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新论文 · v1arXiv:2611.04590

Can Network Models of Science Do Without Pluralist Agents?

Jonas Hallgren, David Hyland, Mahault Albarracin

原文摘要

Science is a process of useful models being proven wrong time and again, but despite its many flaws, it somehow works. Why is this? Paul Feyerabend argued that it works because people dare to hold and defend incompatible rival theories against the accepted one, since a theory's failures often lead to its abandonment in the presence of a more accurate or simpler alternative. He called this view theoretical pluralism. We introduce a theoretical and experimental model in which this can be examined. It compares communities of pluralist agents, which can hold rival theories side by side and maintain beliefs within and between those theories, with communities of monist agents, which can only incorporate what they hear or observe into a single model of their own. In a multi-agent variational inference setting, our experimental demonstrations illustrate how communities with rival theories can go through periods of uncertainty followed by a shift in the theories they hold true, how the information that is communicated between communities affects beliefs when those communities make different observations about the world, and how sharing support for theories and sharing beliefs within them contribute differently to collective belief dynamics.

cs.MA · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.20687

The First-Order Oracle Complexity of Lipschitz Convex Optimization in Nondual Settings

Brian Bullins, Cristóbal Guzmán, David Martínez-Rubio, Mathieu Molina

原文摘要

We study first-order black-box convex optimization over an $\ell_p$-ball for objectives Lipschitz in the $\ell_q$-norm, solving in the affirmative the nonsmooth version of the COLT open question (Guz15b) on whether the geometry of a smaller feasible set ($p < q$) can improve convergence rates in convex optimization, and matching prior lower bounds up to logarithmic factors. Our rates include $\widetilde{O}(1/T)$ for convex Euclidean-Lipschitz optimization over the $\ell_1$-ball, improving on the $O(1/\sqrt{T})$ classical rate under general assumptions. The key technical device is a new online learning game, where the comparator is evaluated using the maximum of affine losses observed so far. We bound the value of this game above and below in terms of a combinatorial online learning quantity: the sequential fat-shattering dimension, which we characterize for the $\ell_p / \ell_q$ case. Our results generally apply when the feasible set $\mathcal{X}$ and the set of possible subgradients $\mathcal{H}$ admit a type of minimax theorem, advancing on a fundamental question by Sridharan (Sri12, Section 10.1.2, Q3). As a geometric consequence of our analysis, of independent interest, we obtain estimates for the expected distance of a convex hull of samples to their mean in several Banach geometries, a version of the celebrated Wendel's theorem (Wen62), but quantitative and for bounded general distributions as opposed to centrally symmetric ones.

math.OC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.16312

Narrative Paradoxes in LLM Inference: Shaping Reasoning Trajectories toward Misalignment

Haoming Yang, Ke Ma, Yangbangyan Jiang, Ligong Zhang, Xiaojun Jia, Longtao Huang, Yingfei Sun, Qianqian Xu 等 9 位作者

原文摘要

Explicit reasoning in large language models (LLMs) is often associated with reliability, yet it can still pose risks when a model infers harmful goals under a benign context. We identify a distinct challenge: ensuring the safety of the \emph{multi-step reasoning process} itself, where locally coherent steps may still lead to globally harmful outcomes. To explore this vulnerability, we propose PDRA (Paradox-driven Reasoning-based Attack), a novel attack method that programs a harmful reasoning trajectory. PDRA first constructs a paradoxical narrative core by pairing a malicious actor (e.g., a terrorist) with a benign task (e.g., writing a safety bulletin). It then embeds this core into a structured analytical framework that forces the model through a mandatory three-stage analysis -- surface purpose, latent intent, and operational reconstruction -- guiding it to produce detailed harmful content while maintaining narrative coherence. Mechanistically, we show PDRA steers the model's internal representations away from refusal-related patterns, suppressing safety-triggering lexicons while activating planning-oriented terms. Empirically, PDRA achieves state-of-the-art attack success rates across diverse LLMs with high efficiency. \textcolor{red}{\textbf{Warning:} This paper may contain sensitive content.}

