CoMPASS: Collaborative Molecular Property Prediction via Adaptive Small-Large Model Synergy

• URL: http://arxiv.org/abs/2608.30674v1
• Date: Mon, 31 Aug 2026 12:14:01 GMT
• Title: CoMPASS: Collaborative Molecular Property Prediction via Adaptive Small-Large Model Synergy
• Abstract summary: Accurate molecular property prediction requires both statistical reliability and chemical reasoning.<n>CoMPASS is a retrieval-calibrated framework for small-large model collaboration.<n>Results suggest that generative reasoning should augment calibrated prediction through evidence-grounded, controlled corrections.
• Score: 8.012730274764019
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate molecular property prediction requires both statistical reliability and chemical reasoning. Graph neural networks can be calibrated directly on labeled assays but remain limited by the coverage of their training data. Large language models (LLMs) can compare molecular evidence and articulate chemical rationales, yet are unreliable as standalone quantitative predictors. The central challenge is therefore to determine when an LLM should influence a calibrated model and by how much. Here we present CoMPASS, a retrieval-calibrated framework for small-large model collaboration. CoMPASS retains a graph attention network (GAT) as the predictive anchor, retrieves locally relevant training molecules, provides attention-grounded evidence to an LLM, and converts its proposal into a bounded correction through an agreement-aware gate. Across six classification and two regression benchmarks, CoMPASS improves the GAT anchor in regions of correctable uncertainty while limiting LLM intervention in high-confidence regimes. Ablations show that the gains arise from validation-calibrated retrieval and bounded fusion rather than prompting alone. These results suggest that generative reasoning should augment calibrated prediction through evidence-grounded, controlled corrections rather than direct output replacement. Code is available at https://github.com/littlepeachs/CoMPASS.

Related papers

• Adapting Evidential Neural Networks to Test-Time Neighbor Fusion Improves Molecular Property Prediction [28.11511793703245]
  A trained molecular property model can be refined at test time by correcting each prediction with the measured labels of the most similar training molecules.<n>This work demonstrates that evidential uncertainty decomposition is not merely a calibration objective but an actionable inference resource.
  arXiv  Detail & Related papers  (2026-07-13T04:59:09Z)
• Uncertainty-aware reinforcement learning for chemical language models [0.5156484100374058]
  Reinforcement Learning (RL) has become a powerful paradigm for de novo molecular design.<n>We show that uncertainty-aware RL enables CLMs to explore chemical space more robustly.<n>This leads to more reliable hit discovery without compromising molecular score.
  arXiv  Detail & Related papers  (2026-06-23T14:42:46Z)
• The Critical Role of Model Selection in Causal Inference: A Comparative Analysis of Classification Models within the InferBERT Framework for Pharmacovigilance [5.94491873775155]
  InferBERT framework integrates transformer models with Do-calculus.<n>Four models were evaluated-XGBoost (baseline), ALBERT, BioBERT, and Med-LLaMA.<n> domain-specific pre-training was decisive.<n>BioBERT achieved the highest accuracy on both datasets.
  arXiv  Detail & Related papers  (2026-06-15T09:01:49Z)
• Discovery of Hidden Miscalibration Regimes [52.452902154360565]
  Models may be systematically overconfident on some kinds of inputs and underconfident on others.<n>We define the corresponding miscalibration field and propose a diagnostic framework for estimating it.<n>Our approach learns a calibration-aware representation of the input space and estimates signed local miscalibration by kernel smoothing in the learned geometry.
  arXiv  Detail & Related papers  (2026-05-13T13:07:50Z)
• KMM-CP: Practical Conformal Prediction under Covariate Shift via Selective Kernel Mean Matching [19.67518257951341]
  Conformal Prediction (CP) provides finite-sample coverage guarantees under exchangeability.<n>We propose KMM-CP, a conformal prediction framework based on Kernel Mean Matching (KMM)<n> Experiments on molecular property prediction benchmarks with realistic distribution shifts show that KMM-CP reduces coverage gap by over 50% compared to existing approaches.
  arXiv  Detail & Related papers  (2026-03-27T13:44:42Z)
• Information Fidelity in Tool-Using LLM Agents: A Martingale Analysis of the Model Context Protocol [69.11739400975445]
  We introduce the first theoretical framework for analyzing error accumulation in Model Context Protocol (MCP) agents.<n>We show that cumulative distortion exhibits linear growth and high-probability deviations bounded by $O(sqrtT)$.<n>Key findings include: semantic weighting reduces distortion by 80%, and periodic re-grounding approximately every 9 steps suffices for error control.
  arXiv  Detail & Related papers  (2026-02-10T21:08:53Z)
• Mitigating LLM Hallucination via Behaviorally Calibrated Reinforcement Learning [32.32593439144886]
  Behavior-calibrated reinforcement learning allows smaller models to surpass frontier models in uncertainty quantification.<n>Our model's log-scale Accuracy-to-Hallucination Ratio gain (0.806) exceeds GPT-5's (0.207) in a challenging in-domain evaluation.
  arXiv  Detail & Related papers  (2025-12-22T22:51:48Z)
• Generalized Correctness Models: Learning Calibrated and Model-Agnostic Correctness Predictors from Historical Patterns [67.24756301536617]
  We propose a Generalized Correctness Model (GCM) to generate accurate and calibrated confidence estimates.<n>We first show that GCMs can be trained on the correctness data from many LLMs.<n>We then use CMs as a lens for studying the source of correctness prediction ability and its generalization.
  arXiv  Detail & Related papers  (2025-09-29T16:19:01Z)
• Composable Score-based Graph Diffusion Model for Multi-Conditional Molecular Generation [85.58520120011269]
  We propose Composable Score-based Graph Diffusion model (CSGD), which extends score matching to discrete graphs via concrete scores.<n>We show that CSGD achieves state-of-the-art performance with a 15.3% average improvement in controllability over prior methods.<n>Our findings highlight the practical advantages of score-based modeling for discrete graph generation and its capacity for flexible, multi-property molecular design.
  arXiv  Detail & Related papers  (2025-09-11T13:37:56Z)
• Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback [91.22679548111127]
  A trustworthy real-world prediction system should produce well-calibrated confidence scores. We show that verbalized confidences emitted as output tokens are typically better-calibrated than the model's conditional probabilities.
  arXiv  Detail & Related papers  (2023-05-24T10:12:33Z)
• Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right? [135.71855998537347]
  We revisit the common practice of evaluating adaptation of Online Continual Learning (OCL) algorithms through the metric of online accuracy. We show that this metric is unreliable, as even vacuous blind classifiers can achieve unrealistically high online accuracy. Existing OCL algorithms can also achieve high online accuracy, but perform poorly in retaining useful information.
  arXiv  Detail & Related papers  (2023-05-16T08:29:33Z)
• MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection [76.97324120775475]
  Cross-domain keypoint detection methods always require accessing the source data during adaptation. This paper considers source-free domain adaptive keypoint detection, where only the well-trained source model is provided to the target domain.
  arXiv  Detail & Related papers  (2023-02-09T12:06:08Z)

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.