In briefA four-step method for checking radiomic survival models in small patient groups
SummaryThis methodological study proposes a four-step validation protocol for radiomic survival analysis in small cohorts, combining FDR-corrected feature selection, comparison of feature selection strategies, repeated nested cross-validation, and a Gaussian noise negative control. Applied to 78 patients with gastroenteropancreatic neuroendocrine tumors, it used 535 CT-based body composition features at the third lumbar vertebra. The findings suggest a modest prognostic signal with considerable uncertainty, and the protocol may transfer to other small-cohort studies.
Mariela Talavera-Mu\~noz, Cecilia Diana-Albelda, Miguel Antonio Sampedro-Nu\~nez et al.
In briefMapping Morocco's Crop Types Nationwide Using Google AlphaEarth Embeddings and Field Boundaries
SummaryThis paper uses publicly available 64-dimensional pretrained embeddings from Google AlphaEarth Foundations with lightweight classifiers to map crop types across Morocco. It classifies three categories: winter cereals, tree crops, and other crops. Predictions are restricted to agricultural parcels using the Fields of the World global field boundary map. Over ten repeated runs on an independent test set, average accuracy was 87.17 ± 0.42%, and nationwide inference took about five hours.
Walid Elbarz, Mohamed Bourriz, Vincent Simonneaux et al.
In briefLibrary of Hands: fixing how commands map to robot motion, not the world model
SummaryThe paper proposes Library of Hands (LoH), a training-free method that keeps a world model frozen and corrects only the mapping from commands to applied actions, called the hand. LoH infers the hand through the frozen model by finding the action that best explains the change between two camera images, and it stores each identified hand so a returning setup is recognised rather than relearned. In closed-loop tests on three simulated tasks, success rose from 28.6% to 62.3%, and performance dropped by only 0.6 percentage points when nothing changed.
In briefAdapting Vision-Language-Action Models with Real and Simulated Data: A Robotic Chess Study
SummaryThis paper studies how to fine-tune a general-purpose vision-language-action model (pi0.5) for a robot arm that moves a chess king between named squares. It varies dataset sizes and update budgets, using real teleoperated demonstrations and simulated demonstrations. In simulation, success improves with more data and updates, a learning-rate cool-down helps checkpoint comparison, and validation loss is unreliable for model selection. On the real arm, simulated pretraining helps most when real data is scarce, while several alternative improvement methods gave no measurable gain over plain behavior cloning.
In briefInterpretability Tools Are Not Model-Specific: Transferring Refusal and Read-out Directions Across Models
SummaryThis paper tests whether interpretability tools can move between language models through a map between residual streams fit only on unlabeled prompts. It transfers a refusal direction and two read-out tools, a correctness direction and a sentiment probe, across 110 model pairs from five families spanning 1B to 72B parameters. The amount transferred depends on the data the map was fit on, and adding unlabeled prompts from the tool's operating region substantially raises recovery.
WhyThe authors transfer interpretability tools via residual-stream maps fit on unlabeled prompts, validated across 110 model pairs with controls and a proof plus coverage score.
Marcus Armstrong, Navid Ayoobi, Pradham Mummaleti et al.
In briefTesting how scribble-labeled image segmentation fails when annotations are imperfect
SummaryScribble-supervised segmentation is usually tested only on clean, fixed annotations, so its robustness to flawed labels is poorly understood. The authors audit 12,031 PASCAL VOC 2012 scribbles and build six controlled perturbation operators, such as class corruption, spatial offset, and stroke-length reduction. Across several backbones and training pipelines, class corruption causes the largest drop, lowering TEL-ResNet101 mIoU on VOC from 70.08 to 1.46. The paper offers a reproducible benchmark for diagnosing annotation robustness beyond clean-set accuracy.
In briefControlled Binary Assignment Flows: a geometric method for learning deep assignment flow parameters for binary graph labeling
SummaryThis paper proposes a geometric approach for representing and learning time-variant parameters of deep assignment flows used for binary data labeling on graphs. Each edge carries a parameter flow optimally controlled within a 2-dimensional totally geodesic submanifold, guided by objectives derived from a non-local linear scale space representation of the input data. Aggregated triplets of these flows are learned from data within 5-dimensional totally geodesic submanifolds, and their fusion, followed by geometric integration, yields the layers of binary deep assignment flows. Numerical results show both the expressivity and the limitations of this highly parallel local geometric synthesis approach.
