arXiv:2609.27417v1 Announce Type: new Abstract: Symbiosis between humans and digital beings offers a vision for the future of human–machine interaction.…
Tag: AI
Just-in-Time Memory: Learning to Curate Task-Adaptive Memory for LLM Agents
arXiv:2609.27334v1 Announce Type: new Abstract: Agentic memory systems reuse past experience to improve future performance, yet most existing designs…
CART: Closed-Loop Adaptive Red Teaming for Large Language Models
arXiv:2609.27336v1 Announce Type: new Abstract: Automated red teaming often replays a fixed set of prompts, which measures known risks but cannot learn…
Alignment Inertia: Auditing the Durability of Training Data Influence Through Policy Override Resistance
arXiv:2609.27333v1 Announce Type: new Abstract: Platform operators increasingly rely on system prompts and fine-tuning to govern model behavior, yet it…
MolDesignBench: Evaluating LLM-based Agent for Scenario-grounded Molecular Design
arXiv:2609.27349v1 Announce Type: new Abstract: Real-world molecular design remains challenging for large language model (LLM)-based agents. It requires…
Learn How to Act from Your Own Interactions: On-Policy Self-Distillation for GUI Agents
arXiv:2609.27307v1 Announce Type: new Abstract: Graphical User Interface (GUI) agents enable the fulfillment of complex user instructions through…
Verifiable Hidden Dynamics Play: Generating Agentic RL Environments from Solved Mechanisms
arXiv:2609.27321v1 Announce Type: new Abstract: Language-model agents increasingly face long-horizon tasks with evolving state, interdependent decisions,…
Stable Geometry with Divergent Task Evidence for Efficient Long-Horizon Agent Compression
arXiv:2609.27332v1 Announce Type: new Abstract: Long horizon agents accumulate growing interaction histories that increase context and inference costs. We…
Large Knowledge Model: From Papers to a Scientific Reasoning Landscape
arXiv:2609.27297v1 Announce Type: new Abstract: Accumulated scientific knowledge advances inquiry when prior findings help researchers choose new…
StateComp: Learning When to Compress History in Long Horizon Agents
arXiv:2609.27298v1 Announce Type: new Abstract: Long-horizon agents continuously accumulate interaction history during task execution, yet the importance…