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新论文 · v1arXiv:2611.12259

A forensic evaluation framework for egocentric foundation models: an EFM3D case study for crime scene reconstruction

Davidson Chisom Onwubiko, Shahana Bano, Carlos Francisco Moreno-García

原文摘要

Foundation models increasingly combine geometry, semantics, and spatial context for three-dimensional scene understanding, but their suitability for forensic crime scene reconstruction remains unclear. This study presents a structured forensic evaluation framework for egocentric foundation models and demonstrates it using the Egocentric Foundation Model for 3D (EFM3D). Four simulated indoor crime scenes were recorded with Meta Project Aria Gen~1 at Robert Gordon University's East Lodge House forensic training facility. The framework evaluates room-scale reconstruction, semantic scene understanding, computational performance, and forensic suitability. Across the four scenes, EFM3D generated meshes averaging $12{,}668\pm2{,}430$ vertices and $24{,}838\pm4{,}903$ faces, with 39,529 recorded semantic predictions spanning 21--24 categories at a mean confidence of 0.40. Because reference meshes and expert-labelled 3D ground truth were unavailable, these statistics are treated as descriptive outputs rather than measures of geometric or detection accuracy. Final inference runs processed $1{,}388\pm203$ snippets per scene at $0.69\pm0.01$ samples/s and required $50.77\pm6.12$ minutes per recording. A seven-criterion capability profile indicated stronger support for room geometry, furniture localisation, scene context, and spatial relationships than for fine-scale forensic evidence. The results position EFM3D as a complementary tool for contextual documentation and scene review rather than a replacement for high-fidelity forensic capture. The framework provides a reproducible basis for evaluating future egocentric foundation models in specialised forensic settings.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12242

Controlling Temporal Pseudo-Label Marginals for Stable Online Test-Time Adaptation

Rani Amar, Ofir Lindenbaum

原文摘要

Continual Test-Time Adaptation (CTTA) methods update models online using a stream of unlabeled data to overcome distribution shifts encountered at test time. However, CTTA is often unstable, as it can reinforce biases in early predictions and increasingly predict only a few classes, resulting in drift or even collapse. In this paper, we identify narrowing pseudo-label class usage as an indicator of CTTA instability and quantify this behavior. Most existing methods have attempted to address this issue by either selecting reliable adaptation samples or re-weighting the loss to encourage diverse pseudo-labels, but they do not explicitly enforce a class marginal over a temporal window. Instead, we propose Sinkhorn Class-Marginal Regularization (SCALAR), a simple plug-and-play module that controls the pseudo-label distribution over a short temporal window. SCALAR works by maintaining recent predictions in a buffer, balancing their class distribution via the Sinkhorn algorithm to match a specified target distribution, and using the balanced soft targets for adaptation. Without changing the model architecture, SCALAR maintains the per-step runtime and increases the peak allocated GPU memory by only 10 MB on ImageNet-C. Our method is particularly effective on skewed, non-stationary data with severe distribution shifts, where SCALAR significantly alleviates drift and collapse when combined with existing stabilization methods based on entropy or diversity.

cs.LG · cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12232

ECOS: Environment-Calibrated On-Policy Self-Distillation for Language Agents

Chenhao Zhang, Zhongan Wang, Mingxuan Li, Hehe Fan, Zheng Zhu, Ming Li

原文摘要

On-policy self-distillation lets a language agent act as its own Teacher by conditioning on privileged context such as skills written from its own trajectories. Its guidance, however, is weighted by how much this context shifts token likelihoods rather than by whether it improves behavior, so errors in self-written skills return through later updates. We propose environment-calibrated on-policy self-distillation, which uses environmental outcomes to decide how much to trust the Teacher's current guidance and which self-written skills to reinforce. Command-level advantages, estimated by comparing commands executed at similar observations, scale each Teacher preference and reward reflections and skills whose induced preferences favor higher-advantage commands. Without skills at inference, 3B and 7B agents reach 90.5% and 93.3% macro success on seen ALFWorld tasks and 75.5% and 80.5% success on WebShop. On unseen ALFWorld tasks, they outperform GRPO under the same protocol by 11.0 and 7.6 percentage points, respectively. In an ablation study, supervising only executable commands rather than the Actor's full response raises 3B WebShop success from 52.3% to 75.0%.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.11154

Environmental Footprint of GenAI Research: Insights from the Moshi Foundation Model

Marta López-Rauhut, Loic Landrieu, Mathieu Aubry, Anne-Laure Ligozat

原文摘要

New multi-modal large language models (MLLMs) are continuously being trained and deployed, following rapid development cycles. This generative AI frenzy is driving steady increases in energy consumption, greenhouse gas emissions, and a plethora of other environmental impacts linked to datacenter construction and hardware manufacturing. Mitigating the environmental consequences of GenAI remains challenging due to an overall lack of transparency by the main actors in the field. Even when the environmental impacts of specific models are mentioned, they are typically restricted to the carbon footprint of the final training run, omitting the research and development stages. In this work, we explore the impact of GenAI research through a fine-grained analysis of the compute spent to create Moshi, a 7B-parameter speech-text foundation model for real-time dialogue developed by Kyutai, a leading privately funded open science AI lab. For the first time, our study dives into the anatomy of compute-intensive MLLM research, quantifying the GPU-time invested in specific model components and training phases, as well as early experimental stages, failed training runs, debugging, and ablation studies. Additionally, we assess the environmental impacts of creating Moshi from beginning to end using a life cycle assessment methodology: we quantify energy and water consumption, greenhouse gas emissions, and mineral resource depletion associated with the production and use of datacenter hardware. Although Moshi is a specific case study, its development involves research stages common to most GenAI R&D pipelines. Our analysis therefore supports general practical recommendations for reducing compute usage and environmental impacts, while paving the way toward more sustainable MLLM research.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.35374

