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版本更新 · v2arXiv:2610.08446

AssemState: Manual and Physical-State-Guided Reasoning for Zero-shot Furniture Assembly

Zhiyuan Qi, Jierui Li, Yifan Shen, Cheng Qian, Jiateng Liu

原文摘要

Multimodal large language models (MLLMs) have made significant progress in visual understanding, but precise 3D spatial reasoning integrated with physical environment remains difficult. Furniture assembly requires not only recovering step-level operations from diagrammatic manuals, but also translating semantic attachment relations into 6D pose updates that enable parts to physically interact with the environment and previously assembled components. To study this problem, we propose AssemState, a zero-shot framework for manual and physical-state-guided furniture assembly. It firstly employs anchor-guided boundary assembly states to decompose manual pages into single-part operations and recover an assembly-tree. Then, it uses iterative after-state feedback refinement to guide successive (SE(3)) updates and corrections, and validates their physical plausibility through simulation-based release tests. Experiments show that compared with the strongest prior baseline, AssemState improves F1 from 38.58\% to 62.80\% and Tree Exact Match from 28.24\% to 53.92\% for assembly-tree recovery. On 243 independently evaluated part-level operations, our proposed iterative refinement improves judge-accepted operations from 0 to 5.3\% and reduces mean Chamfer distance from 5.4111 to 1.7744. However, visually plausible candidate poses may still suffer from collision, floating, mirror-orientation errors, incomplete seating, and wrong-side attachment. These results show that AssemState improves operation-structure recovery and selected local pose metrics, while MLLMs remain limited for spatial relationship reasoning.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.09541

Memory-Efficient Active Perception for Embodied Question Answering in Dynamic Human-Populated Scenes

Xin Lu, Rui Li, Xun Huang, Weixin Li, Jiayuan Li, Chuanqing Zhuang, Zhengda Lu, Jun Xiao

原文摘要

Embodied Question Answering (EQA) couples active visual perception, vision-language reasoning, and sequential decision making, and has traditionally been evaluated in temporally stable environments where visual evidence can be accumulated reliably. In realistic multimedia settings such as dynamic human-populated scenes, however, moving people and transient occlusions introduce human-induced perceptual variations: task-relevant multimodal cues become transient, viewpoint-dependent, and incompletely captured in individual views. Conventional store-then-retrieve strategies generate redundant visual evidence and incur high inference cost, making them ill-suited to such variations. This exposes two core problems for embodied agents: resolving viewpoint-dependent ambiguity, and maintaining compact evidence for efficient inference. To address these challenges, we present DIVRR (Dynamic-Informed View Refinement with Relevance-guided Rotation and adaptive memory selection), a training-free active evidence-acquisition framework for EQA. DIVRR combines question-conditioned room guidance with relevance-triggered view augmentation to guide exploration and additional observation. Selective memory admission uses the same relevance signal as view augmentation to retain high-scoring evidence, reducing stored entries while keeping the retrieved visual context compact. To evaluate EQA under human-induced perceptual variations, we introduce DynHuman-EQA, a dynamic human-populated EQA dataset constructed in Habitat, comprising dynamic and static subsets under shared scene layouts. Experiments on DynHuman-EQA and HM-EQA show that DIVRR outperforms existing baselines in both accuracy and memory efficiency. The gains are most pronounced in dynamic human-populated scenes.

cs.CV · cs.MM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.00284

OCELOT: Planetary-Scale Species Distribution Modeling with Satellite Foundation Embeddings

Bart van Merriënboer, Lauren Harrell, Keith Anderson, Burcu Karagol Ayan, Elise Kleeman, Tom Denton, Drew Purves

原文摘要

Large-scale repositories of species observations now contain billions of occurrence records from around the world, but these sightings cluster heavily in human-accessible areas. Existing models use only a small fraction of these records and are not evaluated on extrapolating to remote, unvisited regions--where species distribution models can be used as geographic priors to help classify data from camera traps and acoustic recorders. We scale relative occurrence species distribution models by training a multimodal implicit neural representation network called OCELOT that fuses spatial coordinates with satellite foundation embeddings on two orders of magnitude more observations than previous work: 2 billion GBIF records across 100,000 taxa. Scaling to this regime requires co-designing the data pipeline, taxonomic representation, loss formulation, and model architecture: spatiotemporal thinning and sub-linear cell sampling correct for spatial redundancy and observer bias, while an additive taxonomic readout shares representations across species, genera, and families to improve predictions for rare taxa. To evaluate performance in remote areas, we benchmark geographic extrapolation using the NDCG metric on spatial block holdouts up to 600 km. Satellite foundation embeddings improve extrapolation by 51% over coordinate-only models and outperform classical bioclimatic rasters. The resulting model also matches or outperforms existing baselines on established interpolation benchmarks, demonstrating that optimizing for geographic extrapolation does not compromise in-distribution accuracy.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.20068

Marginal utility, matrix factorization, and the Key-Value (KV) cache: a unified information-economic framework for sovereign geo-mining inference

