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
1d ago

Evaluating Multimodal LLMs across Text and Audio Modalities for Accessible Disaster Assistance

arXiv:2608. 14651v1 Announce Type: new Abstract: Effective disaster risk communication is a foundational humanitarian challenge, yet current emergency infrastructure fails to meet the needs of individuals with access and functional needs, including hard-of-hearing individuals, pregnant women, mothers with toddlers, and elderly individuals with dementia.

By Anuridhi Gupta, Samara Mansoor, Hemant Purohit
arXiv AI
1d ago

CEDAR-GRPO: Process-Aware Reinforcement Learning for General Abductive Reasoning in LLMs

arXiv:2608. 14791v1 Announce Type: new Abstract: Abductive reasoning, often characterized as inference to the best explanation, is central to explanation under uncertainty, from everyday sense-making and investigation to scientific discovery.

By Moein Salimi, Danial Parnian, Shaygan Adim, Amirmohammad Ebrahiminasab, Nima Alighardashi, Parsa Gholami, Sahand Akramipour, Mahdi Jafari Siavoshani, Mohammad Hossein Rohban
arXiv AI
1d ago

LLMs Can Predict Failure Risk, But Struggle to Predict Which Collaboration Protocol Pays Off: Cost-Aware Protocol Routing Across Reasoning Tasks

arXiv:2608. 14927v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems can improve reasoning by spending more computation, but deployment requires deciding when extra collaboration is worth its cost.

By Chih-Hsuan Yang, Jingyan Jiang, Cheng-Hau Yang, Vikram Vasudevan, Huihuo Zheng, Venkatram Vishwanath, Rajeev Thakur
arXiv AI
1d ago

OGX: An Open-Source, Vendor-Neutral Generative AI Application Server

arXiv:2608. 14580v1 Announce Type: new Abstract: OGX (Open GenAI Stack) is an open-source AI application server and Python library that implements the APIs of major frontier labs (OpenAI, Anthropic, Google) with pluggable backend providers.

By Francisco Javier Arceo, S\'ebastien Han, Matthew Farrellee, Charlie Doern, Yuan Tang, Derek Higgins, Varsha Prasad Narsing, Gordon Sim, Sumanth Kamenani, Ben Browning, Raghotham Murthy
arXiv AI
1d ago

When Do LLMs Apply the Wrong Law? Diagnosing LLM Failures in Temporal Legal Reasoning

arXiv:2608. 14610v1 Announce Type: new Abstract: Legal reasoning tasks such as legal judgment prediction (LJP) require identifying the temporally correct version of the law governing a case -- a capability we term temporal applicable-law determination.

By Yiqian Huang, Shuyuan Zheng, Qianying Liu, Shaowen Peng, Yuntao Kong, Kotaro Funakoshi, Chuan Xiao, Manabu Okumura, Yang Cao
arXiv AI
1d ago

When Agentic Executions Fail: Detecting and Localizing Runtime Faults from Telemetry

arXiv:2608. 14680v1 Announce Type: new Abstract: Reliability in LLM-based agentic systems is a property of the whole execution (its tool calls, model calls, guardrails, and inter-agent messages), not of the final answer alone, yet evaluating only task outcomes reveals little about how or why a run fails.

By Chenkai Zhang, Yiran Li, Yifang Tian, Michalis Bachras, Hans-Arno Jacobsen
arXiv AI
1d ago

Generated Context versus Governed State: Functional Conditions for Accountable Longitudinal Clinical Reasoning

arXiv:2608. 14804v1 Announce Type: new Abstract: Large language models (LLMs) have become the dominant interface of clinical artificial intelligence, yet the interface they expose (text in, text out, one context window at a time) maintains no explicit, persistent, governed representation of what is currently true about a patient.

By Augusto Bernardo Pissarra, Victor Lorena de Farias Souza
arXiv AI
1d ago

Large Language Models and their Awareness of Mechanics and Spatial Geometry

arXiv:2608. 14615v1 Announce Type: new Abstract: Large Language Models (LLMs) perform well on established code-generation and mathematical-reasoning benchmarks, but their capabilities in mechanics and spatial geometry, here denoted as mechanical engineering awareness, has not been quantified systematically.

By Johannes Gerstmayr, Sebastian Weyrer, Tobias M\"oltner, Peter Manzl, Michael Pieber