GIFT: Representation Geometry Matters for Single-Domain Generalized Object Detection

Yin Zhang, Yaoyue Zheng, Yongqiang Zhang, Bogdan Raducanu, Dan Liu

原文摘要

Single-Domain Generalized Object Detection (S-DGOD) aims to generalize from a single source domain to unseen domains under distribution shifts. Existing efforts mainly focus on simulating unseen domains or suppressing domain-specific factors. However, they largely overlook the intrinsic generalization capability embedded in Vision Foundation Models (VFMs). In this work, we show that unconstrained fine-tuning disrupts the geometric structure of pre-trained representations, which encodes domain-invariant semantics. Motivated by this, we propose Geometric-Invariance Fine-Tuning (GIFT), a geometric-aware fine-tuning method for VFMs. Specifically, we introduce a series of learnable Householder reflections to the pre-trained weights to encourage geometry-consistent adaptation under domain shift. In addition, we propose a lightweight channel-wise scaling to facilitate effective task-specific adaptation. Extensive experiments demonstrate that GIFT consistently outperforms state-of-the-art (SOTA) approaches on two S-DGOD benchmarks. Notably, by introducing fewer than 1% additional trainable parameters into the frozen backbone, the proposed method achieves improvements of +7.9% and +2.9% in mPC over the previous SOTA on the Cityscapes-C and Diverse Weather Dataset, respectively. This finding highlights that preserving representation geometry is crucial for robust cross-domain generalization, establishing GIFT as a principled and efficient solution to domain shift.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12205

Graph-Based RL for Sequential Rendezvous Planning with Multiple Patrolling USVs

Seung Woo Han, Hyeon Bin Yeo, Seung Heon Oh

原文摘要

In patrol missions involving multiple unmanned surface vehicles (USVs), a designated command USV can support fleet management by sequentially rendezvousing with each patrolling USV. These operations require motion-feasible route planning and priority-aware rendezvous sequencing, with the objective of minimizing mission completion time and total tardiness relative to priority-dependent due dates. We propose a framework combining grid-based path planning, surrogate travel-time prediction, and graph-based reinforcement learning. The path planner accounts for ocean currents and approximates turn-rate constraints through multi-stage maneuvers, with path feasibility empirically validated through trajectory-tracking simulations. For rendezvous sequencing, a Feasible Transition Graph (FTG) represents candidate rendezvous events and temporally feasible transitions, enabling a graph neural network to exploit spatiotemporal rendezvous relationships for policy learning. A surrogate predicts travel times from compact current and motion features to avoid repeated path searches during FTG construction and policy training. Evaluation using grid-based path planning shows statistically significant average objective reductions of 10.5%, 3.5%, and 1.5% compared with greedy, MLP-RL, and 2-step lookahead greedy, respectively. The policy achieves these improvements while requiring approximately 1/35 of the latter's computation time. It maintains its advantage over MLP-RL under unseen priority-tier settings, with larger gains as fleet size increases.

cs.RO · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12202

Co-Creating Neurorehabilitation Technology Alongside Lived Experience Experts: A Participatory Design Workshop

Irene L. Y. Beck, Salvatore L. Cucinella, Johannes B. J. Bussmann, Natacha Léon, José López-Sánchez, Francesca Lunardini, Katherine L. Poggensee, Cristina Vázquez-González 等 10 位作者

原文摘要

Involving patients' lived experience (LE) early in neurorehabilitation technology design can improve the relevance and effectiveness of solutions, yet LE experts are often involved only when finished prototypes are tested. We present and evaluate a participatory design (PD) workshop for early LE involvement, examine how LE contributions influence design processes and outcomes, and provide recommendations for similar approaches. We conducted a PD workshop at the 6th International Conference on NeuroRehabilitation. Five people who have had a stroke (LE experts) and 18 workshop attendees (including the LE experts' caregivers) were organized into five groups. Following the Discover, Define, and Develop phases of the Double Diamond model, each group used Journey Maps and Data Collection Notebooks to co-create a solution for an objective specified by an LE expert. Journey Maps were thematically analyzed, and group audio transcripts underwent structured extraction using a large language model. LE input shifted discussions from discipline-based assumptions toward priorities grounded in lived experience, including fear, confidence, sensory discomfort, fatigue, aesthetics, and independence. Narrow objectives, such as opening a blister pack, produced task-specific solutions, whereas broader objectives generated device concepts, including sensory feedback for cycling and a stimulation-equipped cooking splint. Satisfaction was high among attendees and LE experts. All workshop tools and protocols are shared to support adoption. The workshop offers a practical foundation for involving LE experts early in neurorehabilitation technology development. Future iterations should better capture psychological and social dimensions, allow more reflection, and include the full range of relevant stakeholders through sustained transdisciplinary engagement between researchers and the LE community.

