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新论文 · v1arXiv:2611.03289

Top-$k$ Retrieval Leaves Memory Value Undetermined

Arman Behnam

原文摘要

A memory-augmented agent must decide which memories to keep, merge and retrieve, and its only evidence of their value is its retrieval log: the memories it retrieved in each episode and whether it succeeded. Existing methods assign every memory a value from this log, even when the log cannot separate one memory's contribution from another's. We treat the log as an experiment that nobody designed and ask which memory values it determines. Two counts answer this before any outcome is observed: how often each memory was retrieved, and how often each pair was retrieved together. From these counts, we prove that top-$k$ retrieval leaves every memory value undetermined, however many episodes are logged, and that a question with its own memories, asked once, determines at most one of their values. On fourteen standard memory benchmarks, no memory value is determined in any of 55 configurations. Both causes are choices about retrieval. We introduce LEDGER, which estimates each determined Shapley value in closed form with a confidence interval and reports every other value as undetermined. It merges memories with identical histories without losing a determined value, and it retrieves so that the next log determines more. On pooled HotpotQA, LEDGER raises the fraction of memories determined from 0.001 to 0.797, at a cost of 2.6 accuracy points on HotpotQA and with a gain of 2.7 on LoCoMo.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03283

ReVA-HPE: Relative 6DoF Head Pose Estimation with Synthetic Pose Supervision and Personalized Virtual Anchors

Vasiliki Vasileiou, Panagiotis P. Filntisis, Petros Maragos, Kostas Daniilidis

原文摘要

Monocular head pose estimation is usually formulated as absolute regression from a single face crop, yet many applications observe the same person over time and can supply an explicit anchor image. Recently, relative head pose estimation was proposed, which casts the problem as estimation of the relative geometric change between an anchor and a query image. However, existing methods still require the anchor image pose to be known at inference, significantly limiting their practical use. We first extend relative head pose estimation to full 6DoF by additionally predicting a depth-normalized relative translation, which composes with the anchor to recover metric query pose in the camera frame. To eliminate the need for a ground-truth anchor pose, we reconstruct a personalized avatar from a single unlabeled enrollment image and render a bank of virtual anchors with known poses. The bank provides robust rotation hypotheses through multi-anchor consensus, while the real enrollment image serves as the real anchor in the translation branch via landmark PnP. We also introduce an efficient variant that amortizes the bank cost once at enrollment, after which each query requires a single real-to-real relative prediction, with limited accuracy sacrifice. The relative pose estimator is trained exclusively on synthetic face renderings, without real pose supervision, pseudo-labels, domain adaptation, or fine-tuning on the evaluation sets. On real 6DoF benchmarks, ReVA-HPE achieves state-of-the-art rotation accuracy on BIWI and, given a near-frontal enrollment image, on FaMoS, while remaining competitive in metric translation despite relying solely on synthetic pose supervision. Project page and code: https://vasilikivas.github.io/ReVA-HPE/

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.25986

The Inference-Compute Frontier for Limit Order Book Prediction

C. Evans Hedges

原文摘要

We identify an empirical inference-compute frontier for limit order book prediction on FI-2010. A power law fitted to low- and mid-compute models extrapolates to an excluded MLPLOB-style mixer family whose work is $56$--$2{,}515$ times the largest budget in the corresponding fit. Across nine folds and five horizons, pooled $R^2$ is $0.934$ on the realized target frontier, with 59\% lower RMSE than constant endpoint extrapolation, and $0.731$ on all excluded operating points. Measured latency gives a weaker relationship and reorders model families. We therefore design FastBiNLOB to pack computation into dense temporal and feature mixing. H120 exceeds the published state-of-the-art macro-F1 on two of four horizons in every one of five seeds. Under our CPU/MPS protocol, H96 reduces median latency by 23.6\% relative to the W128 MLPLOB reference, and H120 by 60.2\% relative to a TLOB-inspired dual-axis attention reference.

cs.LG · q-fin.ST · q-fin.TR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03280

Specificity Bias and the Wikipedia Paradox: An xAI Analysis of AI Text Detectors under Domain Shift

Nico Morloher, Johannes Reschke, Mohamed Mady

原文摘要

The widespread adoption of Large Language Models (LLMs) demands reliable detection of human-authored versus synthetic text. However, current Transformer-based detectors often experience severe performance degradation during domain shifts and operate as opaque classifiers. This paper investigates the robustness and interpretability of fine-tuned Transformer models (DistilBERT, RoBERTa) by evaluating them against a custom out-of-distribution (OOD) dataset of hyper-local news synthesized via open-source LLMs (Llama 3, Mistral, Gemma). To analyze model decision boundaries, we apply a tripartite Explainable AI (xAI) framework using SHAP. Our analysis reveals three key phenomena: First, detectors often rely on a "Call-to-Action" syndrome inherent to instruction-tuned LLMs rather than generic machine text. Second, we observe a "Specificity Bias," where LLMs successfully bypass detection by hallucinating specific named entities and mimicking conversational imperfection. Finally, we illustrate the "Wikipedia Paradox," wherein formal, objective human writing is frequently misclassified as AI due to its low statistical burstiness. These findings demonstrate that purely statistical detection paradigms face systematic limitations in highly structured human domains, emphasizing the critical need for semantically-aware, interpretable detection architectures.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.05109

RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities

Vasiliki Rizou, Pascal Frossard, Dorina Thanou

原文摘要

To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interactions without sacrificing modality-specific information. Learning disentangled representations is a principled way to identify the underlying shared and unique factors that are hidden in observational data. However, while multimodal disentanglement is a compelling paradigm, existing methods are largely confined to the two-modality regime due to its inherent scalability bottleneck. To address this, we propose RePercENT, a self-supervised framework designed to surpass these limitations and unlocks scalable pairwise disentanglement beyond two modalities. Through a multimodal `plug-and-play' architecture, our approach operates directly on pre-extracted embeddings, eliminating the need for extensive joint pre-training while making no assumptions regarding the underlying modalities or foundation model backbones. Moreover, we introduce a joint optimization objective for simultaneously deriving the shared and unique components, and provide formal theoretical guarantees that characterize the optimality of our solution. Spanning modalities from images and videos to clinical reports and omics, and tasks from sentiment analysis to tumor classification, RePercENT successfully recovers disentangled components while maintaining competitive performance and significantly reducing computational complexity.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03274

Strictly proper scoring rules for node selection tasks on graphs

Lorenz Hilfiker, Nathan Cincera Brack, Martin Sterchi

原文摘要

In node selection tasks, the goal is to identify a single unknown target node in a graph. We consider probabilistic supervised node selection, where the estimator outputs a probability distribution over the nodes of the graph and the ground-truth node is available for evaluation. Classical evaluation metrics for such tasks are either statistically ill-conceived or blind to the graph structure. To fill this gap, we introduce two strictly proper scoring rules that are explicitly graph-aware: the Resistance score, which is parameter-free, and the Diffusion score, which operates at an adjustable coarse-graining resolution. We show that the Diffusion score interpolates between the multi-category Brier score and a cluster-level weighted Brier score. The Resistance score generalises two classical scoring rules: the multi-category Brier score and the Ranked probability score. Applied to rooted trees, it also serves as the strictly proper version of the improper Hierarchical error distance used in hierarchical classification. In the same setting, we furthermore establish strict propriety of the Hierarchical cross-entropy family, a recently introduced separate class of hierarchy-aware scoring rules. Experiments on epidemic source detection and on ImageNet classification confirm that the proposed scores detect meaningful differences in estimation quality that classical metrics miss.

cs.LG · cs.SI · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03272

Diverse-Teacher On-Policy Distillation for Generative Recommendation

Zhipeng Yi, Xinyu Lin, Minxuan Shan, Zipeng Chen, Yujiao Zhao, Chuanyu Xu, Wenjie Wang

原文摘要

Generative recommendation generates the semantic ID (SID) of the next item from a user's interaction history. Existing training objectives mainly supervise target SID tokens, while on-policy distillation (OPD) provides denser supervision through a teacher's probability distribution over all tokens for more effective and efficient training. However, OPD can aggravate popularity bias: teacher feedback favors popular-item tokens, transferring this preference to the student, while student rollout favors popular items, leaving unpopular-item SID paths rarely generated and supervised. These two sources of bias reinforce each other and reduce recommendation diversity. We propose HERO, a diversity-aware OPD framework. Heterogeneous teacher distillation trains two teachers specializing in popular and unpopular items and routes each training sample to the teacher matching the popularity group of its ground-truth item. The student learns from the routed teacher's probability distribution along its own generated SIDs. Diversity-aware item rollout incorporates ground-truth SIDs of unpopular items into training, covering SID paths that the student rarely generates. Only the student is used for inference. Offline experiments on an industrial dataset and three public datasets further demonstrate its effectiveness, diversity, and robustness. Moreover, we deploy HERO in the "Guess What You Like" module on the 1688 homepage, where a 15-day online A/B test shows a 3.412% increase in gross merchandise value (GMV).

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03271

A Human-Verified AI Workflow for Annotating Environmental Sound Data

Michael Barasa Murunga, Allan Vikiru

原文摘要

Large-scale annotation of environmental sound recordings presents significant challenges under time and resource constraints, particularly when sound categories are imbalanced and only part of a dataset has been labeled. This paper describes the development and evaluation of a human-AI workflow used during annotation of the Kenya Forest Sounds Dataset (KFSD), using a project-stage working collection of 7,531 audio recordings across 21 categories. We investigated zero-shot classification, a feature-based ensemble, and a fine-tuned Audio Spectrogram Transformer (AST). The fine-tuned AST achieved 89.36% accuracy, 85.98% precision, 89.24% recall, and 87.58% F1 on held-out test data for binary classification of Person talking. Because this result remained below the predefined 95% threshold for autonomous annotation, model predictions were used as suggestions and subjected to human verification. Of the 5,026 initially unlabeled recordings, 2,337 were routed to a speech-verification queue and 2,689 remained for ordinary manual annotation. The verifier reviewed all 2,337 machine-labeled files and detected zero false positives, yielding 100% verification accuracy. The observed average verification time was approximately 4.2 seconds per file. At an average rate of 35 seconds per file for manual annotation, the hybrid workflow reduced work on the remaining queue from approximately 48.9 to 28.9 person-hours, saving about 20.0 hours (41%). The study documents the model-selection process, provenance-aware file organization, verification procedure, observed errors, practical limitations, and opportunities for extending AI assistance beyond one category.

cs.LG · cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03268

VerifLM: Physically Verified Image-Edit-Guided Goal Pose Generation for Robotic Manipulation

