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新论文 · v1arXiv:2611.16138

Individually Fair Facility Location

Rajni Dabas, Samir Khuller, Milind Maiti

原文摘要

We study individually fair facility location, where each client must connect to an open facility within a prescribed radius and the objective is to minimize total opening and connection cost. We show that preserving the radii changes the approximability of metric facility location fundamentally. For every fixed radius violation below three, no deterministic polynomial-time algorithm achieves a sublogarithmic cost factor unless $\mathrm{P}=\mathrm{NP}$. The hardness holds with uniform opening costs and uniform radii. It contradicts the strict-fairness constant-factor guarantee recently claimed by Liu, Xu, and Zhang [Theory of Computing Systems, Vol. 70 (2026)]. We then establish a positive result for arbitrary client radii. We give a randomized polynomial-time algorithm that has cost at most $1.725$ times the strictly fair optimum and serves every client within three times its fair radius (in the Appendix we show how this can be de-randomized). We use the approach of Byrka, Srinivasan and Swamy [IPCO 2010]. The facility-location radius factor three is therefore optimal among deterministic polynomial-time algorithms with constant cost factor, unless $\mathrm{P}=\mathrm{NP}$. Experiments on real-world data indicate that the worst case radius bound is rarely realized in practice: the algorithm serves over 99.8% of clients within their stated radius, and never beyond 1.5 times it. Our code is available at https://github.com/milindmaiti/IFFL_Implementation.

cs.DS · cs.CC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.19931

Expanding the Role of Diffusion Models for Robust Classifier Training

Pin-Han Huang, Shang-Tse Chen, Hsuan-Tien Lin

原文摘要

Incorporating diffusion-generated synthetic data into adversarial training (AT) has been shown to substantially improve the training of robust image classifiers. In this work, we extend the role of diffusion models beyond merely generating synthetic data, examining whether their internal representations, which encode meaningful features of the data, can provide additional benefits for robust classifier training. Through systematic experiments, we show that diffusion models offer representations that are both diverse and partially robust, and that explicitly incorporating diffusion representations as an auxiliary learning signal during AT consistently improves robustness across settings. Furthermore, our representation analysis indicates that incorporating diffusion models into AT encourages more disentangled features, while diffusion representations and diffusion-generated synthetic data play complementary roles in shaping representations. Experiments on CIFAR-10, CIFAR-100, and ImageNet validate these findings, demonstrating the effectiveness of jointly leveraging diffusion representations and synthetic data within AT.

cs.LG · cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.19148

Program Learning with Verifiable Rewards: Distilling Expert Traces into Programs

Vishvesh Bhat, Muhammad Ahmed Mohsin, Asad Aali

原文摘要

Small language models struggle with tool-use tasks that require following multi-step procedures. Whether these failures reflect limited capability or missing procedural knowledge remains unclear. We introduce Program Learning with Verifiable Rewards (PLVR), which distils expert traces into reusable programs without updating model weights. Frontier models turn recurring operations in the traces into typed primitives and rules, and search assembles them using reference actions and training-task checks. A small frozen model executes the primitives, with deterministic verifiers checking their outputs. The resulting program guides a student's reasoning or checks its proposed actions. We evaluate the same program across students on $τ^2$-Bench Retail and Telecom and LiveMCPBench, using synthetic training tasks and unseen benchmark test tasks. On Telecom, PLVR raises an 8B model's success rate from 43.0% to 91.2%, while larger students reach up to 94.4%, compared with 57.9% for the best baseline. On Retail and LiveMCPBench, PLVR is competitive with the best baselines, although tool search remains more dependent on the student model. These results support program learning as a way to transfer expert procedures across frozen models, trading additional inference computation for more reliable tool use.

cs.AI · cs.SC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16126

Split-and-scale Latent 3D Representations

Dorian Chan, Muhammed Kocabas, Xiaoming Zhao, Oncel Tuzel, Jen-Hao Rick Chang

原文摘要

Latent 3D representations have significantly expanded the capabilities of modern shape modeling, enabling compact encoding of 3D scenes and supporting powerful 3D generative models. Despite this progress, current representations struggle to capture the fine details of complex objects, fundamentally limited by the capacity of their latents. However, scaling latent capacity at training time is computationally expensive and often infeasible under memory limits, and naively increasing the number of latents at inference fails to make effective use of the added capacity. In this paper, we address both challenges with \textit{split-and-scale}, a method that effectively expands latent capacity at inference while keeping training cost tractable. Our key observation is that by representing 3D scenes as continuous functions of 3D coordinates, a single pretrained tokenizer can be applied at inference to arbitrarily small sub-regions of a scene, allocating the same latent capacity to a smaller spatial extent and thus capturing far higher detail. We validate this insight by conducting extensive ablations to study the trade-offs between compute, memory, latent capacity, and reconstruction quality, demonstrating our inference-time scaling method achieves quality competitive with much larger models that exceed single-GPU memory limits. We further demonstrate that split-and-scale supports high-quality generative modeling, training an autoregressive 3D generator that performs competitively with state-of-the-art baselines.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.07338

Appear2Meaning: A Cross-Cultural Benchmark for Structured Cultural Metadata Inference from Images

