Publications

Supplementary Table4 from PGV001, a Multi-Peptide Personalized Neoantigen Vaccine Platform: Phase I Study in Patients with Solid and Hematologic Malignancies in the Adjuvant Setting
Mansi Saxena
Thomas U. Marron
Julia Kodysh
John P. Finnigan
Sayali Onkar
Anna Kaminska
Kevin Tuballes
Ruiwei Guo
Rachel Lubong Sabado
Marcia Meseck
Timothy J. O’Donnell
Robert Sebra
Samir Parekh
Matthew D. Galsky
Ana Blasquez
Gustavo Gimenez
Mesude Bicak
Cansu Cimen Bozkus
Daniela Delbeau-Zagelbaum
Denise Rodriguez … (voir 17 de plus)
Ana Acuña-Villaorduña
Krzysztof Misiukiewicz
Marshall R. Posner
Brett A. Miles
Hanna Y. Irie
Amy Tiersten
Deborah B. Doroshow
Andrea Wolf
John Mandeli
Rachel Brody
Andres Μ. Salazar
Sacha Gnjatic
Jeff Hammerbacher
Eric E. Schadt
Philip Friedlander
Alex Rubinsteyn
Nina Bhardwaj
<p>Supplementary Table4: Number of Subjects who had Treatment Emergent Adverse Events by grade, system (13 subjects)</p>
2026-06-16
American Association for Cancer Research (accepté)
doi.org
Supplementary Table5 from PGV001, a Multi-Peptide Personalized Neoantigen Vaccine Platform: Phase I Study in Patients with Solid and Hematologic Malignancies in the Adjuvant Setting
Mansi Saxena
Thomas U. Marron
Julia Kodysh
John P. Finnigan
Sayali Onkar
Anna Kaminska
Kevin Tuballes
Ruiwei Guo
Rachel Lubong Sabado
Marcia Meseck
Timothy J. O’Donnell
Robert Sebra
Samir Parekh
Matthew D. Galsky
Ana Blasquez
Gustavo Gimenez
Mesude Bicak
Cansu Cimen Bozkus
Daniela Delbeau-Zagelbaum
Denise Rodriguez … (voir 17 de plus)
Ana Acuña-Villaorduña
Krzysztof Misiukiewicz
Marshall R. Posner
Brett A. Miles
Hanna Y. Irie
Amy Tiersten
Deborah B. Doroshow
Andrea Wolf
John Mandeli
Rachel Brody
Andres Μ. Salazar
Sacha Gnjatic
Jeff Hammerbacher
Eric E. Schadt
Philip Friedlander
Alex Rubinsteyn
Nina Bhardwaj
<p>Supplementary Table5: Cellular Immunophenotyping antibody panels</p>
2026-06-16
American Association for Cancer Research (accepté)
doi.org
Unsupervised Causal Abstractions Discovery
Theo Saulus
Simon Lacoste-Julien
Dhanya Sridhar
Causal abstractions formalize when a high-level structural causal model (SCM) captures the interventional behavior of a lower-level SCM. Exi… (voir plus)sting applications of this notion largely follow a hypothesis-testing paradigm: an expert proposes a candidate high-level model and then evaluates if the low-level system implements it. We study the complementary problem of learning a high-level model directly from low-level measurements. Our contributions leverage hypotheses from low-rank causal discovery, and can be summarized as follows: (1) we show that observations generated by a low-rank graph induce latents that form a causal abstraction, (2) we provide identifiability results about these latents, and (3) we propose a practical objective to learn this high-level SCM.
2026-06-16
arXiv (prépublication)
doi.org
arxiv.org
Intermediate Bilevel Optimization: Modeling Endogenous Follower Tie-Breaking Behavior
Maria Bazotte
Margarida Carvalho
Thibaut Vidal
In bilevel optimization, optimistic and pessimistic follower behaviors are the most commonly used forms to define how the follower ties-brea… (voir plus)ks among multiple optimal solutions. In this work, we go beyond these extreme tie-breaking behaviors and investigate the intermediate bilevel optimization program (I-BO), where the follower's selected optimal response is a decision-dependent random event, with a probability measure influenced by the leader's decision. We formally introduce a class of such endogenous measures, including the special case of strong-weak decision-dependent I-BO. We reformulate the I-BO as a Transformed I-BO (T-I-BO) with exogenous uncertainty by defining inverse and Markov-chain transformations, which represent the follower's response as a function of the leader's decision and exogenous randomness. We handle the T-I-BO's uncertainty via sample-average approximation (SAA), and we propose tailored approaches for its SAA program according to the chosen transformation. Computationally, our methods solve reasonable-sized instances efficiently and outperform the deterministic equivalent when available. Furthermore, experiments stress the critical need to accurately model follower tie-breaking behavior, particularly depending on its alignment with the leader's objective, as misspecification leads to suboptimal leader decisions.