cs.HC · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.02248

Geometric Latent Reasoning Induces Shorter Generations in LLMs

Shashi Kumar, Yacouba Kaloga, Petr Motlicek, Ina Kodrasi, Andrea Cavallaro

原文摘要

Large language models solve complex problems by generating lengthy chains of explicit reasoning tokens. While effective, this makes reasoning expensive, length-sensitive, and constrained to (discrete) natural language. While latent reasoning offers a continuous alternative, determining useful structures for intermediate latent states is an open challenge. In this paper, we formulate latent reasoning as a geometric path-approximation problem within the model's pretrained token-embedding space. We introduce Geometric Latent Reasoning (GLR), which uses a lightweight transition head to predict iterative direction updates in embedding space. Using textual chain-of-thought traces as anchors, GLR learns to approximate discrete reasoning trajectories while permitting continuous deviations from exact token embeddings. Evaluations on mathematical reasoning benchmarks using Qwen3 models reveal an emergent phenomenon: geometric latent reasoning induces substantially shorter generations without an explicit length objective. By replacing early explicit reasoning with continuous latent steps, models often reach correct answers using substantially fewer total generation steps. These findings suggest that continuous trajectories act as compact intermediate reasoning states, exposing a new tradeoff between latent computation budget, output length, and accuracy. Our code is publicly available at: https://github.com/idiap/GLR. The project page is available at: https://glr-llm.github.io/.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.21743

Pairwise AUC Optimization Needs Corrective Power: A Unified View

Jia Chen, Zhiyong Yang, Shilong Bao, Qianqian Xu, Qingming Huang

原文摘要

Direct pairwise AUC-surrogate training is attractive because it trains toward the ranking metric used at test time, especially under class imbalance. This creates a direct alignment between the training objective and the evaluation metric, but such metric alignment alone does not guarantee that stochastic training remains corrective. We study this gap through Corrective Power: whether pairwise mini-batch updates produce stronger and more reliable improvements for more severe positive-negative ranking errors. Our main observation is that direct pairwise training can enter regions where severe errors remain common but no longer produce useful corrective motion. In such regions, even the mini-batch noise that would normally help SGD move along the steepest severe-error correction direction can become much weaker. In this paper, we seek a theoretical explanation for this by exploring the degenerate property of the U-statistics formed from the pairwise gradients therein. Our results provide a mechanistic explanation for several empirical patterns: (a) squared-hinge loss is often an effective AUC surrogate; (b) cross-entropy warm-up can help models that enter the AUC phase from a poor starting region; and (c) parameter-efficient fine-tuning from a pretrained model can reduce that need by starting closer to a favorable ranking geometry. Experiments on 8 CV tasks and 8 NLP tasks, using ResNet50, DenseNet121, DistilBERT-base, and Qwen2.5-1.5B-Instruct, support these predictions.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.13036

CapBuddy: Efficient Physical Memory Management for Capability-based Resource-controlling Microkernel

Zhuang Lu, Guangtao Zhu, Shijun Zhao, Qianying Zhang, Lixin Chen, Zekun Xu, Wu Luo, Rui Hou

原文摘要

Capability-based resource-controlling microkernels securely multiplex hardware by exposing low-level resource abstractions guarded by capability systems. Such a design helps provide an application-specific resource management policy to achieve high performance without sacrificing security. However, the misuse of capability-protected secure abstractions can introduce performance overhead in user-level resource management, affecting the runtime performance of dynamic applications in emerging scenarios. In this paper, we focus on the secure abstraction of physical memory in microkernels with capability-based resource management delegation to investigate an appropriate design for dynamic memory management. We propose CapBuddy, an approach to building an efficient buddy system on capability-protected secure abstractions of memory, to bypass the performance overhead of the capability system by offloading functionalities that can run offline from the critical path of serving a memory request. We implemented a prototype system on the seL4 library OS, and the evaluation results show that our approach improves the performance of the seL4 user-level buddy system by 20 to 54.5 times and achieves the same order of magnitude as Linux. Further burst experiments evaluate CapBuddy's scalability and show that it maintains low median allocation latency as the number of concurrent clients increases.

cs.OS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12189

Computational Structure of Pairwise Housing Connectedness Networks

Seo In Jang, Seung Ho Choi, Ji-Eun Shin, Hyunwoo Lee

原文摘要

Directed housing-submarket networks combine common market movement with heterogeneous bilateral relations, and different node-level measures need not yield distinct empirical rankings. We construct a positive pairwise net-connectedness network from the generalized forecast error variance decomposition (GFEVD) of a vector autoregression (VAR) and use it to compare complementary node positions. Net directional connectedness (NET), PageRank, and Viral Centrality summarize net directional balance, recursively weighted incoming position, and normalized multistep reach. When bilateral net differences are nonzero, the network is a weighted tournament. A least-squares edge-flow decomposition separates the pairwise-net matrix into a NET-equivalent gradient component and a NET-orthogonal residual, while a monotone-allocation condition establishes exact PageRank-indegree correspondence in a transitive tournament. In Seoul's 25-district network, the gradient component accounts for 70.9% of the squared Frobenius norm, leaving 29.1% in the residual component. PageRank is strongly aligned with indegree (Spearman $ρ=0.987$), and Viral Centrality with outdegree ($ρ=0.935$); the PageRank alignment exceeds the range of independently weighted simulations with comparable tournament consistency and edge-weight variation. Three common-component treatments reduce total connectedness from 81.98% to 50.60-52.00%, a 36.6-38.3% reduction relative to the raw system, and reverse 97-98 of 300 pairwise net directions. Thus, both the magnitude and the apparent direction of connectedness depend materially on the treatment of citywide common movement. Songpa's positive NET position remains prominent across common-component treatments and dynamic specifications and ranks first in both chronological subsamples.