Yuechen Jiang, Enze Zhang, Md Mohsinul Kabir, Chenying Lin, Ying Peng, Fengyuan Liu, Qianqian Xie, Stavroula Golfomitsou 等 10 位作者

原文摘要

Museum records express how people understand heritage objects through cultural and historical relations. Recovering these human attributions from images is a technical challenge for vision-language models (VLMs). We introduce Appear2Meaning, a cross-cultural benchmark of 750 museum objects for image-only recovery of title, culture, period, origin, and creator. Its evaluation framework distinguishes field-level from complete-record compatibility and uses human validation for semantic judgement. Across nine VLMs, mean field accuracy is 36.16--49.82%; 7.20--17.02% of records have all four contextual fields compatible, and 2.13--7.87% have all five, including title. For the two tested models, image-only input yields 12.6--32.2 percentage points higher reference compatibility for culture, period, and origin than a museum-and-title-only control.

cs.CV · cs.CL · cs.MM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16122

Intent-Aware Protection for AI Agents

Fady Copty, Neta Haiby, Max Braitmaiere, Julia Korsunsky

原文摘要

AI agents increasingly access enterprise data, invoke powerful tools, and act with limited human oversight. Existing security defenses primarily detect prompt injection, harmful outputs, or suspicious behavior, but do not determine whether an agent remains faithful to the purpose for which it was deployed. An action may contain no attack pattern and stay within granted permissions while still violating the agent's role, organizational constraints, or purpose. We introduce Intent- Aware Security, a framework that treats trusted agent intent as a first-class security object. We define Intent Preservation and Intent Drift, and operationalize them through an Intent Profile: an agent-specific representation extracted from organizational, role, and developer intent. We further introduce an Intent-Aware benchmark of 887 validated samples across 35 agents, built by a generation procedure that fixes ground-truth labels before LLM realization, and four Intent Profile-based protections: pre-execution detection, post-execution detection, input alignment, and system-prompt hardening. Across all evaluations, Intent Profile-based protection outperforms all profile-free baselines on the primary metric. On our benchmark, it raises four-class acro-F1 from 45% to 79% (Out-of-Scope F1 from 8% to 76%). On more than 10,000 executed AgentDojo trajectories, where ground truth comes from environment state, profile-based post-execution detection reaches 0.949 AUROC versus AlignmentCheck 0.909, 0.897 with raw agent configuration and 0.843 without context. Profile-based input alignment improves protection success by 7 points over DataFilter with a comparable benign utility. These results suggest that an agent's intended purpose can serve as a useful security reference for detecting and mitigating deviations from its deployed role and operational boundaries.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.23983

Sensorless strain-bounded grasping of fruit

Yusei Shuto, Danilo Vasconcellos Vargas

原文摘要

Robotic post-harvest handling must hold produce without bruising it, yet fruit stiffness varies several-fold with ripeness, so no fixed grip force suits every item. We instead bound compression strain using only the encoder and motor effort of a servo gripper. Given a strain bound $\varepsilon$ and a stiffness floor $k_{\min}$ set once per crop and gripper, the controller estimates object size $\hat{D}$ at contact, refines stiffness $\hat{k}$ conservatively by probing, and stops at $\hat{F} = \varepsilon \hat{k} \hat{D}$, keeping true strain below $\varepsilon$ with high probability above a quantified contact-detection floor. In simulation, where fixed force thresholds either drop heavy objects or damage soft ones, the proposed controller carries 92% of objects within its operating range with 0.3% damage. On cherry and medium tomatoes, setting $\varepsilon$ above the detection floor scaled the stop force threefold across fruit sizes, carrying all fruits while reducing markings on cherry tomatoes from 8/10 to 1/10; on medium tomatoes both marked 3/6.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2404.03604

A Unified Algorithmic Framework for Dynamic Assortment Optimization under MNL Choice

Shuo Sun, Rajan Udwani, Zuo-Jun Max Shen

原文摘要

We consider assortment and inventory planning problems with dynamic stockout-based substitution effects, and without replenishment, in two different settings: (1) Customers can see all available products when they arrive, a typical scenario in physical stores. (2) The seller can choose to offer a subset of available products to each customer, which is more common on online platforms. Both settings are known to be computationally challenging, and the current approximation algorithms for the two settings are quite different. We develop a unified algorithm framework under the MNL choice model for both settings. Our algorithms improve on the state-of-the-art algorithms in terms of approximation guarantee and runtime, and the ability to manage uncertainty in the total number of customers and handle more complex constraints. In the process, we establish various novel properties of dynamic assortment planning (for the MNL choice model) that may be useful more broadly.

math.OC · cs.DS

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2508.16731

COSMO-Bench: A Benchmark for Collaborative SLAM Optimization

Daniel McGann, Easton R. Potokar, Michael Kaess

原文摘要

Recent years have seen a focus on research into distributed optimization algorithms for multi-robot Collaborative Simultaneous Localization and Mapping (C-SLAM). Research in this domain, however, is made difficult by a lack of standard benchmark datasets. Such datasets have been used to great effect in the field of single-robot SLAM, and researchers focused on multi-robot problems would benefit greatly from dedicated benchmark datasets. To address this gap, we design and release the Collaborative Open-Source Multi-robot Optimization Benchmark (COSMO-Bench) -- a suite of 24 datasets derived from a baseline C-SLAM front-end and real-world LiDAR data. Data DOI: https://doi.org/10.1184/R1/29652158

cs.RO

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2602.22545

SFL-Net: Dependence-Regularized Latent Pathways for Multi-Contrast MRI-to-tau-PET Synthesis and Component Attribution

