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 AI

Jul 8

Evaluating Fine-Tuning and Metrics for Neural Decompilation of Dart AOT Binaries

arXiv:2607. 06125v1 Announce Type: cross Abstract: Neural decompilation is increasingly studied as a code-generation problem, yet its evaluation methodology remains underdeveloped for modern languages.

By Raafat Abualazm, Ayman AboElhassan, Amr G. Wassal

llms fine-tuning benchmarks

arXiv AI

Jul 8

Privilege and confidentiality in generative AI workflows

arXiv:2607. 05479v1 Announce Type: cross Abstract: Generative AI (GenAI) systems store and process client data in three distinct ways: in the model's parameters through training and memorisation, in the context window during a live session, and in knowledge databases for retrieval-augmented generation (RAG).

By V\'aclav Jane\v{c}ek, Thomas Melham

llms rag benchmarks

arXiv AI

Jul 8

LLM-Guided Measurement Credibility Correction for Trustworthy Industrial Process Inference

arXiv:2607. 06111v1 Announce Type: cross Abstract: Industrial prediction and soft sensing depend on credible input measurements.

By Youcheng Zong, Runda Jia, Dakuo He

llms

arXiv AI

Jul 8

Decision Protocols in Multi-Agent Large Language Model Conversations

arXiv:2607. 05477v1 Announce Type: cross Abstract: Improving the task performance of Large Language Models (LLMs) is essential, yet scaling these models faces significant challenges such as diminishing returns and high costs.

By Lars Benedikt Kaesberg

llms agents benchmarks

arXiv AI

Jul 8

FreqDepthKV: Frequency-Guided Depth Sharing for Robust KV Cache Compression in Long-Context LLM Inference

arXiv:2607. 06519v1 Announce Type: new Abstract: Long-context LLM inference is increasingly limited by the memory and bandwidth cost of KV caches, yet aggressive compression can remove the layer-specific evidence needed for retrieval and multi-step reasoning.

By Anna C\'ordoba, Adam Puente Tercero, Nerea Angulo Hijo, Mar Linares Tercero, Julia Barrientos, Ainhoa Miranda, Jes\'us Olivera

llms nlp efficiency benchmarks

arXiv AI

Jul 8

X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models

arXiv:2607. 06163v1 Announce Type: cross Abstract: Foundation Models for Electronic Health Records (FEMRs) are pretrained on large-scale structured patient data, enabling them to convert longitudinal patient trajectories into generalizable representations for diverse clinical prediction tasks.

By Jie Huang, Pengfei Yin, Zihan Xu, Daniel Capurro, Mike Conway, Ting Dang

llms safety

arXiv AI

Jul 8

Faithful or Findable? Evaluating LLM-Generated Metadata for RDF Dataset Search

arXiv:2607. 05970v1 Announce Type: cross Abstract: Dataset search depends heavily on metadata, making LLM-generated metadata a consequential form of synthetic content in retrieval systems.

By Riccardo Terrenzi, Serkan Ayvaz

llms agents

arXiv AI

Jul 8

Synthetic Consumer Insight Generation with Large Language Models

arXiv:2607. 05761v1 Announce Type: new Abstract: Modern data-driven marketing relies on large amounts of consumer data, yet collecting such data can be costly, time-consuming, and difficult to scale.

By Stephen L. France, Pia. A. Albinsson

llms

arXiv AI

Jul 8

Information Limits and Attractor Dynamics in Economies of Frontier LLM Agents: A Pre-Registered Test

arXiv:2607. 06001v1 Announce Type: new Abstract: We report a pre-registered, two-part experiment on small economies of frontier language-model agents (Claude Opus 4.

By Cheng Qian

llms agents

arXiv Machine Learning

Jul 8

Statistically Meaningful Geometry and Gauge Symmetry Breaking: A Geometric Foundation for Scientific Discovery and Intelligence Emergence

arXiv:2607. 05436v1 Announce Type: new Abstract: The rapid scaling of over-parameterized machine learning architectures, particularly LLMs, raises a profound crisis: do these systems exhibit genuine intelligence, or are they merely sophisticated statistical pattern matchers?