Caroline Gans Combe

原文摘要

This paper builds a theoretical bridge between the economic notion of marginal utility and two machine-learning constructs, matrix factorization and the Key--Value cache of transformer language models. The singular value spectrum of a rating matrix is shown to be a diminishing marginal utility schedule for latent factors, the eigenvalue spectrum of the projected covariance operator to be the marginal utility schedule of a model's learned representation, and cache eviction and low-rank cache compression to be instances of constrained utility maximization under a memory budget. The three collapse into a single allocation rule: retain the top dimensions whose eigenvalue exceeds the shadow price of the binding constraint. The framework is applied to the automated extraction of structured information from geo-mining documents, where it motivates a multi-pass inference protocol, a layer-wise TIES model merging procedure, and a selection policy combining extraction quality, localization drift and energy, scalarized with a Conditional Value-at-Risk term on drift. Two empirical contributions are reported. An 11.2-million-parameter hierarchical classifier, trained in about five minutes on a single GPU, reaches 90.0 per cent level-1 accuracy on a held-out test set from a 973-document uranium-exploration corpus, against 92.0 per cent for a proprietary model on a fifty-document human audit of the same corpus, at a latency of 2.62 ms per card against approximately 2,000 ms for the API and at negligible cost. A diagnostic of uniform-density TIES merging exposes a reproducible degenerate mode in which the merged model returns token-identical outputs across five geographically distinct districts while declaring high confidence; re-executing the merge under layer-wise calibrated densities removes that signature on the diagnostic sample. The full-scale extraction benchmark, including LoRA fine-tuning, is reported as projected rather than measured and remains an empirical extension of this work.

cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.07900

Rethinking the Value of Agent-Generated Tests for LLM-Based Software Engineering Agents

Zhi Chen, Zhensu Sun, Yuling Shi, Chao Peng, Xiaodong Gu, David Lo, Lingxiao Jiang

原文摘要

Large Language Model (LLM) code agents increasingly resolve repository-level issues by iteratively editing code, invoking tools, and validating candidate patches. In these workflows, agents often write tests on the fly, but the value of this behavior remains unclear. This raises a central question: how does agent-written testing relate to task outcomes and resource use during repository-level issue resolution? To better understand the role of agent-written tests, we analyze 5,000 recorded agent runs produced by six strong LLMs on SWE-bench Verified using the lightweight mini-SWE-agent scaffold. Our results show that test writing is common for most models. GPT-5.2 is an exception: it rarely writes new test files while achieving a resolution rate close to the top-ranked model. Test-writing frequency varies widely across models. Within a given model, it differs less between resolved and unresolved tasks. When tests are written, their files contain substantially more statements that print runtime values than assertions that check conditions. We then compare baseline prompts with revised prompts that encourage or discourage writing new test files for four models. Agents start or stop writing test files as requested on 36.8%-93.2% of tasks. Resolved-task totals differ by 0-13 out of 500 per model, with no statistically significant difference, while API calls or token use differ substantially for three models. Similar resolution totals can therefore hide substantial differences in how agents test and how many resources they use.

cs.SE · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.11821

Representing the Non-dominated Set of Multi-objective Network Problems by Supported Non-dominated Points

David Könen, Lara Löhken, Michael Stiglmayr

原文摘要

In multi-objective combinatorial optimization, unsupported non-dominated points typically outnumber supported points and are often significantly more challenging to compute. Recent studies show that extreme supported non-dominated points provide high-quality representations of the non-dominated set for certain binary problems. We demonstrate that this observation does not generalize to capacitated network optimization problems: representation quality decreases with increasing arc capacities, whereas supported non-dominated points consistently provide high-quality representations with respect to several quality indicators. However, supported point sets may still be too large in practical applications, where only a small, fixed number of alternatives is typically desired. Selecting fixed-size representations from the non-dominated set requires its computationally expensive generation and thus diminishes the computational advantages that representations are intended to provide. We therefore suggest the (extreme) supported points as alternative candidate sets in subset selection problems. Our numerical results show that restricting the candidate set to supported non-dominated points yields fixed-size representations of nearly the same quality as those selected from the complete non-dominated set. Overall, supported non-dominated points serve both as high-quality representations and as reasonable candidate sets for subset selection.

cs.DM · cs.NE · math.OC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2507.17988

Synthesis of timeline-based planning strategies avoiding determinization

Dario Della Monica, Angelo Montanari, Pietro Sala

原文摘要

Qualitative timeline-based planning models domains as sets of independent, but interacting, components whose behaviors over time, the timelines, are governed by sets of qualitative temporal constraints (ordering relations), called synchronization rules. Its plan-existence problem has been shown to be PSPACE-complete; in particular, PSPACE-membership has been proved via reduction to the nonemptiness problem for nondeterministic finite automata. However, nondeterministic automata cannot be directly used to synthesize planning strategies as a costly determinization step is needed. In this paper, we identify a fragment of qualitative timeline-based planning whose plan-existence problem can be directly mapped into the nonemptiness problem of deterministic finite automata, which can then synthesize strategies. In addition, we identify a maximal subset of Allen's relations that fits into such a deterministic fragment.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.31620

