#01Sep 1, 2026
cs.CV
BS: Take the Hint - Interactive Multitracer PET/CT Lesion Segmentation with a Scribble-Conditioned ResEnc U-Net
Marven Sherif, Amgad Elmasry, Youssef Ghazal and 1 more
Automated lesion segmentation in whole-body PET/CT is complicated by the variety of physiological tracer uptake patterns and by the differing appearance of lesions across tracers. The autoPET/CT V challenge addresses this by making segmentation interactive: user scribbles marking foreground and background are supplied alongside the image, and the algorithm is expected to exploit them. We present our submission, a scribble-conditioned residual encoder U-Net operating on four input channels: CT, PET, and a sparse scribble map for each of foreground and background. The network is initialised from the autoPET-III winning weights and extended from two to four input channels, with the two scribble channels zero-initialised so that the pretrained representation is preserved exactly at initialisation. Every model is fine-tuned per fold from the corresponding autoPET-III fold checkpoint, so that no validation case is seen during pretraining. PET intensities are normalised against a per-scan aorta blood-pool reference derived from a CT segmentation, which removes tracer- and centre-specific scaling without requiring lesion labels. At inference the five fold models are ensembled by averaging their softmax outputs per sliding-window patch, before Gaussian-weighted stitching. On the challenge's five-fold split, with each fold evaluated on its own validation cases, mean Dice is 0.554 and mean lesion-level F1 is 0.528 without scribbles, rising to 0.751 and 0.733 after five correction rounds. About 85% of that gain follows the first scribble, and the spread between fold models narrows five-fold over the same rounds, so interaction largely compensates for how well or badly a given model segments unaided.
#02Sep 1, 2026
stat.ML
Variable Selection for Feature-Based Newsvendor
Zhaoliang Yuan, Jie Wang
Feature-based newsvendor models use observable covariates to tailor inventory decisions, aiming to balance holding and shortage costs under demand uncertainty. However, high-dimensional feature sets often hinder interpretability and inflate data collection and implementation costs. This paper studies variable selection for the feature-based newsvendor problem under a hard cardinality constraint on the number of selected features. We formulate the resulting $\ell_0$-constrained empirical newsvendor problem with $\ell_2$-regularization, establish its computational hardness, and develop a mixed-integer second-order cone programming reformulation that strengthens the standard Big-$M$ formulation. To enable scalability beyond exact optimization, we develop a randomized-rounding algorithm with a bi-criteria guarantee and a greedy heuristic. Statistically, we provide theoretical analysis of the resulting sparse policy estimator, including finite-sample estimation error, out-of-sample risk bounds, and support recovery guarantees. Extensive experiments on both synthetic and real data illustrate the computational and statistical trade-offs among various baselines. Our results demonstrate that the proposed variable selection framework achieves competitive out-of-sample operational costs while using substantially fewer covariates.
#03Sep 1, 2026
cs.AI
EvoSCM: Scientific Belief Revision Through Causal Model Evolution and Experimentation
Qing Zhao, Haowei Li, Weijian Deng and 2 more
Scientific agents must learn not only how to reason, but also what to believe. However, existing LLM agents typically express scientific hypotheses in free-form text, leaving their beliefs implicit and difficult to test or revise. We introduce EvoSCM, which equips scientific agents with explicit structural causal models that evolve as new experimental evidence is collected. EvoSCM maintains a population of competing SCM hypotheses, each encoding a candidate causal explanation of the environment, and evolves them through a closed discovery loop. In each round, the agent abduces latent mechanisms from accumulated evidence, designs discriminative interventions, and commits to falsifiable predictions that it tests through experimentation. Discrepancies between prediction and observation are inductively distilled into correction rules that revise the causal structures and mechanisms of each hypothesis, and the agent then deductively validates the revised population against accumulated evidence and structural consistency to guide the next round. We evaluate EvoSCM on DiscoverPhysics, a benchmark requiring agents to uncover the hidden dynamics of noncanonical physical worlds through experimentation. EvoSCM consistently improves scientific discovery over baselines, yielding more accurate explanations and predictions while making more effective use of experimental interactions.
#04Sep 1, 2026
math.PR
Pointwise Majorization for sub-Weibull and Mixed Tail Processes with Applications in Quadratic Chaos and Ergodic Diffusions
Haichen Hu, David Simchi-Levi
Classical chaining controls an indexed stochastic process through a single worst-case bound, which can obscure substantial variation across the index set. We establish the first simultaneous pointwise majorization theory for Banach-valued processes with sub-Weibull or two-metric mixed-tail increments. For an anchored sub-Weibull process on a separable index space, write $v(t):=d(t,t_0)$. Given a reference measure $μ$, the envelope at $t$ is governed by the pointwise Fernique-Talagrand functional of order $α$, $Φ_{μ,d}^{(α)}(t):=\int_0^{4v(t)}(\log\frac{1}{μ(B_d(t,r))})^{1/α}dr$. $\forall δ\in(0,1)$, we obtain that $$ \mathbb{P}(\|Z_t\|\lesssim\{Φ_{μ,d}^{(α)}(t)+v(t)(\log(e/δ))^{1/α}\},\forall t)\ge 1-δ. $$ Our bound is determined by the pointwise complexity $Φ_{μ,d}^{(α)}$ rather than a global quantity. The result holds for every $α>0$ and does not involve dyadic logarithmic terms from peeling. For mixed tail processes, with fixed measures $μ_1,μ_2$ and $v_j(t):=d_j(t,t_0)$, $Φ_j(t):=\int_0^{4v_j(t)}(\log\frac{1}{μ_j(B_{d_j}(t,r))})^{1/α_j}dr, j=1,2$, for any $δ\in(0,1)$, we show that $$\mathbb{P}(\|Z_t\|\lesssim\sum_{j=1}^2\{Φ_j(t)+v_j(t)(\log\frac{e}δ)^{1/α_j}\},\forall t)\ge 1-δ.$$ Although the two regimes are coupled in the mixed tail condition, each retains its own pseudo-metric, reference measure, pointwise Fernique-Talagrand functional, and tail exponent. The proof tracks the index-wise costs of measure-generated admissible chains and synchronizes them through a nested common refinement. For applications, we derive matrix-specific bounds for centered quadratic chaos under pseudo-metrics induced by the operator and Frobenius norms, and observable-specific finite-time bounds for diffusion empirical processes.
#05Sep 1, 2026
cs.SE
Efficient SWE Agent Benchmarking via Trajectory-Aware Evaluation
Kefeng Duan, Dewu Zheng, Yanlin Wang and 7 more
Evaluating software engineering agents on realistic benchmarks is costly, since each task may require multi-step code exploration, modification, and test execution. Existing efficient evaluation methods select representative subsets to estimate full-benchmark performance, but are largely result-only: they fit historical pass/fail response matrices or static task semantics, discarding how agents solve problems. We propose PTA-IRT, a Privileged Trajectory-Aware Item Response Theory framework that fuses process and outcome signals. Historical execution trajectories supply process-level evidence beyond pass/fail, such as explored context, attempted edits, and solving paths, which PTA-IRT uses as privileged information for calibration subset selection and ability estimation. Under low calibration budgets, PTA-IRT consistently outperforms prior IRT baselines on score and ranking recovery across four SWE benchmarks. Code and data are publicly available at https://github.com/DeepSoftwareAnalytics/PTA-IRT.