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新论文 · v1arXiv:2611.05550

Energy-efficient End-to-End Communication System with Spiking Neural Networks

Pooja Plavilayil Anil, Te-Chuan Chiu, Nguyen Van Huynh

原文摘要

2030 is expected to witness the first commercial 6G systems. However, traditional modular communication architectures cannot meet the latency, optimisation, and integration requirements demanded by various sub-millisecond, data-intensive emerging 6G applications. End-to-end (E2E) communication addresses these limitations by replacing traditional processing blocks with neural networks. Nevertheless, conventional artificial neural networks (ANNs) in E2E systems use dense computation. This results in substantial energy consumption and computational resource requirements, which may not be feasible in resource constrained devices. This paper addresses the aforementioned challenge by developing a spiking neural network (SNN) encoder that targets lower encoder inference energy while maintaining communication reliability. In particular, the SNN encoder combines sparse spike activity, sinusoidal temporal modulation, and weighted temporal aggregation to map one-hot encoded messages to distinguishable transmit signals. The model is evaluated across antenna configurations and SNN timesteps to investigate the bit error rate (BER)-energy trade-off. The simulation results show that the SNN encoder achieves comparable BER under suitable timestep selections, while reducing the average estimated encoder energy by 68.93%, 52.20%, and 35.69% for SNN timesteps 2, 3, and 4, respectively, across five antenna configurations. These findings highlight the potential of the proposed SNN encoder for energy-efficient E2E communication systems.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.01932

Things that Matter -- Identifying Interactions and IoT Device Types in Encrypted Matter Traffic

Kristopher Alex Schlett, Bela Genge, Ioan Padurean, Savio Sciancalepore

原文摘要

Matter is the latest interoperability standard for the consumer Internet of Things (IoT) market, developed by the Connectivity Standards Alliance (CSA). Matter's design pays particular attention to security and privacy: it provides validated secure session establishment protocols, and it uses robust security algorithms to encrypt communications between IoT devices and Matter controllers. However, little is known about the extent to which information can still be inferred from encrypted traffic. This paper investigates whether a passive adversary can identify Matter interactions and device types by analyzing encrypted traffic metadata. Using datasets collected from both real-world and simulated Matter deployments, we identify repeatable fingerprints based on packet lengths, directions, and packet sequences. Our results show that Matter interactions can be identified with over 95% accuracy even under packet loss and delay conditions. Furthermore, device types such as lighting, lock, and plug appliances can be inferred with accuracy exceeding 88%. These findings reveal a significant privacy risk affecting Matter-enabled deployments regardless of vendor. The CSA acknowledged our findings and indicated that mitigation mechanisms may be considered in future Matter versions.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.16568

Manifold-Matching Autoencoders: Embedding Data into Chosen Geometric Structures

Laurent Cheret, Vincent Létourneau, Isar Nejadgholi, Chris Drummond, Hussein Al Osman, Maia Fraser

原文摘要

Manipulating the structure of data in the latent space of an autoencoder is a long-standing goal of representation learning. We propose a simple regularization, Manifold Matching, that imposes an explicit geometric structure on that latent space. The idea is to take a reference representation R of the data, which need not be the raw input X fed to the network, and to minimize, batch-wise, the mean squared error between the pairwise distances in R and those in the latent space Z. Although the autoencoder still learns to reconstruct X, the shape of its latent space comes to mimic that of R. The objective requires no labels, only reference distances, and because those are scalars, the dimensionality of R is decoupled from that of Z: any reference can guide any bottleneck. Using this single instrument we study how different geometric structures can be imposed on a latent space, comparing against autoencoders built on geometric or topological regularizers. Across fifteen datasets under a shared protocol with statistical testing, the resulting Manifold-Matching Autoencoder (MMAE) matches the global-structure quality of persistence-based autoencoders, statistically tying RTD-AE on distance correlation and triplet accuracy, and stays competitive on the local and topological metrics that other methods lead, at the computational cost of a plain autoencoder: it uses only a batched distance loss, with no persistence computation. We frame MMAE not as a universal winner but as an efficient and controllable regularizer whose reference selects which structure the latent inherits, and which turns any fixed embedding into a reusable out-of-sample encoder. All experiments use a two-dimensional bottleneck.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05549

Whose Transparency is it Anyway? Understanding Generative AI Transparency Needs and Gaps Across Stakeholders

Genevieve Smith, Sahiba Chopra, Natalia Luka, Min Kyung Lee

原文摘要

Transparency has become a central principle in AI governance, yet there is little agreement about what transparency means, for whom, and in what contexts. Drawing on interpretive flexibility within the Social Construction of Technology, this paper examines how different stakeholders understand and experience transparency in generative AI. This study draws from a global survey of 400 end-users, AI builders, and policymakers, complemented by a structured focus group with end-users, grounding the quantitative findings in qualitative insights from the group most underserved by current transparency practice. We present three key findings. First, all three groups share similar understandings of what transparency means and what it should afford, centered on ensuring accuracy and privacy. Second, gaps between the importance of transparency features and their perceived availability are substantially larger for end-users than builders, revealing how power shapes what gets built and who it serves. Only 28% of end-users report that current transparency practices reflect their needs very well, compared to 53% of builders and 49% of policymakers. Third, transparency is situationally constructed, priorities shift with context and task structure, with role-specific frames activating when context demands them and receding when it does not. We argue that meaningful AI transparency requires moving beyond a one-size-fits-all solution, including current emphases on AI disclosure and static documentation, toward pluralistic transparency infrastructures that reflect the needs of diverse users and stakeholders.