2026-06-15
arXiv (prépublication)
doi.org
arxiv.org
Abstention-Aware Personalized Object Rearrangement via Uncertainty-Guided LLM Assistance
Sam Collin
Ali Ayub
Robotic assistance in household environments requires not only predicting where objects should be placed, but also reasoning about when obje… (voir plus)cts should not be placed at all. Existing approaches to personalized object rearrangement primarily focus on placement decisions under the assumption of clean observations and complete actionability, limiting their applicability in realistic, cluttered, and partially erroneous settings. In this paper, we introduce APOLLO, a hybrid framework for abstention-aware personalized object rearrangement that combines a lightweight, personalized embedding model (PEM) with selective large language model (LLM) assistance. PEM is trained for each user-environment pair using a small number of demonstrations, operates entirely on CPU, and produces uncertainty estimates, which are used to selectively invoke LLM-based reasoning only for ambiguous decisions, balancing efficiency, privacy, and reasoning capability. To evaluate this formulation beyond existing benchmarks, we introduce APOR, a synthetic, LLM-generated dataset that captures room-level, multi-furniture environments, diverse organizational profiles, explicit abstention behavior, and noisy partial scene context. Extensive experiments on both PARSEC and APOR provide initial evidence that APOLLO improves over prior LLM-based baselines in controlled benchmark settings while substantially reducing LLM usage. Code is available at https://github.com/PaInt-Lab/APOLLO.
2026-06-14
arXiv (prépublication)
doi.org
arxiv.org
3D Scene Graphs: Open Challenges and Future Directions
Dennis Rotondi
Francesco Argenziano
Sebastian Koch
Nathan Hughes
Martin Buechner
Johanna Wald
Lukas Rosenberger Schmid
Daniele Nardi
Abhinav Valada
Liam Paull
Federico Tombari
Luca Carlone
Kai O. Arras
3D Scene Graphs (3DSGs) have emerged as a powerful representation for spatial AI by combining geometric grounding with semantic and relation… (voir plus)al abstractions of the environment. Their expressiveness has made them relevant to a broad range of problems in robotics and computer vision, including manipulation, navigation, task planning, scene understanding, and many others. However, the field remains fragmented: different communities adopt distinct formulations, construction pipelines, and evaluation protocols, making it difficult to compare methods, identify common assumptions, and assess remaining challenges for robust real-world deployment. This survey provides a unified and critical review of 3DSGs, with particular emphasis on open challenges and future directions. We first formalize 3DSGs under a common definition and analyze the principal modeling choices that characterize existing formulations, including node and edge attributes, hierarchical structure, dynamic scene representations, and affordance-aware extensions. We then review how 3DSGs are built from raw sensory observations, discussing the most common terminologies, conventions, and techniques. Finally, we examine downstream applications and evaluation strategies, from intrinsic graph quality to task-level performance. To support the community, we also provide a dedicated website that organizes and extends the surveyed content, accessible at https://3dscenegraphs.com/.
2026-06-14
arXiv (prépublication)
doi.org
arxiv.org
VISTA: Scale-Aware Visual Navigation via Action History Conditioning
Maeva Guerrier
Koki Kobayashi
Simon Roy
Jana Pavlasek
Giovanni Beltrame
Vision Navigation Foundation Models (VNMs) promise end-to-end learned navigation policies capable of zero-shot deployment across diverse emb… (voir plus)odiments and environments. To maintain generality, many vision-based navigation models predict normalized actions. However, this normalization introduces a critical deployment vulnerability: applying different scaling factors to the same normalized trajectory alters its physical geometry, which degrades navigation performance and increases collision risks. We address this vulnerability by conditioning the model on normalized action histories alongside image observations, providing explicit context on the relationship between the model's predictions and the robot's actual physical displacement. Furthermore, current VNMs often struggle in visually repetitive environments that lack distinct features. To resolve this issue, we integrate a DINOv3 encoder, whose richer representations enable our model to capture both spatial and geometric dimensions between observations. VISTA generalizes robustly to out-of-distribution environments, achieving 100% goal prediction accuracy in zero-shot, real-world deployment in Outdoor, Forest and Office settings, and an average of 95% checkpoints crossed, demonstrating consistent path following in unseen environments.
2026-06-14
arXiv (prépublication)
doi.org
arxiv.org
On Defining Erasure Harms for NLP
Yu Lu Liu
Arnav Goel
Jackie Chi Kit Cheung
Alexandra Olteanu
Ziang Xiao
Su Lin Blodgett
The deployment of NLP systems has raised concerns about harms they might produce, including representational harms. Recent literature has be… (voir plus)gun to conceptualize and measure one such harm, the harm of erasure. Nevertheless, the field lacks a clear and cohesive conceptual foundation for identifying and measuring erasure. Existing conceptualizations of erasure are often broad -- making it difficult to identify what is needed to establish and measure erasure -- or else specific to particular settings -- facilitating measurement for those settings but potentially challenging to adapt to other settings. To address this gap, we develop and propose a structured definition of erasure that clarifies what components are necessary for establishing whether erasure has occurred, which practitioners need to explicitly articulate and operationalize in order to measure erasure.