Bilingual Language Models Capture Cross-Linguistic Influence in Second-Language Reading

Abderrahmane Issam, Irene E. Winther, Stefan L. Frank

原文摘要

While monolingual language model (LM) surprisal is widely established as a predictor of reading times, it cannot capture the non-native (L2) reading dynamics that are shaped by the reader's first language (L1). In this work, we investigate whether the cross-lingual transfer within bilingual models aligns with human L1-driven biases during L2 reading. We evaluate bilingual models on eye-tracking data of readers from 12 diverse L1 backgrounds reading English texts. To isolate L1-specific transfer, we compare bilingual models trained on an L1 that matches the readers' L1 background to both a monolingual English baseline and bilingual models trained on other L1s. We demonstrate that matching the readers' L1 improves the prediction of L2 reading times. This predictive advantage is concentrated in late-stage eye-tracking measures, modulated by L1-L2 script overlap, and peaks among intermediate-proficiency readers. These findings provide evidence that bilingual LMs capture aspects of cross-linguistic influence, and that such influence is present in L2 reading behavior.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.03562

NPCNet: Navigator-Driven Pseudo Text for Deep Clustering of Early Sepsis Phenotyping

Pi-Ju Tsai, Charkkri Limbud, Kuan-Fu Chen, Yi-Ju Tseng

原文摘要

Sepsis is a heterogeneous syndrome. Identifying clinically distinct phenotypes may enable more precise treatment strategies. In recent years, many researchers have applied clustering algorithms to sepsis patients. However, many existing approaches require aggregating or imputing temporal Electronic Health Records (EHRs), which can limit the model's ability to capture disease progression. Furthermore, existing clustering processes seldom incorporate domain-driven constraints, often resulting in phenotypes that lack clear clinical distinction. We propose a novel clustering network, NPCNet, that comprises a text embedding generator, a clustering operator, and a target navigator. We first transformed EHRs into pseudo texts by discretizing continuous clinical measurements and integrated them with static variables to construct the embeddings. The target navigator then infused clinical knowledge into the embeddings through auxiliary tasks, constraining clustering results to better align sepsis phenotypes with clinical significance. Finally, the clustering operator employed an iterative refinement mechanism to jointly optimize phenotype centroids and patient representations under domain-driven constraints. Extensive experiments on public datasets demonstrated that NPCNet achieved superior performance on both internal clustering and clinical metrics. We identify four distinct sepsis phenotypes with divergent SOFA trajectories. Notably, although α and δ exhibit similarly severe conditions initially, NPCNet distinguishes patients likely to improve (α) from those at risk of deterioration (δ). Furthermore, treatment effect analysis suggests that early vasopressor administration may be associated with improved outcomes in the α, \{beta}, and δ phenotypes. These findings highlight NPCNet's potential for phenotype-specific treatment strategies.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.12530

Autonomous Precision Milling of Biological Structures via Generic Anatomical Priors and Active Boundary Perception

Enduo Zhao, Xiaofeng Lin, Yifan Wang, Yuhan Song, Weihan Li, Saul Alexis Heredia Perez, Kanako Harada

原文摘要

Autonomous precision milling of biological structures is challenged by incomplete knowledge of target geometry, local material thickness, and critical internal boundaries. Subject-specific preoperative models can address geometric and thickness variations, but static models cannot determine boundary status encountered during execution, while repeated target-specific imaging limits scalability. This article presents a state-adaptive autonomous milling framework that assigns complementary roles to generic anatomical priors and active boundary perception. A generic anatomical prior provides conservative global guidance and is transformed through semantic-guided registration and hybrid vision-force calibration into robot-executable guidance for individual targets. As milling approaches uncertain boundaries, the robot actively probes the remaining structure and uses relative stiffness changes to estimate boundary status and structural detachability. A state-adaptive controller governs transitions between active perception and spatially selective incremental refinement, repeating this cycle until the termination criterion is satisfied. Hierarchical experiments on biological surrogates and in vivo mouse cranial window creation demonstrate accurate anatomical prior transfer, reliable boundary adaptation, and autonomous precision milling of biological structures.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.15189

The Sampling Complexity of Condorcet Winner Identification in Dueling Bandits

El Mehdi Saad, Victor Thuot, Nicolas Verzelen

原文摘要

We study best-arm identification in large-scale stochastic dueling bandits under the sole assumption that a Condorcet winner exists, i.e., an arm that wins each noisy pairwise comparison with probability at least $1/2$. We introduce a new identification procedure that exploits the full gap matrix $Δ_{i,j}=q_{i,j}-\tfrac12$ (where $q_{i,j}$ is the probability that arm $i$ beats arm $j$), rather than only the gaps between the Condorcet winner and the other arms. We derive high-probability, instance-dependent sample-complexity guarantees that (up to logarithmic factors) improve the best known ones by leveraging informative comparisons beyond those involving the winner. We complement these results with matching lower bounds that establish the optimality of our procedures in all regimes. Overall, our results reveal the general form of the sampling complexity, characterized by a trade-off between the cost of locating informative entries and the verification cost required to achieve the desired confidence. In particular, this complexity drastically differs from what is suggested by pure asymptotic results or by procedures that are tailored to Strongly Stochastic Transitive models.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.28751