cs.AI · cs.CY · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05548

Iterated creation, grading, and revision of data science projects by language models

Rohan Alexander

原文摘要

We evaluate the extent to which agentic AI models can complete an end-to-end data science project, and how this improves with feedback. A 'writer' model with specific tools creates a complete data science project including downloading, cleaning, and analyzing a dataset, and writing a short paper about it. The project is executed and a 'grader' model evaluates it. The writer can then revise its submission, and this repeats for a total of three rounds. We consider seven models from five developers: Anthropic, OpenAI, Google, DeepSeek, and Z.AI. Each model is both a writer and a grader, so there are 49 writer-grader pairs and we run each pair three times. Of the 147 initial submissions, 107 run and render, and the average score increases from 59 per cent to 76 per cent after the final round of feedback. By writer the extent of the increase ranges from 3 percentage points for Anthropic's Opus 5.5 to 46 percentage points for Anthropic's Haiku 4.5, and by grader it ranges from 13 percentage points for Google's Gemini 3.8 Flash to 32 percentage points for Anthropic's Haiku 4.5. When a model grades its own writing the score tends to be about 9 percentage points higher than the average of what the other models give it. About half of the difference that we see from round to round is within the grader's own noise. Feedback generally results in the highest marks, but even just having the chance to revise, or having the additional budget, in terms of time or tool calls, without any feedback, results in considerable increases for some of the models but not for others. We find that how much such a loop helps depends on which model plays which role, but weaker models generally do not improve stronger models.

cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05546

FedSharp: Byzantine Robust Decentralized Federated Learning Under Noisy Channel

Ojaswi Sinha, Amin Farajzadeh, Animesh Yadav, Melike Erol-Kantarci

原文摘要

Decentralized federated learning (DFL) eliminates the central aggregation server but remains vulnerable to data heterogeneity, Byzantine attacks, and channel noise. We show that noise accumulates as models propagate across nodes, becoming increasingly significant as the learning rate decays. Moreover, accumulated noise enables Byzantine attackers to exploit loss-based model selection by mimicking honest model averages. To address these challenges, we propose screened, hop-aware, ranked, proximal averaging, a robust DFL framework (FedSharp). In this approach, each node maintains models from its 2b predecessors, ensuring at least b honest candidates when at most b nodes are Byzantine. FedSharp screens suspicious models using coordinate-wise median deviations, averages the b lowest-loss candidates to reduce fresh channel-noise variance by a factor of b, and applies noise-adaptive gradient-norm regularization and proximal training to mitigate accumulated noise and non-IID drift. Experiments on a 100-node ring with 33 Byzantine nodes show that FedSharp achieves 93.5-94.9% accuracy on MNIST, compared with 29.4-38.1% for a state-of-the-art single-model-selection benchmark, and 32.8-48.8% on CIFAR-10, compared with 10.0-30.5%, across Gaussian and hidden attacks under IID and non-IID data. On MNIST, FedSharp also achieves accuracy within approximately one percentage point of the benchmark operating without channel noise.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05544

A Unified Framework for Training-Dataset Aggregation and Exact Machine Unlearning via Gibbs Probability Measures

Yaiza Bermudez, Samir M. Perlaza, Iñaki Esnaola

原文摘要

In this paper, three operations on Gibbs probability measures are studied: renormalization, normalized log-linear combination, and nesting. The resulting measures are shown to be Gibbs probability measures with explicit expressions for their objective functions, reference measures, and regularization factors. These measures are further characterized as unique solutions to optimization problems involving linear combinations of the original objective functions, subject to either relative entropy regularization or relative entropy constraints. When the reference measures are probability measures, geometric interpretations of these three operations are established involving Pythagorean identities and exponential geodesics.A central result is the equivalence between nesting and normalized log-linear combinations of Gibbs probability measures. These results are then applied to supervised machine learning with Gibbs algorithms, which are represented by Gibbs probability measures. In this context, either through nesting or log-linear combinations, Gibbs algorithms trained on local datasets are used to form new Gibbs algorithms trained upon the aggregation of all local training datasets. Interestingly, the parameters of such operations, determine the weights of each training data point on the aggregated dataset. %Operations on local Gibbs probability measures are shown to control the contributions of training data. This observation is leveraged for achieving: (a) exact machine unlearning in centralized, decentralized, and federated learning systems; and (b) centralized-equivalent performance, in the sense of producing algorithms trained on the aggregation of all local datasets in decentralized and federated learning .

stat.ML · cs.IT · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05543

Generalized Hyperrectangular Prediction Sets in Multivariate Conformal Prediction