In briefTrade Promotion Optimisation for Consumer Goods
SummaryThis paper presents an end-to-end system for optimising trade promotions, estimating price elasticity and recommending promotional prices for consumer goods. Items with weak evidence borrow elasticity and curvature from analogue SKUs selected by a cross-validated ridge model. In a four-week experiment on a 39-SKU consumer brand, the plan lifted units by 20.6% and revenue by 6.9% at flat margin.
In briefCertify, Report, or Refuse: Harm-Constrained Conformal Prediction for Clinical Actions
SummaryThe paper introduces HC-ROCP, a training-free framework that uses frozen classifier probabilities and adds a harm penalty when selecting prediction sets and clinical actions. Policies are tested on disjoint calibration splits with multiplicity adjustment and evaluated retrospectively on COVID-19 radiographs, ISIC 2019 dermoscopy, and EyePACS-to-APTOS retinopathy transfer. Across ten calibration seeds, severe-action errors on COVID-19 fell from 4.68% to 0.54% compared with the exact risk-optimal baseline.
In briefWhen Does Sliced Inference Pay Off? Tiling and Resolution for Small Object Detection
SummaryThis paper examines when sliced inference, such as SAHI, actually improves small object detection in high-resolution images. Comparing seven dense detectors on VisDrone and DOTA, the authors find that much of the gain comes from higher resolution rather than slicing. They propose SCAHI, which accumulates dense outputs from all tiles with coverage-aware weights and outperforms box fusion on identical passes.
WhyThe authors propose SCAHI, replacing box fusion with coverage-weighted dense outputs, and report gains over box fusion on VisDrone and DOTA.
In briefReinforcement learning aided by simulation using control variates
SummaryThe paper proposes a conservative method that uses simulated data only to reduce variance during learning, rather than to directly set the learning target. It is developed first for policy evaluation, then combined with the actor-critic framework for policy improvement and extended to offline reinforcement learning. Experiments on multiple MuJoCo benchmark tasks show improvements over standard simulation mixing approaches.
In briefTrialDesignBench: A Benchmark for Testing Language Models on Phase-I Trial Design
SummaryThis paper introduces TrialDesignBench, a benchmark of 617 Phase-I/I-II oncology dose-finding trials, each paired with an expert-validated reference design aligned with recent FDA regulatory initiatives. The authors evaluate five frontier large language models, and none of them matches the reference standard. Ablation experiments attribute the gap mainly to inferring unrecorded design determinants and to structured decision-tree reasoning.
Yihan Hank Tang (1), Peng Yang (2), Jiayi Xin (1) et al.
In briefPredicting city traffic in bad weather using large language models
SummaryThis paper proposes Multi-STG, a framework that combines semantic priors derived from large language models with graph-based spatiotemporal learning to forecast urban traffic under adverse weather. It converts context descriptions such as weather, POIs, and holidays into semantic embeddings, and uses multimodal attentional fusion and gated residual calibration in its prediction head. Experiments on real private car datasets from three cities show an average 11.82% reduction in MAE relative to the strongest baseline, with stable performance under sparse and non-stationary conditions.
In briefAdapting small language models to Romanized Nepali (Nepali written in Latin script)
SummaryThe paper adapts three 2–3B open base models (Llama-3.2-3B, Qwen2.5-3B, Gemma-2-2B) to Romanized Nepali using QLoRA on 9,000 instruction examples created by deterministic transliteration. Fine-tuning raised chrF++ from 10–14 to 29.0–30.6 and lowered bits per byte from 2.23–2.41 to 0.78–0.84. Likelihood and reference-overlap metrics disagreed on how much the models differed, and the model with the fewest tokens per word scored worst on every measure.