Correctness vs Efficiency: Behavior Control with Weight-Space Extrapolation in Code RL

Kunhao Zheng, Pierre Chambon, Juliette Decugis, Jonas Gehring, Taco Cohen, Benjamin Negrevergne, Gabriel Synnaeve

原文摘要

A model trained with an imperfect reward in RL may learn to exploit the reward with undesired behavior. We study a controlled form of this problem in competitive programming. A program may pass small tests while violating the intended efficiency requirement on larger inputs. We construct a family of rewards that progressively relax the efficiency constraint, making the reward increasingly easy to exploit, and train separate policies. This sweep reveals a correctness-efficiency frontier. Relaxing the reward leads to less efficient programs and more resource-limit failures. Stricter rewards suppress these failures but increase correctness failures near the model's capability boundary. This frontier is also reflected in weight space. Interpolating between the resulting models reproduces the same behavioral change. More surprisingly, extrapolation in both directions further amplifies or suppresses it beyond the trained checkpoints. Extensive experiments show that this pattern holds across pure reasoning, tool use, and agentic coding, and at both 32B and 7B scales. This suggests a simple intervention for this form of reward hacking: use a loose reward to amplify the undesired behavior, use the weight-space difference to a strict-reward model as its steering vector, and then extrapolate past the strict model to suppress it.

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2511.19208

Leader Election via Unique Sink Orientation

Jérémie Chalopin, Maria Kokkou

原文摘要

A proof-labeling scheme (PLS) certifies a global property using only local checks. That is, a prover assigns a certificate to every node and each node accepts or rejects by inspecting only the certificates in its neighborhood, so that every graph satisfying some global property admits a certificate assignment accepted by all nodes (existence) and every certificate assignment accepted by all nodes gives a legal configuration (soundness). In this work, we design PLSs for two classical problems, leader certification and spanning tree certification, assuming that all nodes are anonymous, for a specific class of graphs. Certifying a unique leader is impossible in general anonymous graphs for any certificate size due to a well-known result for rings by [Korman, Kutten and Peleg, 2010]. This raises the question of which graph classes admit a PLS for leader certification with anonymous nodes, ideally with size independent of the size of the graph. Our work contributes to a growing list of such classes, which includes chordal graphs, meshed graphs and simply connected subsets of the triangular grid. We consider dismantlable graphs, a class which contains trees, chordal, bridged and Helly graphs, as well as the $k$-th powers of $δ$-hyperbolic graphs for $k \geq 2δ$. To the best of our knowledge, these are the first PLSs tailored to dismantlable graphs, potentially highlighting structural properties useful for certifying additional problems. Finally, we present a generic transformation that converts any labeling scheme with certificates of finite type into a silent self-stabilizing algorithm by adding only one extra state, assuming a Gouda fair scheduler, whereas known generic transformations require $O(\ell + \log n)$ bits for $\ell$-bit certificates. This transformation may be of independent interest.

cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2508.13288

Hierarchical Conformal Classification

Floris den Hengst, Inès Blin, Majid Mohammadi, Syed Ihtesham Hussain Shah, Taraneh Younesian

原文摘要

Conformal prediction (CP) is a powerful framework for quantifying uncertainty in machine learning models, offering reliable predictions with finite-sample coverage guarantees. When applied to classification, CP produces a prediction set of possible labels that is guaranteed to contain the true label with high probability, regardless of the underlying classifier. However, standard CP treats classes as flat and unstructured, ignoring domain knowledge such as semantic relationships or hierarchical structure among class labels. This paper presents hierarchical conformal classification (HCC), an extension of CP that incorporates class hierarchies into both the structure and semantics of prediction sets. We formulate HCC as a constrained optimization problem whose solutions yield prediction sets composed of nodes at different levels of the hierarchy, while maintaining coverage guarantees. To address the combinatorial nature of the problem, we formally show that a much smaller, well-structured subset of candidate solutions suffices to ensure coverage while upholding optimality. An empirical evaluation on three new benchmarks consisting of audio, image, and text data highlights the advantages of our approach, and a user study shows that annotators significantly prefer hierarchical over flat prediction sets.

cs.LG · cs.AI · stat.ML

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2509.00832

Challenges in Non-Polymeric Crystal Structure Prediction: Why a Geometric, Permutation-Invariant Loss is Needed