Nabil Alami, Souhaib Ben Taieb

原文摘要

In multivariate conformal prediction, many nonconformity scores yield prediction sets with complex geometry (e.g., ellipsoids or density level sets) that are difficult to interpret and visualize. Hyperrectangles offer a practical alternative by providing one interval per output. We extend this notion to Cartesian products of arbitrary one-dimensional sets, which we call generalized hyperrectangles. We introduce a rectangularization framework that turns any continuous multivariate nonconformity score, or conditional density, into one yielding generalized hyperrectangular prediction sets with finite-sample coverage. We establish key geometric properties of our rectangularization framework and provide useful intuitions on generalized hyperrectangles. We further establish a representation theorem showing that a broad class of hyperrectangular conformal methods based on max-aggregated coordinate-wise scores can be interpreted as rectangularizations of implicit conditional densities. Through synthetic and real-world experiments, we quantify the efficiency cost of rectangularization, while highlighting its benefits, especially for practical applications of uncertainty quantification.

stat.ML · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.17590

Revisiting the 16x Pretraining Compute Gap between Diffusion and Autoregressive Language Models

Guanghan Wang, Bing He, Zhan Shi, Benoit Dumoulin, Hanqing Lu, Volodymyr Kuleshov

原文摘要

Diffusion language models (DLMs) have attracted wide attention for their fast, parallel decoding, but they are believed to be far less efficient to train than autoregressive language models (ARMs): a widely cited study reports that a DLM needs roughly 16x the training compute of an ARM to match its likelihood, a figure that has made pretraining DLMs from scratch look prohibitive at scale. We find that the methodology behind this figure has fallen out of step with current DLM practice: it scores the model with its evidence lower bound (ELBO) and trains a full-sequence, decoder-only model. We therefore revisit the scaling laws of DLMs, adopting the exact likelihood under the model's own decoding order, block diffusion training, an encoder-decoder architecture, and a sweep over the block sizes used in practice. Under this new protocol, we study across ten model variants, about 1,700 models in total, and find that pretraining DLMs is far cheaper than previously thought. Concretely, scoring with the exact likelihood reduces the compute multiple to 12.8x; block diffusion training and an encoder-decoder architecture further reduce it to 7.8x at block size 32, and smaller block sizes narrow it to 4.6x at block size 8 and 3.0x at block size 4. We complement these scaling analyses with an empirical rule for the optimal decoder depth of encoder-decoder block diffusion, a held-out compute budget beyond the fitting range at which the gap between variants persists, and downstream benchmark results where likelihood improvements carry over to accuracy. Overall, the 16x figure substantially overstates the pretraining cost gap between DLMs and ARMs, and pretraining DLMs from scratch is far more viable than previously assumed.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05542

Towards Efficient Whole Page Ranking with One-Pass Inference

Qi Xu, Zhigang Hua, Zijie Lei, Yan Xie, Jizhe Zhang, Lynn Lu, Delun Du, Sha Hua 等 13 位作者

原文摘要

Whole-page ranking jointly optimizes advertisements and organic content to balance business outcomes and user engagement under complex layout and business constraints. Existing non-autoregressive (NAR) approaches enable efficient page generation but typically score candidate items independently of previously selected items, limiting their ability to capture order-dependent interactions. Autoregressive (AR) methods address this limitation through sequential conditioning, but incur substantial inference overhead that challenges large-scale deployment. Bridging this quality-efficiency gap remains a challenge in industrial whole-page ranking. We propose Multi-Item Scoring (MIS), a prefix-aware NAR-style framework that incorporates sequential context into efficient multi-item scoring. MIS integrates global candidate interactions, localized prefix conditioning, and position-aware scoring within a shared representation architecture. By computing reusable representations once per request and performing lightweight prefix-conditioned scoring during constrained beam search, MIS captures important sequential dependencies without repeated full-model inference. Experiments on KuaiRand-1K and RL4RS demonstrate that MIS approaches the generation quality of an autoregressive CGA baseline while achieving 4.07x and 3.54x faster CPU decoding, respectively. In a randomized evaluation on a large-scale social-video platform, MIS improves the realization metric by 0.104% and watch time by 0.058%, while reducing ranking-service CPU consumption by 9.73%. These results demonstrate that prefix-aware multi-item scoring can narrow the quality gap between NAR and AR generation while retaining the efficiency required for industrial scalability.

cs.AI · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05540

ProofStudio: Five Proof Assistants in the Browser

Yutaka Nagashima

原文摘要

Recent advances in AI-assisted proof automation are extending the use of proof assistants in software verification and mathematics. However, greater proof automation does not remove users' responsibility to assess whether formal statements capture their intended requirements, making education in formal specification and reasoning essential. To lower the installation and learning barriers associated with mature proof assistants, we present ProofStudio, a browser-based environment integrating Isabelle/HOL, Lean, Rocq, HOL4, and HOL Light with corresponding worked examples, preconfigured proof search, and contextual AI assistance. With ProofStudio, users can explore formal specifications and proofs across five systems without local installation, inspecting native prover feedback and experimenting with automation through a common interface.

cs.SE · cs.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05539

Rewrite, Then Veto: Rewriter-Agnostic Edit Gating for LLM-Based Post-OCR Correction

