Publications

POSTELLAR: Posterior Stellar Spectrum Sampling—An Alternative to Approximate Stellar Spectra for Exoplanetary Analysis
Dhvani Doshi
Nicolas B. Cowan
Yashar Hezaveh
Gabriel Missael Barco
Étienne Artigau
Abstract We present a novel approach to perform posterior sampling of the underlying stellar spectrum in high-resolution spectroscopic obser… (voir plus)vations. Our method, postellar , coherently combines information from empirical observations and physics-based models, enabling more accurate spectral recovery while providing uncertainties that can be propagated into downstream analyses. This is accomplished by treating the intrinsic stellar spectrum as a latent variable and performing posterior sampling under a Gaussian likelihood with an informative prior constructed using a score-based diffusion model trained on PHOENIX stellar models. We validate the framework on synthetic SPectropolarimètre InfraRouge (SPIRou) radial velocity (RV) observations generated from the empirical spectra of Barnard’s Star and Proxima Centauri. Spectra inferred with postellar recover the ground truth more accurately than standard empirical templates. For medium signal-to-noise observations, postellar improves RV accuracy by up to a factor of three, while RVs derived from empirical templates tend to be biased. This method performs particularly well in low signal-to-noise and low-cadence regimes. The postellar framework is broadly applicable to other high-resolution spectroscopy science cases, including stellar abundance analyses and exoplanet atmospheric characterization.
2026-09-13
Astrophysical Journal (publié)
doi.org
arxiv.org
DiffDose: Differentiable Programming for Personalized Dose-Regimen Optimal Control
Shayan Hajhashemi
Amin Emad
Morgan Craig
Dose-regimen design requires choosing how much drug to give, when to give it, and how treatment should vary across patients. Mechanistic pha… (voir plus)rmacokinetic-pharmacodynamic (PK/PD) and quantitative systems pharmacology (QSP) models can predict treatment responses, but optimizing dosing inputs depends on model-specific sensitivity derivations or derivative-free search. Here, we introduce DiffDose, a differentiable programming framework for mechanistic open-loop dose-regimen optimization that uses automatic differentiation (AD) to handle clinically interpretable dose amounts and administration times as differentiable controls. We evaluate our method in three settings: fixed-schedule dose-amplitude optimization in OptiDose PK/PD benchmarks; dose-timing optimization in a chemotherapy-induced neutropenia model with state-dependent delay; and individualized mosunetuzumab dosing in a QSP virtual population. Across these examples, AD produced gradients consistent with references, reduced model-specific derivative work, and shortened benchmark time to solution. DiffDose thereby turns mechanistic PK/PD and QSP models from tools that evaluate prespecified regimens into gradient-based engines for individualized regimen design.
2026-09-12
bioRxiv (accepté)
doi.org
Learning Multi-Agent Task Assignment and Navigation in the Factory: from Simulation to Real Robots
Abdalwhab Bakheet Mohamed Abdalwhab
Giovanni Beltrame
David St-Onge
Reinforcement learning (RL) has shown considerable promise for robotic decision-making, yet deploying multi-agent RL (MARL) on physical mult… (voir plus)i-robot systems in industrial environments remains challenging. This paper investigates the real-world applicability of decentralized MARL for multi-robot multi-machine tending. We propose Feature-fusion Multi-Agent Proximal Policy Optimization (FMAPPO), which fuses 2D LiDAR measurements with task-specific state information to enable safe decentralized multi-robot task assignment and navigation. A complete simulation-to-reality pipeline was developed using high-fidelity robotic simulation and ROS2 and deployed on physical mobile-manipulator platforms operating under realistic real-world conditions, with the robotic arms disabled during the experiments. We further investigate the sensitivity of the learned policy to command update frequency, an important consideration for real-world deployment. Comparative evaluation in simulation demonstrated that FMAPPO significantly outperformed state-of-the-art baselines with a large effect size, achieving improvements of 106\% and 21\% in parts delivery and 48\% and 11\% in parts collection over MAPPO and SMAPPO, respectively. FMAPPO also increased machine utilization by 31 and 10 percentage points, respectively, while reducing collisions by 18\% and 15\% and increasing the safety score by 14 and 6 percentage points compared with MAPPO and SMAPPO, respectively. Furthermore, real-world experiments demonstrated that the learned decentralized policies can coordinate multiple robots to service multiple machines while maintaining safe operation under real-world sensing and control constraints. Videos of the real-world experiment are available online https://anonymouspapers123.github.io/FMAPPO/.
