most citedOmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization

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cs.AI20262 cited

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization

Keane Ong, Sabri Boughorbel, Luwei Xiao +9

Socially intelligent AI systems must reason across diverse human behavioral tasks and generalize to new social contexts. However, behavioral data is inherently heterogeneous, compr…

cs.AI2026

AlphaOPT: Formulating Optimization Programs with Self-Improving LLM Experience Library

Minwei Kong, Ao Qu, Xiaotong Guo +12

Optimization modeling underlies critical decision-making across industries, yet remains difficult to automate: natural-language problem descriptions must be translated into precise…

cs.AI2026

MobEvolve: An Agentic Self-Evolving Heuristic System for Interpretable Human Mobility Generation

Junlin He, Yihong Tang, Tong Nie +6

Human mobility generation aims to synthesize realistic trip chains for target populations based on individual features. Existing paradigms, including deep generative models, LLM-ba…

cs.AI2026

DecisionBench: A Benchmark for Emergent Delegation in Long-Horizon Agentic Workflows

Yuxuan Gao, Megan Wang, Yi Ling Yu +2

We introduce DecisionBench, a benchmark substrate for emergent delegation in long-horizon agentic workflows. The substrate fixes a task suite (GAIA, tau-bench, BFCL multi-turn), a…

cs.AI2025

HugAgent: Benchmarking LLMs for Simulation of Individualized Human Reasoning

Chance Jiajie Li, Zhenze Mo, Yuhan Tang +11

Simulating human reasoning in open-ended tasks has long been a central aspiration in AI and cognitive science. While large language models now approximate human responses at scale,…

cs.AI2025

ITINERA: Integrating Spatial Optimization with Large Language Models for Open-domain Urban Itinerary Planning

Yihong Tang, Zhaokai Wang, Ao Qu +11

Citywalk, a recently popular form of urban travel, requires genuine personalization and understanding of fine-grained requests compared to traditional itinerary planning. In this p…