Multimodal Modular Chain of Thoughts in Energy Performance Certificate Assessment
- URL: http://arxiv.org/abs/2603.00115v1
- Date: Fri, 20 Feb 2026 14:46:01 GMT
- Title: Multimodal Modular Chain of Thoughts in Energy Performance Certificate Assessment
- Abstract summary: This paper presents a cost-efficient framework that leverages Vision-Language models for automated EPC pre-assessment from limited visual information.<n>Experiments on a multimodal dataset of 81 residential properties in the United Kingdom show that MMCoT achieves statistically significant improvements over instruction-only prompting.
- Score: 2.5501895454488666
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Accurate evaluation of building energy performance remains challenging in regions where scalable Energy Performance Certificate (EPC) assessments are unavailable. This paper presents a cost-efficient framework that leverages Vision-Language models for automated EPC pre-assessment from limited visual information. The proposed Multimodal Modular Chain of Thoughts (MMCoT) architecture decomposes EPC estimation into intermediate reasoning stages and explicitly propagates inferred attributes across tasks using structured prompting. Experiments on a multimodal dataset of 81 residential properties in the United Kingdom show that MMCoT achieves statistically significant improvements over instruction-only prompting for EPC estimation. Analysis based on accuracy, recall, mean absolute error, and confusion matrices indicate that the proposed approach captures the ordinal structure of EPC ratings, with most errors occurring between adjacent classes. These results suggest that modular prompt-based reasoning offers a promising direction for low-cost EPC pre-assessment in data-scarce settings.
Related papers
- The Evaluation Context Protocol (ECP): A Portable Contract for AI Agent Evaluation [0.0]
This paper explores the critical methodologies for evaluating AI agents and the essential role of advanced observability infrastructure.<n>We propose the Evaluation Context Protocol (ECP), an early-stage, vendor-neutral framework intended to act as a contract layer for agentic systems.<n>We describe an open-source reference implementation that includes adapters for LangChain, LlamaIndex, CrewAI, and PydanticAI.
arXiv Detail & Related papers (2026-08-18T07:14:36Z) - A Human-Centered Validation of the Explainability-Performance Coefficient [0.0]
We propose a model-agnostic metric, the EPC score, that quantifies explanation quality by explicitly balancing the trade-off between feature selection sparsity and preserved model performance.<n>We show that the EPC score effectively uncovers operational dependencies among network activations, data dimensionality, and explainer performance.
arXiv Detail & Related papers (2026-07-31T16:50:51Z) - From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs [6.041312734387813]
This report presents an analytically structured, empirically calibrated, GPU-level methodology for estimating large language model (LLM) inference energy on NVIDIA H100-class accelerators without direct measurement.<n>The proposed estimator combines parameter-scaled transformer FLOP accounting, memory-traffic factors, and hardware-specific energy coefficients for FP16/BF16 tensor-core computation and high-bandwidth-memory movement.<n>The methodology further decomposes total energy into compute, parameter-access, key-value-cache write, and attention-read components, allowing the scaling behavior with model size, context length, and generated-token count to
arXiv Detail & Related papers (2026-07-29T07:50:55Z) - ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning [110.46431027868954]
ThinkBooster is a framework for seamless test-time compute scaling of large language model (LLM) reasoning.<n>It consists of a modular Python library implementing state-of-the-art TTC scaling strategy and scorer families.<n>A deployable OpenAI-compatible proxy service enables drop-in integration of adaptive reasoning into real-world applications.
arXiv Detail & Related papers (2026-06-05T05:28:46Z) - A Multi-dimensional Framework for Evaluating Generalization in EEG Foundation Models [48.385356148620815]
Recent EEG foundation models have demonstrated promising transfer capabilities across tasks and datasets.<n>We propose a multi-dimensional evaluation framework for assessing EEG models under realistic low-resource conditions.
arXiv Detail & Related papers (2026-05-27T14:51:54Z) - On Predicting the Post-training Potential of Pre-trained LLMs [60.07459271263409]
We introduce a new task of predicting post-training potential - forecasting a base model's performance before post-training.<n>We propose RuDE, a unified framework that bypasses the generation gap of base models by leveraging response discrimination.<n>Experiments demonstrate a correlation greater than 90% with post-training performance.