2026-09-12
arXiv (prépublication)
doi.org
arxiv.org
Question's Gambit: The First Move Matters in Agentic Deep Search
Radin Hamidi Rad
Amin Bigdeli
Negar Arabzadeh
Sajad Ebrahimi
Charles L. A. Clarke
Benjamin C. M. Fung
Ebrahim Bagheri
Deep research agents answer complex questions through iterative loops of searching, reading, and reasoning. Recent work on reasoning-intensi… (voir plus)ve benchmarks such as BrowseComp-Plus shows that well-configured lexical retrieval can surface high-quality evidence, yet agents may still fail to connect documents carrying evidence to the gold documents. We identify a deep research agent's first retrieval move as an important design decision for this setting. We introduce Question's Gambit, a first-move retrieval module that decomposes the question into a set of clues, reformulates them into complementary searches, consolidates the retrieved results, and reranks the candidate pool before the agent begins its iterative search-and-reasoning process. This produces an opening context designed to support both clue aggregation and final-answer verification. We further evaluate on MultiHop-RAG to test whether these benefits transfer beyond BrowseComp-Plus to a more conventional multi-hop question structure. Experiments on BrowseComp-Plus show that Question's Gambit improves retrieval recall and downstream agent accuracy over strong baselines, improving answer accuracy from 83.1% to 90.5% with gpt-5.5 over Pi-Serini, the strongest reported agentic baseline. Our results confirm that effective agentic deep research depends not only on the tools available inside the loop, but also on the quality of the first move. We published our implementation publicly at https://github.com/radinhamidi/Question-s-Gambit.
2026-09-12
arXiv (prépublication)
doi.org
arxiv.org
Temporal Recurrence Favors Fewer Layers
Ivan Anokhin
Johan Obando-Ceron
Irina Rish
Sebastian Risi
In streaming tasks, recurrent models can carry latent computation across time, allowing each update to build on representations produced ear… (voir plus)lier. This raises a basic question: once temporal recurrence provides sequential computation across steps, how much depth is still needed within each step? Prior work has shown that recurrence can make shallow models competitive. We instead study this question as a compute-allocation problem, varying within-step depth, expert width, and the number of parallel experts per layer across several compute budgets. For each budget, we compare the best observed recurrent and non-recurrent allocations and the performance they achieve under approximately matched per-step computation. Across Sokoban and autoregressive FineWeb language modeling, we find that temporal recurrence shifts the best observed compute allocation toward substantially fewer layers, with comparable or better performance.
2026-09-10
arXiv (prépublication)
doi.org
arxiv.org
The Iterative Equivariant Filter
Pieter van Goor
James Richard Forbes
This paper presents the iterative equivariant filter (IterEqF). The iterative extended Kalman filter (IterEKF) replaces the standard EKF cor… (voir plus)rection step with an iterative correction step that is the Gauss-Newton solution to a nonlinear weighted least squares problem. The standard equivariant filter (EqF) exploits and respects the underlying symmetry of state estimation problems posed on homogeneous spaces and Lie groups. The motivation behind the IterEqF is to combine the features of both the IterEKF and the EqF, thus leading to a high-performance state estimation solution that is well suited to navigation problems. This paper derives the iteration procedure for the IterEqF update step and shows how the intrinsic nonlinearity of the approach naturally results in a `reset' of the filter's covariance into new coordinates. Monte-Carlo simulations of range-based localisation for a mobile robot demonstrate the improvement in performance relative to a standard EqF, especially during the transient convergence.
2026-09-10
arXiv (prépublication)
doi.org
arxiv.org
Time to redefine psychosis: completing an integral conceptualization
Emmanuel Stip
Alexandre Hudon
Guillaume Dumas
Alexandre Dumais
To the Editor,We read with great interest the article An Integral Conceptualization of Psychosis (Benning, 2026). By integrating biological,… (voir plus) psychological, phenomenological, cultural, and social pe...