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04586

Conditional independence and graphical models for rankings

Aritz Pérez

原文摘要

Ranking models define probability distributions over rankings and are fundamental in applications such as preference learning and decision-making. Without structural assumptions, these models are intractable due to their factorial complexity. Although independence assumptions are key to reducing complexity and enabling interpretability, standard notions of conditional independence must account for the mutual exclusivity constraints inherent to rankings. We introduce relative rank models, a probabilistic framework based on structured coarsenings of the ranking space that relax these constraints while preserving relative order information. This representation enables a well-defined notion of conditional independence for rankings and allows rankings to be modeled using Bayesian networks. As a result, standard methods for learning and probabilistic inference can be applied to rankings, with interpretable representations based on conditional independence. We further show that two prominent ranking models based on independence assumptions tailored to rankings arise as special cases, providing a unifying framework for independence in ranking models.

stat.ML · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04583

Multi-Path Mamba Diffusion for 3D Point Cloud Semantic Scene Completion

Li Liang, Naveed Akhtar, Jordan Vice, Anwaar Ulhaq, Ajmal Saeed Mian

原文摘要

3D semantic scene completion plays a critical role in enabling various downstream tasks within autonomous systems by estimating both missing geometric structures and semantic information in scene data. In this work, we introduce a unique neural framework that integrates state space modeling with diffusion-based generative approaches to enhance 3D point cloud semantic scene completion. We propose \textit{MPath Mamba} diffusion network, leveraging a skip-triple Mamba structure, dimensional decomposition residuals, and multi-directional dilations within multi-path data to efficiently process long-sequence data for feature extraction. The proposed method operates in the conditioned latent space of a variational autoencoder, enhanced by a state-space mechanism to improve 3D point cloud representation and reconstruction. Extensive experiments conducted on the SemanticKITTI benchmark demonstrate that our approach achieves highly competitive performance for 3D point cloud semantic scene completion.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04580

Confidence-Aware Aggregation for Multimodal Audio Reasoning

Michel Olvera, Paraskevas Stamatiadis, Ga{ë}l Richard

原文摘要

Large Audio Language Models (LALMs) enable generative reasoning over complex acoustic scenes but often exhibit prediction instability. We show that this variability can serve as a reliability metric via inference-time self-consistency and confidence-aware aggregation. Using a log-probability--based scoring framework, candidate answers are weighted by model likelihoods, leveraging reasoning diversity and self-certainty without additional training. Experiments on four LALMs, including Qwen3-Omni and Audio Flamingo 3, demonstrate that confidence-aware aggregation improves accuracy and calibration, with additive methods like Borda voting outperforming classical Majority Vote, especially under high disagreement. This approach transforms LALM variability into a source of reliability for scalable and robust multimodal audio understanding. Project code is available at https://github.com/michelolzam/lalm-self-consistency.

cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04579

FreeForm Harness: A Mobile-Native Agent Harness for Open and Composable Extensions

Bo Zhang, Pengbo Zhang, Jiaxin Jiao, Yue Liu

原文摘要

Mobile phones support many everyday agent tasks, but current mobile agents bundle capabilities into monolithic pipelines with no open recombination point. Users and third parties cannot publish, install, and compose new behaviors, leaving personalized daily-life requests underserved. We argue that the missing piece is a substrate: although phones are the Web's dominant access point, open publication and composition under decentralized authorship stop at the agent harness. We present FreeForm Harness (FFH), a mobile-native agent harness for open, composable extensions. A frozen base and a permanence-aware required layer handle mobile constraints, including location, proactive triggering, and scarce channels. A uniform plugin contract makes independently authored extensions composable, checkable, and replaceable over a frozen socket set. FFH also supports end-user authorship through a tiered commissioned-authoring path. When no existing extension matches a user's intent, a Web coding agent generates a candidate artifact, which is validated and admitted on device under the same contract as other extensions. An Android prototype spans device, market, and cloud. Evaluation shows that architectural boundaries are enforced at runtime; realistic daily-life requests fit the declared composition space without widening it; and user intent reaches on-device execution through retrieval or commissioned authoring, with final trust decisions remaining device-local. FFH advances the agentic Web from "everything is a plugin" toward "everyone is a developer" on mobile.

cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04577

The Lovász Local Lemma Is Sharp for the Hat Guessing Number

Thomas Delépine, Matthieu Rosenfeld

原文摘要

In the hat guessing game on a graph $G$, each vertex is occupied by a player wearing a hat whose color is chosen from a set of $q$ colors. Each player knows the graph and their position in it and sees only the hat colors of their neighbors. The players may agree on a strategy beforehand but must simultaneously try to guess their own hat colors without communicating. They win if at least one player guesses their own hat color correctly. The hat guessing number of $G$, denoted by $\textrm{HG}(G)$, is the largest integer $q$ for which the players have a strategy that guarantees a win for every assignment of hat colors. A direct application of the symmetric Lovász Local Lemma shows that $\textrm{HG}(G) < eΔ$ for every graph $G$ of maximum degree at most $Δ\ge 1$. We prove that this bound is asymptotically sharp: for every $\varepsilon > 0$ and every sufficiently large $Δ$, there exists a graph $G$ of maximum degree at most $Δ$ with $\textrm{HG}(G) \ge (e - \varepsilon)Δ$. This answers a question of Alon et al., disproves the conjecture $\textrm{HG}(G) \le 2Δ(G)$ of Bosek et al., and improves on the construction of Latyshev and Kokhas, which achieves $\textrm{HG}(G) = 4Δ(G)/3$. The hat guessing number thus provides a natural combinatorial problem in which the Lovász Local Lemma is asymptotically tight.

math.CO · cs.DM

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04576

ValQID: Learning the Net Value of Semantic Query Groups for Web Device Identification

Yifan Yang, Zhen Huang, Weijia Shi, Xichen Tan, Zhihuang Liu, Xionglve Li, Zhiping Cai

原文摘要

Web interfaces are widely used to access network services and manage networked devices. Identifying the vendors and types of devices behind Web interfaces is a critical step in asset inventory, security management, and risk assessment. Device identity cues are dispersed across page text, configuration fields, and management functions, and interpreting them often requires linking related Web objects. Responses and objects require different amounts of semantic analysis, so exhaustively processing all candidate content can introduce unnecessary computation. We present ValQID, which uses a relation graph to combine dispersed device identity cues, evaluates semantic questions efficiently with DSEJEV, and accounts for complementarity among semantic questions when estimating computation value. Based on the current identification state, ValQID weighs each question group's expected identification benefit against its execution cost and adaptively allocates computation to identify device vendors and types. Evaluation on real Web response data shows that ValQID achieves relative gains of up to 47.1% in joint macro-F1 and up to 82.1% in macro-F1 for low-frequency vendors over a state-of-the-art method. DSEJEV speeds up semantic processing by up to 8.58 times compared with general-purpose language models.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04574

Spectral Quantum Memory for Implicit Neural Representations

Daniel A. Moroz-Broitman, Eliya Nachmani

原文摘要

Implicit neural representations (INRs) model signals as continuous functions from coordinates to values, but their performance depends strongly on how spectral capacity is allocated. Yet most Quantum INR (QINR) architectures treat spectral components as independent outputs of separate quantum blocks. We introduce the Spectral Quantum Memory Model (SQMM), a memory coupled quantum INR in which sequential hybrid data reuploading layers (bands) communicate through a shared coherent quantum memory register. Each band reads the memory, computes on its own coordinate upload, and writes part of its state back, so later bands can build on the computation of earlier ones. When memory read and write layers are input independent, we prove that the memory state forms an operator valued Fourier series whose support is contained in the Minkowski sum of the initial memory spectrum and the local band spectra. Downstream bands then condition their Fourier coefficients on the spectral content stored by earlier bands, and the memory coupling creates no new primitive frequencies. This theory motivates a spectral memory alignment loss that encourages memory to encode the spectrum expressed by the deployed prefix of the model. Across audio and image reconstruction benchmarks, SQMM improves over compact QINR and INR baselines.

quant-ph · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04571

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

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

原文摘要

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

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04570

Setting the Stage: Enhancing Music Recommendations with Spoken Introductions to Stimulate Serendipity

Brett Binst

原文摘要

People like to keep their music listening fresh and interesting, but recommender systems often struggle to support this need. Even when a recommended song is potentially refreshing and enriching, listeners may not perceive it as such. Building on Silvia's appraisal theory of interest, this paper studies whether enhancing recommendations with spoken introductions, immersive or informative, can stimulate two distinct flavors of experienced serendipity: taste-broadening (sparking interest in music outside one's typical preferences) and taste-deepening (deepening interest in music within one's typical preferences). Results from a large-scale user study show that both introduction types stimulate taste-broadening and taste-deepening serendipity. Taste-broadening is most effectively stimulated through transportation (absorption in a narrative world), whereas taste-deepening is most effectively stimulated through cognitive elaboration (learning something new about the artist or the social context in which the music emerged). We discuss how these findings could be translated into practice through curated taste-broadening playlists, where both introduction types appear promising, and taste-deepening playlists, where informative introductions seem particularly effective.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04569