arXiv Detail & Related papers (2026-05-12T11:33:49Z) - Gated Multimodal Learning for Interpretable Property Energy Performance Prediction and Retrofit Scenario Analysis [4.59166421255722]
This study introduces a gated multimodal model to predict Standard Assessment Procedure (SAP) energy efficiency and Environmental Impact (EI) scores.<n>In a Westminster, London case study, the model predicts SAP and EI scores with MAEs of 4.03 and 4.76 points and R2 values of 0.757 and 0.748, respectively.<n>Overall, the framework provides scalable property-level evidence for retrofit screening, intervention prioritisation, and net-zero housing transitions.
arXiv Detail & Related papers (2026-05-06T16:23:11Z) - UniDial-EvalKit: A Unified Toolkit for Evaluating Multi-Faceted Conversational Abilities [70.79422099851506]
We present UniDial-EvalKit (UDE), a unified evaluation toolkit for assessing interactive AI systems.<n>UDE standardizes heterogeneous data formats into a universal schema, streamlines complex evaluation pipelines through a modular architecture, and aligns metric calculations under a consistent scoring interface.
arXiv Detail & Related papers (2026-03-24T13:01:31Z) - Matching Features, Not Tokens: Energy-Based Fine-Tuning of Language Models [102.20309135516186]
Cross-entropy (CE) training provides dense and scalable supervision for language models.<n>We introduce a feature-matching objective for language-model fine-tuning.<n>To optimize this objective efficiently, we propose energy-based fine-tuning.
arXiv Detail & Related papers (2026-03-12T17:57:50Z) - Rethinking Probabilistic Circuit Parameter Learning [47.13236967317809]
We introduce anemone, a new mini-batch EM algorithm for PCs.<n>Anemone consistently outperforms existing datasets in both convergence speed and final performance.
arXiv Detail & Related papers (2025-05-26T13:41:06Z) - In-context Ranking Preference Optimization [65.5489745857577]
We propose an In-context Ranking Preference Optimization (IRPO) framework to optimize large language models (LLMs) based on ranking lists constructed during inference.<n>We show IRPO outperforms standard DPO approaches in ranking performance, highlighting its effectiveness in aligning LLMs with direct in-context ranking preferences.
arXiv Detail & Related papers (2025-04-21T23:06:12Z) - Supervised Optimism Correction: Be Confident When LLMs Are Sure [91.7459076316849]
We establish a novel theoretical connection between supervised fine-tuning and offline reinforcement learning.<n>We show that the widely used beam search method suffers from unacceptable over-optimism.<n>We propose Supervised Optimism Correction, which introduces a simple yet effective auxiliary loss for token-level $Q$-value estimations.
arXiv Detail & Related papers (2025-04-10T07:50:03Z) - DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs [86.76714527437383]
This paper proposes DSMoE, a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks.<n>We implement adaptive expert routing using sigmoid activation and straight-through estimators, enabling tokens to flexibly access different aspects of model knowledge.<n>Experiments on LLaMA models demonstrate that under equivalent computational constraints, DSMoE achieves superior performance compared to existing pruning and MoE approaches.
arXiv Detail & Related papers (2025-02-18T02:37:26Z) - Energy-based Automated Model Evaluation [19.90797626200033]
We propose a novel measure -- Meta-Distribution Energy (MDE) -- that allows the AutoEval framework to be both more efficient and effective.
MDE establishes a meta-distribution statistic, on the information (energy) associated with individual samples, then offer a smoother representation enabled by energy-based learning.
We provide extensive experiments across modalities, datasets and different architectural backbones to validate MDE's validity.
arXiv Detail & Related papers (2024-01-23T11:54:09Z) - c-TPE: Tree-structured Parzen Estimator with Inequality Constraints for
Expensive Hyperparameter Optimization [45.67326752241075]
We propose constrained TPE (c-TPE), an extension of the widely-used versatile Bayesian optimization method, tree-structured Parzen estimator (TPE) to handle these constraints.
Our proposed extension goes beyond a simple combination of an existing acquisition function and the original TPE, and instead includes modifications that address issues that cause poor performance.
In the experiments, we demonstrate that c-TPE exhibits the best average rank performance among existing methods with statistical significance on 81 expensive HPO with inequality constraints.
arXiv Detail & Related papers (2022-11-26T00:25:11Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.