AGENTIC-SYSTEMSApril 3, 20261 min read12 views

Beyond Symbolic Solving: Multi Chain-of-Thought Voting for Geometric Reasoning in Large Language Models

Researchers have introduced MARS-GPS, a novel framework that enhances geometric reasoning in LLMs using multi-parallel reasoning rollouts and Python-based verification. The system achieved a record 88.8% accuracy on the Geometry3K benchmark, significantly outperforming previous state-of-the-art models.

Computer Science > Artificial Intelligence

Title:Beyond Symbolic Solving: Multi Chain-of-Thought Voting for Geometric Reasoning in Large Language Models

Geometric Problem Solving (GPS) remains at the heart of enhancing mathematical reasoning in large language models because it requires the combination of diagrammatic understanding, symbolic manipulation and logical inference. In existing literature, researchers have chiefly focused on synchronising the diagram descriptions with text literals and solving the problem. In this vein, they have either taken a neural, symbolic or neuro-symbolic approach. But this solves only the first two of the requirements, namely diagrammatic understanding and symbolic manipulation, while leaving logical inference underdeveloped. The logical inference is often limited to one chain-of-thought (CoT). To address this weakness in hitherto existing models, this paper proposes MARS-GPS, that generates multiple parallel reasoning rollouts augmented with Python code execution for numerical verification, ranks them using token-level entropy as a confidence signal, and aggregates answers through a multi-stage voting and self-verification pipeline. Empirical results show that MARS-GPS with 8 parallel rollouts achieves 88.8% on Geometry3K, a nearly +11% improvement over the prior state-of-the-art, with accuracy scaling consistently as the number of rollouts increases from 1 to 16 (+6.0% on ablation subset). We provide our code and data in an anonymous repository.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

VeriSimpl: Robust Optimization Modeling from Natural Language using Simplification-based Verification

Researchers have introduced VeriSimpl, a novel framework that leverages LLMs and simplification-based verification to accurately translate natural language descriptions into executable optimization formulations.

agentic-systems

SonicSampler: Unified Tile-Aware Kernels for LLM Sampling and Speculative Verification

Researchers introduced SonicSampler, a unified suite of tile-aware Triton kernels designed to accelerate LLM sampling and speculative verification. By fusing the entire pipeline into a single CUDA Graph-compatible kernel, SonicSampler achieves up to 16x speedups over state-of-the-art baselines.

agentic-systems

PersonaTrail: Benchmarking Personalized Web Agents through Browsing Trails

Researchers have introduced PersonaTrail, a novel benchmark evaluating personalized web agents through realistic browsing histories. Alongside it, the PACMem memory framework structures raw browsing data to significantly boost agent performance in inferring user preferences.

agentic-systems

Benchmarking the Personalization Capabilities of Large Language Models

A new study introduces SDR-Bench and SDR-Arena to benchmark the personalization capabilities of LLMs in two-party persuasion scenarios, revealing a personalization plateau among frontier models.

agentic-systems

Incomplete Prompt Jailbreaks in Large Language Models

Researchers have conceptualized Incomplete Prompt Jailbreaks (IPJ), a vulnerability where incomplete harmful prompts bypass LLM safeguards due to delayed refusal behaviors. The discovery of functional neurons offers a novel path for precise neuron-level defense interventions.

agentic-systems

Benchmarking Large Language Models on Multi-Sensor Physical Hazard Assessment

A new benchmark evaluates five leading large language models on multi-sensor physical hazard assessments, revealing a critical vulnerability when evaluating combined sensor risks below individual thresholds. Despite near-perfect accuracy on single-sensor breaches, all models failed to issue precautionary signals for multi-sensor hazards.