Abelian embeddings make useful promises: approximation of detrivialised equations

Tamio-Vesa Nakajima, Zephyr Verwimp, Stanislav Živný

原文摘要

We study the following family of search problems: given a system of equations $x+y+z\equiv 0\pmod p$ promised to admit a solution that is nonconstant on every equation, find a $c$-colouring of the variables that makes no equation monochromatic. For $p\in\{2,3\}$, the problem recovers known tractable Promise Constraint Satisfaction Problems (PCSPs). For $p > 3$, we obtain a complete complexity classification: the problem is polynomial-time solvable for $c \geq 4$, yet even its decision version is NP-hard for $c = 3$, and it is not well-defined for $c = 2$. Our main conceptual contribution is a general tractability framework for PCSPs based on Abelian embeddings, a notion introduced by Bhangale, Khot, and Minzer [STOC 2022] in the study of quantitative approximation of satisfiable CSPs. We show that suitable Abelian embeddings yield polynomial-time PCSP reductions realised by singleton algorithms in the sense of Zhuk [FOCS 2026]. On the hardness side, the promise relation is far removed from the reconfigurable regime covered by the conditional hardness framework of Banakh, Barsukov, and Nakajima [LICS 2026]; our proof instead relies on a new additive-combinatorial rigidity theorem classifying special $3$-colourings of $\mathbb{Z}_p$.

cs.CC · cs.DM · cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04566

A research world model of power engineering learns its dynamics and reveals what-ifs

Fanping Sui, Ying Zhang, Liwei Lin

原文摘要

Decisions about the direction of research in power engineering determine which technologies mature and, through them, shape infrastructure that lasts decades, yet their consequences cannot be observed before they are taken, and existing forecasts of research fields extrapolate a single trajectory. Here we introduce research world models, which treat a research field as a dynamical system that can be acted upon, and establish one for power engineering from 1.1 million papers. A distilled language model reads each paper into role-typed concepts, two sophisticated, high-fidelity graph-temporal neural network cores learn how the resulting concept network evolves, and operators learned from natural experiments in the publication record estimate the effects of interventions. In temporally held-out tests, the model forecast years ahead which concepts would grow and which method-target pairs would be joined for the first time, outperforming tuned machine-learning baselines in most settings, and its estimated intervention effects tracked those realized in natural experiments it had not seen. Its training scheme and three of its operators replicated in two fields withheld from development. Questions posed in natural language are compiled by a domain-specialized 27-billion-parameter language model into executable actions and answered as paired simulations with and without the intervention. The answers were rated 4.1 of 5 for faithfulness by an independent language-model judge. By revealing the latent dynamics of a research field and its response to interventions, research world models offer a new way to simulate and test research decisions before they are made.

cs.AI · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04565

Curriculum unlearning in humans and neural networks

Jirko Rubruck, Alice Zhang, Andrew Saxe, Christopher Summerfield

原文摘要

During learning, humans often benefit from curricula that teach simple rules before exceptions. However, we lack a computational explanation for this phenomenon. Here, using a canonical behavioural task and modelling with neural networks, we develop and test a theory for why simple-to-complex curricula succeed. We show that during learning, both humans and neural networks show simplicity biases, a tendency to learn simple rules that are underfit to the data. In neural networks this bias is accompanied by compression of neural representations. Simple-to-complex curricula help both types of learners overcome this compression, developing representations (and policy) with appropriate dimensionality for the task. We test and confirm a further prediction from this theory: learners that have been pre-exposed to simple tasks and with more compressed initial representations benefit more from curricula. These findings suggest that curricula help with ``unlearning'' of simple rules.

cs.LG · q-bio.NC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.38044

A Tale of Two Walks: From Kac to KMP and Back

Qian Chen, Jingcheng Liu, Minglong Qin, Leonard J. Schulman, Fang Song, Penghui Yao, Mingnan Zhao