Jorge Ramón Fonseca Cacho

原文摘要

LLMs can repair noisy OCR transcripts of historical documents, but they also change text that was already correct. We propose EditVeto, a veto gate placed after a full-context LLM rewrite. The rewrite is character-aligned to the OCR input, its differences are split into blocks, and a 0.6B-parameter gate keeps or reverts each block. An oracle analysis motivates the design: given the true error locations, per-span generation is worse than no correction, whereas vetoing a full rewrite's edits helps. Trained on rewrites by two Qwen3 models and evaluated, frozen, on the eight HIPE-OCRepair-2026 test sets, the gate improves all five rewriters tested, two of them unseen in training: weighted cMER falls by 0.0026-0.0044 and the preference score rises by 0.15-0.25 (paired bootstrap, p ~ 0.0002 each). On published competition outputs it lowers cMER for three of four teams but raises it for the winning run (0.0050 to 0.0065). In a second, disclosed test look, a retrained gate still improves LoRA-tuned rewriters (cMER -0.0011 to -0.0021), and an ensemble of their gated edits reaches a cMER of 0.0069 and a preference score of 0.928, mostly through fine-tuning and pooling rather than gating. Built only on public models and data, it is behind the winner (0.0050) and tied with the second-ranked run (0.0071), both from the only team using domain pre-training, and ahead of all other published runs. A third, secondary look shows the unchanged gate improving zero-shot Qwen3.8-27B (cMER 0.0114 to 0.0108) but only the preference score of its LoRA-tuned version.

cs.CL · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05538

Recasting Visual Language Models as EEG Foundation Models for Clinical Neurodiagnostics

Xin Zhou, Elena Sergeeva, Nan Bi, Wenyu Song, Lijun Yin, Hao Deng

原文摘要

Historically automated electroencephalography (EEG) systems was done using task-specific supervised models trained on purposely annotated data. A more recent modeling paradigm relies on creating a unified representation, or a 'foundation model', for multiple modality-specific tasks through large-scale domain-specific pretraining on the unannotated EEG signal. The quality of this unified representation directly depends on the availability of the modality-specific datasets. At the same time, generalist Vision Language Models (VLMs) exhibit broad few-shot learning capacities as well as good fine-tuning adaptability to different visual and language tasks. Can VLMs learn how to read and interpret EEG visually, emulating to a degree the way human clinicians inspect the EEG patterns? In this work we make a case that they can: we show that generalist VLMs without large-scale EEG pretraining perform competitively across a collection of clinical neuro-diagnostic tasks, can adapt to different tasks in a few-shot learning setting better than specialist foundation models, and exhibit better performance with model scaling when modality-specific models fail to exhibit.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2604.01650

AromaGen: Interactive Generation of Rich Olfactory Experiences with Multimodal Language Models

Awu Chen, Silang Wang, Yunge Wen, Jianing Yu, Zinab Eisa, Ana Julia Santos Lopes, Jas Brooks, Hiroshi Ishii 等 9 位作者

原文摘要

Smell's connections with food, memory, and social experience have motivated researchers to bring olfaction into interactive systems. Multimodal AI opens new possibilities for generating smells from natural language, yet it remains unclear whether pretrained models encode sufficient olfactory knowledge to translate language into perceptible compositions. We present AromaGen, an AI-powered 12-channel wearable olfactory system that maps free-form natural-language descriptions to 12 base odorants selected to cover a semantically derived olfactory space, while supporting iterative refinement through natural-language feedback. In a between-subjects study (N=60) across 50 real-world benchmark smells, participants distinguished AromaGen-generated target smells above chance in a three-alternative forced-choice task, with retrieval-augmented AI composition performing comparably to human-expert composition and zero-shot AI composition. Natural-language feedback significantly improved the perceived similarity of both AI- and human-expert compositions. Our findings demonstrate the feasibility of using pretrained multimodal AI, with human aroma composition data, to generate perceptible olfactory experiences from natural language.

cs.HC · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05528

The Confidence Score: a New Measure on Kernels

Elizabeth Hofer, Martin v. Mohrenschildt

原文摘要

Across a wide range of applications, kernel measures are used to tune hyperparameters, select among candidate kernels, and assess the suitability of kernels for specific learning tasks. For classification, kernel measures are used to predict which kernels may produce accurate kernel classifiers. Many existing kernel measures, such as alignment to the ideal kernel, are designed to reward kernels which captures all samples from the same class as similar. While effective in many settings, this requirement is unnecessarily restrictive for classification, where classes may exhibit substantial internal structure. In this work, the "Confidence Score", a new kernel measure, is proposed. Unlike alignment-to-the-ideal kernel measures, the Confidence Score measures the alignment between an encoding of the ground truth and that ground truth projected by the kernel, allowing kernels that preserve intra-class structure to achieve high scores. Through experiments, it is demonstrated that the Confidence Score exhibits a higher correlation with classification accuracy across a variety of kernels on both real-world and synthetic datasets, compared to select other kernel measures.

cs.LG · eess.SP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05527

Tight Lower Bounds under Matched High-Order Hölder Smoothness and Uniform Convexity

Cedar Site Bai

原文摘要

In this paper, we provide tight lower bounds for minimizing high-order Hölder-smooth and uniformly convex functions when their orders match. Specifically, we consider functions whose $p^{th}$-order derivatives are Hölder continuous with degree $ν$ and parameter $H$, and that are uniformly convex with degree $q$ and parameter $σ$. When these orders match, namely, $q = p + ν$, we show that any deterministic algorithm with access to derivatives up to order $p$ requires $Ω\left(\left(\frac{H}σ\right)^{\frac{2}{3(p+ν)-2}}\log\frac{Δ_0}ε\right)$ oracle calls to reduce an initial optimality gap of $Δ_0$ to $ε$. This matches the corresponding upper bound and establishes a higher-order uniformly convex counterpart to Nesterov's classical first-order lower bound for strongly convex functions. Rather than directly generalizing Nesterov's classical quadratic-chain construction, we develop a multiscale phase-wise construction that preserves previous oracle responses while maintaining global Hölder smoothness and uniform convexity. Our result resolves the matched case left open by existing lower bounds for the two asymmetric regimes.