Agamdeep S. Chopra, Caitlin Neher, Tianyi Ren, Juampablo E. Heras Rivera, Hesamoddin Jahanian, Mehmet Kurt

原文摘要

Structural MRI-to-tau-PET synthesis estimates molecular imaging patterns from indirect anatomical correlates. We present SFL-Net, a vector-quantized architecture with shared, T1-specific, FLAIR-specific, and joint-input latent pathways. Dependence regularization differentiates these pathways, while latent-derived structural conditioning supports decoding without direct encoder-decoder feature skips. We evaluated SFL-Net and capacity-matched VAE, VQ-VAE, U-Net, and SPADE-U-Net variants using 605 training and 83 internal validation subjects pooled from ADNI-3 and OASIS-3. SFL-Net achieved the lowest raw PET MAE and MSE and the highest PSNR among the implemented models, while SPADE-U-Net variants performed better on several SUVR and staging metrics. Exact coalitional Shapley attribution assigned the largest average marginal contribution to the joint-input pathway, with substantial variation across subjects. These contributions describe decoder dependence under latent replacement and retained structural conditioning; they do not establish identifiable information-theoretic synergy or the incremental benefit of combining MRI contrasts. Predictions showed dynamic-range compression at higher tau burden, and several staging metrics were close to a constant Stage 1 reference. SFL-Net provides a framework for structured latent-component interrogation in multi-input image synthesis, linking architectural pathways to reconstruction behavior.

cs.CV · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16116

Robo-SWE: An Executable Issue-Resolution Dataset Mined from Robot Software Repositories

Vahid Haratian, Vahid Ansari, Mohammadhossein Malmir, Alois Knoll

原文摘要

Coding agents are evaluated mostly on general-purpose software, but domain-specific systems require domain-aware evaluation. Robot software is a particularly challenging case: Robot Operating System 2 (ROS 2) packages have their own build tools, simulators and learning frameworks are built on physics engines, and behavior that depends on timing and hardware is hard to test. We present Robo-SWE, a dataset of 236 executable issue-resolution tasks mined from merged pull requests in 28 public robotics repositories in four domains: ROS 2 systems, simulation and physics engines, robotics algorithm libraries, and robot-learning frameworks. Each task contains the original issue text, a container recipe for the repository at the commit before the fix, the developers' fix, and the developers' tests, which fail before the fix and pass after it in repeated offline runs. The dataset also keeps a provenance ledger of all 1,567 candidate pull requests, with the stage each reached and the reason for every rejection, and, as a first use, 3,540 graded attempts by five coding-agent setups with recorded patches and 3,529 available trajectories. Across three runs per primary setup, resolve rates range from 45.8% for Claude Sonnet 5.5 to 56.5% for Claude Opus 5.5; GPT-6.1 Sol reaches 54.1% at maximum effort and 52.5% at medium effort, and GPT-6 Astra 51.4%. GLM-5.3 resolves 46.2%, and Kimi K3.1 Pro 43.9%. The tasks are validated by execution; verification of their fairness by human reviewers is future work. The dataset and public leaderboard are available online.

cs.SE · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16114

ADAPT: Agentic Diff-Aware Patch Testing for Pull Requests

Vahid Haratian, Atakan Akar, Eray Tüzün

原文摘要

Modern software development relies on pull requests (PRs) that introduce small, targeted code changes. Once merged, these changes should be protected by focused tests that exercise the behavior the PR introduces, yet developers often submit PRs without such tests, leaving the new behavior unguarded against future regressions. Most test generation techniques target entire software units and optimize objectives such as code coverage rather than the behavior a change introduces, and prior diff-aware approaches have had limited success in capturing it. We propose ADAPT (Agentic Diff-Aware Patch Testing), a regression test generation approach that uses an autonomous coding agent to produce change-focused tests for PRs. Given a PR, the agent analyzes the diff between the base and head versions to identify the modified functions and their context, then plans and generates candidate unit tests. A dedicated fail-to-pass (F2P) verification tool runs each candidate on both versions and, after F2P success, reports which executable changed lines remain uncovered; guided by this feedback, the agent iteratively refines its tests. We evaluate ADAPT on the same 426 real-world PRs used by the prior state-of-the-art baseline, with GPT-4o and GPT-5-mini. ADAPT+GPT-4o reaches an F2P rate of 62.0% (264/426), compared with BLAST's reported 35.4% (151/426), while ADAPT+GPT-5-mini reaches 89.9% (383/426). On 137 PRs shared with Change And Cover (ChaCo), ADAPT achieves full patch coverage on 83.9% of PRs, compared with 79.6% for ChaCo, at considerably lower time and cost. A manual evaluation confirms that the F2P-valid tests target each PR's intended change, with mean scores of up to 4.54/5 for importance, 4.35/5 for assertion strength, and 4.49/5 for readability, often covering behavior beyond the developer-written tests.