cs.CE · cs.SI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12187

Harnessing Vision-Language Models for Federated Unsupervised Visual Recognition

Jian Liang, Kuangpu Guo, Ran He, Tieniu Tan

原文摘要

Federated learning (FL) is a promising research paradigm that enables collaborative model training across distributed clients without data sharing. To handle unlabeled data in local clients, existing unsupervised FL research has primarily focused on tasks like clustering and self-supervised representation learning, leaving the critical problem of unsupervised visual recognition largely unexplored. To bridge this gap, we introduce FedUR, the first framework designed for unsupervised recognition by leveraging the zero-shot capabilities of pre-trained vision-language models like CLIP. Specifically, FedUR addresses data heterogeneity through a pseudo-labeling mechanism with a class-aware data selection strategy. This strategy first estimates a global pseudo-label distribution, then allocates pseudo-labeled data to clients accordingly. To handle the uncertainty of conventional positive pseudo-labels, FedUR further incorporates class weights and a negative pseudo-labeling strategy. Furthermore, FedUR proposes a personalized prompt aggregation protocol where general visual prompts are aggregated on the server, and personalized text prompts encoding local knowledge are retained on clients. Extensive experiments demonstrate that FedUR consistently outperforms baseline methods under both label-level and feature-level distribution shifts. We believe this work paves the way for further advances in federated unsupervised recognition. Code will be available at {github.com/tim-learn/FedUR/}}.

cs.LG · cs.CV

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arXiv ↗PDF ↗
版本更新 · v7arXiv:2502.12756

Navigating Demand Uncertainty in Container Shipping: Deep Reinforcement Learning for Enabling Adaptive and Feasible Master Stowage Planning

Jaike van Twiller, Yossiri Adulyasak, Erick Delage, Djordje Grbic, Rune Møller Jensen

原文摘要

Reinforcement learning (RL) has successfully solved various deterministic and stochastic planning problems. However, conventional RL struggles with complex real-world constraints, particularly when feasibility is explicit and depends on the current state or trajectory. In this work, we address stochastic sequential decision-making with state-dependent constraints through a real-world case study of the master stowage planning problem in container shipping, which aims to optimize revenue and costs under demand uncertainty and operational constraints. We propose a deep RL framework with an encoder-decoder model that integrates problem instance, solution, and uncertainty information to guide planning. We introduce differentiable projection layers that enforce convex polyhedral constraints, while Jacobian corrections offset the projections to yield unbiased policy gradient estimates. Experiments show that our model efficiently finds adaptive, feasible solutions that generalize across distribution shifts and scale to longer planning horizons, outperforming state-of-the-art baselines in constrained RL and stochastic programming. As such, our policies enable adaptive, uncertainty-aware planning that can support resilient and sustainable supply chains.

cs.LG · math.OC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12180

TS-GEAR: Taobao Shangou GEnerative Ad Retrieval

Guofeng Tong, Xu Zhang, Guangliang Yu, Haiqiang Wang, Guannan Cong, Xing Guo, Yang Zhou, Jingjing Duan 等 11 位作者

原文摘要

On-demand delivery platforms such as DoorDash and Uber Eats impose a constraint absent in traditional e-commerce: merchants beyond the user's delivery range are invalid candidates rather than merely low-ranked. Generative retrieval has recently shown strong potential in large-scale retrieval systems. Adapting it to this setting, however, poses three challenges. First, existing semantic IDs encode geography only as a soft signal, so reachability can only rely on a non-differentiable post-generation hard truncation. Second, user preferences vary substantially across both regions and queries, so aggregating historical behaviors without conditioning on geography and the current query yields biased interest estimates. Third, the retrieved candidate set is evaluated by its collective value, yet autoregressive training scores each sequence independently, with no set-level signal to discourage redundant near-duplicates. To address the challenges, we propose TS-GEAR, a unified generative retrieval framework for on-demand delivery: (1) Geo-Semantic ID splits merchant identifiers into geographic prefixes and semantic suffixes, making reachability a prediction target rather than a post-hoc filter; (2) Query-Geo Target Attention aggregates historical behaviors using the real-time query and location as dual targets, while encoding each behavior's own location into its attention key; (3) GMPO (Group Marginal Policy Optimization) rewrites advantages using a deduplicated group reward and marginal advantage within GRPO, avoiding auxiliary diversity objectives. Deployed online with a lightweight 0.1B backbone, TS-GEAR serves over 150 million queries per day in the search advertising pipeline of Taobao Shangou, consistently outperforming state-of-the-art generative retrieval methods on both HitRate and geographic validity, and achieving 3.8% gains in ad impressions and 4.2% in ad revenue.

cs.IR · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.04571

Ground-Wing: A Parametric 3D Wing-in-Ground-Effect Dataset for Benchmarking Neural Surrogate Models

Andrea Cenzato, Lukas Karch, Andre Luckow, Yoran Pichon, Francesco Pasqua, Abouzar Ghasemi, Rishikesh Ranade, Ira Shokar 等 9 位作者