In briefFrom spikes to fields: how turbulence-like brain dynamics arise in spiking networks
SummaryThe paper builds a spatial spiking neural network and converts spikes into activity-density and phase fields to study how turbulence-like brain patterns arise. A delayed, distance-dependent passive-dendrite regime reproducibly generates turbulence-like field structure. The authors find that firing rate, vortex count, and avalanche fits alone are insufficient, so phase defects must be interpreted together with spatial scaling and spike-level propagation.
In briefReconstructing and recognizing objects hidden behind fragmented occlusion using video
SummaryThis paper presents a method that reconstructs an object hidden behind dense, fragmented occlusions, such as foliage, from a sequence of grayscale video frames captured by a single camera. The approach aligns the frames so that the average of the aligned images, called the integrated image, has maximum contrast, and this integrated image serves as the reconstruction. Experiments on synthetic and real videos show accurate reconstruction at occlusion densities above 90%, and the integrated image can be classified reliably by standard classifiers.
Roman Pflugfelder, Michael Lindenbaum, Daniel Cremers
In briefType-logical grammars and formal languages: language classes and complexity of first-order linear logic
SummaryThe paper shows that type-logical grammars with standard rules for product, associativity, extraction and infixation have an NP-complete fixed recognition problem. It also shows that grammars in first-order linear logic can simulate quasi-linear grammars, which recognise languages in non-deterministic linear time. The author argues that first-order linear logic can be seen as the logic of non-deterministic linear-time string languages, graph languages and automata.
WhyThe authors report NP-completeness of the fixed recognition problem for type-logical grammars and simulation of quasi-linear grammars by first-order linear logic.
In briefContinuous-time transformer for indoor localization from asynchronous WiFi and IMU sensors
SummaryThis paper proposes a continuous-time transformer that treats WiFi signal strength and inertial measurements as an unordered set of timestamped tokens. A single attention block fuses information across modalities and time without resampling or modality-specific branches. During training, entire modalities and individual observations are randomly masked so the model can handle missing or outdated inputs. Experiments on simulated and real datasets, including data from unseen days, show improvement over an open WiFi fingerprinting baseline under cross-session drift and competitive results against learned fusion and dead-reckoning baselines.
In briefDirectly conditioning audio diffusion transformers on fMRI to reveal cortical contributions to sound reconstruction
SummaryThe paper introduces MAAS, an end-to-end framework that conditions a pretrained audio diffusion transformer (Stable Audio) directly on region-of-interest fMRI responses, without first training a decoder to predict a stimulus representation. On a 7T fMRI dataset, it reports consistent gains over baselines, including a two-stage fMRI-to-latent approach, on both acoustic and semantic metrics. The authors also propose a fidelity metric based on a computational model of auditory neural processing to quantify how much each auditory cortical region contributes to the reconstructed audio.
Matteo Ciferri, Tonio Weidler, Matteo Ferrante et al.
In briefFour Traveling Thief Benchmark Instances Are Actually Knapsack Problems
SummaryThe traveling thief problem asks a thief to choose a route and a packing together, and a 9,720-instance benchmark is widely used to compare its heuristics. This paper finds that four instances have a renting rate of 0.00, so the route does not affect the objective and each reduces to a 0-1 knapsack problem. The authors define corrected renting rates using a published reference solution, show that the corrected instances behave as proper TTP instances, and release reference results. They also release ttplib, an open-source Python library for loading the instances.
WhyThe authors identify four benchmark instances with zero renting rate, reduce them to knapsack problems, and report corrected rates, reference results, and an open-source library.
In briefFairness in AI explanation texts: how language models create group gaps in their explanations
SummaryThis paper examines whether large language models that turn SHAP-style explainable AI results into plain-language narratives treat social groups unequally. The authors propose a framework that generates narratives with the protected attribute disclosed, withheld, or counterfactually swapped, and evaluate five LLMs on four credit scoring, education and employment datasets. They find significant disparities across all datasets, metrics and models, arising from feature values and from the protected attribute itself.
WhyThe authors introduce fairness in XAI narratives, using a three-condition framework across four datasets and five LLMs to report significant disparities.
