work injects a \emph{single} semantic direction into the residual stream.

By Subramanyam Sahoo, Justin Shenk

llms

arXiv AI

Jun 16

ACCORD: Action-Conditioned Contextual Grounding for Language Agents

arXiv:2606. 16432v1 Announce Type: cross Abstract: User instructions are often underspecified because humans rely on implicit assumptions about the surrounding environment.

By Lai Jiang, Cheng Qian, Zhenhailong Wang, Pan Lu, Heng Ji, Hao Peng

llms agents robotics benchmarks

arXiv AI

Jun 16

AQ4SViT: An Automated Quantization Framework with Search Gating Policy for Compressing Spiking Vision Transformers

arXiv:2606. 15523v1 Announce Type: cross Abstract: Spiking Vision Transformers (SViTs) have emerged as alternative low-power ViT models, but their large sizes hinder their deployments on resource-constrained embedded AI systems.

By Rachmad Vidya Wicaksana Putra, Saad Iftikhar, Muhammad Shafique

llms efficiency benchmarks

arXiv AI

Jun 16

FORTIS: Benchmarking Over-Privilege in Agent Skills

arXiv:2605. 09163v3 Announce Type: replace Abstract: Large language model agents increasingly operate through an intermediate skill layer that mediates between user intent and concrete task execution.

By Shawn Li, Chenxiao Yu, Han Wang, Wei Yang, Ryan Rossi, Franck Dernoncourt, Xiyang Hu, Philip Yu, Chaowei Xiao, Huan Zhang, Yue Zhao

llms agents benchmarks safety

arXiv AI

Jun 16

The Faithfulness Gap: Certifying Semantic Equivalence Between Natural-Language and Formal Mathematical Statements

arXiv:2606. 16541v1 Announce Type: new Abstract: Autoformalization, translating natural-language mathematics into formal proof assistants, is bottlenecked not by translation fluency but by \emph{faithfulness}: a formal statement can typecheck and be provable, yet still encode a different theorem than the source intended.

By Noor Islam S. Mohammad, Tamim Sheikh

llms benchmarks

arXiv Machine Learning

Jun 16

Taming Curvature: Architecture Warm-Up for Stable Transformer Training

arXiv:2606. 16768v1 Announce Type: new Abstract: Training billion-parameter Transformers is often brittle, with transient loss spikes and divergence that waste compute.

By Sameera Ramasinghe, Ajanthan Thalaiyasingam, Hadi Mohaghegh Dolatabadi, Chamin Hewa Koneputugodage, Gil Avraham, Violetta Shevchenko, Yan Zuo, Karol Pajak, Alexander Long

llms benchmarks

arXiv AI

Jun 16

AI-Driven Framework for Adaptive Water Network Management with Proof-of-Concept Implementation: Addressing Non-Revenue Water in Jordan

arXiv:2606. 15709v1 Announce Type: new Abstract: Jordan faces severe water scarcity with 50\% of water produced is lost to leakage, theft and metering issues also known as non-revenue water (NRW).

By Mohammed Fasha, Nahel Al-Maayta, Bilal Sowan, Mohammad Athamneh, Husam Barham

llms rag agents safety

arXiv AI

Jun 16

AutoDojo: Adaptive Attacks Expose Superficial Defenses and User-Underspecification Limits in LLM Agents

arXiv:2606. 15057v1 Announce Type: cross Abstract: Indirect prompt injection (IPI) is a major security threat to LLM-powered agents.

By Xinhang Ma, Taoran Li, Chaowei Xiao, Zhiyuan Yu, Ning Zhang, Yevgeniy Vorobeychik

llms agents multimodal benchmarks

arXiv AI

Jun 16

DynaDebate: Breaking Homogeneity in Multi-Agent Debate with Dynamic Path Generation

arXiv:2601. 05746v2 Announce Type: replace Abstract: Recent years have witnessed the rapid development of Large Language Model-based Multi-Agent Systems (MAS), which excel at collaborative decision-making and complex problem-solving.

By Zhenghao Li, Zhi Zheng, Wei Chen, Jielun Zhao, Yong Chen, Tong Xu, Enhong Chen

llms agents benchmarks

arXiv AI

Jun 16

Unified Multimodal Model for Brain MRI Imputation and Understanding

arXiv:2606. 16484v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) hold great potential for medicine, as they inherit knowledge from LLM and allow multiple data modalities to be integrated, analysed and interpreted in natural language.