原文摘要

We reveal an unexpected connection between two interacting particle systems: Kac's walk and the Kipnis-Marchioro-Presutti (KMP) process. We first establish a mixing-time analogue of Aldous's conjecture for the KMP process on general graphs: the mixing time of the $t$-particle process on any connected $d$-vertex graph is at most $O(\log d)$ times the corresponding single-particle mixing time, uniformly in $t$. As an application, we improve the mixing-time bound for coordinate hit-and-run on the $n$-dimensional standard simplex from $\widetilde{O}(n^3)$ [KV26] to $O(n\log n)$, while removing the dependence on the initial distribution. We then show that the twirling channel induced by the parallel Kac's walk on the symmetric subspace can be exactly encoded by a Markov chain on partitions, which naturally lifts to a parallel KMP chain on the complete graph. We prove that $O(\log d+\log(1/\varepsilon))$ repetitions of the parallel Kac's walk suffice to approximate Haar twirling on the symmetric subspace of $(\mathbb{C}^d)^{\otimes t}$ to error $\varepsilon$, uniformly in all $t$. Our main technical contribution is a new structural property of KMP-type dynamics: conditional product structure, which holds for both the standard KMP processes on general graphs and the parallel KMP processes on complete graphs. Conditioned on suitable auxiliary randomness, the evolution of the labeled KMP process factorizes into independent single-particle updates. Combining this structure with an exact coupling, we remove the dependence on the number of particles from the mixing bounds for both unlabeled KMP models. This bypasses the $\log(π_{\min}^{-1})$ dependency from spectral-gap arguments, which is often prohibitively large for many-particle systems. Using this framework, we prove mixing bounds that are sharp up to logarithmic factors for both unlabeled KMP models.

quant-ph · cs.DS · math.PR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04563

Optimized size, shape and orientation of rhombic transposition flaps and excisions by gradient-based methods

Niels Aage, Brandon Marshal Parks

原文摘要

This work presents a new method for gradient-based optimization of the rhombic transposition flap and the excision it closes, which set the strain of the skin after closure. Previous studies swept at most three parameters. Here, using structural optimization methods, up to eight variables, two flap angles, two cut lengths and the size, shape and position of the rhombic excision, are optimized under up to 32 geometric and stress constraints. The skin is a pre-stretched, incompressible hyperelastic membrane that wrinkles instead of carrying compression. Smoothed level-sets on a fixed grid represent the cuts, and springs between interpolated points the sutures, so designs change without remeshing and the adjoint method gives the sensitivities. The energy the surgery adds to the skin is minimized, and the model reproduces the energies of a published 48-design sweep with a median deviation of 5%. The optimal flap angles, $(-23^\circ, 31^\circ)$, lie outside the swept range and add 63% less energy than the Limberg flap and 15% less than the best swept design. Under unequal in-vivo tension, the optimal flap turns with the direction of the tension. Fitting the excision to the lesion and its margin lowers the energy by a further 35%, and with all eight variables it is 78% below the Limberg flap. This optimum is the same for finer grids, narrower cuts and wider undermining, although wider undermining reduces its gain. Lowering its suture-line stress by 41% costs only 2.3% more energy.

cs.CE · physics.med-ph

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.25740

A mathematical study of the excess growth rate

Steven Campbell, Ting-Kam Leonard Wong

原文摘要

The excess growth rate, defined as the gap in Jensen's inequality for the logarithm, is a fundamental functional in portfolio theory. In this paper, we present a mathematical study motivated by information theory. We begin by establishing its properties and showing that it has rich connections with information-theoretic concepts such as the Helmholtz free energy, L. Campbell's measure of average code length and large deviations. Our main results consist of three axiomatic characterization theorems of the excess growth rate, in terms of (i) the relative entropy, (ii) the gap in Jensen's inequality, and (iii) the logarithmic divergence that generalizes the Bregman divergence. Furthermore, we study maximization of the excess growth rate and compare it with Kelly's growth optimal portfolio. Our results not only provide theoretical justifications of the significance of the excess growth rate, but also establish new connections between information theory and quantitative finance.

cs.IT · math.PR · q-fin.MF · q-fin.PM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02936

