Overconfident Oracles: Limitations of In Silico Sequence Design Benchmarking

• URL: http://arxiv.org/abs/2502.17246v1
• Date: Mon, 24 Feb 2025 15:25:21 GMT
• Title: Overconfident Oracles: Limitations of In Silico Sequence Design Benchmarking
• Abstract summary: We examine 12 sequence design methods that utilise ML oracles common in the literature.<n>We find that there are significant challenges with their cross-consistency and robustness.<n>We propose supplementing the evaluation with a suite of biophysical measures to assess the viability of generated sequences.
• Score: 5.359378066251386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning methods can automate the in silico design of biological sequences, aiming to reduce costs and accelerate medical research. Given the limited access to wet labs, in silico design methods commonly use an oracle model to evaluate de novo generated sequences. However, the use of different oracle models across methods makes it challenging to compare them reliably, motivating the question: are in silico sequence design benchmarks reliable? In this work, we examine 12 sequence design methods that utilise ML oracles common in the literature and find that there are significant challenges with their cross-consistency and reproducibility. Indeed, oracles differing by architecture, or even just training seed, are shown to yield conflicting relative performance with our analysis suggesting poor out-of-distribution generalisation as a key issue. To address these challenges, we propose supplementing the evaluation with a suite of biophysical measures to assess the viability of generated sequences and limit out-of-distribution sequences the oracle is required to score, thereby improving the robustness of the design procedure. Our work aims to highlight potential pitfalls in the current evaluation process and contribute to the development of robust benchmarks, ultimately driving the improvement of in silico design methods.