math.OC · cs.LG · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.09258

Back to the Familiar Future: Failure Recovery for VLA Policies via Pre-Imagined Milestone Selection

Suyeon Shin, Juwon Kim, Hyeonbin Park, Hyunseo Kim, Hyundo Lee, Hyung-Sin Kim, Byoung-Tak Zhang

原文摘要

Vision-language-action (VLA) policies can deviate from nominal trajectories during manipulation, even when tasks remain physically feasible. Recovering from these deviations is challenging, as they push the policy into unfamiliar state spaces where direct re-planning frequently destabilizes action sequences. We propose Back to the Familiar Future (B2FF), a recovery framework for foresight-driven VLAs that leverages future visual conditioning as a recovery interface. Before execution, the VLA generates a milestone bank of familiar future states conditioned on the clean initial observation. At recovery time, a recoverability-aware selector selects a recovery milestone from this bank and enforces it as a fixed visual goal. This enables the VLA to robustly map off-trajectory observations back to a familiar future. On failure-injected LIBERO, under controlled recovery timing aligned with the injected failure, B2FF increases the average success rate of a baseline VLA from 56.3% to 74.0%, demonstrating that pre-imagined milestones can guide recovery without fine-tuning the low-level action generator.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05524

MA-SafeDiffuser: Safe Multi-Agent Planning with Diffusion Probabilistic Models

Kiran Ravish, Ankita Kushwaha, Preeti, Pawan Kumar

原文摘要

We propose MA-SafeDiffuser, a multi-agent extension of SafeDiffuser that equips diffusion-based trajectory planners with finite-time diffusion invariance guarantees for joint-agent safety specifications. Building on the single-agent construction that embeds control barrier function (CBF) constraints into reverse-diffusion updates, we: (i) formalize joint safe sets as intersections of per-agent and pairwise barrier sets; (ii) derive centralized and decentralized (communication-aware) constrained denoising procedures with provable invariance under mild assumptions; (iii) address local-trap and deadlock phenomena via time-varying specifications and liveness CBFs; and (iv) develop a lightweight benchmarking suite including a multi-agent Maze2D domain. Empirically, MA-SafeDiffuser reduces violation counts relative to unconstrained diffusion baselines, while retaining planning quality. We provide algorithms, proofs, and reference implementations.

cs.AI · cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.32503

Pareto-optimal Non-Clairvoyant Scheduling with Predictions

Ziyad Benomar, Vianney Perchet

原文摘要

We study preemptive single-machine non-clairvoyant scheduling with predictions, where a prediction of the order of the jobs (or of their sizes) is available, and the objective is to minimize the total completion time. An algorithm is $c$-consistent if it is $c$-competitive when the prediction is correct, and $R$-robust if it is $R$-competitive whatever the prediction. Since the introduction of this problem as one of the first examples of learning-augmented algorithms, the best possible trade-off between consistency and robustness has remained open. We determine it exactly for deterministic algorithms. For every $R>2$, we give an $R$-robust algorithm whose consistency is \[ c(R)=R\Big(1-\Big(1-\frac{R+2}{R^2}\Big)^{\frac R{R+2}}\Big), \] and we prove that no deterministic $R$-robust algorithm, with either predicted order or predicted job sizes, has better consistency. In particular, consistency $1+\varepsilon$ is achievable with robustness $\varepsilon^{-1/2}+\frac16+O(\sqrt\varepsilon)$, whereas all previous algorithms required robustness $Θ(1/\varepsilon)$. Our algorithm is greedy: it follows the predicted order as fast as possible, subject to the invariant that the cost paid so far is at most $R$ times the optimum of the instance in which every unfinished job would end immediately. The matching lower bound rests on the observation that every robust deterministic algorithm must satisfy the same invariant along its run on correct predictions. Furthermore, we give a simple randomized algorithm, using predicted sizes, with consistency $1+\varepsilon$ and robustness $(3\varepsilon)^{-1/2}+O(1)$, strictly below the deterministic frontier. The question of determining the Pareto-optimal frontier among randomized algorithms remains open.

cs.DS · cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05522

From Activation to Intervention: Interpreting Sparse Visual Features through Reconstruction

Jianyang Gu, Jacob Beattie, Mridul Khurana, Julia Chae, Xi Xiao, Cheng Zhang, Anuj Karpatne, Wei-Lun Chao 等 10 位作者

原文摘要

Sparse autoencoders (SAEs) decompose visual representations into individual features, offering a promising basis for explaining model behavior. Interpreting these features typically relies on images or pixel regions that most strongly activate them. However, this evidence becomes ambiguous when multiple features with different semantics frequently activate in the same locations, such as texture and color in the same patterns. We propose Reconstructive SAE Intervention (RecSAEI), a framework that shifts feature explanation from passive observation to active visual reconstruction. By intervening on representations along SAE decoder directions and reconstructing the visual output, we provide direct evidence of a feature's semantic meaning. We demonstrate that our method successfully disambiguates feature meanings across multiple datasets and vision encoders. Furthermore, our framework grounds [CLS] SAE interventions in a sparse set of patch SAE decoder directions. Beyond stable target response, this attribution also exposes the compositional relationship between elementary visual patterns and high-level semantics. Our results advocate for intervention reconstruction as an essential complementary signal for explaining neural networks.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2403.15191