Why Tabular Foundation Models Excel on Relational Data They Have Never Seen

Linjie Xu, Yiqun Hu, Jixiang He, David Wipf

原文摘要

Given a relational database (RDB) storing heterogeneous tabular information, how can we predict missing (or future) values in some target column of interest? As candidate targets are numerous in enterprise settings, it is preferable to avoid learning a new model from scratch each time there is a new prediction task. Frozen foundation models (FMs) based on in-context learning (ICL) and RDB-specific pre-training provide a viable solution, but the ideal design still remains largely unresolved. Perhaps unexpectedly, preliminary evidence has recently shown that existing single-table FMs (aka TFMs), when combined with suitable parameter-free relational encoders, can be quite competitive in practice despite zero exposure to RDB pre-training or relational data of any kind. In this work we first empirically confirm this phenomenon with a more diverse array of experiments. We then turn to our main contribution, namely, surfacing multiple complementary explanations for why TFMs behave so effectively. These explanations ultimately hinge on innate synergies whereby parameter-free encoders map relational effects to fixed-length embeddings that loosely mimic distributions for which TFMs were originally designed; in so doing, desirable single-table properties transfer to relational settings (e.g., convergent posterior predictive distributions). Collectively, our analysis forges new pathways for improving RDB FMs, particularly when we prioritize interpretability and inevitable integration within agentic systems.

cs.AI · cs.DB · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04561

Distributed LTL Synthesis under Arbitrary Message Adversaries

Benedikt Bollig, Patricia Bouyer-Decitre, Matthias Függer, Dhrubajyoti Ghosh, Thomas Nowak, Nathalie Sznajder

原文摘要

Reactive synthesis is the problem of generating a program that produces outputs satisfying a logical specification for every input stream. We study distributed synthesis for two processes communicating in rounds over unreliable links, with at least one message delivered in every round. Despite this delivery guarantee, we show that synthesis from linear temporal logic specifications is undecidable, even when messages carry the processes' complete causal histories. More generally, we give a complete characterization of the communication restrictions that make synthesis decidable. Restrictions are described by a message adversary $A$, a set of allowed infinite graph histories: sequences of communication graphs, each specifying which messages are delivered in a round. Iterated chromatic subdivision associates these histories with points in the unit interval, yielding an image $π(A) \subseteq [0,1]$. For every $ω$-regular message adversary $A$, synthesis is decidable if and only if $π(A)$ is nowhere dense. In the decidable cases, our procedure takes a deterministic parity automaton for $A$ and a specification, decides realizability, and constructs a realizing distributed algorithm whenever one exists. Further, our topological arguments shed new light on the fundamental requirements of synthesis. They show that, for $ω$-regular message adversaries, synthesis is decidable if and only if consensus is repeatedly solvable in bounded time. This links two previously unrelated problems from formal verification and distributed computing through the processes' uncertainty about communication.

cs.DC · cs.FL · cs.LO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.26307

Word Length and Diameter in Permutation Groups

Markus Lohrey, Alexander Thumm

原文摘要

In the binary diameter problem, the input consists of explicitly given permutations generating a finite group $G$ and a binary-encoded number $k$, and the question is whether every element of $G$ is a product of at most $k$ of the given generators. In the binary length problem, the input additionally contains a permutation $g \in G$, and the question is whether $g$ is such a product. We show that the binary diameter problem is PSPACE-complete, thereby confirming a conjecture of Lohrey and Rosowski (2023). Moreover, we show that PSPACE-hardness already holds for solvable groups of derived length at most 3, whereas the problem is $Π_2^{\mathrm{P}}$-complete for 2-step nilpotent groups. For the binary length problem, we establish a sharp complexity threshold: the problem is NP-complete for 2-step nilpotent groups, which extends a result of Lohrey and Rosowski for abelian groups, and PSPACE-complete for groups that are simultaneously 3-step nilpotent and metabelian, which strengthens the main result of Jerrum (1985). Finally, we show that the unary diameter problem, where $k$ is encoded in unary, is $Π_2^{\mathrm{P}}$-complete for generating sets of the full symmetric group, confirming another conjecture of Lohrey and Rosowski.

math.GR · cs.CC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04559

Distributionally Robust Search: The Power of Information and the Limits of Moments