2026-09-09
Psychosis (publié)
doi.org
ZipCodec: Ultra-Low-Frame-Rate Streaming Speech Coding
Luca Della Libera
Cem Subakan
Mirco Ravanelli
Neural audio codecs are a fundamental component of modern speech generation systems. While recent codecs achieve increasingly low bitrates, … (voir plus)reducing frame rate remains challenging, as each token must preserve more information while maintaining reconstruction quality. We present ZipCodec, a streaming neural speech codec operating at 6.25 Hz and 0.80 kbps with a theoretical latency of 160 ms. Our approach combines large-scale WavLM distillation with a redesigned transformer-based architecture, a scalar spherical quantizer, and a latency-aware streaming decoder. Experiments show that ZipCodec substantially outperforms existing streaming codecs at comparable bitrates in both reconstruction and downstream tasks, while operating at a significantly lower frame rate. Despite its 842M parameters, ZipCodec achieves real-time single-stream inference on a consumer-grade CPU. Demo samples, code and checkpoints are available at https://lucadellalib.github.io/zipcodec-web/.
2026-09-09
arXiv (prépublication)
doi.org
arxiv.org
Fixed State, Long Reach: What a Constant-Size Cache Buys Block Diffusion at Scale
Vaibhav Singh
Pierre-Andre Noel
Torsten Scholak
Eugene Belilovsky
Oleksiy Ostapenko
Diffusion language models decode tokens in parallel, but their bidirectional denoiser rules out the naive key--value (KV) cache behind fast … (voir plus)autoregressive inference. Block diffusion restores caching by decoding block-by-block, and the block caches deployed on it so far are tied to attention: O(L)in memory and, if used as training-free retrofits, only an approximation of the model's computation. Both constraints can be overcome: sequence mixers that summarize finalized blocks into a reusable state support block caching, and the corresponding block-causal training objective makes the cache exact. We study this recipe at scale, pretraining three 3B block-diffusion denoisers (attention, mamba, and hybrid) on 300B tokens under one single-frontier objective and decoding all three through a single cached interface. Only the state-space cache is O(1) in sequence length: its memory and per-step latency stay constant at any context length, while an attention cache remains O(L). At 256k tokens (where attention has grown to 82GB and 29 ms/step), the Mamba cache delivers 4.3x lower latency, 11x less memory, and 2.6x higher single-stream throughput; and because that footprint is constant it scales with batch as well, reaching 14x the aggregate throughput, where attention cannot run beyond a single stream. The same linear-state bias lets the Mamba and hybrid backbones keep retrieving out to 8-16x their training length, whereas attention's retrieval collapses at 2x, at no measured quality cost.
2026-09-08
arXiv (prépublication)
doi.org
arxiv.org
Normative Reference Values and Age-Related Variation in Advanced Diffusion MRI of the Pediatric Spinal Cord: DTI, DKI, and NODDI Metrics across Cervical and Thoracic Levels Using Hybrid Diffusion Imaging
Zahra Sadeghi Adl
Devon Middleton
Laura Krisa
Sara Naghizadehkashani
Mahdi Alizadeh
Slimane Tounekti
Scott H Faro
Adam Flanders
Jürgen Finsterbusch
Julien Cohen-Adad
Feroze B. Mohamed
BACKGROUND AND PURPOSE Normative diffusion values are essential for detecting microstructural abnormalities in the spinal cord. While diffus… (voir plus)ion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) provide additional sensitivity beyond DTI, existing normative datasets are limited to adults with restricted cervical coverage. Pediatric spinal cord data remain scarce and predominantly DTI-based, leaving a gap in characterizing age-related microstructure. The purpose of this study was to establish a normative database of pediatric spinal cord diffusion metrics (DTI, DKI, NODDI) from C1-T12 using a clinically feasible Hybrid Diffusion Imaging (HYDI) protocol and to assess agreement between HYDI-derived and conventional DTI metrics. MATERIALS AND METHODS One hundred typically developing children (mean age 12.07 ± 3.09 years), aged 6-17 years, underwent a 3T MRI scan including a T2-weighted, multi-shell, multiband, HYDI (b=0, 800, 2000 s/mm2) sequence covering C1-T12 and a validation subset, a single-shell DTI