cs.SE · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.01178

riMESA: Consensus ADMM for Real-World Collaborative SLAM

Daniel McGann, Michael Kaess

原文摘要

Collaborative Simultaneous Localization and Mapping (C-SLAM) is a fundamental capability for multi-robot teams as it enables downstream tasks like planning and navigation. However, existing C-SLAM back-end algorithms that are required to solve this problem struggle to address the practical realities of real-world deployments (i.e. communication limitations, outlier measurements, and online operation). In this paper we propose Robust Incremental Manifold Edge-based Separable ADMM (riMESA) -- a robust, incremental, and distributed C-SLAM back-end that is resilient to outliers, reliable in the face of limited communication, and can compute accurate state estimates for a multi-robot team in real-time. Through the development of riMESA, we, more broadly, make an argument for the use of Consensus Alternating Direction Method of Multipliers as a theoretical foundation for distributed optimization tasks in robotics like C-SLAM due to its flexibility, accuracy, and fast convergence. We conclude this work with an in-depth evaluation of riMESA on a variety of C-SLAM problem scenarios and communication network conditions using both synthetic and real-world C-SLAM data. These experiments demonstrate that riMESA is able to generalize across conditions, produce accurate state estimates, operate in real-time, and outperform the accuracy of prior works by a factor >7x on real-world datasets.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16110

Harnessing Environmental Scatterers for Endogenous Target Covertness in 6G ISAC: A New Paradigm for Sensing Security

Yifan Zhang, Boxuan Xie, Mingyang Yang, Ying Liu, Riku Jäntti, Zan Li

原文摘要

Integrated sensing and communication (ISAC) is a cornerstone of 6G networks, enabling simultaneous data transmission and environmental perception. However, the ubiquitous radiation of sensing waveforms introduces a critical security vulnerability where the physical existence and characteristics of targets are continuously exposed to unauthorized wardens. Meanwhile, existing sensing-secure methods primarily focus on degrading a target warden's parameter-estimation accuracy, yet they fail to prevent wardens from detecting the target's physical presence. In this article, we first analyze sensing security threats in ISAC systems and survey existing sensing-secure methods. To address the critical gap, we propose a novel security paradigm termed endogenous target covertness. By intentionally harnessing environmental scatterers, such as known objects, ambient backscatter devices, and reconfigurable intelligent surfaces (RIS), as ghost targets, the proposed scheme ensures a low probability that the warden detects the true target for the true target at the warden. This deliberately renders the true target's existence indistinguishable to the warden, while the legitimate base station can extract the true target's response using prior knowledge of the scatterers. We demonstrate the effectiveness of the proposed scheme through a case study, proving that absolute target covertness can be achieved without consuming dedicated jamming power. Finally, we outline key open challenges and promising research directions to realize truly secure, ubiquitous sensing in future ISAC ecosystems.

cs.CR · cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2508.18787

Design, Implementation and Evaluation of a Real-Time Remote Photoplethysmography (rPPG) Acquisition System for Non-Invasive Vital Sign Monitoring

Constantino Álvarez Casado, Sasan Sharifipour, Manuel Lage Cañellas, Nhi Nguyen, Le Nguyen, Miguel Bordallo López

原文摘要

The growing integration of smart environments and low-power computing devices, coupled with mass-market sensor technologies, is driving advancements in remote and non-contact-based physiological monitoring. However, deploying these systems in real-time on resource-constrained platforms introduces significant challenges related to scalability, interoperability, and performance. This paper presents a computationally efficient real-time remote photoplethysmography (rPPG) system, designed to extract physiological signals, such as heart rate, respiratory rate, and oxygen saturation (SpO2), from facial video streams. The system is built on the Face2PPG pipeline, which processes video frames sequentially for rPPG signal extraction and analysis, while leveraging a multithreaded architecture to manage video capture, real-time processing, network communication, and graphical user interface updates concurrently. This design targets continuous operation at 30 frames per second (FPS), with adaptive feedback through a collaborative user interface to guide optimal signal capture conditions. The network interface includes both an HTTP server for continuous video streaming and a RESTful API for on-demand vital sign retrieval. To ensure accurate performance despite limited computational resources, we use a hybrid programming model combining Functional Reactive Programming (FRP) and the Actor Model, allowing event-driven processing and efficient task parallelization. We evaluate HR accuracy on three public datasets (COHFACE, LGI-PPGI, and UBFC-RPPG), and conduct preliminary evaluations of RR on COHFACE and SpO2 on PURE. We also assess the processing time and end-to-end operation of the system on two computers, including live HR and SpO2 agreement with a medical-grade fingertip pulse oximeter across five 10-minute sessions. Our work addresses key challenges in real-time contactless biosignal monitoring.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16107

Trained on What Screen Readers Cannot Read: Accessibility Debt in the GUI Corpora Behind Computer-Use Agents

