InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery

Key points

• Problem: The authors argue that many existing AI Scientist systems are tied to domain-specific designs, which makes unified reasoning across scientific fields difficult. Their optimization often refines proposals through updates within one trajectory, and most systems keep no persistent memory across extended research cycles.
• Input and output: This is a system paper with an implementation and experiments. The input is either an algorithm discovery task, given an objective and the original baseline codebase, or an empirical discovery task, given observations and a research question. The output is hypotheses and method plans, executed code or experimental protocols, measurements and reports, and each candidate solution is a full experimental proposal covering code logic, parameter settings and physical operation protocols.
• Method: Generation produces hypotheses and method plans through deep research built on a literature knowledge graph and a flow graph that links subtasks as a DAG (directed acyclic graph), Verification evaluates them computationally or experimentally, and Evolution stores the outcomes in memory for the next cycle. Verification search replaces the tree of MCTS (Monte Carlo Tree Search) with a solution graph that pools all prior experiments, and builds new proposals with four operators: parent-only expansion, intra-branch evolution, cross-branch reference and multi-branch aggregation. Memory is split into reusable procedures distilled from past reasoning (Strategy-Procedural Memory, SPM), the method and metrics stored after each experiment (Task-Episodic Memory, TEM), and principles drawn from pairwise comparison of methods at the end of each batch plus a graph of explored research directions (Semantic-Knowledge Memory, SKM).
• Implementation: On GAIA (real-world tasks that need reasoning, web navigation and tool use) and HLE (Humanity's Last Exam), the system was compared on three base models, Qwen3-235B, o4-mini and a Gemini-3-pro plus o4-mini combination, and on reaction outcome prediction (ChemCoTBench) it called RDKit to compute molecular descriptors. For fluorescent protein design it used ESMFold and ProSST, and in that wet-lab stage SCP (Science Context Protocol) coordinated automated devices for DNA assembly, expression, purification and fluorescence measurement. The official repository README states that it releases launch_discovery.py for runs from idea generation to experiments, launch_qa.py for deep research question answering, a tasks folder with a prompt.json and baseline code per algorithm discovery task, and a memory module.
• Results: On the 165-question GAIA validation set, the Gemini-3-pro plus o4-mini configuration averaged 86.06%, above Manus at 73.30% and above the 80.8% of MiroThinker-v1.5-235B, which was evaluated only on the text-only subset. On the AutoEAP scientific data task, HK-PCC (housekeeper Pearson correlation coefficient between predicted and measured enhancer activity) rose from 0.65 for the DeepSTARR baseline to 0.91. The ablation without SPM reached 82.42% average GAIA accuracy with 22.69 average tool calls, worse than the 86.06% and 18.52 of the full system.
• Limits: The two life science cases reproduce targets reported before, GPR160 in hepatocellular carcinoma reported by OriGene and ARG2 in colorectal cancer from an original study, and the wet-lab fluorescent protein experiment confirmed the brightness and folding efficiency of the variant sfGFP reported in the literature. The only numeric memory table is the SPM ablation on GAIA, while TEM is shown through performance curves and SKM through prompt-based case studies. The authors name tighter coupling of computational reasoning with experimental validation, and a faster path from generated hypotheses to verifiable results, as future work.

Paper links

External research summaries. These are not HDATF publications or measured product results.