A multi-agent pipeline iteratively refines topology optimization outputs to match natural language preferences for branched structures, achieving 60% success rate across replicates in cantilever and phone-stand tasks.
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Robin: A multi-agent system for automating scientific discovery
13 Pith papers cite this work. Polarity classification is still indexing.
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AgentForesight introduces an online auditor model that predicts decisive errors in multi-agent trajectories at the earliest step using a coarse-to-fine reinforcement learning recipe on a new curated dataset AFTraj-2K.
RosettaSearch applies LLM-driven multi-objective search at inference time to improve backbone-conditioned protein sequences, recovering designs with 18-68% better structural fidelity and 2.5x higher success rates than single-pass models like LigandMPNN.
Kosmos is an AI scientist that maintains coherence over hundreds of agent steps via a shared world model, executes thousands of code lines and reads thousands of papers per run, and produces traceable reports with 79.4% statement accuracy according to independent reviewers.
DiscoPER uses code generation, statistical validation, and second-order meta-reflection on accumulated discoveries to recover 8 of 9 known ecological patterns on a new benchmark at 72.7% support rate.
Introduces an auditable four-stage diagnostic for LLM physics reasoning in novel frameworks and applies it to three parallel worlds, yielding pass rates of 6/15, 6/15, and 0/15 on frontier models with noted qualitative-quantitative asymmetry.
Decentralized AI agent teams self-organize around hypotheses, critique proposals, and share knowledge to outperform single-agent baselines on biomedical ML, language-model optimization, and protein fitness tasks.
EvoMemBench evaluates 15 memory methods for LLM agents and finds long-context baselines competitive with no single memory approach working consistently across settings.
PRL-Bench evaluates frontier LLMs on 100 real physics research tasks and finds the best models score below 50, exposing a gap to autonomous discovery.
An agentic system produces traceable review packages and an un-finetuned 196B model using it covers more major issues than Gemini-3.1-Pro on 134 ICLR 2025 submissions while winning most blind comparisons to human committees.
Sibyl-AutoResearch introduces self-evolving trial-and-error harnesses with auditable conversion units that link trial signals to updated research behaviors and harness repairs in autonomous systems.
Claw AI Lab presents an interactive multi-agent platform for autonomous AI research that supports customizable teams, real-time control, and a code harness for experiment integration and result integrity.
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Autonomous Scientific Discovery via Iterative Meta-Reflection
DiscoPER uses code generation, statistical validation, and second-order meta-reflection on accumulated discoveries to recover 8 of 9 known ecological patterns on a new benchmark at 72.7% support rate.