Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv:2606. 21257v4 Announce Type: replace-cross Abstract: openPangu models are attractive targets for private and domestic large-language-model deployment, yet their robustness under aggressive post-training quantization on Ascend NPUs has not been systematically characterized.
arXiv:2608. 07460v1 Announce Type: cross Abstract: While post-training improves the capabilities of large language models (LLMs), it generally lowers their output diversity and creativity, negatively impacting tasks that explicitly require creativity (e.
arXiv:2509. 00559v3 Announce Type: replace Abstract: Humans intuitively navigate social interactions by simulating unspoken dynamics and reasoning about others' perspectives, even with limited information.
arXiv:2608. 06992v1 Announce Type: cross Abstract: We present a web demo for exploring a large-scale disambiguated knowledge base (KB) materialized from a large language model (LLM).
arXiv:2608. 06756v1 Announce Type: new Abstract: Vision-language models are increasingly serving as the reasoning core of embodied agents.
arXiv:2608. 06752v1 Announce Type: new Abstract: This paper proposes DKG-MTI, a dual knowledge graph framework for unified multi-task user intent inference from online travel reviews.
arXiv:2608. 06578v1 Announce Type: new Abstract: Frontier language models are trained using distinct data, objectives, and safety pipelines.
arXiv:2608. 07435v1 Announce Type: cross Abstract: Vision-language models (VLMs) are improving rapidly, but benchmark development lags behind, making weaknesses hard to identify.
arXiv:2608. 07033v1 Announce Type: new Abstract: This work investigates whether Electroencephalograph (EEG) foundation models (EFMs) can be made faster and locally deployable without sacrificing accuracy.
arXiv:2608. 07040v1 Announce Type: new Abstract: Solving combinatorial optimization problems (COPs) requires not only efficient algorithms but also carefully crafted formulations.
arXiv:2608. 06926v1 Announce Type: new Abstract: Designing autonomous agents that effectively assist human teams hinges on understanding team dynamics, often without task specific knowledge.
arXiv:2605. 29738v2 Announce Type: replace-cross Abstract: Legal NLP benchmarks overwhelmingly evaluate a single language or aggregate tasks that differ fundamentally across jurisdictions, making cross-lingual comparison impossible.
arXiv:2411. 00028v3 Announce Type: replace-cross Abstract: Socioeconomic prediction aims to leverage various urban data to predict the socioeconomic indicators of regions such as population and commercial activity level, which plays an important role in understanding urban regions and supporting decision-making.
arXiv:2608. 06485v1 Announce Type: cross Abstract: Personality-conditioned LLM agents (PC-Agents) are increasingly used in emotional support, social simulation, and role-playing, motivating the development of lifelong agents that remain coherent over extended interactions.
arXiv:2605. 19035v2 Announce Type: replace Abstract: The rapid advancement of Large Language Models has given rise to autonomous LLM-based agents capable of complex reasoning and execution.
arXiv:2608. 07395v1 Announce Type: cross Abstract: Large Language Model (LLM)-based automated algorithm design typically evolves algorithms as complete, indivisible programs.
arXiv:2608. 06909v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly operate through long-horizon trajectories involving user instructions, tool use, external observations, and memory.
arXiv:2608. 07176v1 Announce Type: cross Abstract: Developing foundation generative models for endoscopy is limited by the gap between natural and clinical images and the computational cost of training large Diffusion Transformers.
arXiv:2604. 27540v2 Announce Type: replace Abstract: Scientific reasoning rarely stops at what is directly observable; it often requires uncovering hidden structure from data.
arXiv:2608. 07023v1 Announce Type: cross Abstract: Organizing thousands of unstandardized, multilingual expertise declarations is a persistent challenge for Human Resources (HR) platforms, directly impacting downstream tasks like accurate talent matching.