🦾 LLM Agent

📹 ICCV2025 · 4 paper notes

Embodied Image Captioning: Self-supervised Learning Agents for Spatially Coherent Image Descriptions

A three-stage self-supervised framework is proposed that significantly improves cross-view description consistency and accuracy for the same object in indoor environments, achieved through agent-driven multi-view observation collection, LLM consensus-based pseudo-label generation, and contrastive fine-tuning of the captioner.

GTR: Guided Thought Reinforcement Prevents Thought Collapse in RL-based VLM Agent Training

This paper identifies the "thought collapse" phenomenon in RL-based VLM Agent training — where CoT reasoning rapidly degenerates into state-agnostic, templated thoughts that lead to ineffective actions — and proposes the GTR framework, which combines a VLM corrector for automatic thought correction (SFT) with PPO-based action optimization in a dual-objective training scheme, achieving 3–5× success rate improvements on the 24-Point Game and ALFWorld.

Less is More: Empowering GUI Agent with Context-Aware Simplification

This paper proposes SimpAgent — a context-aware simplification framework that achieves SOTA on multiple GUI navigation benchmarks while reducing FLOPs by 27%, via masking-based element pruning (randomly masking irrelevant element regions during training) and consistency-guided history compression (directly dropping historical visual tokens at intermediate LLM layers with a KL divergence consistency constraint).

UIPro: Unleashing Superior Interaction Capability for GUI Agents

UIPro is proposed to achieve state-of-the-art GUI interaction performance across mobile, web, and desktop platforms by constructing 20.6M GUI understanding samples for pre-training and introducing a unified action space to integrate heterogeneous GUI agent task data.