sequence covering C2-C5. Ten microstructural metrics were quantified for white matter (WM), gray matter (GM), and whole cord at each vertebral level. Multivariable regression modeled age and sex. Agreement between HYDI-derived and conventional DTI at C3 was evaluated using paired t-tests, Pearson correlation, and Bland-Altman analysis. RESULTS Normative C1-T12 vertebral diffusion values were established. WM-GM contrast was evident for FA, NDI, MD, and AD, with significant differences across most cervical and thoracic segments. All metrics demonstrated significant age dependence (p .01) after adjustment for sex, with higher FA, kurtosis, ODI and NDI and lower diffusivity and fISO in older children. Sex effects were mod
2026-09-08
American Journal of Neuroradiology (publié)
doi.org
ReactHuman: A Physics-Grounded Benchmark for Human-Like Reactive Decision-Making in Embodied Multimodal LLMs
Yizhan Li
Jianxin You
Mengyang Xiong
Yinhuan Chen
Zicheng Zhao
Dekun Wu
Dongqing Zhang
Bang Liu
Reacting to sudden physical hazards (catching a slipping plate, dodging a falling knife) is both a meaningful test of embodied intelligence … (voir plus)and a hard requirement for deploying multimodal large language models (MLLMs) as the decision coreof household robots. Existing evaluations, however, probe intuitive physics passively through question answering over videos, or target deliberate, long-horizon tasks such as navigation and rearrangement; none measure whether a model can turn physical understanding into immediate, safety-critical action. We introduce ReactHuman, the first physics-grounded benchmark for human-like reactive decision-making, in which the evaluated MLLM acts as the brain of a simulated humanoid facing sudden household hazards; it spans 17 event families and over 1,000 bit-for-bit reproducible scenes with exact, annotation-free ground truth derived from 240 Hz rigid-body simulation, including adversarial objects whose appearance contradicts their physics (a foam anvil, a steel apple). We further design a five-metric suite that scores each reaction along three axes: reasonable, safe, and physically grounded. We physically execute every committed plan so that decisions have observable consequences. With this harness we evaluate seven representative MLLMs. Results show that reactive safety is far from solved: models mishandle roughly one hazard in three, act from fixed dispositions rather than the observed scene, trust appearance over motion, and miss interception points at meter scale even when the chosen action is correct; none of these failures shrink with model scale. ReactHuman thus offers both a fine-grained diagnosis and a scalable training signal toward physically grounded, safety-aware embodied agents. The benchmark can be found here: https://huggingface.co/datasets/Alan123/reacthuman-benchmark-scaled
2026-09-08
arXiv (prépublication)
doi.org
arxiv.org
Shape-guided Gaussian Splatting for Sparse-View X-ray 3D Reconstruction
Pranav Poudel
Florence Dell’Aniello Picard
Nairouz Shehata
Frédéric Lavoie
Hervé Lombaert
Sparse-view X-ray 3D reconstruction is essential for reducing radiation exposure, but recovering a density field from a handful of X-ray pro… (voir plus)jections is severely ill-posed. Recently, 3D Gaussian Splatting has achieved state-of-the-art performance in sparse-view reconstruction by representing the volume using explicit, optimized primitives, but it requires dozens of projected views. With fewer views, reconstruction quality degrades severely since the explicit primitives are optimized freely without any anatomical information. Anatomical structures, in contrast, share similar geometry and density across a population. Their variations are bounded within a limited range that statistical shape models can capture. This paper proposes a shape-guided Gaussian splatting framework for sparse-view X-ray 3D reconstructions. Our contribution lies in driving Gaussian positions toward anatomically valid configurations, alongside atlas-based density regularization. Our method ensures anatomically consistent reconstruction and improves PSNR by 2.83 dB over a state-of-the-art Gaussian splatting baseline with as few as 5 views. Code Available: https://github.com/polyshape-lab/ShapeGuidedGaussian
2026-09-08
arXiv (prépublication)
doi.org
arxiv.org