Emmanuel Jehanno, Romain Menegaux, Julien Mairal, Sergei Grudinin

原文摘要

Crystalline structure prediction is an essential prerequisite for designing materials with targeted properties. Yet, it is still an open challenge in materials design and drug discovery. Despite recent advances in computational materials science, accurately predicting three-dimensional non-polymeric crystal structures remains elusive. In this work, we focus on the molecular assembly problem, where a set~$\mathcal{S}$ of identical rigid molecules is packed to form a crystalline structure. Such a simplified formulation provides a useful approximation to the actual problem. However, while recent state-of-the-art methods have increasingly adopted sophisticated techniques, the underlying learning objective remains ill-posed. We propose a better formulation that introduces a loss function capturing key geometric molecular properties while ensuring permutation invariance over $\mathcal{S}$. Remarkably, we demonstrate that within this framework, a simple regression model already outperforms prior approaches, including flow matching techniques, on the COD-Cluster17 benchmark, a curated non-polymeric subset of the Crystallography Open Database (COD). Our code is available at https://github.com/inria-thoth/SinkFast.

cs.LG · cond-mat.mtrl-sci

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.22787

Conformal PM2.5 Mapping Under Spatial Covariate Shift: Satellite-Reanalysis Fusion for Africa's Green Industrial Transition

Yaw Osei Adjei, Davis Opoku, Ephraim Abotsi, Kwadwo Owusu Amanqua, Oliver Kornyo, Elisha Soglo-Ahianyo, Cephas Anertey Abbey, Ransford Oppong

原文摘要

Africa's green industrialization imperative demands reliable infrastructure for monitoring air quality. We present a satellite-reanalysis PM2.5 fusion system trained on 2,068,901 records from 404 monitoring locations in 29 African countries (OpenAQ, 2017-2022), combining LightGBM with leakage-resistant spatial cross-validation and conformal prediction to quantify predictions and their geographic applicability limits. Under 5-fold location-grouped spatial cross-validation, LightGBM achieves RMSE = 30.83 +/- 5.07 ug/m3, MAE = 14.54 +/- 1.66 ug/m3, R2 = 0.134 +/- 0.023, and macro F1 = 0.336 +/- 0.018. This R2 is substantially below random-split benchmarks (>0.90) but reflects true geographic generalisation difficulty rather than model failure. Split conformal prediction targeting 90% marginal coverage reveals severe East Africa degradation (actual PICP = 65.3% vs. nominal 90%), consistent with medium-strength covariate shift (humidity KS = 0.2237, sat_pblh KS = 0.2558). We operationalise these findings through regional reliability flags (High/Medium/Low/Unreliable) and a monitor prioritisation score directing infrastructure expansion toward highest-burden unmonitored populations, directly supporting Africa's green industrial transition and SDGs 3.9, 7.1.2, 9, 11.6.2, and 13.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.27149

Temporally Ordered Region-Token Mamba with Logit-Space Diffusion for Remote Sensing Change Detection

Maneet Chatterjee, Aparup Ghosh, Anuvab Sen, Udayon Sen, Arnav Aditya, Yixin Zhang

原文摘要

Change Detection (CD) in remote sensing requires jointly modeling bitemporal context and fine-grained spatial details to accurately localize changed regions. While dense attention effectively captures bitemporal relationships, its computational cost limits scalability to high-resolution imagery. In contrast, conventional feature fusion and coarse decoding often struggle to distinguish true changes from appearance variations and preserve object boundaries. To overcome these limitations, we propose Bitemporal Mamba-Diffusion for Change Detection (BMD-CD), a framework that integrates temporally structured state-space modeling with logit-space diffusion refinement. BMD-CD first converts deep bitemporal features into region tokens and organizes them into explicit temporal partitions for bidirectional state-space propagation. It then employs a Learnable Trigonometric Fusion (LTF) module to integrate temporal information and a Conditional Diffusion Decoder that refines coarse predictions in logit space with a short DDIM schedule to generate fine-grained, high-resolution change maps. Extensive experiments on LEVIR-CD, WHU-CD, DSIFN-CD, CDD, and S2Looking demonstrate the effectiveness of the proposed approach. BMD-CD achieves IoU scores of 0.923, 0.957, 0.882, and 0.979 on WHU-CD, DSIFN-CD, LEVIR-CD, and CDD, respectively, and further exhibits strong zero-shot robustness on ValaisCD and B-FLAIR-test.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.17414

A decomposition approach for large virtual network embedding

Amal Benhamiche, Pierre Fouilhoux, Lucas Létocart, Nancy Perrot, Alexis Schneider

原文摘要

Virtual Network Embedding (VNE) is the core combinatorial problem of Network Slicing, a 5G technology which enables telecommunication operators to propose diverse service-dedicated virtual networks, embedded onto a common substrate network. VNE asks for a minimum-cost mapping of a virtual network on a substrate network, encompassing simultaneous node placement and edge routing decisions. On a benchmark of large virtual networks with realistic topologies we compiled, the state-of-the art heuristics often provide expensive solutions, or even fail to find a solution when resources are sparse. We introduce a new integer linear formulation together with a decomposition scheme based on an automatic partition of the virtual network. This results in a column generation approach whose pricing problems are also VNE problems. This method allows to compute better lower bounds than state-of-the-art methods. Finally, we devise an efficient Price-and-Branch heuristic for large instances.