Xuanyou Chen, Jinyu Chen, Glen Chou

原文摘要

Many manipulation goals are easier to visualize than to specify numerically through explicit geometric constraints. We present VerifLM, a framework that converts image-edited goal configurations into physically verified 6-DoF object poses for planning. Using an edited image, VerifLM recovers an initial relative pose between an active object and a passive object, then prompts a vision-language model to generate axis-wise sampling constraints and a task-specific validation program. Candidate poses are evaluated in a physics simulator, and the VLM selects the most task-consistent retained candidate. To operate without predefined object meshes or physical parameters, VerifLM reconstructs metric object geometry from RGB-D observations and constructs multiple hypothetical simulations with varying physics and geometry. Parallel-world validation evaluates candidates across geometry--physics variations and ranks them by the number of worlds in which they satisfy the validation program. We evaluate VerifLM on nine manipulation tasks. VerifLM achieves the highest task success on eight of nine tasks, with the largest performance gain on goals requiring precise orientation and contact relationships.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03264

DeepWaveRL: Self-Supervised Full Waveform Inversion via Reinforcement Learning

Peng Jin, Yinan Feng, Yinpeng Chen, Youzuo Lin, Sharon Huang

原文摘要

Full Waveform Inversion (FWI) is a fundamental inverse problem in geophysics to estimate subsurface properties, such as velocity, from seismic measurements. Recently, self-supervised learning methods remove the need for ground-truth velocity maps during training by leveraging a forward operator to simulate seismic data from predictions and backpropagate the gradients of reconstruction loss through it. In this work, we consider a more challenging setting without accessing such gradients. To this end, we incorporate reinforcement learning into self-supervised FWI and propose a framework named DeepWaveRL. Specifically, we reformulate FWI as a policy learning problem, where the policy network predicts velocity maps as actions, and a forward operator is used solely for simulation. A global scalar reward is computed based on data misfit for each prediction and used to weight policy gradients. Thus, learning proceeds without accessing the gradients from the forward operator. Given the indirect and coarse supervision signals, we further identify key strategies to stabilize learning and mitigate the spatial credit assignment bottleneck. Experimental results show that, despite relying entirely on a single scalar reward, DeepWaveRL achieves competitive performance compared to supervised and self-supervised baselines. These findings suggest that our framework offers a flexible and complementary learning paradigm for the FWI research community.

cs.LG · physics.geo-ph

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.41275

TRACE: Target-State Risk-Aligned Conditional Execution for Embodied Tracking

Mingyang Lyu, Gongce Cao, Yiyang Jia, Lu Jia, Erliang Lin, Yuhao Zhang, Yinqian Sun, Pin Lyu 等 10 位作者

原文摘要

Embodied visual tracking requires a robot to follow a language-specified person under occlusion, look-alike distractors, and ambiguous instructions. We show that improving tracking persistence can simultaneously increase safety violations. Using a compact 0.6B vision-language-action (VLA) tracker with a frozen backbone, we update only the planner head with proximal policy optimization (PPO). PPO improves success and following rates across all three EVT-Bench settings, but increases the collision rate on distracted tracking because episodes that previously ended in target loss can instead end in contact. We introduce TRACE, an execution-time target-state risk gate that preserves the learned policy and intervenes selectively. A lightweight risk head processes 84 cues, including candidate boxes, identity margins, a learned range-and-bearing probe, and the policy's commands. When the calibrated risk exceeds a threshold, TRACE rescales the translational component of the next waypoint while leaving yaw unchanged. The executed action is identical to PPO's on more than 98% of steps. In paired evaluations on identical episodes, TRACE reduces the distracted-tracking collision rate by 3.96 percentage points, while further improving success and following rates. Matched slowdown controls without target-state timing do not reproduce this improvement. Concurrent evaluation reproduces a collision reduction of 3.77 points, compared with 0.29 points for identical-arm null pairs, indicating that the effect exceeds evaluation variability. These results show that target-state-aware execution can recover the safety cost of policy post-training with minimal intervention.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.41273

SPT-Flow: One Integration, Many Futures in Continuous-Time World Models

Mingyang Lyu, Yinqian Sun, Gongce Cao, Yuhao Zhang, Lu Jia, Qian Zhang, Bo Wang, Pin Lyu

原文摘要

Continuous-time world models generate a latent trajectory through a single ODE integration, but adding stochastic conditioning does not ensure that the model uses it. In our teacher-forced baseline, posterior means vary by only about 1% of the prior scale, and matched and swapped latents yield indistinguishable reconstruction PSNR at the reported precision. We study a supervision-shortcut hypothesis: ground-truth intermediate states can reduce the need for trajectory-associated latent information. We introduce SPT-Flow, which samples one latent per trajectory and matches a free-running ODE rollout to demonstration states at multiple readout times. Re-anchoring experiments support the proposed mechanism. On LIBERO, with both models trained for 30k optimizer steps, SPT-Flow improves best-of-10 PSNR over our retrained ODEWorld baseline by 2.0 and 2.4 dB at the mid-trajectory and final frames and reduces mean LPIPS by 23%, with comparable per-sample inference cost. The rollout control recovers most of the accuracy gain; learned latent conditioning adds diversity and best-of-K gains while reducing mean sample accuracy relative to this control. The diagnostics support trajectory-associated latent utilization; semantic mode coverage under matched conditions in the robotic setting remains unverified.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.06014

Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Proven

Ran Ben-Basat, William Kuszmaul, Michael Mitzenmacher, Amit Portnoy, Shay Vargaftik

原文摘要

Uniform random rotations (URRs) are a common preprocessing step in modern quantization approaches used for gradient compression, inference acceleration, KV-cache compression, model weight quantization, and approximate nearest-neighbor search in vector databases. In practice, URRs are often replaced by randomized Hadamard transforms (RHTs), which preserve orthogonality while admitting fast implementations. The remaining issue is the performance for worst-case inputs. With a URR, each coordinate is distributed as a shifted beta distribution, which converges to a Gaussian distribution in high dimensions. Generally, one RHT is not suitable in the worst case, as individual coordinates can be far from these distributions. We show that after composing two RHTs on any $d$-sized input vector, the marginal distribution of every fixed coordinate of the normalized rotated vector is within $O(d^{-1/2})$ of a standard Gaussian both in Kolmogorov distance and in $1$-Wasserstein distance. We then plug these bounds into the analyses of modern compression schemes, namely DRIVE and QUIC-FL, and show that two RHTs achieve performance that asymptotically matches URRs. However, while two RHTs suffice for scalar quantization, they may be insufficient for Vector Quantization (VQ), which depends on the joint distribution across fixed-size blocks of coordinates (as opposed to only marginal distribution convergence for single coordinates). We prove that a composition of three RHTs leads to decaying coordinate covariance. This ensures that any fixed, bounded, multi-dimensional VQ codebook optimized for URRs has the same expected error when using three RHTs, up to an additive term that vanishes with the dimension. Finally, because practical inputs are rarely adversarial, we propose a linear-time $O(d)$ check on the input's moments to dynamically adapt the number of RHTs used at runtime to improve performance.

cs.LG · cs.AI · cs.DS · cs.NI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03257

Rethinking Fairness Metrics for Multi-Class and Multi-Group Assessment: Not Everything is Binary

Matteo Grazioso, Fabio Naccarella, Chiara Gallese, Marco S. Nobile

原文摘要

Artificial Intelligence, and Machine Learning in general, is becoming pervasive across all domains and disciplines. By means of this data-driven approach, algorithms can learns complex task just by learning from samples. However, if the data-set is biased, they might lead to the development of unfair computer systems, that is, characterised by a prejudice towards specific individuals or groups, which might get an unmotivated output from the system. This circumstance lead to the development of multiple metrics designed to quantify the level of unfairness of an algorithm. However, a generalised assumption in the literature is that the sensitive features and the output labels are binary. When the data is not binary, it is forcefully binarized using a one-versus-the-rest simplification. In this work, we discuss the limitations of existing metrics and propose novel, mathematically sound, and generalized multi-group and multi-class formulations of the metrics. Ultimately, our results highlight the empirical assessment distortion caused by binarization strategies, which reveals the limitations of binary fairness metrics in the multi class setting.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03256

Poisson Super Learning For Survival And Competing Risks On A Discretized Time Scale

Gabriele Pittarello, Thomas Gerds, Pernille Falberg Rønn, Helene Charlotte Rytgaard

原文摘要

We introduce the Poisson Super Learner, a scalable ensemble method for survival and competing risks prediction with right-censored data. The approach exploits the correspondence between the likelihood of piecewise-constant hazard models and the Poisson likelihood. After expanding the data over a chosen time partition, candidate hazard learners are trained under a common Poisson loss, and cross-validated log-hazard predictions are combined by a Poisson meta learner. The resulting ensemble is a geometric combination of candidate hazards, and can be used to estimate survival probabilities and cause-specific absolute risks. The Poisson Super Learner framework is modular and can in principle incorporate any learner that can estimate interval-specific hazards, including parametric, penalised, additive, and other machine-learning hazard models. Training targets the cause-specific hazard directly and does not require explicit modelling of the censoring distribution. For competing risks, separate cause-specific ensembles are fitted and subsequently combined to obtain individualised cumulative incidence predictions. We illustrate the approach using data from the Steno Diabetes Center collected between 2005 and 2022 to predict cardiovascular disease risk in patients with type 1 diabetes from detailed covariate information. The applied analysis is complemented by simulation studies designed to reflect key features of the data and to examine the behaviour of the method under controlled settings. All analyses were implemented using the freely available R software package poissonsuperlearner.

stat.ML · cs.LG · stat.CO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.03203

MedCUA-Bench: A Screenshot-Only Benchmark for Clinical Computer-Use Agents

Jia Yu, Zilong Wang, Xinyang Jiang, Dongsheng Li, Shuo Wang

原文摘要

Computer-use agents could automate repetitive screen-based clinical work, but their reliability in medical graphical user interfaces remains largely unvalidated. Existing benchmarks focus on general web or desktop tasks and underrepresent medical software, which requires domain knowledge, exhibits markedly different UI design from mainstream applications, lacks public testing environments, and demands safety validation beyond task completion. We introduce MedCUA-Bench, an interactive benchmark for clinical computer-use agents. It covers 18 clinical scenarios across 10 medical domains, reconstructed from real product manuals and open-source medical systems to capture authentic clinical interfaces while avoiding licensing and privacy constraints. Each task ships with paired intent- and step-level goals to disentangle clinical reasoning from UI execution, and is evaluated by a deterministic checker over task completion and five clinical safety dimensions. Across 23 agents, the best closed-source model reaches 54.2% strict success, while all models remain below 9% on the real OpenEMR. Open-source agents average only 2.5%, with the best reaching 16.2%. MedCUA-Bench exposes the gap between current agents and reliable clinical software use, providing a reproducible testbed for future research.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03765