Obada Kraishan, Kulsawasd Jitkajornwanich

原文摘要

Computer-use agents learn to operate mobile interfaces from corpora of captured screens and accessibility trees. The same trees are what a screen reader consumes, so an agent trained on them inherits whatever labelling gaps the underlying applications carry. This paper measures one of those gaps, content-description coverage, in two Android corpora: Rico, collected in 2017, and AndroidControl, collected in 2024. Across 17,373,614 interface elements, 17.74% of the Rico elements that need a content description under our coding rule carry one, against 57.27% in AndroidControl. The difference of 39.5 percentage points holds under five alternative coding rules and when whole apps are resampled. It describes two datasets that also differ in app selection, capture method and platform version; it does not measure change over time. Input controls are the least labelled role in both corpora (2.20% and 27.21%, or 30.99% in AndroidControl if hint text counts as a name). Descriptions in the older corpus are also five times more likely to be placeholders (10.25% against 1.95%), so the difference is not an artefact of developers typing filler. Resolving 26,205 task actions to the elements they touched shows that, among tappable graphics with no text, demonstrations operate elements described 9.0 points more often than the others on the screens they visit, about 3.5 points of it from the choice of element within a screen. That is a selection effect inside the benchmark. A two-coder reliability check (kappa = 0.91 and 0.96) and the full pipeline accompany the paper.

cs.HC · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.00140

Steering Away from Memorization: Reachability-Constrained Reinforcement Learning for Text-to-Image Diffusion

Sathwik Karnik, Juyeop Kim, Sanmi Koyejo, Jong-Seok Lee, Somil Bansal

原文摘要

Text-to-image diffusion models are susceptible to memorization, revealing a fundamental failure to generalize beyond the training set. Current mitigation approaches often sacrifice image quality or prompt alignment to reduce memorization. To address this, we propose Reachability-Aware Diffusion Steering (RADS), an offline-trained, sampling-time control framework that mitigates memorization while maintaining generation fidelity. RADS models the diffusion denoising process as a dynamical system and learns a policy-conditioned reachability value function that estimates future memorization risk along the denoising trajectory. This motivates a constrained reinforcement learning (RL) formulation, where a policy learns to steer the trajectory away from memorization via perturbations in the caption embedding space. Empirical evaluations show that RADS achieves a stronger empirical Pareto frontier between generation diversity (SSCD), quality (FID), and alignment (CLIP) compared to state-of-the-art inference-time baselines. Crucially, RADS applies the learned controller during sampling time, separating memorization mitigation from the pretrained model. Our website is available at: https://s-karnik.github.io/rads-memorization-project-page/.

cs.CV · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16103

Low Probability of Target Detection in Wireless Sensing by Harnessing Reconfigurable-antenna based Reflectors

Yifan Zhang, Zhentian Zhang, Jantti Riku, Yichuan Wang, Xingwang Li, Tuo Wu

原文摘要

In this paper, we introduce low probability of target detection (LPTD) as a new security objective for wireless sensing systems, where the existence of a target is concealed from passive wardens. Unlike conventional communication security and covert communication, LPTD aims to protect the sensed target itself from malicious detection, rather than concealing transmitted signals or communication activities. Specifically, we propose a reconfigurable-antenna-based reflector-assisted target covertness scheme, where passive reflectors deployed around the target are harnessed as artificial scattering sources to confuse the warden. By adjusting their reflection coefficients, these reflectors reshape the local scattering environment and mask the target-induced echo. We then analyze the warden's optimal detection strategy and derive the false-alarm and missed-detection probabilities, followed by a closed-form expression for its minimum total detection error probability. Numerical results validate the analysis and show that sufficient cooperation among reflectors can increase the warden's minimum total detection error probability to approximately 0.94--0.96, making target detection close to random guessing.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16100

Hiding a Gap Does Not Close It: The Price of Removing the Mean Group Contrast from Learned Models of Learning Dynamics

Sosthène Bidojesussi Hounzinhin, Ahmed Ratnani, Abdeladim El Akri

原文摘要

Adaptive tutors use models learned from student data, and a common fairness remedy erases the group signal from these models. We turn the cost of this remedy into an audit tool. Building on mean-parity regression, we give the minimum extra error, relative to the best predictor, of removing the mean group contrast from a predictor that cannot use the group, $p_0p_1Δ^2/R^2$, and the exact cost of any correction built from an estimated group model, which, for the best predictor, is never below this minimum. A three-stage audit (fit on training units, announce the cost on validation units, test on independent units) estimates this cost before the correction is used. In a controlled study, the price can be large even when the state predicts the group poorly. On three real data sets, erasing the group from a learned model of learning dynamics predicts almost no gap where one is observed. In a selected knowledge-tracing network, erasing the hidden state leaves most of the gap; a correction of the prediction removes it at an extra Brier score of 0.021 on average. On Eedi, with the network selected on separate units, the audit accepted the announced cost in all five splits, the measured cost fell inside the announced interval in all five, and the gap reduction transferred to new classes in four; on new ASSISTments schools the audit abstains. In a tutoring simulator, the objective and the help budget matter more than the erasure, and floors on each group's knowledge separate narrowing a gap from levelling down. Hiding a gap in a model does not close it.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16099

CupOFMoCA: Coupled Objective-Guided Discrete Flows for Molecular Conjugate Assembly