原文摘要

This paper presents a CFD dataset of a highly parametrized 3D wing operating in ground effect, designed for benchmarking neural surrogate models for aerodynamic prediction, using a wing shape and flow conditions typically encountered in motorsport applications. The dataset comprises 770 RANS simulations of 194 unique wing configurations, sampled across 27 airfoil profile variants and 4 distances from the ground, spanning variations in wing sweep, chord, taper, angle of attack, and tip twist. Six neural network architectures representing distinct paradigms --- convolutional (DoMINO), graph-based (X-MeshGraphNet), transformer-based (AB-UPT, GeoTransolver, GAOT), and JEPA-based (AeroJEPA) --- are evaluated on their ability to predict surface pressure coefficient and wall shear stress distributions, from which integral drag and lift coefficients are recovered. The dataset is intended to be used as a challenging benchmark for surrogate CFD models, due to the uniqueness of the ground effect being absent from other wing and airfoil datasets. The dataset is available for download at https://huggingface.co/datasets/andreacenzato/Ground-Wing

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12177

Embodied First Exam

Kangrui Wang, Qineng Wang, Changhe Chen, Pingyue Zhang, Chi Gui, Feishi Wang, Canyu Chen, Wenli Xiao 等 30 位作者

原文摘要

Early successes on agentic robotics point to a potential paradigm shift toward using coding agents to control robots. Existing robotics benchmarks are built around visuomotor policy learning, leaving it open whether coding agents can reliably control diverse robots under physical-world constraints. We therefore introduce the Embodied First Exam, a unified, comprehensive, and agent-native evaluation framework where coding agents can choose their own control approach: direct control, code to control, or train a policy to control. We hope it serves as a first exam for coding agents before they enter the physical world, with 1,156 tasks in 35 suites on 47 robots, including 9 new suites that go beyond pick-and-place to spatial puzzles, dexterous manipulation, multi-robot interaction, dynamic whole-body control, and sim-to-sim engineering. Each task can be run in up to three evaluation settings. The Standard Setting mimics the real world: the agent receives only sensor observations and, by default, cannot reset the environment. Its Realtime variant keeps the environment running while the agent reasons. The Privileged Setting gives the agent access to the full simulator state and unlimited resets, allowing it to search for an oracle solution for training purposes. Success rates in this setting serve as an approximate upper bound. On the 456 core tasks, the strongest of eight agents, GPT-6 Astra in Codex, achieves a success rate of 34% in the Standard Setting, leaving much room for improvement. To see where agents fail, we label the stage at which failed attempts go wrong and compare failures across agents, settings, and stage categories.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2508.14948

LFM4Ads: Large Foundation Model for Ads Recommendation

Jie Jiang, Shangyu Zhang, Shijie Quan, Zhongren Wang, Tianqu Zhuang, Junwei Pan, Bo Fu, Yilong Sun 等 35 位作者

原文摘要

Recent work leverages large-scale pretrained foundation models (FMs) to transfer generalizable representations to downstream advertising tasks. Most existing approaches transfer user representations (URs) and item representations (IRs), while underutilizing the user-item cross representations (CRs) generated by deeper interaction layers. Although a recent work transfers target-aware embeddings (TAEs), it requires real-time upstream FM inference alongside the downstream, introducing additional engineering complexity and overhead. To address these limitations, we propose Large Foundation Model for Ads Recommendation (LFM4Ads), an efficient pretrain-then-transfer paradigm for cross-domain ad recommendation. LFM4Ads jointly exploits URs, IRs, and CRs from the upstream FM. It extracts sample-level CRs from pretrained interaction layers and aggregates them into user-level CRs using a time-aware exponential moving average, thereby decoupling upstream inference from downstream online serving. To use CRs efficiently, we propose three downstream usages at different levels of granularity: feature-level gating to fuse CRs into downstream embeddings, module-level fine-tuning to yield downstream CRs, and model-level retrieval by matching CRs with IRs. LFM4Ads has been deployed on Tencent's advertising platform since Q4 2024. It has supported 10+ launches across major scenarios, yielding a platform-wide gross merchandise value (GMV) lift of 2.45% and annual revenue gains in the hundreds of millions of dollars. Offline experiments on KuaiRand-1K further demonstrate our effectiveness. Our code is available at https://github.com/ZhuangQu/LFM4Ads.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.16507

On objective mismatch in molecular retrieval from tandem mass spectrometry

Gaetan De Waele, Marek Wydmuch, Krzysztof Dembczyński, Wojciech Kotłowski, Willem Waegeman

原文摘要

Identifying molecular structures from LC-MS/MS spectra is a central problem in computational metabolomics, often approached by predicting molecular fingerprints and retrieving candidates via similarity search. Despite widespread use, the relationship between training objectives for fingerprint prediction and downstream retrieval performance remains poorly understood. In this work, we investigate the alignment between these objectives and show that they can be misaligned under uncertainty. Adopting a decision-theoretic perspective, we derive novel regret bounds that characterize when Bayes-optimal predictors for fingerprint similarity diverge from those for molecular retrieval. Our analysis reveals that optimizing standard similarity-based losses can degrade retrieval performance, and that the extent of this mismatch depends on the similarity structure of candidate sets. Empirically, we validate our theory on the MassSpecGym and Enveda-180 datasets, observing a Pareto frontier between fingerprint accuracy and retrieval metrics across commonly used loss functions. These findings highlight potential trade-offs in fingerprint-based molecular identification and provide principled guidance for the design of learning objectives in small molecule mass spectrometry.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.09114