cs.DM · cs.NI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.23676

Web World Models

Jichen Feng, Yifan Zhang, Chenggong Zhang, Yifu Lu, Shilong Liu, Mengdi Wang

原文摘要

Language agents increasingly require persistent worlds in which they can act, remember, and learn. Existing approaches sit at two extremes: conventional web frameworks provide reliable but fixed contexts backed by databases, while fully generative world models aim for unlimited environments, but the world is constructed primarily through generation, making it harder to maintain a fixed, deterministic global framework and reducing controllability. In this work, we introduce the Web World Model (WWM), a middle ground where world state and ``physics'' are implemented in ordinary web code to ensure logical consistency, while large language models generate context, narratives, and high-level decisions on top of this structured latent state. We build a suite of WWMs on a realistic web stack, including an infinite travel atlas grounded in real geography, fictional galaxy explorers, web-scale encyclopedic and narrative worlds, and simulation- and game-like environments. Across these systems, we identify practical design principles for WWMs: separating code-defined rules from model-driven imagination, representing latent state as typed web interfaces, and utilizing deterministic generation to achieve unlimited but structured exploration. Our results suggest that web stacks themselves can serve as a scalable substrate for world models, enabling controllable yet open-ended environments.

cs.AI · cs.CL · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.07346

GreedTok Is a 0.3287-Approximation for Tokenization, Within 3% of the Best Possible Constant

Kundan Baliga, Aiden Xie, Aditya Byju

原文摘要

A tokenizer's vocabulary is chosen once and used on every input, so it matters how much compression a greedy vocabulary can lose. GreedTok (Lim et al., 2025) picks $k$ tokens one at a time, each time the string that saves the most tokens; its worst-case ratio was open. We prove that GreedTok saves at least 0.3287 of the optimal number of tokens saved, on every corpus and budget, while junction corpora show that no constant above 0.3387 is possible. The lower-bound proof is written out in full and uses no computer certificates. Its key idea is to track the order of greedy's choices: given that order, the pattern greedy leaves inside an optimal token depends on two numbers, and an induction peels off one chosen string at a time; without the order, a needed inequality fails for a 20-character token. Over 256 symbols, GreedTok's ratio exceeds 0.33861 for every $k\ge 104{,}910$, and over $k^c$ symbols with $c<1/2$ it is at least 0.4386 in the limit. We also show that the objective is APX-hard: it has no PTAS unless P = NP.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.19730

Design and Analysis of Temporal Representations in Neural Networks

Alban Colas-Gattepaille, Alexandre Muzy

原文摘要

Neural networks, spiking or not, compute with amplitudes: activation values, spike counts, firing rates, never event times. No measure exists of how much a network uses time. We propose one, the degree of temporal representation: the fraction of a network's four functions (encoding, activation, decoding, learning) whose variables or parameters are temporal, and hypothesise that it predicts performance on temporal tasks. At fixed architecture, accuracy and energy efficiency increase with the degree: a delay network of maximal degree reaches 98.94\% on Google Speech Commands with 175k parameters, above transformers. To test whether networks of different degree really differ, we convert the delay network into artificial and spiking networks with the same inputs and outputs. From this common start, the artificial one learns differently and temporal gradients depend on event times. Hence we obtain a design rule: for temporal data, make event times variables of a network's functions. Spiking networks, converted after training, keep the delay network's accuracy, unreachable by spiking networks trained from scratch.

cs.NE

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.13644

Learning Equilibria in Coordination Games via Minorization-Maximization

Ashok Krishnan K. S., Helene Le Cadre, Ana Busic

原文摘要

This paper considers games where the utilities for agents are the sum of a term proportional to a social utility, and another term that is an individual cost or reward. The agents are assumed to be irrational in their perception of the individual cost or reward. The multi equilibrium game is regularized, and its strictly concave potential function is used to select a unique equilibrium. This selected equilibrium is shown to be an $ε-$equilibrium of the original game, where $ε$ is parametrized by the regularizing function. A minorization-maximization based iterative learning scheme is proposed to learn equilibria in this game. This scheme converges to the potential-optimal equilibrium, and has superior convergence behavior in comparison to gradient and best response methods.

cs.GT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2508.03471

Learned Adaptive Indexing with Updatable Learned Index

Suvam Kumar Das, Suprio Ray

原文摘要

Indexes can significantly improve search performance in databases. However, if the query workload changes frequently or new data updates occur continuously, it may not be worthwhile to build a conventional index upfront for query processing. Building a conventional fine-grained index, like B+-tree on the entire dataset can be very expensive since it may involve processing terabytes to petabytes of data. Moreover, with a traditional indexing, data ingestion tends to slow down in case of heavy updates as the index also needs to be updated. In these cases, adaptive indexing is a viable solution instead of traditional indexing. Adaptive indexing is a technique in which an index gets built on the fly as a byproduct of query processing. In recent years, research has taken on a new direction where machine learning models are employed for the purpose of indexing. These indexes, known as learned indexes, are more efficient compared to traditional indexes in terms of memory footprints and query performance. To the best of our knowledge, no learned indexes exist yet for adaptive indexing. We propose a novel learned approach, called FLAIRS, for adaptive indexing. It is built on the fly as queries are submitted. We also introduce an updatable learned index TULSI that is utilized by FLAIRS for indexing data. FLAIRS can employ a query workload prediction technique that makes future workload projection based on past workload data. We have evaluated FLAIRS against existing adaptive indexes for various query workloads. Our results show that our approach performs better than others in most cases, offering significant improvement in query performance.

