arXiv Machine Learning
Aug 10
Beyond Attention: Signed Integrated Gradients Attribution in a BiomeGPT-Style Microbiome Transformer
arXiv:2608. 06486v1 Announce Type: new Abstract: In a feature-tokenized transformer (arXiv:2106.
Large language models
Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv AI
Aug 10
An Empirical Study of openPangu Quantization on Ascend NPUs
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 AI
Aug 10
CreativeInstruct: Scalably Teaching LLMs to Balance Quality, Creativity, and Diversity
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 AI
Aug 10
Social World Models
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 AI
Aug 10
Capek 0.5: An Execution-Centric Vision-Language Model for Embodied Intelligence
arXiv:2608. 06756v1 Announce Type: new Abstract: Vision-language models are increasingly serving as the reasoning core of embodied agents.
arXiv AI
Aug 10
Mind the Gap: A Dual Knowledge Graph Framework for Unified Multi-task User Intent Inference
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 AI
Aug 10
Divergent Response Modes in Frontier Language Models Under Steering Pressure
arXiv:2608. 06578v1 Announce Type: new Abstract: Frontier language models are trained using distinct data, objectives, and safety pipelines.
arXiv AI
Aug 10
SABRE: Scalable and Automated Benchmarking of VLMs under Stress
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 AI
Aug 10
ZIPBrain: Can EEG Foundation Models Be Faster, Locally Deployable, but Accurate?
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 AI
Aug 10
Not All Problems Are Best Modeled as MILP: A DSL-Centric Framework for Flexible and Accurate Optimization Modeling
arXiv:2608. 07040v1 Announce Type: new Abstract: Solving combinatorial optimization problems (COPs) requires not only efficient algorithms but also carefully crafted formulations.
arXiv AI
Aug 10
TRIBE: Predicting Team Performance via Communication Behavior Ensembles
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 AI
Aug 10
Multi-Legal-Bench: Evaluating LLMs on Legal Reasoning Across Jurisdictions, Languages, and Legal Traditions
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 AI
Aug 10
Harnessing the Synergy between LLM Agents and Knowledge Graphs for Urban Socioeconomic Prediction
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 AI
Aug 10
Do AI Personas Grow? Analyzing and Benchmarking Personality Evolution in LLM Agents After Life Events
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 AI
Aug 10
Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On
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 AI
Aug 10
PACE: Primitive-Aware Code Evolution for Automated Algorithm Design
arXiv:2608. 07395v1 Announce Type: cross Abstract: Large Language Model (LLM)-based automated algorithm design typically evolves algorithms as complete, indivisible programs.
arXiv AI
Aug 10
Long-Horizon Agent Trajectory Attribution: A Unified Benchmark and Fine-Grained Annotation Framework
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 AI
Aug 10
Representation-driven Endoscopic Visual Embedding Alignment for Latent Generation
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 AI
Aug 10
In-Context Examples Suppress Scientific Knowledge Recall in LLMs
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 AI
Aug 10
An Agentic Hybrid Top-Down and Bottom-Up Approach to Knowledge Graph Generation
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.