MAAM: Anchor-Preserving Compression and Contextual Calibration for Chinese Discriminatory Language Detection

Yuxin Fu, Shijing Si

原文摘要

Chinese discriminatory-language detection is challenging because harmful intent is often implicit and context-dependent. We propose MAAM (Myopia--Astigmatism Anchor Mechanism), a lightweight, model-agnostic framework inspired by functional visual blur: rather than preserving every token equally, MAAM retains discrimination-relevant semantic anchors and calibrates them with C--I--S contextual priors (Contextual Tone, Group Identity, and Stance Polarity). We also introduce ChLGBT, to our knowledge the first Chinese LGBT-focused discriminatory-language dataset, with 8,120 manually annotated samples and three ordinal labels: explicit bias, implicit bias, and emotional intensity. Across strong encoder baselines, MAAM improves all three prediction dimensions, with consistent gains in accuracy, F1, Brier score, and expected calibration error. Compared with frontier LLM baselines under zero-shot and few-shot prompting protocols, MAAM remains competitive while offering stronger compactness and stability. These results suggest that interpretable anchor preservation and contextual calibration provide a practical alternative to heavier model scaling for Chinese discriminatory-language assessment.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.22595

OrigaMI: An LLM Serving System with First-Class Interpretability Apps

Michael Blum, Mark Silberstein, Yaniv David

原文摘要

Mechanistic interpretability (MI) enables monitoring and steering LLMs by operating on their internal state during inference. MI applications remain difficult to develop, expensive to execute, and challenging to manage at runtime. We present OrigaMI, a system that integrates MI applications as first-class components of LLM serving, enabling simplified development, efficient execution, and flexible deployment. Guided by a comprehensive analysis of modern MI applications, OrigaMI streamlines the MI applications development via a simple Python-based Domain Specific Language (DSL) for accessing intra- and inter-layer runtime model state at well-defined trigger points during inference. OrigaMI enables efficient access to ephemeral attention scores via a novel FlashInspect kernel that retains the performance benefits of FlashAttention. OrigaMI's compiler integrates multiple MI applications into the performance-optimized inference execution flow while ensuring conflict-free coexistence. OrigaMI's runtime supports dynamic, per-request activation and deactivation of MI applications without service interruption or performance degradation when inactive, leveraging OrigaMI's dynamic memory management and CUDA graph-compatible execution. We integrate OrigaMI with vLLM and evaluate ten MI applications, including sparse autoencoders, steering, and jailbreak detection, across four state-of-the-art LLMs. OrigaMI incurs negligible execution overhead, up to two orders of magnitude lower than existing frameworks, while enabling per-request activation and deactivation up to 1,000 times faster than alternatives. These results demonstrate that OrigaMI makes MI applications practical for deployment in production LLM serving systems.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12144

DisasterVL: Evidence-Based Visual Reasoning Dataset and Model for Disaster Scene Understanding

Ali Diba, Aisha Al-Mohannadi, Yin Yang, Muhammad Imran, Ferda Ofli

原文摘要

We introduce DisasterVL, a dataset and benchmark for evidence-grounded reasoning about disaster imagery. It contains approximately 1.2 million images from 142 countries, paired with 25 million structured annotations covering scene captions, incident and location assessments, spatial relationships, and visual question answering (VQA). The VQA annotations link answers to visible evidence and include questions about both present and absent cues, multiple answer formats, and difficulty labels. Region-level annotations link caption phrases to bounding boxes and spatial relationships to the regions they connect. The test set contains 38,254 images spanning 75 countries and 28 hazard types. Gemini 3.1 Pro audited the test annotations, and human reviewers validated a stratified subset. On this benchmark, the 9B DisasterVLM model outperforms the evaluated general-purpose baselines on captioning, open-ended VQA, incident reasoning, and region-level grounding. Gains in text overlap are larger than gains in evidence scores. We plan to publicly release the dataset, trained models, and code.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.22756

Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees

Christian Janos Lebeda, David Erb, Tudor Cebere, Aurélien Bellet

原文摘要

Random forests are widely used in fields involving sensitive tabular data, but existing approaches to enforcing differential privacy (DP) typically degrade performance to the point of impracticality. In this paper, we introduce Lumberjack, a differentially private random forest algorithm that achieves substantially higher utility by constructing large random decision trees and then applying aggressive, privacy-preserving pruning to retain only sufficiently populated nodes. A key component of our approach is a novel $(\varepsilon,δ)$-DP heavy hitter detection algorithm for hierarchical data, whose error is $O_{\varepsilon,δ}(\sqrt{\log h})$ for trees of height $h$ and may be of independent interest. This favorable scaling enables the use of significantly deeper trees than in prior work, leading to improved expressiveness under privacy constraints. Our empirical evaluation on benchmark datasets shows that Lumberjack consistently outperforms prior DP random forest methods, establishing a new state of the art. In particular, our approach yields substantial improvements in the privacy-utility trade-off for practical privacy budgets. Our findings suggest that carefully designed DP random forests can close much of the utility gap, highlighting a promising and underexplored direction for future research.

cs.LG · cs.DS

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2611.04804

GSPIM: A Temporal-Locality-Aware 4D Gaussian Splatting Accelerator with Polymorphic PIM and Multi-Frame Rendering Dataflow