Nicole Megow, Cliff Stein, Jose Verschae

原文摘要

We study binary search for a target drawn from a distribution on $n$ ordered elements. The cost is the expected number of comparison queries. In our distributionally robust framework, the algorithm knows only an uncertainty set containing the distribution and minimizes its worst-case approximation ratio against an optimal strategy that knows the distribution. Collections of uncertainty sets capture different forms of information: quantiles, moments, general linear measurements, or approximate CDFs. This framework quantifies how much and what form of information is needed for near-optimal search. Our main result separates moments from general linear measurements. Although $n-1$ moments determine the distribution exactly, for every constant $δ\in(0,1)$, the first $n^{1-δ}$ exact moments can be as uninformative for search as no information: the worst-case approximation ratio remains $Ω(\log n)$. In contrast, for every constant $\varepsilon\in(0,1)$, $O(n^\varepsilon\log^4 n)$ suitably chosen linear measurements suffice for an $O(1/\varepsilon)$-approximation. The limitation lies in the measurements chosen, rather than linearity itself. Using LP duality and polynomial bounds of Rakhmanov and Bernstein, we construct distributions with identical moments and mass concentrated at different positions. For the upper bound, we combine dyadic sketches and Johnson--Lindenstrauss embeddings with a truncated median-search algorithm that converts uniform CDF approximation into search guarantees. Measuring information in bits, we prove a lower bound of $Ω(1/\varepsilon)$ on the approximation ratio for summaries of any form using at most $\frac12 n^\varepsilon\log n$ bits, for every sufficiently small constant $\varepsilon>0$. Quantiles match this within constant factors. For Kolmogorov balls of radius $n^{-\varepsilon}$ the optimal approximation ratio is $Θ(1/\varepsilon)$.

cs.DS · cs.DM

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新论文 · v1arXiv:2611.04556

Inter-Agent Influence: Evaluating Persuasion, Deception, and Coercion in Multi-Agent Systems

Chandler Smith, Qi Guo, Cecilia Elena Tilli, Sophia Hatz, David Demitri Africa, Patricia Paskov, Christian Schroeder de Witt, Philip Torr 等 9 位作者

原文摘要

The deployment of AI agents in multi-agent settings enables inter-agent influence, whereby agents can strategically steer each other's behaviour towards an intended outcome. Such capabilities pose novel risks, as malicious agents could direct others towards harmful actions. In this work we investigate three such capabilities - persuasion, deception, and coercion - across five realistic evaluation environments and a variety of agents instantiated with frontier language models. We observed significant inter-agent influence capabilities; in oversight environments, all tested models shifted overseer decisions from rejection to approval through persuasion, deception, or coercion. We also observed successful influence in non-hierarchical 'peer-to-peer' settings through redirection of a peer's safety research trajectory. As multi-agent deployments become increasingly commonplace, these results point to a pressing need for work on mitigating the risks from inter-agent influence. Code and data are available at https://github.com/Cooperative-AI-Foundation/inter-agent-influence.

cs.AI · cs.MA

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新论文 · v1arXiv:2611.04555

CosmosAlpha: Multi-Relational Graph Search with Pareto-Guided Exploration for LLM-Driven Alpha Mining

Zhan-He Wang, Zi-Yang Lang, Yi-Yang Li, Qing-Yu Lan, Yun-Song Piao

原文摘要

Discovering predictive alpha factors from noisy financial data is a fundamental yet challenging problem in quantitative finance. LLM-driven formulaic alpha mining offers an attractive paradigm by combining interpretable symbolic expressions with iterative generation and refinement guided by quantitative feedback. However, existing generation-centric and tree-based search strategies primarily organize factors through generation lineage, leaving structural and statistical relationships across search branches underutilized. Consequently, information remains localized along parent--child trajectories, which can confine exploration to redundant regions. Moreover, effective factor discovery must account for multiple distinct objectives that cannot be adequately captured by a single search criterion. We introduce CosmosAlpha, a graph-based LLM factor-mining framework that represents discovered factors as a multi-relational graph encoding generation lineage, expression-structure similarity, and cross-sectional exposure correlation. A Graph Flow mechanism propagates search utilities across the graph, allowing each factor to be assessed using relational evidence from nearby factors rather than in isolation. Pareto-guided selection jointly considers quality, fertility and diversity, explicitly modeling their trade-offs within a multi-objective search framework. Across CSI 300 and CSI 500, CosmosAlpha-Graph achieves the best mean rank over seven predictive and portfolio metrics and outperforms its controlled CosmosAlpha-MCTS counterpart on every reported metric, showing a consistent advantage for relational graph search in LLM-driven factor mining. The source code is available at https://github.com/wzhmtldswz/CosmosAlpha

q-fin.CP · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.10660

Accounting for Purchased Artificial Intelligence Inference in Corporate Greenhouse Gas Inventories: A Token-Based Method

