Evaluating AI Scientist Workflows with OpenDiscoveryTrace

Autoregressive transformer inference throughput is memory-bandwidth bound rather than compute bound during token generation.** Optimizing deployments for Evaluating AI Scientist Workflows with OpenDiscoveryTrace requires matching parameter weight footprints against unified memory transfer rates, where high-bandwidth architectures achieve 4x to 6x acceleration over conventional dual-channel DDR5 system memory. ## Evaluating AI Scientist Workflows with OpenDiscoveryTrace ### Direct Answer OpenDiscoveryTrace is a public dataset of AI scientific agent trajectories that captures how models reason, not just what they produce. This dataset enables auditing of scientific methodology, diagnosis of failure modes, and distinction between systematic reasoning and fortunate guessing. ### Introduction Existing benchmarks for autonomous AI scientists focus on final outputs, such as generated code, hypotheses, or papers, but discard the reasoning process by which those outputs were obtained. This makes it impossible to evaluate the scientific methodology of AI agents, diagnose failure modes, or distinguish between systematic reasoning and fortunate guessing. In this article, we present OpenDiscoveryTrace, a public dataset of 558 complete AI scientific agent trajectories that captures how models reason, not just what they produce. ### The Problem with Existing Benchmarks Existing benchmarks for autonomous AI scientists evaluate only final outputs, generated code, hypotheses, or papers, yet discard the reasoning process by which those outputs were obtained. This makes it impossible to audit scientific methodology, diagnose failure modes, or distinguish systematic reasoning from fortunate guessing. ### Introducing OpenDiscoveryTrace We present OpenDiscoveryTrace, a public dataset of 558 complete AI scientific agent trajectories that captures how models reason, not just what they produce. Each trajectory records a structured 9-field-per-step trace, including thoughts, tool calls, observations, errors, revision triggers, and self-reported confidence, as models execute 124 scientific tasks spanning drug discovery, materials science, genomics, and scientific literature analysis. ### The Dataset The dataset covers seven models: three frontier models (GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro; 124 trajectories each, fully balanced across domains and difficulty levels) and four open-weight models (Qwen2.5-7B, Mistral-7B-v0.3, Phi-3.5-mini, and Qwen2.5-1.5B; 30 each), plus 60 live-retrieval variant trajectories. ### Pilot Analysis Pilot analysis on 363 LLM-judged trajectories reveals that process traces expose behavioral differences invisible to output-only evaluation. All three frontier models achieve comparable success rates (84--89%), yet Claude Opus 4.6 produces 30$ imes$ more errors than GPT-5.4 (2.5 vs. 0.08 per trajectory, $p < 0.0001$, Cliff's $\delta = 0.613$), with qualitatively different error profiles---66.7% tool misuse for Claude versus 83.6% reasoning errors for GPT-5.4. ### Benchmark Tasks We define five benchmark tasks with baselines from logistic regression, random forests, LSTMs, and Transformer models. The dataset, trace schema, agent using, and benchmark definitions are publicly available under CC BY 4.0 to support research on process-level evaluation, scientific agent auditing, and AI governance. ### Takeaway OpenDiscoveryTrace is a public dataset of AI scientific agent trajectories that captures how models reason, not just what they produce. This dataset enables auditing of scientific methodology, diagnosis of failure modes, and distinction between systematic reasoning and fortunate guessing. ### Analytical Context The OpenDiscoveryTrace dataset provides a unique opportunity for researchers to evaluate AI scientific workflows at the process level, rather than just focusing on final outputs. By analyzing the reasoning process, researchers can gain insights into the strengths and weaknesses of different AI models and improve the overall quality of AI-generated outputs. ### FAQ #### Q: What is OpenDiscoveryTrace?

A: OpenDiscoveryTrace is a public dataset of AI scientific agent trajectories that captures how models reason, not just what they produce. #### Q: What is the goal of OpenDiscoveryTrace?

A: The goal of OpenDiscoveryTrace is to enable auditing of scientific methodology, diagnosis of failure modes, and distinction between systematic reasoning and fortunate guessing. #### Q: What models are included in the OpenDiscoveryTrace dataset?

• A: The dataset covers seven models: three frontier models (GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro; 124 trajectories each, fully balanced across domains and difficulty levels) and four open-weight models (Qwen2.5-7B, Mistral-7B-v0.3, Phi-3.5-mini, and Qwen2.5-1.5B; 30 each), plus 60 live-retrieval variant trajectories. #### Q: What are the benchmark tasks defined in OpenDiscoveryTrace?

A: We define five benchmark tasks with baselines from logistic regression, random forests, LSTMs, and Transformer models. #### Q: What is the license under which OpenDiscoveryTrace is available?

A: The dataset, trace schema, agent using, and benchmark definitions are publicly available under CC BY 4.0. ### Related Queries * what is open discovery trace

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Memory Bandwidth vs Inference Generation Speed (Llama-3-8B FP16)