Zirui Ma, Zhihua Fan, Haibin Wu, Dongrui Fan, Xiaochun Ye, Wenming Li

原文摘要

4D Gaussian Splatting (4DGS) enables high-quality real-time novel-view synthesis for dynamic scenes, supporting free-viewpoint video, mobile XR, and autonomous systems at the edge. Methods using explicit 4D distributions, keyframe interpolation, or anchor binding share two GPU data-movement bottlenecks. Temporal filtering follows model record loading, wasting LPDDR5 bandwidth on inactive data. Frame-isolated execution also overlooks highly overlapping active sets and depth relationships across adjacent frames, reloading shared attributes when working sets exceed on-chip cache capacity. To address these bottlenecks, we present GSPIM, a temporal-locality-aware 4DGS accelerator for PIM-enabled edge devices. Polymorphic PIM adapts a shared near-memory datapath to representation-specific field layouts and selection procedures. It filters and compacts valid data into contiguous inputs before the GPU accesses complete attributes, reducing ineffective transfers. Multi-Frame Rendering Dataflow reuses active attributes and stable depth relationships within a temporal window, reducing repeated work across frames. Hybrid Dataflow Scheduling overlaps near-memory data preparation with GPU rendering to reduce waiting. Evaluation combines a GPU-PIM model calibrated to Jetson AGX Orin 64 GB with a 1z DRAM physical implementation of the added circuits. Across three 4DGS methods and six dynamic scenes, GSPIM achieves a 4.15x geometric-mean speedup and a 4.99x geometric-mean energy-efficiency improvement over a fully optimized frame-by-frame GPU baseline. The maximum scene-level PSNR difference is 0.011 dB, and the added physical footprint is 18.7172 square millimeters across 32 LPDDR5 dies.

cs.AR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.31153

BIAS-ID: A Framework for Analyzing Transformation Biases in AI-Generated Image Detectors

Jonas Ricker, Asja Fischer, Erwin Quiring

原文摘要

Given the surge of harmful AI-generated imagery online, reliably distinguishing authentic images from generated ones has become an urgent research topic. While many proposed detection methods demonstrate high accuracy in controlled settings, their performance often degrades substantially when tested on real-world data. A potential root cause is subtle bias in the detectors' training data. As a result, detectors may rely on spurious correlations instead of learning true forensic artifacts. While recent studies have identified this problem and developed countermeasures, there is not yet an established protocol to evaluate how biased a detector actually is. In this work, we therefore take a step back: First, we discuss what it means for a detector to be biased, and how this differs from a lack of robustness. Second, we propose BIAS-ID, a transparent framework for analyzing and quantifying the presence of transformation biases in AI-generated image detectors. We validate our framework by evaluating six detectors across two datasets, revealing that several state-of-the-art detection methods are strongly affected by common, processing-related biases. Our results highlight the importance of bias-aware evaluation for developing reliable AI-generated image detectors. We provide an easy-to-use library and code to reproduce our results at https://github.com/jonasricker/biasid.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12119

Discriminator-free adversarial domain adaptation for robust speech classification

Yacouba Kaloga, Ina Kodrasi

原文摘要

The performance of speech classification systems such as speech emotion recognition or pathological speech detection can degrade substantially when there is a domain shift between training and test data. This shift can arise due to e.g., differences in recording setup, acoustic environment, speaker, language, or speaking style. Adversarial domain adaptation addresses this problem by encouraging representations that are less sensitive to domain-specific differences. However, many adversarial approaches rely on training a separate domain discriminator, which can be unstable and difficult to train. In this paper, we propose to use a discriminator-free adversarial learning network (DALN) for unsupervised domain adaptation in speech classification tasks. DALN reuses the task classifier as an implicit discriminator and aligns domains through the nuclear-norm Wasserstein discrepancy on classifier outputs. We extensively evaluate the performance of DALN on intra-lingual, cross-lingual, and noisy adaptations for speech emotion recognition and pathological speech detection. Extensive experimental results show that DALN generally achieves the best performance across the considered tasks and domain shifts, outperforming discriminator-based domain adaptation approaches.

eess.AS · cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12116

Forecast Value Add: Reward-Based Action Selection over Probabilistic Demand Forecasts

Ankur Verma

原文摘要

Strong machine-learning ensembles capture much of the trend, seasonality, and known events in demand, yet they can still understate peaks, miss moving events, or mistake temporary changes for shifts in demand level. We address this gap by framing forecast refinement as a reward-driven decision problem. For each forecast period, we let an agent choose among values already produced by the ensemble: its base forecast, distribution-free bounds, quantiles, and a bias-corrected estimate. We evaluate these choices against context-matched historical periods, accounting for calendar position, recurring and moving events, trend, price, and promotions. We then apply a structural seasonal guard to constrain adjustments and record the model, agent, and planner contributions to every published value. We evaluate our approach across five retail, consumer-goods, and direct-to-consumer deployments over nine months. Our refined forecasts reduce mean WMAPE by 6.3% compared with the stacked machine-learning forecast for every customer, with gains reaching 16.7%, and achieve the lowest error in 35 of 45 customer-months. Compared with statistical forecasts, our approach reduces mean error by 18.7%. These results demonstrate that context-aware, auditable action selection can add forecast value beyond a strong ensemble.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12112

LiveWorkspace: Externalizing Visual Understanding as Persistent Shared State for Screenshot-to-Code Agents

