The paper introduces a training‑free ‘socket’ that connects a DNA foundation model to a frozen large language model (LLM) using a single ridge‑regression plug. The plug maps Evo 2 DNA features to the LLM’s internal representation of a gene’s symbol, name, and description, replacing hidden states at placeholder positions without back‑propagation. Evaluated on the GeneHop benchmark, the socket consistently outperforms shuffled DNA across four hosts, achieving significant gains in accuracy and demonstrating robust gene‑identity transfer without additional training.
arXiv:2608. 20054v1 Announce Type: new Abstract: Multi-module systems often expose every module to the full input.
arXiv:2609.35877v1 Announce Type: cross Abstract: Many DNA foundation models are pretrained by masking parts of a sequence and asking the model to reconstruct them. Standard masked pretraining expose...
The study investigates whether independently trained language‑model societies share a common packet language and how inherited interface states affect learning. A comprehensive audit of 30 pairwise interactions among six restricted societies shows that only one pair is fully interoperable, another is partially compatible, and the remaining 26 pairs fail across all alignment levels. Further experiments reveal that a globally trained communication interface can act as a severe negative‑transfer prior, but inherited interfaces never outperform fresh‑interface controls by the preregistered margin.
arXiv:2610.34639v1 Announce Type: cross Abstract: How do later loops in a pretrained looped language model repair two-hop retrieval? We study Ouro-1.4B and Ouro-2.6B by transplanting attention querie...
The study investigates whether independently trained language‑model societies share a common packet language and how inherited interface states affect learning. A comprehensive audit of 30 pairwise interactions among six restricted societies shows that most cross‑initialization pairs fail to interoperate, with only one pair achieving full bidirectional compatibility. Further experiments reveal that reinitializing only the packet reader, writer, and mouth dramatically improves accuracy, while inherited interfaces never outperform fresh ones by the preregistered margin.
arXiv:2410.02343v2 Announce Type: replace Abstract: Large language models (LLMs) routinely fail to output the correct option in multiple-choice question answering (MCQA) while encoding the answer int...
The study evaluates a frozen Geneformer representation for predicting CRISPRi perturbation effects under a tightly controlled, pre‑registered protocol. While the representation shows significant predictive power within the Virtual Cell Challenge dataset, it fails to transfer to external screens, with negative zero‑shot Spearman correlations. The analysis also reveals that the VCC endpoint is heavily influenced by sampling depth, as cell count alone explains most of the variance, indicating a sampling‑depth entanglement that could mask transfer failures in less controlled settings.
arXiv:2607. 29431v1 Announce Type: new Abstract: Large language models increasingly generate optimization models from natural language, but existing evaluation often reduces a generated model and its ground truth to a single equivalent/not-equivalent verdict or an execution-success rate--labels that are neither independently checkable nor faithful to the multiple distinct senses in which two formulations can agree.
Depth pruning removes entire Transformer blocks to shrink and speed up language models, but the resulting damage is uneven across capabilities. In a study of four 1.2–1.7 B instruction‑tuned models, function calling and mathematical reasoning collapsed in every case, while other skills varied in order of degradation. Fine‑tuning a short ‘healing’ run can restore some abilities, with the choice of which blocks to retrain and the allocation of a limited token budget influencing recovery outcomes.
The paper introduces FateMultiplicity, a label‑free framework that builds a Rashomon set of statistically admissible models for single‑cell trajectory inference by cross‑fitting held‑out genes and non‑inferiority testing. It shows that model multiplicity is large and depends more on the diversity of the model space than its size, and that per‑cell certified fate margins do not improve assignment reliability compared to baseline models. The study also identifies two robust constructions— a margin‑erosion ratio for branch‑point detection and a certification filter that reduces disagreement with clonal outcomes— that merit further investigation.
arXiv:2610.33958v1 Announce Type: cross Abstract: Coding agents are given embedding search and code-graph tools on the premise that a repository's structure is something the model lacks and the tool...
arXiv:2609.16145v1 Announce Type: new Abstract: We study a practical question: can a small correction module fix errors in a frozen language model's outputs without degrading its base capabilities? W...