VagueBench: Benchmarking the Robustness of Embodied Agents on Ambient Context Illusions

Yang Qu, Qiang Fu, Zonghao Ying, Jiayi Zhao, Yaoyuan Zhang, Jinyang Guo, Jian Yang, Aishan Liu 等 10 位作者

原文摘要

Embodied agents are increasingly deployed in open-world environments, yet current robustness evaluations remain biased toward hazardous instructions and jailbreak vulnerabilities. A more fundamental issue is largely overlooked: agents are rarely tested under semantically sufficient but contextually minimal instructions (i.e., common in real-world human communication), where auxiliary contextual cues are absent. For the first, we identify a novel phenomenon termed Ambient Context Illusion, where agents fail to execute tasks correctly when non-essential contextual details are omitted, despite unchanged task semantics. To systematically study this, we establish VagueBench, a benchmark built upon the AI2-THOR interactive simulation environment for the evaluation of context dependency in embodied agents. We systematically consider key semantic components of instructions (entities, procedures, constraints, and prior knowledge) and derive a unified taxonomy with four categories and nine subtypes to reduce auxiliary information. VagueBench contains 1,303 instruction pairs across diverse indoor environments, constructed via a human-agent co-generation framework that integrates semantic redundancy estimation, taxonomy-guided transformation, and rigorous semantic-invariance verification. Through extensive evaluation on 6 state-of-the-art embodied agents, we observe significant degradation on VagueBench in both task performance and executability (relatively -19.65% in task success rate and relatively +54.10% in empty plan rate) revealing a strong reliance on auxiliary linguistic signals rather than core task semantics. Furthermore, we demonstrate that this phenomenon persists in real-world robotic systems, where it can lead to incorrect decisions and potential safety risks.

cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03254

TRAILS: Trajectory Generation for Automated Indoor Locomotion Studies

Juho Kalliokoski, Evan G. Center, Steven M. LaValle, Timo Ojala, Basak Sakcak

原文摘要

Virtual reality experiments involving navigation and simulated robot motion often require large, realistic environments and numerous distinct trajectories to avoid repetitive navigation while providing sufficient variation for controlled experiments. Designing such scenarios manually is labor-intensive, particularly when trajectories must satisfy predefined geometric and kinematic constraints while remaining diverse and comparable across experimental conditions. This paper presents TRAILS, an automated framework for generating complete virtual navigation scenarios consisting of an environment layout, a path, and a time-parameterized motion profile. The framework combines a constraint-based procedural environment generator inspired by Wave Function Collapse, trajectory generation using optimized cubic Bézier curves, and speed-profile generation subject to kinematic constraints. The generated scenarios provide diverse indoor environments with corridors, open spaces, and obstacles, together with trajectories of identical duration that vary in path geometry, wall proximity, and velocity profiles while minimizing systematic dependencies between these characteristics. The framework is designed as an easy-to-use tool requiring only a small number of intuitive user-defined parameters to automatically generate large sets of distinct navigation scenarios satisfying prescribed geometric and kinematic constraints. By relying on explicit, seed-controlled algorithmic procedures rather than data-driven models, the generated scenarios remain transparent, reproducible, and configurable, making the framework well suited for telepresence and human-subject experiments. The code is available at https://github.com/JucciK/Trails .

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.18321

CoSMO: Conformal Semantic Mapping for Open-World Safe Robot Navigation

Shuqi Wang, Yiwei Zheng, Pingrui Zhang, Yue Gao, Xiang Yin

原文摘要

Safe autonomous navigation requires not only geometric obstacle avoidance but also semantic awareness. Different object categories demand different safety clearances, and the set of relevant categories may expand as the robot explores novel environments. Vision-language models (VLMs) offer rich semantic representations for building maps of unknown scenes, yet their confidence scores are poorly calibrated, making raw predictions unreliable for safety-critical planning. Conformal prediction (CP) can provide coverage guarantees, but existing methods assume a fixed label set and stationary data, limiting their use in open-world settings. We propose Conformal Semantic Mapping for Open-World Safe Robot Navigation (CoSMO), a semantic safety mapping framework that transforms VLM embeddings into online-calibrated coverage sets and safety-aware obstacle dilations under distribution shift. By calibrating directly in the embedding space, CoSMO avoids the score rescaling that arises in confidence calibration as the vocabulary expands. Experiments on the Matterport3D (MP3D) dataset show that CoSMO reduces semantic collisions by 61.6% relative to a geometry-only baseline, while maintaining formal coverage guarantees even under delayed expert feedback. Real-world experiments on a quadruped robot further demonstrate generalization by achieving long-run empirical coverage of 90.73% against the 90% target. Project website: https://yangming911.github.io/cosmo-page/.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03250

