{"total":12,"items":[{"citing_arxiv_id":"2605.21083","ref_index":16,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"AIMBio-Mat: An AI-Native FAIR Platform for Closed-Loop Materials Discovery and Biomedical Translation","primary_cat":"physics.app-ph","submitted_at":"2026-05-20T12:18:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"AIMBio-Mat is a conceptual blueprint for an AI-native, FAIR, governance-aware decision layer that formulates biomedical-materials discovery as constrained multi-objective optimization under uncertainty.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20744","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Hack-Verifiable Environments: Towards Evaluating Reward Hacking at Scale","primary_cat":"cs.LG","submitted_at":"2026-05-20T05:46:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Presents Hack-Verifiable TextArena, a benchmark that embeds verifiable reward hacking opportunities into environments to enable deterministic measurement of exploitation by language models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.15564","ref_index":1,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"CrystalBoltz: End-to-End Protein Structure Determination via Experiment-Guided Diffusion for X-Ray Crystallography","primary_cat":"cs.LG","submitted_at":"2026-05-15T03:11:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CrystalBoltz performs experiment-guided posterior sampling with diffusion models on structure-factor amplitudes for protein structure determination, reporting lower RMSD and R-factors than baselines with 33x faster runtime.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18816","ref_index":1,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Symmetry in the Wild: The Role of Equivariance in Neural Fluid Surrogates","primary_cat":"cs.LG","submitted_at":"2026-05-12T10:47:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Explicit E(3)-equivariance in neural CFD surrogates improves generalization on diverse-geometry hemodynamics benchmarks but degrades in-distribution performance on strongly aligned aerodynamics data, consistently beating data augmentation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08953","ref_index":1,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A putative, computationally stable structure of homotrimeric BP180/collagen XVII","primary_cat":"q-bio.BM","submitted_at":"2026-05-09T13:50:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A putative homotrimeric structure of BP180 is predicted with Boltz-2 and shown to remain mostly folded over 500 ns MD trajectories, with a stiff NC16A domain and flexible Col-15.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.04265","ref_index":156,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Benchmarking open-source tools for in silico antiviral drug discovery","primary_cat":"q-bio.BM","submitted_at":"2026-05-05T19:59:39+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Boltz-2 and fine-tuned DrugFormDTA lead ML-based binding prediction while GNINA leads docking tools on a cleaned antiviral dataset, with performance varying by viral protein.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"made available a web-based interface that allowed people to run the model a few times for free for non-commercial use, and then pay for further non-commercial use. This sparked backlash from the academic community. Eventually, Google released AlphaFold3 with an open-source, non-commercial license in November 2024. That episode spurred the development of Boltz-1, the first iteration of which was published a preprint on November 20, 2024.[156] Loosely speaking, Boltz-1 can be thought of as a fully open- source version of AlphaFold3 with an additional \"affinity prediction\" branch added on. Boltz-2 was announced in June 2025.[157] AlphaFold is a very complex system, consisting of extensive sequence preprocessing and six interacting modules that operate in a recursive fashion. A full exposition of how AlphaFold works"},{"citing_arxiv_id":"2604.23924","ref_index":5,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Agentic AI platforms for autonomous training and rule induction of human-human and virus-human protein-protein interactions","primary_cat":"cs.AI","submitted_at":"2026-04-27T00:47:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Agentic AI platforms autonomously train 87%-accurate PPI prediction models on protein-disjoint data and induce aligning human-readable rules for human-human and virus-human interactions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.22026","ref_index":37,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Rethinking Publication: A Certification Framework for AI-Enabled Research","primary_cat":"cs.AI","submitted_at":"2026-04-23T19:40:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A two-layer certification framework decouples knowledge validity from human authorship to accommodate AI-enabled research in existing publication systems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.18467","ref_index":11,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"An Integrated Deep-Learning Framework for Peptide-Protein Interaction Prediction and Target-Conditioned Peptide Generation with ConGA-PepPI and TC-PepGen","primary_cat":"cs.LG","submitted_at":"2026-04-20T16:20:23+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"An integrated framework with ConGA-PepPI for PepPI prediction and binding-site localization plus TC-PepGen for target-conditioned peptide generation reports 0.839 accuracy and 0.921 AUROC in cross-validation along with 40.39% of generated peptides exceeding native templates on AlphaFold 3 ipTM.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.17175","ref_index":1,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Design","primary_cat":"cs.LG","submitted_at":"2026-04-19T00:20:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.18603","ref_index":9,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Dual Triangle Attention: Effective Bidirectional Attention Without Positional Embeddings","primary_cat":"q-bio.QM","submitted_at":"2026-04-09T19:32:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Dual Triangle Attention achieves effective bidirectional attention with built-in positional inductive bias via dual triangular masks, outperforming standard bidirectional attention on position-sensitive tasks and showing strong masked language modeling results with or without positional embeddings.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"For nucleotide modeling, where functional and structural dependencies can span large genomic distances, DTA's context extension capability may complement existing long-range approaches. Future work should focus on MLM-specific variants of position dropping, potentially enabling robust long-context extension in bidirectional settings without full long-context pretraining. Methods Data sources Argmax position probe.Synthetic sequences were generated by sampling integers uniformly from[0,v)wherev= 64is the vocabulary size. Labels were the 0-indexed position of the first occurrence of the maximum value. Sequence length was fixed atl= 64. Batches of 1,024 sequences were generated on-the-fly during training; evaluation used 16 batches of 1,024 sequences each. Natural language.We used FineWeb-Edu (45), a large-scale filtered web corpus designed for language model pretraining. Text was tokenized using a custom Byte-Pair Encoding (BPE) tokenizer (51) with a vocabulary of 4,096 tokens, chosen to reduce vocabulary size relative to standard tokenizers while preserving reasonable subword granularity. Training sequences were truncated or padded to 256 tokens. Validation and test sets were constructed by filtering documents with at least 1,024 tokens, then splitting the remaining documents into 1,000 documents each for validation and testing. Training data was streamed and filtered to exclude validation and test documents. Halleeet al.| arXiv | April 22, 2026 | 5-12 Fig. 5.DroPE recovery analysis. (a) NLP extended-context validation loss, accuracy, MCC, and F1 before and after dropping positional embeddings at 70% of training. (b) Protein extended-context validation loss, accuracy, MCC, and F1. The vertical dashed line marks the drop point. Shaded regions represent±1 standard deviation across three seeds. (c) NLP final test loss, accuracy, MCC, and F1 comparing RoPE (kept throughout) vs. RoPE-off (dropped at 70%). (d) Protein final test loss, accuracy, MCC, and F1. Signi"},{"citing_arxiv_id":"2409.10588","ref_index":2,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"ADIOS: Antibody Development via Opponent Shaping","primary_cat":"q-bio.PE","submitted_at":"2024-09-16T14:56:27+00:00","verdict":"CONDITIONAL","verdict_confidence":"UNKNOWN","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ADIOS applies opponent shaping in a meta-learning setup to create antibodies that target current and future viral variants while biasing evolution toward weaker strains, demonstrated in Absolut! simulations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}