Related papers

• Neuro-Evolved Heuristics for Variable Gapped Common Subsequence Identification [40.920500181764986]
  This study addresses the Variable Gapped Longest Common Subsequence Problem (VGLCSP)<n>VGLCSP is a variant of the classical longest common subsequence problem with additional gap constraints.<n>We propose a learning-based approach for automatically designing more effective data-driven robustnesss.
  arXiv  Detail & Related papers  (2026-08-01T22:36:35Z)
• CellScientist: Dual-Space Hierarchical Orchestration for Closed-Loop Refinement of Virtual Cell Models [62.281480231694]
  Virtual Cell Modeling (VCM) requires models that not only predict perturbation responses, but also support targeted revision when predictions fail.<n>Current LLM-assisted modeling face a refinement-routing problem: prediction discrepancies are observed through executable implementations.<n>We propose CellScientist, a dual-space hierarchical framework that couples a high-level hypothesis space with a low-level executable implementation space.
  arXiv  Detail & Related papers  (2026-05-08T06:40:24Z)
• Evaluation-driven Scaling for Scientific Discovery [77.20820317940581]
  We introduce Simple Test-time Evaluation-driven Scaling (SimpleTES), a framework that combines parallel exploration, feedback-driven refinement, and local selection.<n>We find substantial gains unlocked by scaling evaluation-driven discovery loops along the right dimensions.<n>Our results establish effective evaluation-driven loop scaling as a central axis for advancing scientific discovery.
  arXiv  Detail & Related papers  (2026-04-21T11:24:09Z)
• Dissecting Linear Recurrent Models: How Different Gating Strategies Drive Selectivity and Generalization [5.057995083193427]
  Linear recurrent neural networks have emerged as efficient alternatives to the original Transformer's softmax attention mechanism.<n>Existing benchmark tasks are either too simplistic to reveal substantial differences or excessively resource-intensive for experimentation.<n>We introduce SelectivBench, a set of lightweight and customizable synthetic benchmark tasks for systematically evaluating sequence models.
  arXiv  Detail & Related papers  (2026-01-18T21:49:21Z)
• Taming Imperfect Process Verifiers: A Sampling Perspective on Backtracking [54.43083499412643]
  Test-time algorithms that combine the generative power of language models with process verifiers offer a promising lever for eliciting new reasoning capabilities.<n>We introduce a new process-guided test-time sampling algorithm, VGB, which uses theoretically grounded backtracking to achieve provably better robustness to verifier errors.
  arXiv  Detail & Related papers  (2025-10-03T16:21:14Z)
• On Evaluating Performance of LLM Inference Serving Systems [11.712948114304925]
  We identify recurring anti-patterns across three key dimensions: Baseline Fairness, Evaluation setup, and Metric Design.<n>These anti-patterns are uniquely problematic for Large Language Model (LLM) inference due to its dual-phase nature.<n>We provide a comprehensive checklist derived from our analysis, establishing a framework for recognizing and avoiding these anti-patterns.
  arXiv  Detail & Related papers  (2025-07-11T20:58:21Z)
• EVA-MILP: Towards Standardized Evaluation of MILP Instance Generation [13.49043811341421]
  Mixed-Integer Linear Programming (MILP) is fundamental to solving complex decision-making problems.<n>The proliferation of MILP instance generation methods, driven by machine learning's demand for diverse datasets, has significantly outpaced standardized evaluation techniques.<n>This paper introduces a comprehensive benchmark framework designed for the systematic and objective evaluation of MILP instance generation methods.
  arXiv  Detail & Related papers  (2025-05-30T16:42:15Z)
• Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute [61.00662702026523]
  We propose a unified Test-Time Compute scaling framework that leverages increased inference-time instead of larger models.<n>Our framework incorporates two complementary strategies: internal TTC and external TTC.<n>We demonstrate our textbf32B model achieves a 46% issue resolution rate, surpassing significantly larger models such as DeepSeek R1 671B and OpenAI o1.
  arXiv  Detail & Related papers  (2025-03-31T07:31:32Z)
• Case for a unified surrogate modelling framework in the age of AI [0.0]
  We argue for the urgent need for a unified framework to guide the development and evaluation of surrogate models.<n>This paper seeks to improve the reliability of surrogate modelling, foster cross-disciplinary knowledge transfer, and, as a result, accelerate scientific progress.
  arXiv  Detail & Related papers  (2025-02-10T18:31:15Z)
• BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning [78.63421517563056]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks.<n>We present a unified probabilistic framework that formalizes LLM reasoning through a novel graphical model.<n>We introduce the Bootstrapping Reinforced Thinking Process (BRiTE) algorithm, which works in two steps.
  arXiv  Detail & Related papers  (2025-01-31T02:39:07Z)
• Can We Further Elicit Reasoning in LLMs? Critic-Guided Planning with Retrieval-Augmentation for Solving Challenging Tasks [68.49251303172674]
  State-of-the-art large language models (LLMs) exhibit impressive problem-solving capabilities but may struggle with complex reasoning and factual correctness. Existing methods harness the strengths of chain-of-thought and retrieval-augmented generation (RAG) to decompose a complex problem into simpler steps and apply retrieval to improve factual correctness. We introduce Critic-guided planning with Retrieval-augmentation, CR-Planner, a novel framework that leverages fine-tuned critic models to guide both reasoning and retrieval processes through planning.
  arXiv  Detail & Related papers  (2024-10-02T11:26:02Z)
• Decomposable Transformer Point Processes [2.1756081703276]
  We propose a framework where the advantages of the attention-based architecture are maintained and the limitation of the thinning algorithm is circumvented. The proposed method attains state-of-the-art performance in predicting the next event of a sequence given its history.
  arXiv  Detail & Related papers  (2024-09-26T13:22:58Z)
• MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
  Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.<n>We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.<n>Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
  arXiv  Detail & Related papers  (2024-06-20T03:50:23Z)
• Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality [84.94877848357896]
  Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models. We analyze this behavior of seq2seq models and identify two contributing factors: a lack of mutual exclusivity bias and the tendency to memorize whole examples. We show substantial empirical improvements using standard sequence-to-sequence models on two widely-used compositionality datasets.
  arXiv  Detail & Related papers  (2022-11-28T17:36:41Z)
• Scalable Intervention Target Estimation in Linear Models [52.60799340056917]
  Current approaches to causal structure learning either work with known intervention targets or use hypothesis testing to discover the unknown intervention targets. This paper proposes a scalable and efficient algorithm that consistently identifies all intervention targets. The proposed algorithm can be used to also update a given observational Markov equivalence class into the interventional Markov equivalence class.
  arXiv  Detail & Related papers  (2021-11-15T03:16:56Z)
• Symbolic Regression by Exhaustive Search: Reducing the Search Space Using Syntactical Constraints and Efficient Semantic Structure Deduplication [2.055204980188575]
  Symbolic regression is a powerful system identification technique in industrial scenarios where no prior knowledge on model structure is available. In this chapter we introduce a deterministic symbolic regression algorithm specifically designed to address these issues. A finite enumeration of all possible models is guaranteed by structural restrictions as well as a caching mechanism for detecting semantically equivalent solutions.
  arXiv  Detail & Related papers  (2021-09-28T17:47:51Z)
• Continuous surrogate-based optimization algorithms are well-suited for expensive discrete problems [9.655888042539495]
  We present empirical evidence showing that the use of continuous surrogate models displays competitive performance against state-of-the-art discrete surrogate-based methods. Our experiments on different discrete structures and time constraints also give more insight into which algorithms work well on which type of problem.
  arXiv  Detail & Related papers  (2020-11-06T15:27:45Z)
• AdaLead: A simple and robust adaptive greedy search algorithm for sequence design [55.41644538483948]
  We develop an easy-to-directed, scalable, and robust evolutionary greedy algorithm (AdaLead) AdaLead is a remarkably strong benchmark that out-competes more complex state of the art approaches in a variety of biologically motivated sequence design challenges.
  arXiv  Detail & Related papers  (2020-10-05T16:40:38Z)

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.