cs.DB

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.08671

Greedy Byte-Pair Encoding Achieves 0.391 of the Optimum, Past the One-Third Barrier for Packing-Based Analyses

Kundan Baliga, Aiden Xie, Aditya Byju

原文摘要

Byte-Pair Encoding (BPE) builds most language-model tokenisers by repeatedly merging a most frequent adjacent pair of symbols. How much shortening can this greedy rule lose against the best $k$ merges? Kozma and Voderholzer proved that greedy achieves at least one third of the optimal shortening; the best known upper bound is $(1-e^{-2})/2\approx 0.432$. We show that one third is a barrier for packing-based analyses, which see the optimum only through a pair packing, and we prove that greedy achieves at least $0.391$ of the optimum on every input, with any tie-breaking. The key idea is rule determinism: all copies of a rule of the optimum spell the same letters, so when one greedy replacement destroys three of the optimum's merge points, half a unit can be refunded on each side. A weaker bound, $0.374$, has a short proof that needs no computer; $0.391$ adds a certificate in exact arithmetic. We also show that the worst-case ratio is exactly $3/4$ for two merges and at most $7/12$ for three.

cs.DS · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.18634

EffiNav: Fusing Depth and Vision-Language for Efficient Object Goal Navigation

Zecheng Yin, Benedict Jun Ma

原文摘要

To locate a target object while exploring the unknown environment is a fundamental capability for autonomous agents, with applications ranging from search-and-rescue to field robots. A simplified version of such task is Object Goal Navigation (ObjNav). In ObjNav, successful arrival at the target object provides a basic measure of performance; however, the efficiency of the navigation trajectory is equally important, as it indicates how intelligently the agent explores and how much time remains for subsequent tasks. In unknown environments, the key to efficient navigation lies in deciding where to explore next. While many prior works aim to address this core challenge and achieved promising performance in certain settings, recent training-based models and non-training frameworks still suffer from generalization and efficiency issues respectively, which in the worst cases can lead to excessive exploration of already-visited areas or redundant back-and-forth motion. We evaluate EffiNav on two widely used simulation benchmarks Habitat Matterport 3D (HM3D) and Open-Vocabulary Object goal Navigation (OVON), and further validate its effectiveness on physical robots in real-world settings. We conduct failure analysis on massive simulation episodes. With minimal modification, we also extend EffiNav to a memory-augmented ObjNav task on the GOAT-BENCH dataset, demonstrating its adaptability beyond standard ObjNav settings. Across two standard metrics--Success Rate (SR) and Success weighted by Path Length (SPL), EffiNav matches or outperforms recent baselines, reflecting its efficiency, robustness, and practical applicability. Recognizing the different emphases of the two datasets, the performances reveals this framework is more balanced and generalizable for efficient ObjNav.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.02302

Improving Evaluation Realism with Inference-Time Compute and Deployment Scaffolds

Axel Ahlqvist, Richard Guan, Juan-Pablo Rivera, Adeline Kassler, Dmitrii Troitskii, Alexandra Souly, Kai Fronsdal, Robert Kirk 等 9 位作者

原文摘要

A core obstacle to alignment evaluation is evaluation awareness: capable models can tell when they are being tested rather than deployed, weakening the conclusions a safety evaluation can support. We present two techniques that make simulated alignment evaluations harder to distinguish from real deployments. Our first technique, critique refinement, spends additional inference-time compute on each simulator action: the simulator generates multiple candidate actions, refines them using feedback from an instance of the target model on how to make them more realistic, and continues the evaluation with the most deployment-like candidate. Our second technique, DISH (Deployment-Imitating SWE-Agent Harness), wraps the target in an agent harness, reducing the gap between simulated and real deployment environments in coding settings. We test the techniques on multiple target models and find that applying both yields larger realism gains than either alone. We show that automated approaches can improve the realism of alignment evaluations, and that they compare favorably to spending the extra compute on more audit attempts.

cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2511.00203

Diffusion LLMs are Natural Adversaries for any LLM

David Lüdke, Tom Wollschläger, Paul Ungermann, Stephan Günnemann, Leo Schwinn

原文摘要

We introduce a novel framework that transforms the resource-intensive (adversarial) prompt optimization problem into an \emph{efficient, amortized inference task}. Our core insight is that pretrained, non-autoregressive generative LLMs, such as Diffusion LLMs, which model the joint distribution over prompt-response pairs, can serve as powerful surrogates for prompt search. This approach enables direct conditional generation of prompts, effectively replacing costly discrete optimization with a small number of parallelizable samples. We provide a probabilistic analysis demonstrating that under mild fidelity assumptions, only a few conditional samples are required to recover high-reward (harmful) prompts. Empirically, our method substantially outperforms prior attacks in both success rate and compute cost, producing low-perplexity, diverse jailbreaks that transfer to a wide range of black-box target models, including robustly trained and proprietary LLMs. Beyond adversarial prompting, our framework opens new directions for red teaming, automated prompt optimization, and leveraging emerging Flow- and Diffusion-based LLMs.

cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.19732

Controlling the latency in temporal coded spiking models for neuromorphic datasets

Alban Gattepaille, Alexandre Muzy

原文摘要

Spiking neural networks (SNNs) promise energy-efficient, event-driven computation on neuromorphic hardware, where response time, or latency, matters as much as energy for real-time applications. Time-to-first-spike (TTFS) coding with learnable synaptic delays is a natural way to exploit the timing of events. However, in state-of-the-art spiking neuron models, spike times emerge from nonlinear membrane dynamics and thresholds: no closed-form expression links network parameters to spike times, so latency can only be measured empirically, not specified or guaranteed at design time. Moreover, TTFS models are mostly evaluated on static datasets, which hides their latency on truly temporal data. Our hypothesis is that, if spike times are computed directly as closed-form functions of presynaptic spike times and synaptic delays, bypassing membrane potential and thresholding, latency becomes an explicit function of the learnable parameters that can be bounded, and therefore controlled, analytically. We build on this hypothesis with the Delay Neural Network (DeNN) in real time, which turns event streams into successive single-spike images, computes each spike time from standardised inter-spike intervals and synaptic delays, and links successive images through a long-term memory. We prove lower and upper bounds on the latency of every neuron of a layer, and show that bounding the synaptic parameters after training with a single hyperparameter places the latency anywhere between these bounds, without retraining. On a ten-class subset of the Google Speech Commands dataset, this post-training control exposes the accuracy--latency trade-off: a three-hidden-layer network reaches 94.25\% accuracy while deciding, on average, 477~ms after the end of the one-second input, and about 250~ms with confidence-based early decisions. Learning is also shown to concentrate the long-term memory on a few neurons. Latency thus becomes a design parameter of spiking models rather than a quantity measured after the fact, a step towards neuromorphic systems with real-time guarantees.

cs.NE

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2605.30732

How To Track Qubits Through Space and Time (Or: Sailing in a Quantum Boat)

James Bartusek, Zikuan Huang, Leo Orshansky, Henry Yuen

原文摘要

While quantum position verification aims to certify a prover's location using quantum information, existing security definitions only guarantee that part of the successful adversarial party is in the claimed location. This leaves open the possibility that a distributed team of adversaries can jointly simulate a prover in a way that defeats the intended meaning of `being at a location' in position-based cryptography. We introduce stronger notions of position verification that we call quantum localization, which requires that there is a specified, unclonable state at the verified spacetime point-and that this state can be found nowhere else. We show that quantum localization leads naturally to a meaningful notion of trajectory verification, in which quantum information is verifiably tracked through space and time. We construct quantum localization and trajectory verification protocols using quantum anchor states, which generalize coset states from unclonable cryptography. The security of our schemes is proven in the classical oracle (i.e. ideal obfuscation) model, which can be heuristically instantiated in the plain model using post-quantum indistinguishability obfuscation. We also introduce and instantiate the concept of functionality localization, which guarantees that the adversary has the ability to compute a secret function at the verified spacetime point, and this function cannot be computed anywhere else. This raises the intriguing possibility of localizing computational capabilities in space and time. More broadly, we believe our notions of quantum localization and subsequent feasibility results provide stronger foundations for position-based cryptography. At a conceptual level, our results also explore the tight link between two fundamental notions in physics-location and information-thus serving as a natural foundation for cryptographic capabilities tied to physical spacetime.

quant-ph · cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.10009

Towards Multi-Source Domain Generalization for Sleep Staging with Noisy Labels

Kening Wang, Di Wen, Yufan Chen, Ruiping Liu, Junwei Zheng, Jiale Wei, Kailun Yang, Rainer Stiefelhagen 等 9 位作者

原文摘要

Automatic sleep staging is a multimodal learning problem involving heterogeneous physiological signals such as EEG and EOG, which often suffer from domain shifts across institutions, devices, and populations. In practice, these data are also affected by noisy annotations, yet label-noise-robust multi-source domain generalization remains underexplored. We present the first benchmark for Noisy Labels in Multi-Source Domain-Generalized Sleep Staging (NL-DGSS) and show that existing noisy-label learning methods degrade substantially when domain shifts and label noise coexist. To address this challenge, we propose FF-TRUST, a domain-invariant multimodal sleep staging framework with Joint Time-Frequency Early Learning Regularization (JTF-ELR). By jointly exploiting temporal and spectral consistency together with confidence-diversity regularization, FF-TRUST improves robustness under noisy supervision. Experiments on five public datasets demonstrate consistent state-of-the-art performance under diverse symmetric and asymmetric noise settings.

cs.LG · cs.CV · cs.RO

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arXiv ↗PDF ↗