Anomaly-ranked confirmation slows new-class learning

Argyro Sasli, Michael Coughlin

原文摘要

Anomaly detectors rank unfamiliar examples for expert confirmation, and the confirmed examples teach a classifier to recognise a new class. Across five benchmarks from physics, medicine and computer vision, the members flagged by the detector are those the classifier learns first. In three systems, confirming the highest-ranked members of a new class gives lower recognition than confirming the same number of random members. Handing subsequent selection to the promoted classifier recovers part of this loss in two systems. A paired intervention further shows that replacing nearby known classes with distant ones sharply reduces the confirmations needed for recognition. These results distinguish finding a new class from selecting examples that teach its extent: within the tested classes, concentrating confirmations at the top of the anomaly ranking makes learning less efficient.

astro-ph.IM · cs.LG · physics.data-an

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.02745

Bolek: A Multimodal Language Model for Molecular Reasoning

Frederic Grabowski, Jacek Szczerbiński, Maciej Jaśkowski, Kalina Jasińska-Kobus, Paweł Dąbrowski-Tumański, Tomasz Jetka, Bartosz Topolski

原文摘要

Molecular property models increasingly support high-stakes drug-discovery decisions, but their outputs are often difficult to audit: classical predictors return scores without rationale, while language models can produce fluent explanations weakly grounded in the input molecule. We introduce Bolek, a compact multimodal language model that grounds natural-language reasoning in molecular structure by injecting a Morgan fingerprint embedding into an instruction-tuned text decoder. Bolek is fine-tuned on molecular alignment tasks, including molecule description, RDKit descriptor prediction, and substructure detection, and on downstream reasoning over 15 TDC binary classification tasks using synthetic chains-of-thought anchored in concrete molecular features. Across these tasks, Bolek outperforms its Qwen3-4B-Instruct base on all endpoints in yes/no mode and on 13 of 15 in chain-of-thought mode, raising mean ROC/PR AUC from 0.55 to 0.76. It also outperforms TxGemma-9B-Chat on 13 of 15 binary classification tasks despite being less than half its size. Bolek's explanations are more grounded than those of the baseline LLMs: it cites numerical descriptors 10-100x more often per chain-of-thought, and the cited values agree strongly with RDKit for key descriptors such as TPSA, MolLogP, and MolWt (Spearman rho = 0.87-0.91). Generalisation extends beyond the training panel: on 15 unseen TDC classification endpoints, Bolek matches TxGemma on five, and it produces non-trivial rank correlations on three held-out regression endpoints despite never seeing downstream regression during training. These results suggest that targeted modality injection and reasoning supervision tied to verifiable molecular features can yield compact, auditable molecular reasoning models.

cs.LG · cs.AI · q-bio.BM

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新论文 · v1arXiv:2611.03247

R$^2$-Omni: Reducing Structural and Precision Redundancy in Omni-LLMs for Long-Form Generation

Bolong Feng, Pengfei Zhou, Zhenglin Wan, Wei Wang, Wangbo Zhao

原文摘要

Omni-modal large language models (Omni-LLMs) typically process large numbers of multimodal tokens from audio-visual inputs, incurring substantial computational costs and a large memory footprint for the key-value (KV) cache. Aggressive token pruning can alleviate this problem while maintaining competitive performance on multiple-choice tasks, which require only a single option as output. However, we observe substantial quality degradation when generating long-form, open-ended responses. We attribute this gap to query-guided selection, which can retain sufficient evidence for answer selection while discarding details needed for comprehensive generation. This motivates reducing overlapping information across tokens, termed structural redundancy, while preserving diverse content. However, the retained representations may still contain precision redundancy due to unnecessarily high numerical precision in their cached values. To address these two levels of redundancy in token representations, we introduce a training-free framework named R$^2$-Omni. First, Structure-Aware Token Consolidation (STC) consolidates redundant representations using spatiotemporal similarity in video and local continuity in audio. Then, Semantic-Sensitivity Guided Precision Allocation (SSPA) allocates value precision to the retained representations according to the estimated reduction in attention-output error under quantized keys, with query attention and audio-visual alignment providing a semantic prior. Extensive experiments on long-form generation benchmarks demonstrate an average 5.0$\times$ reduction in the memory footprint while preserving 97.98% of baseline generation quality and achieving a 1.48$\times$ end-to-end speedup, highlighting the benefits of jointly reducing structural and precision redundancy.

cs.CV

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版本更新 · v10arXiv:2209.06865

Sketch of a novel approach to a neural model

Gabriele Scheler

原文摘要

We present an account of neuroplasticity with respect to cell-internal processing pathways in relation to membrane and synaptic plasticity. We think traditional synapse-centric, weight-based models of memorization are not sufficient or adequate to capture the complexity of neuroplasticity. In these accounts, the model is a network of neurons connected by adaptive transmission links. The adaptation of the transmission links relies on weight changes according to use of the transmission link (short-term and long-term potentiation/depression). In contrast, we propose a paradigm switch from a synapse-centric model (each synapse learns independently, based on its history of use) to a neuron-centric model (each neuron uses signal selection for intracellular pathways to express plasticity at the membrane). A neural model consists of (a) expression of parameters at the membrane, in particular dendritic synapses or spines, and axonal boutons (b) internal parameters in the sub-membrane zone and the cytoplasm with its protein signaling network and (c) core parameters in the nucleus for genetic and epigenetic information. In a neuron-centric model, each node (=neuron) in the network has its own internal memory. Neural transmission and information storage are separated, not automatically combined by coupling strength. There is filtering and selection of signals for storage. Not every transmission event leaves a trace. This represents an important conceptual advance over synaptic weight models. We present the neuron as a self-programming device, rather than as passively determined by ongoing input. We believe a new approach to neural modeling is necessary, because the experimental evidence is not well captured by traditional synapse-centric models. Ultimately, we are interested in the possibilities of a flexible memory system that processes external signals according to its inherent structure.

