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

Jul 27

Reliability Scales Inversely: Bigger Language Models Compound Mistakes Faster

arXiv:2607. 18292v2 Announce Type: replace Abstract: As language models scale, answers start truer but degrade faster: scaling buys capability but erodes reliability.

By Kushal Chakrabarti

llms safety

arXiv Machine Learning

Jul 27

Quantifying Political Partisanship for Cross-Platform Analyses

arXiv:2607. 21842v1 Announce Type: cross Abstract: Research on political polarization on social media depends on the ability to reliably measure partisanship in user-generated content.

By Fathima Ameen, Christopher G. Healey

llms rag safety

arXiv Machine Learning

Jul 27

FBLayout: Optimizing Memory Layout for Efficient LLM Finetuning on Mobile GPUs

arXiv:2607. 21624v1 Announce Type: cross Abstract: Transformer-based models have enabled unprecedented capabilities across language, vision, and multimodal tasks.

By Kahou Tam, Wei Niu, Yu Bao, Xiaomin Ouyang, Chengzhong Xu, Li Li

llms fine-tuning efficiency multimodal safety

arXiv Machine Learning

Jul 27

How Do AI Coding Agents Contribute to Software Development? an Empirical Study of Agentic Pull Requests

arXiv:2607. 21832v1 Announce Type: cross Abstract: Recent advances in large language models and their rapid adoption across software engineering tasks have made Artificial Intelligence (AI) coding agents an integral component of modern software development workflows.

By Iren Mazloomzadeh, Mohammad Mehdi Morovati, Foutse Khomh

llms agents

arXiv Machine Learning

Jul 27

Small Vision-Language Models Know When They Are Wrong But Cannot Say So: A Two-Model Study of Stated versus Internal Confidence Under Realistic Image Degradation

arXiv:2607. 22034v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed on consumer hardware where input images are degraded by compression, camera shake, and poor lighting.

By M M Asif Ferdous

llms multimodal

arXiv Machine Learning

Jul 27

Multi-Agent Debate and Visual Information Extraction for SeePhys Pro: A 1st-Place Technical Report from ICML 2026 AI4Math Track 3 Challenge

arXiv:2607. 21946v1 Announce Type: new Abstract: This technical report presents our approach to Challenge Track~3: SeePhys Pro at the 3rd AI for Math Workshop, where the task is to answer college-level physics questions whose statement and figure may be given partly or entirely as an image.

By Jiseok Kwak, Suhyeon Jo, Taewoo Kim, Yeongmin Kim, Byeonghu Na, Il-chul Moon

llms agents benchmarks

arXiv Machine Learning

Jul 27

IQ-JEPA: A Joint-Embedding Predictive Architecture with a Hermitian Vision Transformer for Sound Speed and Attenuation Estimation from Ultrasound IQ Data

arXiv:2607. 22351v1 Announce Type: new Abstract: The speed of sound in tissue is a prerequisite for well-focused imaging and has diagnostic value, but recovering it from raw pulse-echo channel data is fundamentally a nonlinear inverse problem.

By Masashi Sode, Gianmarco Pinton

llms rag computer-vision fine-tuning

arXiv Machine Learning

Jul 27

TRACE-ROUTER: Task-Consistent and Adaptive Online Routing for Agentic AI

arXiv:2607. 22465v1 Announce Type: cross Abstract: Routing to select large language models (LLMs) with different cost-quality trade-offs has become a fundamental deployment feature of enterprise AI.

By Ritik Raj, Souvik Kundu, Sarbartha Banerjee, Dheemanth Joshi, Ishita Vohra, Tushar Krishna

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 27

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models

arXiv:2505. 23378v3 Announce Type: replace Abstract: Speaker-dependent modelling can substantially improve performance in speech-based health monitoring applications.

By Roseline Polle, Agnes Norbury, Alexandra Livia Georgescu, Nicholas Cummins, Stefano Goria

llms rag

arXiv Machine Learning

Jul 27

Numerical Fragility in Transformers: A Layer-wise Theory for Risk Estimation and Selective Stabilization

arXiv:2510. 21770v2 Announce Type: replace Abstract: Low-precision execution can induce substantial forward discrepancies in Transformers even for fixed weights and input, yet these discrepancies are usually monitored only at the output and lack a layer-wise theoretical account.

