NeuronSifter is a decision‑centric Agent‑in‑Twin framework that uses an evolving biological digital twin instead of a fixed predictive model to prioritize central nervous system interventions. It compiles interventions into state‑conditional target‑occupancy representations, propagates them through a mechanism‑grounded World Action Model, and selects informative measurements to reduce decision uncertainty. The system is evaluated on Alzheimer’s disease scenarios, demonstrating adaptive evidence acquisition, uncertainty‑aware planning, and robustness to model discrepancy, while continuously refining its decision‑relevant representations with externally verified evidence.
By Haowei Xu, Wanyi Fu, Hongbin Han, Zhaoheng Xie
The paper introduces Synthetic Universes, a benchmark that pairs well-known theoretical worlds with closely related twisted variants governed by noncanonical mechanisms. It evaluates scientific agents on each law twice: by testing predictive performance on unseen data and by checking if the law recovers the underlying generating mechanism. Results from a 60‑cell study show a dissociation between predictive adequacy and mechanism recovery, indicating that these are distinct scientific claims requiring separate tests.
By Kargi Chauhan
DNative‑Twin is a graph‑native digital twin that records an AI agent’s committed decision as a typed trajectory, linking observed state, decision path, and authority. It re‑executes the decision mechanism under declared conditions, synchronizing and replaying the process in isolation to compare outcomes under controlled changes. Experiments on enterprise decision logs show that adding replay‑contract state and verification evidence improves unresolved‑divergence recall from 0 to 1.0, while end‑to‑end processing time rises from 0.794 to 8.889 seconds across 500–5,000 cases.
By Junjie Pang, Zhenzhen Xie, Haoke Han, Ying He, Jing Wang, Gang Liu
The paper treats human interventions during LLM agent failures as labels that pinpoint the internal computation that went wrong, rather than merely training data. In a controlled environment, the authors analyze one failure type—over‑asking a clarifying question when the request is already clear—using rule‑based overseer labels validated by blind human judgment. They find that the decision to ask versus act can be causally localized across several models, that over‑asking exhibits distinct mechanistic signatures, and that steering or weight consolidation can mitigate the issue, though outcomes vary by model family.
By Cesare Zavattari, Alessandro Tommasi, Giuseppe Prencipe
arXiv:2605. 09366v3 Announce Type: replace Abstract: Transforming neuroimaging data into clinically actionable biomarkers is a knowledge-intensive and labor-intensive process.
By Keqi Han, Songlin Zhao, Yao Su, Xiang Li, Yixuan Yuan, Lifang He, Carl Yang
The paper introduces CELLAUDIT, a method for auditing whether inputs claimed to influence predictive models actually do so. By testing if an input can enter the computation, whether predictions depend on it, and if that dependence improves observed responses, the authors evaluate agent-generated predictors on a morphology‑transcriptomics benchmark (BBBC047). Their findings show that many models claim compound contributions that are not supported by the data, and that falsification‑guided revisions can recover genuine input effects while improving performance.
By Mengran Li, Bo Li, Chengyang Zhang, Yang Yan, Jinfeng Xu, Zhenchao Tang
arXiv:2607. 10439v3 Announce Type: replace-cross Abstract: We present a physics-inspired classical digital twin of brain-computer- interface (BCI) data: a graph neural network constrained to a band-stratified, metriplectic port-Hamiltonian form, with parameters learned from scalp EEG recorded during rest and motor imagery.
By Dibakar Sigdel
arXiv:2608. 09696v1 Announce Type: new Abstract: Predicting the answer to interventional ``what if'' questions --- the outcome of an action never taken --- requires a \emph{mechanistic}, causal model, not a curve fit; and learning such a model requires \emph{experiments}, because passive data leaves its mechanisms unidentified.
By Kevin Murphy
arXiv:2608. 14490v1 Announce Type: new Abstract: We present a Test-time World-model Inference (Twin) system, in which a frontier coding agent writes an executable world model for completing continual learning tasks, such as ARC-AGI-3 games.
By Alexy Skoutnev, Kirill Acharya, Gaston Longhitano, Madeleine Udell, Kevin Ellis, Iddo Drori
arXiv:2609.07987v1 Announce Type: new
Abstract: LLM-based digital twins promise to reduce repeated human data collection by generating person- specific responses, yet existing evaluations provide lit...
By Steven Wang, Kyle Hunt, Shaojie Tang, Kenneth Joseph
arXiv:2608. 08159v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly reported to exhibit human-like neural and cognitive signatures, including concept cells, mental number lines, and cognitive maps.
By Yuqi Wu, Shengming Zhao, Jie Chen
TwinCheck is an inference‑time verification policy for stateful tool agents that only replaces a proposed tool call when a trace‑grounded counterfactual alternative, called a negative twin, satisfies structural checks and is preferred by a pairwise verifier in both candidate orders. The method uses exact replay to isolate intervention effects, and in experiments on 159 multi‑turn BFCL V4 tasks, it increased GPT‑5.6 Sol’s task success from 45.3% to 58.5% without any observed success‑to‑failure regressions.
By Jiaxuan Dai, Tianyi Huang