Ruoxi Rosie Zhang, Ziang Li, Jiatao Gu, Pranam Chatterjee

原文摘要

Molecular conjugates, including PROTACs and peptide-drug conjugates (PDCs), derive their function from the joint behavior of multiple coupled components, yet most generative approaches design these components independently and combine them only after generation. Such staged pipelines ignore cross-component dependencies and often produce conjugates that are chemically invalid, fall outside empirical conjugate distributions, or lose function upon assembly. In this work, we introduce Coupled Objective-Guided Discrete Flows for Molecular Conjugate Assembly (CupOFMoCA), a discrete generative method that enforces cross-component chemical constraints during conjugate generation. CupOFMoCA restricts guided updates to chemically feasible conjugate states and reweights local transitions using molecular design objectives. Across three PDC systems, coupled generation achieved 100% conjugation validity among valid peptides and increased valid yield over matched post-hoc filtering, with comparable predicted binding scores across the full valid sets. In three-seed evaluations of three PROTAC systems, CupOFMoCA achieved the highest assembly validity on two systems and generated more assembly-valid molecules per second than the evaluated baselines. We further evaluated the generated PROTACs with post-hoc DegradeMaster scoring and explicit-solvent molecular dynamics, supporting their predicted activity and conformational properties. Together, these results establish CupOFMoCA as an efficient generator of molecular conjugates, supporting therapeutic development via coordinated control of assembly and molecular properties.

q-bio.BM · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16098

VISTA: View-Independent micro-Doppler Reconstruction with Multistatic ISAC

Jacopo Pegoraro, Marco Canil, Aurora Peloso, Jesus O. Lacruz, Xitao Gong, Richard A. Stirling-Gallacher, Michele Rossi, Joerg Widmer

原文摘要

The micro-Doppler (uD) spectrum provides detailed characteristics of target movement, making it highly valuable for applications such as target classification and human activity recognition. However, traditional Integrated Sensing And Communication (ISAC) systems are highly view-dependent and limited by blind spots and angle dependencies. Existing approaches rely on either configuration-specific solutions or data-driven models. The former results in poor generalization when device deployments or target orientations change, while the latter requires acquiring extensive multi-view datasets, which is time-consuming and not always feasible. To address these limitations, in this work we propose VISTA, a novel method for reconstructing the uD spectrogram in an absolute coordinate frame, yielding view-independent Doppler features that are invariant to the spatial distribution of ISAC nodes. We formulate the uD estimation as a sparse reconstruction problem that jointly leverages measurements from all ISAC receivers. This is solved using a modified Orthogonal Matching Pursuit (OMP) algorithm, which iteratively alternates between jointly selecting candidate velocity vectors across all receivers (RXs) and independently estimating the complex channel gain amplitudes for each RX. Our results show that, provided sufficient spatial diversity, VISTA successfully reconstructs the targets' absolute uD spectrograms with a median error \leq 0.69 m/s for speeds \leq 15 m/s.

cs.AR

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新论文 · v1arXiv:2611.16097

A Matching Lower Bound for Online Smooth Calibration

Kaixuan Ji, Quanquan Gu

原文摘要

We study smooth calibration error in sequential binary prediction, which measures how far a sequence of probabilistic forecasts is from being calibrated and, unlike the classical expected calibration error, is robust to small perturbations of the forecasts. Against adaptive adversaries, the minimax smooth calibration error is known to lie between $Ω(T^{1/3})$ and $O(\sqrt{T})$ (Qiao and Zheng, 2024), where $T$ is the number of rounds. In this paper, we close this gap by constructing a new adversary whose outcome mean moves with the forecaster's cumulative residual. This design makes compensating for past residuals costly, and forces every forecaster to incur an expected smooth calibration error of $Ω(\sqrt{T})$. This lower bound matches the known upper bound up to universal constants, and further implies $Ω(\sqrt{T})$ lower bounds for calibration distance and lower calibration distance. Overall, our results settle the minimax rates of these calibration measures in sequential prediction. The proof ideas are partially developed by GPT-6 Astra under the authors' guidance.

cs.LG · stat.ML

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新论文 · v1arXiv:2611.16096

The Ultimate Karp-Lipton Collapse

Edward A. Hirsch, Ilya Volkovich

原文摘要

Karp and Lipton proved that if $NP\subseteq P/poly$, then $PH$ collapses. A long line of work pushed the collapse to smaller classes. In particular, Chakaravarthy and Roy obtained two such collapses, to (apparently) incomparable classes $O_2^P$ and $P^{prMA}$. We united them earlier by a collapse to the input-oblivious version $P^{prOMA}$ of $P^{prMA}$, which is contained in both; and, jointly with Williams, to a slightly smaller class. Two other deepest known collapses are to $ZPP^{ONP}$ (following from Köbler and Watanabe) and to $OL_2^P$ (proved in this paper). We unite the three collapses by a collapse to a class contained in all of them. We use a three-stage computational model OGTD (oblivious generator-tester-decoder): a polynomial-time generator with an oracle computes an advice string from $1^n$, a tester certifies it, and a polynomial-time decoder uses it (it is the only one that sees the actual input). Let $OM$ be the class of OGTD languages defined by a restricted version of $prOMA$ as the generator's oracle, by a co-$ONP$ tester, and a $P$ decoder. We show that it is contained in the three collapse classes above and prove that $PH$ collapses to $OM$ under $NP\subseteq P/poly$. The collapse also holds for an unambiguous version of $OM$. All these input-oblivious collapse classes lie in $P^{prSBP}$, and so do even the non-input-oblivious versions $P^{prMA}$, $L_2^P$ and $S_2^P$: we prove that $P^{prS_2^P}\subseteq P^{prSBP}$. This strengthens the containment $S_2^P\subseteq P^{prAM}$ of Chakaravarthy and Roy and our earlier containment $L_2^P\subseteq P^{prSBP}$. The proof unites the Goldreich-Wigderson-type procedure of Chakaravarthy and Roy with our earlier approximate-counting technique. Its input-oblivious counterpart concerns $O_2^P$, the first collapse class of Chakaravarthy and Roy: every language in $O_2^P$ has polynomial-size circuits constructible in $FP^{prSBP}$.