Your Trust, Your Terms: A General Paradigm for Near-Instant Cross-Chain Transfer

Di Wu, Jingyu Liu, Xuechao Wang, Jian Liu, Yingjie Xue, Kui Ren, Chun Chen

原文摘要

Cross-chain transactions today remain slow, costly, and fragmented. Existing custodial exchanges expose users to counterparty and centralization risks, while non-custodial liquidity bridges suffer from capital inefficiency and slow settlement; critically, neither approach guarantees users a unilateral path to recover assets if the infrastructure fails. We introduce the Delegated Ownership Transfer (DOT) paradigm, which decouples key ownership from value ownership to enable secure, high-performance cross-chain payments. In DOT, a user deposits funds into a sandboxed on-chain Temporary Account (TA) (value ownership) while delegating its private key (key ownership) to an abstract Trusted Entity (TE). Payments and swaps are thus reframed as near-instant, off-chain ownership handoffs. Security follows from dual guarantees: the TE's exclusive control prevents double-spending, while a pre-signed, unilateral recovery transaction ensures users retain ultimate authority over their assets. Building on this foundation, we design a novel off-chain atomic swap that executes optimistically in near real time and remains fair even if the TE fails. We formalize the security of DOT in the Universal Composability framework and present two concrete instantiations: a high-performance design based on Trusted Execution Environments (TEEs) and a cryptographically robust variant leveraging threshold cryptography. Our geo-distributed prototype shows that cross-chain payments complete in under 16.70 ms and atomic swaps in under 33.09 ms, with costs fully decoupled from Layer-1 gas fees. These results provide a practical blueprint for building secure, efficient, and interoperable cross-chain payment systems.

cs.CR · cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.18439

RegimeVGGT: Layer-Wise Spatially Preserving Redundancy Removal for Visual Geometry Grounded Transformer

Jinhao You, Shuo Lyu, Zhuohang Lyu, Tanxuan Li, Zibo Zhao, Jiaxiang Hu, Kai Tang, Yichen Guo

原文摘要

Visual Geometry Grounded Transformer (VGGT) recovers dense 3D scene structure from multi-view images in one forward pass, but quadratic cross-frame attention limits its scalability. Existing training-free accelerators exploit token, frame, or attention redundancy; we investigate how layer-wise functional roles can guide compression. Our spectral, probing, and causal analyses reveal three regimes: shallow layers lack cross-view structure, middle layers drive cross-view alignment, and deep layers are redundant for dense geometry yet their cross-frame attention remains essential for pose. RegimeVGGT applies layer-wise U-shaped compression along two axes: Saliency-Guided Banded Merging protects geometry- and edge-salient tokens, while Selectively Protected K/V Downsampling preserves cross-frame spatial coverage and the pose-critical path through a phase-shifted spatial grid, a reference-frame anchor, and uncompressed camera/register tokens. Without any training, RegimeVGGT achieves a 6.7x speedup over the memory-optimized VGGT* at matched reconstruction quality. Project page: https://tracyou07.github.io/regimevggt/

cs.CV · cs.RO

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新论文 · v1arXiv:2611.05517

When to Ask, When to Answer: Strategic Clarification for Ambiguous Reference Resolution in Dialogue

Ibrahim Khebour, Nikhil Krishnaswamy

原文摘要

For humans, referential disambiguation is a collaborative process involving clarification questions asked when they will meaningfully change the outcome. However, large language models (LLMs) have been shown to be biased toward premature answering, preferring definite single-turn responses over more clarification exchanges. In this paper, we study the problem of "strategic clarification" -- asking clarification questions in a reference game when their answers will narrow the set of candidate referents. We construct a controlled test environment from the SIMMC 2.1 dataset that records how many questions each ambiguous reference needs to be successfully resolved, and examine how baseline LLM behavior and common post-training methods ask clarifying questions vs. attempt to answer the referent. We also propose BACE (Belief-Aware Critic with Epistemic tracking), a lightweight action chooser method on top of a frozen LM backbone, and show that BACE more consistently chooses the optimal number of clarifying questions than methods such as GRPO and ArCHer.