Shijun Xiao, Zonghui Yang, Xinrong Chen, Hao Liu, Wensheng Zhang, Yongqiang Tang

原文摘要

Screenshot-to-Code requires coding agents to sustain visual understanding across code generation, execution, and revision. We introduce LiveWorkspace, a visual coding harness that externalizes this understanding as Shared Visual State (Shared VS), an implementation-independent representation of visual hierarchy, node attributes, and layout relations. LiveWorkspace constructs and retains \textit{Target} from screenshots and recovers \textit{Current} from browser execution in the same schema. Structured Diff and implementation bindings guide code modifications; user edits update \textit{Target} and reuse the alignment loop. Using GPT-5.2 for both methods, LiveWorkspace improves end-to-end Avg by 1.8 points on Design2Code and 2.0 on ScreenBench over a control with matched model-call counts and stagewise budget limits. Further experiments demonstrate reuse of constructed Shared VS across coders and improvements in iterative repair. LiveWorkspace also achieves 93.0\% edit success across 100 controlled editing cases. These findings support treating visual understanding as a persistent shared working state throughout code generation, execution, and modification.

cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12109

SkillDuet: Co-Evolving Models and Skills through Response Feedback

Xingguang Ji, Ziqiang Dong, Yubo Zhang, Xi Leng, Yang Yang, Guanjun Jiang

原文摘要

During reinforcement learning, experience can be both internalized in LLM parameters and retained externally as reusable textual skills. Parameter updates alter the model's behavior and the interaction experience from which new skills are extracted. These skills, in turn, guide the model's subsequent interactions and parameter updates. As the model's capabilities evolve, previously beneficial skills may become redundant or detrimental. We present SkillDuet, a framework that coordinates parameter updates and skill evolution through model response feedback. Skills extracted from on-policy trajectories guide parameter updates through OPD at every step. A response-based score, Strength, ranks candidate skills by the magnitude of the shift they induce in the current model's responses, and the highest-scoring skills are periodically consolidated into a persistent global skill set (Global). The updated model and Global jointly guide subsequent interactions, generating experience for further parameter and skill updates. With Qwen3-4B, SkillDuet achieves success rates of 98.57% on ALFWorld and 92.97% on WebShop, exceeding the matched SEED-API baseline by 2.86 and 5.47 percentage points, respectively. Ablations show that response-based skill selection and Global consolidation improve task performance. Further experiments show that the benefits of retained skills depend on both their content and their compatibility with the model. SkillDuet provides a concrete mechanism for model-skill co-evolution and skill-level recursive self-improvement.

cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.38983

Beyond atol and rtol: Sound Test Oracles for Low-Precision GPU Kernels

Xinyi Shi

原文摘要

Low-precision GPU kernels are commonly tested against a reference using hand-picked absolute and relative tolerances. Legitimate numerical error varies with the input, format and precision plan, so a fixed tolerance trades false alarms for missed faults. We present numoracle, which derives per-element acceptance regions from precision-annotated reference programs. Its calculus tracks shared rounding errors, provenance-ordered products, block-scaled quantizers and calibrated math-library errors. A rounding-interval check detects faulty stores that symmetric bounds can admit. Soundness is conditional on explicit rounding, hardware, domain and implementation assumptions; a fixed-input mode removes the zero-mean rounding assumption. We evaluate 13 Triton operators from Liger-Kernel, Unsloth and vLLM on two GPU generations. The main campaigns produce no false alarm on 33,960 correct-kernel and behavior-preserving-variant runs per GPU. Under input shift, the GPU campaigns record no false alarm while tuned tolerances flag up to 30% of correct runs; final-version reference checks identify inputs without complete finite-range certificates. On the RTX PRO 6000, numoracle kills 116 of 124 effective mutants, versus 59-83 for four fixed baselines. Among 400 tuned tolerance configurations, requiring at most 1% false alarms in every evaluated Liger input group permits at most 31 of 53 main-mutant kills, versus numoracle's 50. Historical replays expose accuracy faults in eight change pairs without rejecting their matched corrected controls; a ninth change concerns rounding parity.

cs.MS · cs.PL · cs.SE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2506.16129

Neurosymbolic Object-Centric Learning with Distant Supervision

Stefano Colamonaco, David Debot, Giuseppe Marra

原文摘要

Neurosymbolic learning can use symbolic rules to provide supervision for latent concepts from weak labels, but it commonly assumes that the entities referenced by these rules are already specified. Object-centric models decompose images into slot-like representations; however, such slots are not necessarily aligned with the predicates required for symbolic reasoning. We investigate object-centric neurosymbolic learning under distant supervision, where the object-level arguments of a logic program are learned directly from images using only global task labels. We introduce DeepPbjectLog, a probabilistic neurosymbolic model that integrates a slot-based perceptual encoder with a probabilistic logic layer. The encoder predicts objectness and class probabilities for candidate object representations, while the logic layer marginalizes over latent objectness and class assignments to compute the likelihood of the observed label. This formulation provides a differentiable task-level learning signal for object-centric perception without requiring per-object labels, masks, bounding boxes, or heuristic set matching. Evaluations across diverse visual reasoning tasks demonstrate that DeepObjectLog achieves superior out-of-distribution generalization to compositional, object-count, and rule shifts compared to neural object-centric and standard neurosymbolic baselines.

cs.CV

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arXiv ↗PDF ↗