q-bio.NC · cond-mat.dis-nn · cs.AI · cs.NE · q-bio.MN

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新论文 · v1arXiv:2611.03246

A Common Language for Dynamic Graphs: Interaction Tokenization Enables Foundation Models

Fuyuan Ma, Yili Wang, Xin He, Xin Zhang, Ying Wang, Xin Wang

原文摘要

Dynamic graphs capture evolving interactions in Web systems, including online communication, user engagement, and recommendation. Graph foundation models aim to reuse predictive capabilities across networks, but extending this capability to dynamic graphs faces two challenges. First, node identities are graph-specific, and similar interaction patterns can appear at different temporal and activity scales, making histories difficult to represent consistently across networks. Second, the predictive roles of historical patterns vary across interaction dynamics, limiting transfer from a narrow range of training processes. In this paper, we show that a common interaction language, combined with learning across diverse interaction processes, offers a basis for dynamic graph foundation modeling. We propose CL-DGFM, a Common Language Dynamic Graph Foundation Model for temporal link prediction. Its Dynamic Interaction Tokenizer encodes interaction memory, historical connectivity, and ordered destination transitions into calibrated tabular inputs, making dynamic histories accessible to existing predictors without an additional trainable graph encoder. Its Dynamic Interaction Prior trains a pretrained tabular foundation predictor across diverse synthetic dynamics. Labeled historical context guides prediction, with a Contextual Value Adapter modulating attention values. The model transfers to real graphs without target-graph parameter updates. Experiments on TGB and TGB-Seq demonstrate competitive performance. Further comparisons show that the common language supports basic machine learning and neural models, and enables direct use of frozen tabular foundation models on dynamic graphs.

cs.LG · cs.SI

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新论文 · v1arXiv:2611.03245

HyperFlow: Class-Incremental Semantic Change Detection via Hyperbolic Flow

Manon Béchaz, Léo Buecher, Emanuele Dalsasso, Ciprian Tomoiaga, Patrick Jayet, Marcin Detyniecki, Laetitia Chapel, Lucas Drumetz 等 10 位作者

原文摘要

Semantic change detection in remote sensing aims to identify and characterize land-cover transitions observed in pairs of satellite or aerial images. Existing methods assume a fixed set of change categories, limiting their applicability in real-world scenarios where changes of interest may vary, for example across geographies or over time. In this work, we tackle the problem of class-incremental semantic change detection, where a model progressively learns to detect new types of semantic change. To do so, we exploit the hierarchical structure of land-cover semantics, where newly introduced categories correspond to fine-grained refinements of existing ones, and represent said semantic categories in hyperbolic space, which is particularly well suited to represent such hierarchical relationships. To capture semantic changes, we introduce HyperFlow, a method relying on Busemann prototypes, modeling change as an anomaly in a learned semantic flow within the hyperbolic embedding space induced by segmentation. HyperFlow couples change detection to class-incremental semantic segmentation, enabling class-incremental semantic change detection. We also present HiChange, a new large-scale, high resolution remote sensing dataset with detailed, fine-grained hierarchical labels. Experimental results demonstrate that HyperFlow achieves both strong change detection and semantic segmentation performance across incremental steps. In particular, we show that progressively refining semantic categories, rather than considering them all directly, yields improved change detection, highlighting the benefits of incremental learning for semantic change detection.

cs.CV

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新论文 · v1arXiv:2611.03244

Forecasting How RLHF Responds to Reward-Model Edits Before Training

Zixiao Wang, Duzhen Zhang, Xingbo Du, Dongxia Wu, Le Song

原文摘要

Responses with headers, lists and greater length receive high scores from reward models (RMs), and RLHF policies learn to produce these features even when true quality does not improve. Practitioners therefore edit their RMs, but they learn whether an edit works only after an RL run. We show that the response of a policy to an RM edit can be forecast before training, from samples of the current policy and the scores of the old and new RM. If we read the RM as a set of prices, the first-order theory of KL-regularized RL implies that each feature moves in proportion to its within-prompt covariance with the change in reward. We implant controlled price changes into retrained RMs under a 2^(4-1) fractional factorial design with a self-distilled placebo, run 54 RL runs from six parent policies, and fix every forecast before the runs. With the covariance predictor, we predict the direction of all 20 changes in supply whose forecast has the same sign at every parent and explain 82% of the standardized change. With counterfactual prices, the classical alternative, we explain 50%. The forecast also holds under GRPO and for real RM upgrades, where we explain 66% out of sample with a coefficient frozen from the factorial runs. For rent, the gap between the proxy reward and a gold reward from stronger judges, we rank RM edits correctly with the same covariance over the first 30 steps (Spearman 0.94). This measures the horizon of the forecast: by step 200, the ranking of the factorial edits reverses under both RLOO and GRPO, while the real upgrades keep their order. When we vary the KL coefficient beta, the initial response does not depend on beta, and the response saturates below the KL equilibrium. In one experiment, the long-run response to a price rise is consistent with an additional implicit regularizer beta_0 of about 0.003 to 0.007.