cs.CL · cs.AI

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新论文 · v1arXiv:2611.05515

TSFM Meets LLM: Context as Covariates

Hemanth Ram, Sai Shankar Narasimhan, Shubhankar Agarwal, Sandeep Chinchali

原文摘要

Textual information is ubiquitous in real-world time series datasets, appearing as news, reports, and annotations that hold key predictive signals not deducible from numerical history alone. Large Language Models (LLMs) interpret text but lack numerical precision, while Time Series Foundation Models (TSFMs) are quantitatively accurate but cannot reason about the effects of text. Moreover, the lack of large-scale paired multimodal datasets limits progress in bridging this gap. In this work, we address this challenge by providing a scalable approach for generating realistic multimodal datasets by annotating real-world time series with LLMs. Building on this, we propose Context as Covariates (CoCo), a forecasting framework in which an LLM distills its textual reasoning into numerically grounded forecast and confidence covariates that guide a TSFM backbone, thereby exploiting the strengths of both LLMs and TSFMs. We align the two models in stages, first fine-tuning the LLM via Group Relative Policy Optimization (GRPO) with a reward tied to covariate-induced improvement in forecasting performance, and then fine-tuning the TSFM on covariates generated by the aligned LLM. Our experiments show that models trained on our synthetic multimodal corpus generalize to real benchmarks spanning finance, economics, weather, traffic, and security. Notably, CoCo with Qwen3-4B-Instruct-2507 as the LLM and Chronos2 as the TSFM achieves up to 12% MAE improvement over state-of-the-art TSFMs and LLM forecasters, such as GPT-4o. Code, checkpoints, and the evaluation datasets are publicly available.

cs.LG · cs.AI

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新论文 · v1arXiv:2611.05514

Where models of minimal height are hard to find: cubic rational maps with a multiple fixed point

Elira Shaska

原文摘要

Given a rational point of the moduli space of cubic rational maps, we seek a map in its class with the smallest integer coefficients. A recent algorithm (Shaska, arXiv:2610.33990) combines an exact descent and minimisation of the resultant with a reduction and a local search; it is nearly exact where the minimal height is known. Beyond that range, a certified deeper search shows that the instances in which a smaller model lies beyond its reach are not random: a classifier predicts them from the six invariants of the point, and asking what it relies on leads to an exact algebraic surface, the maps with a triple fixed point. Invariant theory and the stability hypothesis of reduction theory (Hutz and Stoll, 2019) then identify the larger locus that contains it: the hypersurface $ξ_1^3=6ξ_2^2$ of maps with a multiple fixed point, on which the fixed-point quartic is not stable. It holds most of the hard instances. On it the two invariants of the fixed-point quartic are $(ξ_1,ξ_2)=(6m^2,6m^3)$ for an integer $m\ge0$; we parametrise the integer points of the surface, and show that on its generic part the sign of $ξ_5$ is the sign of the cubic coefficient at the parabolic fixed point. A covariant defined on the locus, added to the reduction, resolves only a minority of the hard instances: they appear to be mainly a matter of the search's reach. These findings lead to a certified structured search that never returns a larger model than the original algorithm and finds most of the smaller models within reach, also at heights not used to design it, at a small multiple of its running time.

cs.SC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05512

SlackBench: Benchmarking Agents on Collaborative Projects Grounded in Real Code Repositories

Mingxuan Li, Huanzhi Mao, Ruitao Zheng, Jarod Stanbury James, Yunze Li, Natanya Anderson, Joseph E. Gonzalez, Alex Dimakis

原文摘要

AI Agents are becoming digital coworkers that can browse the web, write code, run experiments, and assist humans over extended horizons. Yet existing benchmarks fall short of real knowledge work: many rely on synthetic data or isolated tasks, missing the complex, multi-party dynamics of collaborative projects, where critical information is scattered across conversations, code, and evolving decisions. Even simple workplace questions may require locating relevant channels and direct messages, recovering context from long discussions, inspecting repository state, reconciling stale or conflicting evidence, and synthesizing across sources. We introduce SlackBench, a benchmark for evaluating agents on realistic research and engineering workflows that require joint reasoning over workplace communication and evolving repository state. Grounded in privacy-preserving reenactments of real academic research projects, SlackBench provides reproducible environments with realistic multi-party dialogue, evolving decisions, and corresponding code changes grounded in GitHub repositories and Google Docs. It contains 140 queries spanning heterogeneous-source agentic search, abstention on unanswerable or false-premise questions, goal-directed summarization, and circumstance inference over long conversations. These tasks are not reducible to static corpus question answering: agents must determine which conversations matter, traverse threads, inspect repository state, and synthesize evidence across communication and code artifacts. Across 33 frontier models and agent harnesses, the best system scores around 80% and most agents score below 66%, highlighting substantial headroom for improvement. SlackBench thus provides an extensible framework for evaluating agents in collaborative work environments. The SlackBench dataset is available at https://huggingface.co/datasets/HuanzhiMao/SlackBench .

cs.AI · cs.CL

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新论文 · v1arXiv:2611.05511

Improved lower bounds on the approximability of Multiway Cut using Sherali-Adams relaxations

Joshua Brakensiek, Neng Huang, Aaron Potechin, Uri Zwick

原文摘要

The Multiway Cut problem is among the most well-studied problems in the area of combinatorial approximation algorithms. The currently best approximation ratio for the problem is 1.2787, obtained recently by the authors (2026). The best lower bound on the approximation ratio that can be achieved for the problem in polynomial time, under the Unique Games Conjecture (UGC), is 1.20016, obtained by Bérczi, Chandrasekaran, Király, and Madan (2020), building on and slightly improving a bound of $\frac65=1.2$, obtained by Angelidakis, Makarychev, and Manurangsi (2017) by introducing the important framework of non-opposite multiway cuts. We obtain a substantially improved lower bound of 1.22232 on the approximation ratio of the Multiway Cut problem. Manokaran, Naor, Raghavendra, and Schwartz (2008) showed that under UGC, the approximation ratio of Multiway Cut is equal to the integrality ratio of a simple LP relaxation, known as the CKR relaxation, introduced by Călinescu, Karloff, and Rabani (2000). Thus, stronger LP relaxations are not required for obtaining an optimal approximation algorithm for the problem. We show, however, that relying on the framework of Angelidakis, Makarychev, and Manurangsi (2017), stronger LP relaxations from the Sherali-Adams hierarchy for non-opposite multiway cuts can be used to obtain improved integrality ratios for the CKR relaxation, and hence improved lower bounds on the approximation ratio of the Multiway Cut problem. One of the novel features of our analysis is considering a continuous limit of discrete LPs defined on a simplex. Our proof of the new 1.22232 bound is completely analytic. We also present strong numerical evidence that suggests that our techniques can be used to improve the lower bound to at least 1.23003.