By Jinwoo Baek

llms benchmarks

arXiv Machine Learning

Jul 27

Toward User-Conditioned Evaluation of Personal LLM Agents under Temporal Interventions

arXiv:2607. 21635v1 Announce Type: new Abstract: Personal agents maintain memories, learned skills, tool configurations, and policy state that evolve with each user.

By Pin Qian, Su Wang, Yihang Chen, Qiaolin Yu, Xiaoyuan Wang, Zhitong Guo, Zhicheng Wang, Junxian You

llms agents benchmarks safety

arXiv Machine Learning

Jul 27

IFCLoRA: Topology-Aware Rank Allocation for Parameter-Efficient Fine-Tuning

arXiv:2607. 22251v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning method for large language models, but its performance depends strongly on how a fixed rank budget is distributed across Transformer modules.

By Wei Zhang, Xinwu Liu, Yihang Cheng

llms fine-tuning

arXiv Machine Learning

Jul 27

LLM-Extracted Covariates for Clinical Causal Inference: Rethinking Integration Strategies

arXiv:2604. 16763v3 Announce Type: replace Abstract: Causal inference from electronic health records (EHR) is fundamentally limited by unmeasured confounding: critical clinical states such as frailty, goals of care, and mental status are documented in free-text notes but absent from structured data.

By Lei Liu, Jialin Chen, Kathy Macropol

llms nlp safety

arXiv Machine Learning

Jul 27

DBA-Bench: A Production-Fidelity Benchmark for LLM-Based Database Operations Agents

arXiv:2607. 22165v1 Announce Type: cross Abstract: LLM-based database agents show promise, but differing task scopes, testbeds, and metrics hinder comparison.

By Junming Chen, Junyang Jiang, Xu Chen, Zibo Liang, Kai Zheng

llms agents benchmarks safety

arXiv Machine Learning

Jul 27

Math Education Digital Shadows for Investigating Learning with GenAI: Mathematics Performance, Anxiety, and Confidence in LLMs

arXiv:2604. 27618v2 Announce Type: replace-cross Abstract: Understanding the impact of large language models (LLMs) on mathematics education requires data on LLMs' mathematical performance and biases.

By Naomi Esposito, Anthony Tricarico, Luisa Porzio, Ali Aghazadeh Ardebili, Massimo Stella

llms

arXiv Machine Learning

Jul 27

Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecasting

arXiv:2607. 22299v1 Announce Type: cross Abstract: Forecasting multiple time-series with high-dimensional covariates presents a core challenge: unifying common temporal patterns while retaining meaningful series-specific information.

By Wan Zhang, Qinjie Lin, Chan Lee, Weijian Li, Han Liu, Kai Zhang

llms fine-tuning efficiency benchmarks safety

arXiv Machine Learning

Jul 27

Cross-Tokenizer On-Policy Distillation via Byte-Prefix Marginalization

arXiv:2607. 22334v1 Announce Type: new Abstract: Open-weight language models from different families exhibit complementary capabilities, motivating their consolidation into a compact student through on-policy distillation (OPD).

By Hao Wang, Kun Yuan, Wenlin Zhong, Minglei Zhang, Han Xiao, Ming Sun, Honggang Qi

llms nlp efficiency benchmarks

arXiv Machine Learning

Jul 27

Agentic Root Cause Analysis through Evidence-Grounded Reasoning

arXiv:2607. 22385v1 Announce Type: cross Abstract: Diagnosing the root cause of anomalies is essential for safe industrial operation.

By Amaury Wei, Olga Fink

llms agents

arXiv Machine Learning

Jul 27

Encoding Invisible Causation for Bridge Diagnostic Agents: Triple-Guided Retrieval-Augmented Fine-Tuning with QLoRA

arXiv:2607. 21680v1 Announce Type: new Abstract: Bridge infrastructure deteriorates gradually, yet its root causes---salt intrusion, freezing, fatigue cracking, and others---remain invisible to the naked eye.

By Takato Yasuno

llms rag agents fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 27

SCOPE and SCION: A Benchmark and an Auditable Reference Pipeline for Schema Induction and Fusion from Text

arXiv:2607. 21610v1 Announce Type: cross Abstract: Schema graphs are an upstream bottleneck of schema-grounded information extraction and knowledge graph construction, yet most extraction systems assume the schema is already available.

By Miaobo Hu, Xiaobo Guo, Shuhao Hu, Bokun Wang, Rui Chen, Xin Wang, Daren Zha, Jun Xiao

llms reinforcement-learning benchmarks

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