By Bing Cheng, Yi-Shuai Niu, Howell Tong, Shing-Tung Yau

llms agents safety

arXiv Machine Learning

Jul 8

Beyond Heuristic Tuning: Power-Calibrated LLM Watermarking

arXiv:2607. 05694v1 Announce Type: cross Abstract: Logit-based watermarking is a widely used mechanism for identifying LLM generated content, yet its effectiveness is governed by a fundamental trade-off between detectability and semantic distortion.

By Xiaopu Wang, Zelin He, Chengyuan Liu, Runze Li

llms

arXiv AI

Jul 8

aiAuthZ: Off-Host, Identity-Bound Authorization for AI Agents

arXiv:2607. 05518v1 Announce Type: cross Abstract: AI agents issue tool calls on the basis of text they cannot verify, so any party who controls part of the context can forge the appearance of authority.

By Sai Varun Kodathala

llms agents safety

arXiv AI

Jul 8

The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities

arXiv:2607. 05743v1 Announce Type: cross Abstract: AI coding agents now read repositories, call tools, and execute shell commands with limited human oversight, and a fast-growing body of work studies whether the execution layer around them is actually safe.

By Mohammadreza Rashidi

llms agents benchmarks safety

arXiv Machine Learning

Jul 8

A Constrained Optimization Perspective of Unrolled Transformers

arXiv:2601. 17257v2 Announce Type: replace Abstract: We introduce a constrained optimization framework for training transformers that behave like optimization descent algorithms.

By Javier Porras-Valenzuela, Samar Hadou, Alejandro Ribeiro

llms nlp

arXiv AI

Jul 8

Agent Step Value: Probing the Observer Effect in Black-Box Traces

arXiv:2607. 04419v2 Announce Type: replace Abstract: Final-answer scores hide which agent transitions helped or harmed a trace.

By Andrew Zhang, Chengzhan Li

llms agents

arXiv AI

Jul 8

NegROI: Click-Centric Uncertainty-Guided Refinement with Scene-Conditioned Negative Prompts for Robust Interactive 3D Segmentation

arXiv:2607. 05955v1 Announce Type: cross Abstract: Interactive 3D segmentation aims to extract object masks in point clouds with minimal user clicks.

By Shuheng Zhang, Feng Wu

llms computer-vision benchmarks

arXiv AI

Jul 8

The GenAI Skill Bypass: Mapping Divergent Pathways of University Students and Staff AI Literacy

arXiv:2607. 05411v1 Announce Type: cross Abstract: Higher education institutions are increasingly expected to ensure that both students and staff develop Generative AI (GenAI) literacies.

By Eduardo Oliveira, Narelle English, Tracii Ryan, Kamila Misiejuk, Cory dal Ponte, Sonsoles L\'opez-Pernas, Mohammed Saqr

llms rag

arXiv AI

Jul 8

Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion

arXiv:2607. 06546v1 Announce Type: cross Abstract: Denoising graphs is a fundamental problem in graph learning and the core operation of graph diffusion models.

By Shervin Khalafi, Igor Krawczuk, Sergio Rozada, Charilaos Kanatsoulis, Antonio G Marques, Alejandro Ribeiro

llms diffusion

arXiv AI

Jul 8

VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection

arXiv:2607. 06254v1 Announce Type: cross Abstract: Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors.

By Sharayu N. Deshmukh, Md Rashidunnabi, Nelton Tiago Gemo, Kurundkar G. D., Mahamune M. R., Nilesh K. Deshmukh

llms diffusion multimodal benchmarks safety

arXiv AI

Jul 8

What Images Cannot Say: Language-Guided Olfactory Representation Learning

arXiv:2607. 06402v1 Announce Type: cross Abstract: Images tell us what a scene looks like, but rarely what it would feel like to be there.

By Eleftherios Tsonis, Xi Wang, Vicky Kalogeiton

llms rag multimodal

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