cs.DS · cs.CC

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新论文 · v1arXiv:2611.05509

From Claws to Conferences: Device-Independent Key Agreement Beyond Non-Signaling

Suvradip Chakraborty

原文摘要

Device-independent conference key agreement (DI-CKA) allows a group of users to establish a common secret key without trusting the internal operation of their quantum devices. Existing DI-CKA protocols derive their security from multipartite Bell tests and therefore require the device components not to communicate during each round. We present, to the best of our knowledge, the first DI-CKA protocol that removes this non-signaling assumption: all interfaces and their mutual communication are treated as one untrusted device, which only has to be quantum polynomial-time while the protocol runs. Assuming that the Learning With Errors (LWE) problem is hard for quantum algorithms with quantum advice during the execution, we prove finite-size security against coherent attacks, in which the device may keep quantum memory and correlate and adapt its behavior across rounds. For two parties the protocol is a computational DI-QKD protocol with an explicit finite-size proof against coherent attacks. Technically, we lift the claw state underlying the classical-verifier test of Brakerski et al.\ (FOCS 2018) to multipartite entanglement, obtaining a \emph{GHZ-correlated claw state}. %that is prepared with one coherent claw-free function evaluation and $N-1$ CNOT gates. We then introduce the \emph{collective adaptive hardcore bit}, a multiparty generalization of the adaptive hardcore bit of Brakerski et al.\ and show that it follows from the original property by a tight reduction. Combined with Jordan's lemma, an entropic uncertainty relation, and entropy accumulation, this certifies smooth min-entropy of the key against an external quantum adversary. Our protocol achieves \emph{everlasting security}: LWE needs to hold only during the execution, because the final key is statistically secure and its secrecy persists even if LWE is broken after the protocol ends.

quant-ph · cs.CR

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新论文 · v1arXiv:2611.05507

Ctrl-F: Force as an Interface for Humanoid Loco-Manipulation

Mengjie Zhao, Yuxin Chen, Yiyang Shao, Chuye Hong, Jiakang Chen, Koushil Sreenath, Masayoshi Tomizuka

原文摘要

Humanoid motion tracking has scaled to general policies that reproduce diverse human motion, but the resulting behavior is in free-space: the tracker knows where the body should be, not how it should interact with the environment. Other work makes the object part of the objective, yet abstracts that interaction to contact or geometry, which leaves the demanding cases under-explored. We argue that force is a natural command channel between motion imitation and object interaction, one that specifies not just where to make contact but how hard. We present Ctrl-F, a hierarchical motion-force framework that augments motion tracking with an explicit force command, a 3-D force at each designated contact body such as a hand or a foot. The hierarchical framework comprises: (1) A unified motion-force controller tracks the reference motion while applying the commanded force to the world. (2) A high-level force planner that issues a force command online based on object state and proprioception observations. In simulation and on hardware, Ctrl-F completes loco-manipulation that demands large, sustained interaction force and adjusts its command online as the load changes, both out of reach of a force-blind tracker. On hardware, Ctrl-F demonstrates hand-based dragging, pushing, and lifting, as well as foot-based kicking, successfully manipulating boxes weighing up to 20kg in pushing and kicking tasks where the force-blind baseline fails, while adapting online to additional loads introduced mid-task.

cs.RO · cs.LG

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新论文 · v1arXiv:2611.05506

Objective-Conditioned Active Preference Learning for Multi-Objective Alignment

Duc Anh Vu, Thong Nguyen, Hoang Pham, Ponhvoan Srey, Luu Anh Tuan

原文摘要

Aligning large language models (LLMs) with several objectives, such as helpfulness, harmlessness, and honesty, typically relies on preference judgment for each objective, growing the annotation cost linearly with the number of objectives. Active preference learning can reduce this cost by selecting the comparisons whose labels are expected to change the policy most. Existing remedies either lack an objective-aware query selection mechanism for the multi-objective setting or regard conflicting preferences as harmful, therefore excluding them from the training data. We introduce OGC-DPO (Objective-conditioned Gradient Coverage DPO), an active learning framework that treats each (response pair, objective) combination as a separate query and measures its informativeness through the policy's own implicit gradient, without relying on an external reward model. OGC-DPO then selects the queries that most expand each objective's gradient coverage. Empirical experiments across varying numbers of objectives demonstrate the effectiveness of OGC-DPO over existing active preference learning methods under the same annotation cost. Further analysis demonstrates that informative conflicts, rather than being uniformly harmful, provide a useful policy training signal.

cs.LG · cs.AI

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