Wannes Van den Bulck, Sofie Goethals, David Martens
In briefTRACE: Adaptive Trust-Region Bayesian Optimization for Open-Ended Molecular Design
SummaryThis paper introduces TRACE, a framework for finding good molecules within a small number of evaluations, generating candidates on demand rather than choosing from a fixed library. It combines graph-perturbation proposals with a Gaussian process model using fingerprints and fragment descriptors. Across six molecular optimization benchmarks with a 1,000-evaluation budget, TRACE showed the strongest overall sample efficiency. The authors also applied it to iron phthalocyanine stability, raising the computed resistance to Fe dissolution from 0.42 to 2.59 eV over 80 designed evaluations.
Madhav R. Muthyala, Mahsa Askari, Farshud Sorourifar et al.
In briefAgentOPSD: Turn-by-Turn Credit Assignment via Recursive Belief Revision
SummaryAgentOPSD is a critic-free reinforcement learning method that assigns credit recursively, one interaction turn at a time, instead of spreading one advantage over a whole trajectory. Within each turn, token-level log-probability gaps between a skill-conditioned self-teacher and the student are summed and used as evidence in a recursive Bayesian update in log-odds space. With Qwen2.5-3B and Qwen2.5-7B on ALFWorld, it consistently improves on GRPO and self-distillation baselines and reaches 89.1% success at the 7B scale.
In briefStylization as Geodesic Navigation on Diffusion Manifolds
SummaryThis paper introduces Stylus, a training-free framework for facial stylization that treats style transfer as navigation on the curved latent manifold of a pretrained diffusion model. It refines content-style interpolation by lowering a ratio-weighted discrete geodesic energy under the pullback metric, using a rank-one power-iteration approximation of the Jacobian. The dual control of latent interpolation and self-attention query optimization enables continuous adjustment of stylization strength while preserving identity-critical facial structure. Experiments reportedly show the strongest identity and content preservation at competitive stylization strength.
In briefTurning likelihood accuracy into parameter accuracy for POMDP learning
SummaryThis paper studies how to learn the parameters of partially observable Markov decision processes (POMDPs) from data. For any candidate parameter, it bounds the parameter recovery error using the likelihood gap to the ground truth, the structural conditioning of the observation and transition kernels, and the behavior policy used for data collection. The bound covers both undercomplete and overcomplete settings and yields finite-sample guarantees for maximum-likelihood estimation and similar procedures.
In briefChecking whether mammography AI models' answers, confidence, and explanations agree
SummaryThis paper examines vision-language models that read mammography images and output an answer, a confidence level, and a written explanation. The authors test four models on breast density and BI-RADS categorization across two datasets, applying controlled changes to questions, option orders, and images. They find that confidence does not drop even when the image is altered, and that shuffling answer options changes most models' answers. Some models also give reasoning that contradicts their final answer.
Rebeca Nam, Chase M. Fensore, Christian A. Fauria-Robinson et al.
In briefLawKAN: Group-law layers that bake known rules into learned models
SummaryLawKAN embeds an exact non-commutative group law in a self-contained layer, learns local increments on its edges, and uses a zero-initialized correction term to capture unknown deviations from the law. On fixed-angle SU(2) composition and real robot arms, it outperforms baselines with fewer parameters. It matches classical fits when the data follow the imposed law.
Anna Mishchenko, Nikolai Gavrilin (Independent researchers)
In briefGHiML: A Vision-Language Model-Guided Method for Hierarchical Multi-Label Classification of Seafloor Images
SummaryThe paper proposes GHiML, which extracts conceptual variables from a vision-language model, generates sample-specific captions, and fuses their text embeddings with image embeddings via cross-attention and a gated router. It also introduces the d_inv score, which evaluates hierarchical multi-label predictions using graph traversal distance and a cardinality penalty. Experiments on BenthicNet, FathomNet and iNaturalist report improvements in macro F1 and d_inv over a vision-only baseline.
In briefWanderBench: Can World Models Explore Dynamic Worlds from Video?
SummaryThis paper introduces WanderBench, a benchmark for testing whether video-conditioned world models can explore dynamic scenes from an input video. It includes 745 single-shot real and AI-generated videos across three difficulty tiers, paired with keyboard-based camera routes, and evaluates six dimensions using 24 human-validated sub-metrics. Tests on eight models show that longer clips preserve the static scene and visual quality but lose camera control and source dynamics, and no model keeps revisited places consistent. The authors also propose WanderLand, a training-free framework that improves five models across every dimension, with source-dynamics fidelity gains of up to 8.3 points.