By Zhiyun Song, Che Liu, Tian Xia, Avinash Kori, Wenjia Bai

llms rag multimodal safety

arXiv AI

Jun 16

CmdNeedle: Measuring the Incompleteness of Command Denylists for AI Agents

arXiv:2606. 15549v1 Announce Type: cross Abstract: The adoption of AI agents is increasing rapidly.

By Chuyang Chen, Zhiqiang Lin

llms agents

arXiv AI

Jun 16

DeepRoot: A KG-Coordinated Multi-Agent System for Therapeutic Reasoning over Historical Medical Texts

arXiv:2606. 15931v1 Announce Type: cross Abstract: Historical medical archives and traditional medicines hold immense potential for drug discovery and remain a primary source for current drug development.

By Zijian Carl Ma, Sean J. Wang, Sijbren Kramer, Li Erran Li

llms rag agents

arXiv Machine Learning

Jun 16

Circuit Tracing in Autoregressive Protein Language Models

arXiv:2606. 16044v1 Announce Type: new Abstract: Protein language models (pLMs) can generate novel protein sequences with properties beyond those observed in nature, yet the mechanisms underlying protein generation remain poorly understood.

By Darin Tsui, William Deinzer, Daniel Saeedi, Amirali Aghazadeh

llms safety

arXiv AI

Jun 16

Revisiting Chebyshev Polynomial and Anisotropic RBF Models for Tabular Regression

arXiv:2602. 22422v2 Announce Type: replace-cross Abstract: Smooth-basis models such as Chebyshev polynomial regressors and radial basis function (RBF) networks are well established in numerical analysis.

By Luciano Gerber, Huw Lloyd

llms efficiency benchmarks

arXiv AI

Jun 16

Towards Verifiable Agentic Data Science: Solving Irregular TSQA Via Tool-Grounded Reasoning

arXiv:2606. 15107v1 Announce Type: new Abstract: Time series data in real-world deployments is overwhelmingly irregular.

By Sanhorn Chen, Xiaoyang Chen, Boyu Liu, Roy Zhao

llms agents nlp benchmarks

arXiv AI

Jun 16

Dual-Uncertainty Guided Policy Learning for Multimodal Reasoning

arXiv:2510. 01444v3 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has advanced reasoning capabilities in multimodal large language models.

By Rui Liu, Dian Yu, Tong Zheng, Runpeng Dai, Zongxia Li, Wenhao Yu, Zhenwen Liang, Linfeng Song, Haitao Mi, Pratap Tokekar, Dong Yu

llms reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Jun 16

Multimodal Evaluator Preference Collapse: Cross-Modal Contagion in Self-Evolving Agents

arXiv:2606. 16682v1 Announce Type: new Abstract: When AI agents use language models to evaluate their own outputs in a feedback loop, systematic biases emerge.

By Zewen Liu

llms agents multimodal

arXiv AI

Jun 16

JE-IRT: A Geometric Lens on LLM Abilities through Joint Embedding Item Response Theory

arXiv:2509. 22888v2 Announce Type: replace Abstract: Standard LLM evaluation practices compress diverse abilities into single scores, obscuring their inherently multidimensional nature.

By Louie Hong Yao, Nicholas Jarvis, Tiffany Zhan, Saptarshi Ghosh, Linfeng Liu, Tianyu Jiang

llms rag safety

arXiv Machine Learning

Jun 16

Zero-order Parameter-free Optimization for LMO-based Methods: Novel Approach for Efficient Fine-tuning

arXiv:2606. 14970v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) has become a central application of modern optimization, enabling pretrained models to adapt to diverse downstream tasks and domain-specific data.

By Dmitriy Bystrov, Daniil Medyakov, Dmitry Bylinkin, Aleksandr Beznosikov

llms fine-tuning

arXiv AI

Jun 16

State-Grounded Multi-Agent Synthetic Data Generation for Tool-Augmented LLMs

arXiv:2606. 16307v1 Announce Type: new Abstract: Training tool-augmented LLM agents requires large corpora of multi-turn, tool-grounded conversational data that is expensive to annotate, privacy-constrained in production settings, and largely absent from public datasets.

By Rahul Khedar, Eshita, Sneha Teja Sree Reddy Thondapu, Mayank Malhotra, Arup Das, Jitesh Chandra, Yun-Shiuan Chuang, Chaitanya Kulkarni, Arun Menon, Linsey Pang, Avinash Karn, Mouli V, Prakhar Mehrotra

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