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Sakana AI introduces AB-MCTS, an inference-time scaling algorithm that enables multiple frontier AI models (Gemini 2.5 Pro, o4-mini, DeepSeek-R1-0528) to cooperate, significantly outperforming individual models on the ARC-AGI-2 benchmark.
SciOrch presents an 8B vision-language model trained with MCTS to coordinate multiple expert LLMs for multimodal scientific reasoning, achieving superior performance while reducing API costs.
This paper introduces Multimodal Multi-Dimensional Scalarization Process Reward Modeling (MMS-PRM), which enforces the worst dimension's robustness in multimodal reasoning to prevent failures like visual hallucinations from being masked by strong text logic.
This paper introduces Program-of-Layers (PoLar), a method that allows LLMs to dynamically skip or loop pretrained layers per input, improving accuracy and efficiency over fixed-depth inference.
StepPRM-RTL is a novel framework combining stepwise trajectory modeling, process-reward modeling (PRM), and retrieval-augmented fine-tuning (RAFT) to improve LLM-based RTL code generation for Verilog and VHDL, outperforming prior methods by over 10% in functional correctness metrics.
Proposes COMPASS, a cognitive MCTS-guided process alignment framework to enhance safety in LLM-powered search agents by synthesizing attack trajectories and isolating risky actions, achieving a favorable safety-utility trade-off with less training data.
DRS-GUI proposes a training-free dynamic region search framework for GUI grounding, using a lightweight UI Perceptor with human-like perceptual actions and Monte Carlo Tree Search to progressively locate instruction-relevant elements. Experiments show a 14% improvement on ScreenSpot-Pro for both general and GUI-specific MLLMs.