cs.LG

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新论文 · v1arXiv:2611.03764

The role of injected noise in diffusion model generalization

Vincent Emmerling, Felix Günther, David Kappel

原文摘要

Diffusion models trained on distinct datasets can produce highly similar samples when prompted with the same noise input, suggesting that they learn a generalized data distribution. We investigate the role of noise injected during the sampling process, and show that it plays an important role in making or breaking this strong generalization effect. Based on this finding we investigate the robustness of sample similarity to changes in the injected noise, and find that its impact is largest in the sampling regime where broad structure already exists but the precise identity of the sample is not yet determined.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03238

ScienceCUA: Resource-to-Skill Construction and Execution-Guided Skill Evolution for Scientific Computer Use Agents

Daiqiang Li, Jingyi Peng, Rui Hu, Jia Li, Xuyang Liu, Guanren Qiao, Yujia Xiang, Qiuzhuang Sun 等 10 位作者

原文摘要

Computer-use agents for scientific software require application-specific expertise. Much of this expertise is documented in human-authored software manuals and web pages. However, turning these resources into executable, reusable skills remains challenging due to mismatches between documented and deployed software versions and differences in scope between documentation coverage and task requirements. We introduce ScienceCUA, a two-stage framework combining resource-to-skill construction with execution-guided skill evolution. First, ScienceCUA identifies tasks supported by the software from its documentation, infers operational trajectories, and generalizes them into reusable Skills. Command catalogs, syntax constraints, and shortcuts are organized separately as References for on-demand lookup. Then, ScienceCUA uses execution feedback to drive skill self-evolution by contrasting successful and failed trajectories for the same task, identifying deficiencies, revising existing skills or adding new ones, and updating the skill library only after repeated validation in real environments. To enable reliable evaluation without benchmark noise or task leakage, we introduce two improved benchmarks. ScienceBoard-Verified provides an audited and corrected basis for measuring task performance, while ScienceBoard-VE separates skill evolution from final testing through disjoint evolution and test tasks and further supports native-agent evaluation. Experiments show that the initial Skills and References improve average task success rate by up to 7.23 percentage points over the base model, while skill self-evolution increases the total gain over the baseline to as much as 17.12 percentage points. ScienceCUA turns software documentation into an evolving set of executable skills and queryable references, offering a practical way for general-purpose agents to use scientific software reliably and at scale.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.03602

SV-WAM: An Efficient Surround-View World-Action Model for End-to-End Autonomous Driving

Jinyang Wang, Shiwei Li, Junjian Wang, Zhiqiang Deng, Jianbin Gao, Yihang Zhao, Liu Liu, Yongjia Zhao 等 15 位作者

原文摘要

World models (WMs) have demonstrated strong potential for end-to-end autonomous driving by learning predictive representations of future scene dynamics. However, generating future videos during inference introduces substantial computational overhead, leading many recent driving WMs to adopt a single front camera as input for efficient deployment. This design restricts spatial coverage in safety-critical maneuvers such as lane changes, merges, and turns. To address this limitation, we propose SV-WAM, a surround-view world-action model (WAM) that preserves full six-camera observations while maintaining efficient inference. SV-WAM leverages future-video prediction as dense training supervision for action learning within a shared generative model, rather than as an inference-time output. At the core of this design is an action-centered causal mask that prevents action tokens from attending to future-video tokens during joint action-video denoising. Consequently, the video branch can be discarded at deployment, enabling efficient action-only planning. Furthermore, we introduce a differentiable drivable-area compliance regularizer that penalizes vehicle-footprint corners approaching or crossing drivable boundaries, improving planning safety and boundary awareness. Extensive experiments on the closed-loop NAVSIMv2 benchmark and the open-loop nuScenes benchmark demonstrate that SV-WAM achieves state-of-the-art planning performance with low inference latency and competitive zero-shot transfer capability.

cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03232

Calibrating Multimodal Test-Time Adaptation under Reliability and Availability Modality Shifts

Jiangmeng Li, Jinglin Xu, Yi Li, Yumeng Liu, Jiahuan Zhou, Fanjiang Xu, Hui Xiong, Gang Hua

原文摘要

Multimodal test-time adaptation (TTA) enables pretrained multimodal models to accommodate distribution shifts during inference using only unlabeled test data, without recourse to the original training data. Compared with unimodal adaptation, multimodal adaptation is further complicated by the coexistence and interaction of multiple representation perspectives. This complexity is particularly pronounced under modality reliability shifts, which preserve modality availability but degrade information quality, and modality availability shifts, which render the corresponding modality entirely inaccessible. Category-conditional Gaussian modeling offers a natural starting point for handling such distributional variations, having proven effective in unimodal test-time adaptation for characterizing representation distributions, yet its extension to multimodal settings requires a more elaborate formulation. To this end, we propose Adaptive Probabilistic Gaussian Calibration (AdaPGC), which extends category-conditional Gaussian modeling across modality-specific and multimodal fusion representations for probabilistic calibration under multimodal distribution shifts. AdaPGC continuously updates category-conditional Gaussian models over modality-specific and fusion representations, using the resulting probabilistic predictions to calibrate source model predictions and guide reliability based rectification, while exploiting their joint distributions to recover unavailable fusion representations. Extensive experiments under diverse modality corruption and missingness conditions demonstrate that AdaPGC effectively improves robustness under both modality reliability and availability shifts. Our source code will be released at https://github.com/XuJinglinn/AdaPGC-plus.

cs.CV

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