2026-06-13
arXiv (prépublication)
doi.org
arxiv.org
Radiomic prediction of substantial LVSI in endometrial cancer using reduced field of view DWI- a feasibility study
Akiyo Takada
Daniel A. Di Giovanni
Takuro Horikoshi
Takahiro Tsuboyama
Hajime Yokota
Sakurako Harada‐Kagitani
Evan McNabb
Jérémy Dana
Rita Zakarian
Haruto Sugawara
Yuka Matsumoto
Yuji Habu
Hirokazu Usui
Kaori Koga
Katsuhiro Nasu
Takashi Uno
Martin Vallières
Caroline Reinhold
2026-06-13
European Journal of Radiology (publié)
doi.org
Engineered Nonheme Iron Enzymes Enable Asymmetric Hydrogenation of Alkenes
Yunfei He
Shuang-Yu Dai
Mei‐Yan Xu
Baixu Ma
Jian Tang
Lizhi Tao
Zhen Liu
Developing biocatalytic systems capable of reducing simple alkenes is highly desirable for synthetic chemistry and biosynthesis, yet existin… (voir plus)g enzymes remain largely restricted to their ability to convert polarized, electron-deficient substrates. Here, we present a nonheme iron metalloenzyme platform that enables hydrogenation of styrenes, conjugated nitriles and amides, and nonconjugated olefins through a putative iron–hydride mechanism. Starting from the Fe(II)/ α -ketoglutarate-dependent dioxygenase GOX, iterative rounds of directed evolution produced an engineered “alkene hydrogenase” (AHase-6) containing 16 mutations and promoting NaBH 4 -driven reduction across diverse C═C bond motifs. Kinetic analysis indicates that this enzymatic hydrogenation process proceeds via formation of an enzyme–substrate ternary complex through a sequential mechanism. Mechanistic studies further reveal that alkene insertion occurs with regioselectivity governed primarily by substrate electronics and sterics. These findings establish nonheme iron enzymes as an unrecognized scaffold for metal–hydride-based hydrogenation and highlight their potential as sustainable, tunable alternatives to traditional catalytic systems.
2026-06-12
Journal of the American Chemical Society (publié)
doi.org
Artificial intelligence-assisted ganglion cell detection in Hirschsprung's disease: A comparative evaluation of two deep learning approaches
E Wang
Karl Grenier
Peter Savadjiev
Dan Poenaru
Background. Definitive diagnosis of Hirschsprung's disease (HD) requires pathological identification of enteric ganglion cells. This process… (voir plus) is time-consuming and subject to inter-observer variability. Artificial intelligence (AI) tools have the potential to standardize and accelerate this workflow, but no study has determined which AI approach best serves intraoperative HD pathology diagnostics. Method. This study compared the U-Net and You Only Look Once version 26 (YOLO26) frameworks for ganglion cell detection using a single-centre retrospective dataset of 54 whole-slide images (WSIs) from rectal biopsies. WSIs were tiled into 397,731 image patches (128x128 pixels), further partitioned into training (70%), validation (15%), and testing (15%) sets. Models were evaluated on tile- and patient-level diagnostic metrics and processing latency. Results. The U-Net achieved a tile-level sensitivity of 82.9%, showing no statistically significant difference compared to YOLO26 (79.1%; p = 0.097). However, YOLO26 demonstrated a statistically significant advantage in tile-level specificity (96.1% vs. 93.9%; p < 0.001) and reduced mean inference latency (7.64 ms vs. 11.57 ms/tile). At the patient level, both models achieved 100% diagnostic sensitivity. Despite low patient-level specificity (0.0% U-Net; 11.8% YOLO26), the tissue-level diagnostic burden of false positives was 6.00% for U-Net and 3.50% for YOLO26. Conclusion. The U-Net is preferred when nominal gains in sensitivity are prioritized, while the YOLO26 is an alternative that optimizes efficiency and false positive suppression. Both models serve as robust screening filters to augment the pathologist's workflow and should be selected based on workflow requirements. Prospective validation on larger, multi-centre datasets is required before clinical implementation.
2026-06-11
medRxiv (accepté)
doi.org
Characterizing Cultural Localization in AI-Generated Stories
Shaily Bhatt
Supriti Vijay
Jeremiah Milbauer
Fernando Diaz
The global use of artificial intelligence has increased interest in assessing the ability to generate culturally localized content, includin… (voir plus)g stories. Cultural localization in stories often occurs through either templated localization -- the use of cultural markers (e.g., names, locations) in a generic narrative -- or holistic localization -- the variation of plots, values, and themes, in addition to cultural markers. We propose a method to measure the degree to which content was generated through templated localization. Specifically, we identify the lexical tokens that distinguish stories across nationalities and measure the similarity of the narratives that remain after removing them. In stories generated by five models on 125 topics for 193 nationalities, our method is able to detect that only a small subset (9-17%) of the vocabulary accounts for the variation across nationalities and that the narratives that remain after removing them contain repeated multi-word sequences, suggesting the presence of a shared culturally-agnostic narrative template. Finally, we characterize the cultural markers for their stereotypicality and offensiveness, finding that markers from 19 countries, mostly located in the Global South, are on average offensive.
2026-06-11
arXiv (prépublication)
doi.org
arxiv.org