cs.CC

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版本更新 · v2arXiv:2512.10095

ReflFlow: Learning Geometry-Guided Ray Tracing for Dynamic Specular Reconstruction

Jiachen Tao, Junyi Wu, Haoxuan Wang, Zongxin Yang, Dawen Cai, Yan Yan

原文摘要

We present ReflFlow, a novel framework for high-fidelity rendering of dynamic specular scenes by addressing two key challenges: precise reflection direction estimation and physically accurate modeling. To achieve this, we propose a Residual Material-Augmented 2D Gaussian Splatting representation that models dynamic geometry and material properties, allowing accurate reflection ray computation. Furthermore, we introduce a Dynamic Environment Gaussian and a hybrid rendering pipeline that decomposes rendering into diffuse and specular components, enabling physically informed specular synthesis via rasterization and ray tracing. Finally, we devise a coarse-to-fine training strategy to improve optimization stability and promote physically meaningful decomposition. Extensive experiments on dynamic scene benchmarks demonstrate that ReflFlow outperforms prior methods quantitatively and qualitatively, producing sharper and more realistic specular reflections in complex dynamic environments.

cs.CV

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版本更新 · v2arXiv:2610.19323

TopicMerge: A Divide and Conquer Approach to Topic Modeling

Matthew Raffel, Lizhong Chen, Sai Munikoti, Sameera Horawalavithana

原文摘要

Recent advancements in topic modeling have split into two main paths: efficient embedding-based methods that offer scalability but are often outperformed on standard clustering metrics, and Large Language Model (LLM) approaches that achieve state-of-the-art clustering performance but at the cost of prohibitive computational expense and poor scalability. This paper introduces TopicMerge, a novel multi-stage pipeline that bridges these approaches to achieve both state-of-the-art clustering performance and computational efficiency. TopicMerge first employs an efficient clustering pipeline to deliberately over-segment a document corpus into a large set of fine-grained, semantically pure micro-clusters, which are then given initial semantic labels. It then introduces a novel, iterative merging algorithm where an LLM, guided by local neighborhood context, arbitrates on consolidating candidate pairs. These approved merges are then selectively executed and semantically relabeled by the LLM, hierarchically building broader, more coherent topics. Extensive experiments on the Bills and Wiki datasets show that TopicMerge achieves state-of-the-art clustering performance while reducing required LLM calls by over 92% compared to document-level methods, offering a practical and scalable solution.

cs.CL · cs.LG

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新论文 · v1arXiv:2611.16093

Measuring Preferred Representation in AI-generated Images with Community Scorers

Melanie F. Pradier, Camilla Longden, Anja Thieme, Rita F. Marques, Nari Johnson, Sidhika Balachandar, Xinnuo Xu, Linda Y. Wen 等 12 位作者

原文摘要

AI-generated images often misrepresent historically marginalised communities, with real-world consequences beyond the images themselves. However, conventional metrics fail to measure how communities want to be represented in AI-generated images. To address this, we introduce the concept of preferred representation. Building on previous work, we propose a measurement instrument that operationalises this concept through a Community Scorer - a model that evaluates a specific community's preferred representation in AI-generated images. We interrogate the validity of Community Scorers on three diverse disability communities that span the Global North and South. The results show that Community Scorers align significantly better with community ratings compared to other widely used text-to-image metrics. We then demonstrate the usefulness of these scorers in a downstream content filtering task. We release the Community Preference Dataset to enable others to optimise how Community Scorers can be scaled for culturally specific evaluation of AI-generated visual media.

cs.LG

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版本更新 · v2arXiv:2605.31304

DUNE: Disentangling Polysemantic Neurons Without Losing Predictive Performance

Doğukan Bağcı, Bernt Schiele, Simone Schaub-Meyer, Jonas Fischer, Robin Hesse

原文摘要

Deep neural networks (DNNs) are widely used, but understanding what they actually learn remains difficult. A major obstacle is that individual neurons often respond to multiple unrelated concepts, obscuring the network's internal decision process. Existing approaches, such as sparse autoencoders, can disentangle these mixed signals into more meaningful, ``monosemantic'' features, but typically introduce approximation errors that may degrade downstream performance. Consequently, these methods exhibit a Pareto frontier between monosemanticity and functional faithfulness. To address this, we introduce DUNE (disentangling units with no error), a method that decomposes DNN neurons into more monosemantic subunits while preserving their original function by design. DUNE identifies concept-specific contributions from the preceding layer and re-routes them into separate subunits whose aggregation exactly recovers the original activation. DUNE can be applied directly to pretrained models, without labels or training of an auxiliary model. Across several models, including DINOv2, DUNE achieves state-of-the-art or competitive monosemanticity while exactly preserving functional faithfulness, substantially advancing the existing Pareto frontier between these properties. DUNE operates efficiently and enables fine-grained interventions such as concept-specific steering.