In briefSciMARS: Self-improving multi-agent system for closed-loop scientific discovery
SummaryThis paper proposes SciMARS, a framework that recursively adapts multi-agent systems to individual scientific tasks. It uses a shared evidence graph and separate editors that adjust agent roles and coordination as well as how agents record and retrieve evidence. On Autonomous Science Bench, ScienceAgentBench, and AppWorld, the memory-augmented version solved more tasks than the base multi-agent team.
In briefA strict upper bound for three-point weighted selection in planar regression
SummaryThe paper proves that the weighted selection constant F_weighted(2,2,3) for minimum-Frobenius-norm least-squares regression with two input and two output coordinates is strictly less than 11/6, improving the previous upper bound of 15/8. The proof uses an exact scalar certificate, rigidity of equality in planar selection, and a compactness argument to control disappearing weights. Combined with a known lower bound, this yields 13/8 ≤ F_weighted(2,2,3) < 11/6.
In briefBeyond the Document: Compiled Context and Governed Agents for Any Work Product
SummaryThe paper proposes a Portable Agent Format (PAF) package that delivers a work product together with compiled context, evidence, provenance, and rules for later interaction. Four newly created packages (an investment proposal, fund research, financial-statement analysis, and an engineering incident review) were tested with 400 frozen questions and 12,000 evaluated responses. A hosted governed reader succeeded on 96% of questions, compared with 86% using instructions plus raw-file retrieval.
In briefTeamknitt: Configuring, observing and intervening in LLM-agent teams using dynamical-systems and human-team theory
SummaryThis paper introduces teamknitt, a framework and open reference implementation for configuring teams of 1 to 16 AI agents, choosing among fourteen routing topologies or custom graphs and limiting information sharing through sharing profiles. It describes observing team processes through non-system and system views, with human-led interventions applied at defined boundaries. The work is an idea and system-description paper offering falsifiable hypotheses, and it reports no comparative results.
In briefLearning a faster inexact alternating method for convex problems with quadratic coupling
SummaryThis paper proposes learning an inexact alternating minimization scheme in which neural networks approximate each subproblem solution, reducing the costly inner solves. For convex problems with quadratic coupling that satisfy a quadratic growth condition, the authors derive a worst-case bound on the optimality gap and combine it with a PAC-Bayes argument for a high-probability bound over problem instances. On robust matrix completion and low-dose CT reconstruction with dictionary regularization, the learned scheme achieves 5x to 13x speedups over an inexact first-order baseline at matched accuracy, rising to 30x with brief end-to-end fine-tuning.
Paul H\"ausner, Jevgenija Rudzusika, Jens Sj\"olund et al.
In briefK-prop: A method for efficient online learning in leaky recurrent networks
SummaryThis paper proposes K-prop, an online learning method for leaky neural networks with self-recurrence. By propagating kernel gains, it greatly reduces the computational and memory costs of real-time recurrent learning (RTRL). In sequence learning, classification, and reinforcement learning tasks, K-prop outperforms existing RTRL approximations and performs comparably to backpropagation through time.
In briefPerceived agency in virtual reality when control of an avatar-agent switches
SummaryThis within-subject VR study with 24 participants compared virtual humans under four control conditions: always computer-controlled, computer-to-human, human-to-computer, and always human-controlled. Participants acted as interviewers and were told the initial controller but not about any transitions. Perceived agency was strongly shaped by the controller presented at the start and did not update after control switched. Likeability and co-presence were generally higher when human control came later and covered more of the interaction.
In briefWaveGen: Generating speech waveforms end-to-end by learning an internal spectral trajectory
SummaryWaveGen is a diffusion-based framework that generates waveforms directly from text in one model, using a method called Spectral Forcing to learn an internal spectral trajectory alongside the target waveform. It avoids external intermediate representations and alignment modules such as duration predictors. The paper reports state-of-the-art results on the Emilia training dataset, with a WER of 1.47 and a SIM of 0.75 on the Seed-en benchmark. Code and checkpoints are publicly available.
