EvoStruct: Bridging Evolutionary and Structural Priors for Antibody CDR Design via Protein Language Model Adaptation
EvoStruct addresses vocabulary collapse in GNN-based antibody CDR design by combining a frozen protein language model with an E(3)-equivariant GNN through a cross-attention adapter. The method introduces progressive PLM unfreezing and R-Drop consistency regularization to recover functionally important amino acid diversity. On CHIMERA-Bench, EvoStruct improves sequence recovery by 16%, reduces perplexity by 43%, and achieves 2.3x greater amino acid diversity compared to the best GNN baselines.
Related guides (2)
Related events (8)
PLAID: Repurposing Protein Folding Models for Multimodal Protein Generation with Latent Diffusion
PLAID is a generative model that simultaneously produces protein 1D sequences and 3D all-atom structures by learning a diffusion model over the latent space of ESMFold, a protein folding model. It requires only sequence data for training—leveraging databases 2-4 orders of magnitude larger than structure databases—and decodes structure at inference via frozen folding model weights. The approach supports compositional prompting by function and organism, addressing practical drug-design constraints like humanization and solubility. A companion compression model, CHEAP, addresses the high-dimensionality of transformer latent spaces to make the diffusion training tractable.
ESMFold2: The Bitter Lesson is Coming for Proteins — Alex Rives, BioHub
A Latent Space interview/commentary piece featuring Alex Rives of BioHub discussing ESMFold2 and the application of the 'bitter lesson' (scale and general methods beating hand-crafted inductive bias) to protein structure prediction and biology. The piece covers the tension between dataset scale versus domain-specific inductive bias in biological ML, and touches on world models and programmable biology. This represents a significant perspective from a leading researcher in protein language models on the next generation of biological foundation models.
LLM-guided MAP-Elites evolution improves medical decision pipelines at inference time
Researchers propose using LLM-guided MAP-Elites evolutionary search as an inference-time alternative to fine-tuning for adapting LLMs to clinical workflows, formulating triage, consultation, and image classification as evolutionary searches over executable artifacts. Across three medical settings, evolved programs substantially outperform manually designed baselines: triage accuracy improves from 77.3% to 87.1% and emergency recall from 0.60 to 0.97, with gains also shown on MIMIC-ESI, iCRAFTMD, and PneumoniaMNIST. The approach works across Llama-3, Qwen-3.5, and Gemma-4 backbones and produces interpretable program-level mechanisms rather than superficial prompt changes.
LLM-Based Grammar Adaptation for Metamodel-Grammar Co-Evolution in Model-Driven Engineering
This paper proposes using LLMs to automate grammar adaptation when metamodels evolve in model-driven engineering, replacing tedious manual work and outperforming rule-based methods. Evaluated on six real-world Xtext DSLs using Claude Sonnet 4.5, ChatGPT 5.1, and Gemini 3, all three LLMs achieved 100% adaptation consistency on test DSLs versus 62-84% for rule-based approaches. A longitudinal study on QVTo showed LLMs successfully reused learned adaptations across all evolution steps without manual editing. However, on large-scale grammars (EAST-ADL, 297 rules), LLM adaptation consistency dropped well below 90%, revealing a scalability limitation.
Deep Learning with Proteins
A Hugging Face blog post covering the application of deep learning techniques to protein science, likely covering protein language models, structure prediction, and related tooling. Published in late 2022, this sits in the context of AlphaFold2's impact and the emerging ecosystem of protein ML models. The post likely surveys models, datasets, and frameworks available for computational biology on the Hugging Face platform.
EvoArena benchmark and EvoMem memory paradigm for LLM agents in dynamic environments
Researchers introduce EvoArena, a benchmark suite that evaluates LLM agents in dynamic environments by modeling changes as progressive update sequences across terminal, software, and social domains. Alongside it, they propose EvoMem, a patch-based memory paradigm that records memory evolution as structured update histories to help agents reason about environmental change. Current agents score only 39.6% average accuracy on EvoArena, while EvoMem yields consistent gains on EvoArena and also improves performance on GAIA and LoCoMo benchmarks. The work highlights a significant gap between static-benchmark performance and real-world dynamic deployment requirements.
MLEvolve: Self-evolving multi-agent framework for automated ML algorithm discovery
MLEvolve is a new LLM-based multi-agent framework for end-to-end machine learning algorithm discovery, addressing limitations of existing MLE agents including information isolation and memoryless search. The system introduces Progressive MCGS (a graph-extended tree search), Retrospective Memory for experience accumulation, and decoupled strategic planning from code generation. Evaluated on MLE-Bench, it achieves state-of-the-art medal and valid submission rates within a 12-hour budget, and also outperforms AlphaEvolve on mathematical algorithm optimization tasks.
Evolved Policy Gradients: OpenAI Meta-Learning via Loss Function Evolution
OpenAI released Evolved Policy Gradients (EPG), a meta-learning method that evolves the loss function used to train reinforcement learning agents rather than hand-designing it. The approach enables faster adaptation to novel tasks, with agents demonstrating generalization to test-time scenarios outside their training distribution, such as navigating to objects placed in new locations. EPG represents an experimental direction in automated algorithm discovery for RL.