cs.LG · cs.CV

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新论文 · v1arXiv:2611.16088

Subsurface schematisation from sparse CPT data using the tabular foundation model TabPFN

Taiga Saito, Fabian Campos Montero, Bruno Zuada Coelho, Ana Martins Teixeira, Stephen Wu, Timo Schweckendiek, Yu Otake

原文摘要

Subsurface schematisation supports geotechnical decisions but remains challenging because sparse site-investigation data leave substantial gaps in information about ground conditions. This study explores TabPFN, a foundation model pretrained on general synthetic tabular data, for this task. Using only grid coordinates and soil behaviour type index observations from sparse cone penetration tests (CPTs), we first reconstruct two-dimensional sections without geotechnical fine-tuning or predefined layer boundaries. We evaluate the approach on synthetic sections and at two field sites in the Netherlands, comparing it with SchemaGAN, a conditional generative adversarial network trained on synthetic geotechnical sections, and with reference interpolation methods. In the baseline comparisons, TabPFN achieved mean errors close to SchemaGAN's on synthetic sections and lower mean errors at both field sites. On synthetic sections, TabPFN's mean absolute error decreased at each tested increase in CPT count, and, under the baseline input setting, its pointwise prediction intervals had mean coverage close to nominal levels. Subsequent fine-tuning on the synthetic sections used to train SchemaGAN improved synthetic test accuracy but increased mean errors at both field sites. These findings indicate that general-purpose tabular foundation models offer a promising route for updating subsurface predictions from sparse and evolving site-investigation data without retraining.

cs.LG · cs.AI

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版本更新 · v3arXiv:2605.25437

Does Seeing More Mean Knowing More? Mono-Anchored Advantage Normalization for Multi-Source Visual Reasoning

Fanhu Zeng, Zhicong Luo, Zefan Wang, You Li, Chi Chen, Maosong Sun

原文摘要

Visual reasoning through reinforcement learning with verifiable rewards (RLVR) has achieved remarkable progress. However, when dealing with multi-source inputs, existing approaches tend to treat them as a mere accumulation of information, lacking explicit mechanisms to distinguish whether integrating additional sources yields information gain or introduces interference. Therefore, they struggle to effectively model dynamic interaction when integrating multiple sources, particularly when they differ significantly in physical properties and semantics, e.g., infrared and depth, leading to inferior performance to mono-source reasoning when a certain source holds the dominant signal. To address this issue, we propose MARS, a novel mono-anchored multi-source reasoning framework that models each visual modality as an independent information source. Specifically, by treating mono-source rewards as dynamic anchors, our method explicitly incorporates the information gain introduced by multi-source fusion into advantage normalization and adaptively emphasizes mutual promotion between sources while suppressing potential noise or conflicts during RLVR. From theoretical analysis, our method effectively quantifies information gain introduced by multi-source integration in gradient estimation, enabling consistent modality regulation. Empirical results also show impressive 3.2% and 4.9% performance gains on GRPO and DAPO across diverse datasets, confirming the effectiveness of our method. Code is available at https://github.com/AI9Stars/MARS.

cs.CV

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版本更新 · v3arXiv:2611.05206

Diffusion Plug-in Priors for Self-Supervised Ultrasound Video Restoration

Chu Chen, Mingduo He, Yining Li, Yinhao Wu, Wei Tang, Yiwen Luo, Yixuan Chen, Pasquale Cascarano 等 9 位作者

原文摘要

Ultrasound videos suffer from hardware-constrained spatial resolution and signal-dependent speckle, while paired low- and high-resolution acquisitions are rarely available. We propose UltraDiff, a self-supervised framework for joint ultrasound video super-resolution and despeckling that uses a frozen pre-trained diffusion model as a plug-in prior. Rather than retraining the model, UltraDiff restores each frame by optimizing the sampler's latent seed under a range-constrained maximum a posteriori-inspired formulation. We derive a speckle-aware data-fidelity term from a Rayleigh scale-family model and characterize its relative-residual and locally variance-adaptive weighting properties. To address degeneracy near zero intensities, we introduce a stabilized surrogate fidelity that is bounded below and strictly convex in log intensity for positive observations in its unclamped form. Total-variation regularization complements the removal of mixed noise, and a temporal seed update warm-starts each frame from the optimized seed of its predecessor, accelerating the convergence. On EchoNet-LVH with simulated speckle and additive noise, UltraDiff with a natural-image prior improves peak signal-to-noise ratio by 1.24\,dB over the strongest baseline, and ultrasound-related priors yield further gains. On 100 real EchoNet-Dynamic videos, UltraDiff reduces the mean absolute error of zero-shot ejection fraction prediction from 7.31 to 6.37 percentage points compared with DUP. These results support ground-truth-free ultrasound video restoration using frozen diffusion priors and a speckle-aware fidelity, without paired training data or model retraining.

cs.CV

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