Guillermo Llopis-Garcia

原文摘要

Purchased artificial intelligence (AI) inference belongs in Scope 3 Category 1 of a corporate greenhouse gas inventory, yet practice omits it or applies a spend-based factor that gives 1.7 to 12.3 times the physical estimate and cannot tell one model or region from another. This paper sets out a token-based method and open dataset: a cradle-to-gate boundary, four tiers of activity data, an effective-token basis, a four-term factor (serving electricity, embodied hardware, data-centre construction, disclosed final training runs) and a rule resolving any model identifier and region to a factor. Inference footprints span twelve times between model classes and nine between regions. The mid-class factor, 0.039 kg of carbon dioxide equivalent (CO2e) per million billed tokens at the world-average grid, is the low end of a stated structural range: a less efficient serving configuration raises it 2.8 times, and replacing the training term with all model development, on its own boundary, gives 1.5 to 2.9 times it. Input bands alone give -18% to +29%. Published per-query energy estimates that can describe a mid-class model are 2.4 to 7.2 times the mid-class energy, above its structural range in four of five rows; those that can describe a frontier-class model bracket it at 0.7 to 1.6 times. Worked examples give 8.5 kg CO2e a year for a 200-person firm, and 2.1 tonnes for fifty developers using agentic coding tools on a mid-class model or 8.3 tonnes on a frontier-class one. The contribution is a traceable figure; accuracy against metered energy is untested.

cs.CY · cs.AI

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版本更新 · v2arXiv:2607.13940

A Self-Evolving Agent for Longitudinal Personal Health Management

Haoran Li, Jiebi Deng, Tong Jin, Jinghong Han, Yuxin Wang, Zexin Wang, Jinyu Sun, Qingyi Si 等 14 位作者

原文摘要

Medical large language models and agents show strong single-encounter question answering and evidence interpretation but struggle to accumulate and govern experience across encounters. We present HealthJev, an open-source agent separating shared safety rules and medical knowledge from a private longitudinal memory and distilling lessons from completed cases. To filter out erroneous lessons before they affect future behaviour, we self-train JevGate, a lightweight System-One model that decides which lessons enter memory; each decision takes a median of 86 milliseconds and about 1,000 input tokens, sustaining experience accumulation at negligible run-time overhead. On a longitudinal benchmark of 90-day simulated daily interactions for 20 users built from real MIMIC-IV trajectories, HealthJev outperformed a memory-free ReAct agent and three memory-augmented frameworks, with all paired comparisons significant. Across six tasks spanning clinical records, medical imaging, genomics and clinical text, HealthJev improved every task with a mean gain of 13.3 percentage points over the base condition, surpassing all baselines. The frozen library transferred to six further backbones with macro-average gains of 6.8 to 15.1 percentage points, and one task's library brought gains on other tasks. These results indicate that governed, self-evolving memory supports longitudinal personal health management beyond single encounters. HealthJev is publicly available at https://github.com/HC-Guo/Self-Evolving_HealthJev.

cs.AI

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新论文 · v1arXiv:2611.04554

SEEK-VAU: Towards Evidence-Faithful Video Anomaly Understanding via Agentic Search

Minghe Wang, Weijun Zeng, Dapeng Luo, Xinyue Wang, Nong Sang

原文摘要

Most video anomaly understanding (VAU) systems still infer anomaly judgments from evidence collected in a feed-forward way under predefined rules, typically sampled frames, or events clustered from such frames. This protocol fails when the collected evidence is incomplete under imperfect predefined rules, because once reasoning begins the model can no longer search for the missing evidence. We address this limitation with SEEK-VAU, which reframes VAU as an agentic search-and-verify interaction protocol under a bounded search budget: instead of passively reasoning over pre-collected evidence, the policy actively gathers evidence across the three stages of an anomaly event chain (precursor, trigger, and aftermath) and verifies its sufficiency before finalization, turning event-chain completeness into an explicit design rather than an outcome of predefined rules. A bounded budget caps the cost of this search, but does not by itself teach the policy where to look or when to stop. To make this behavior learnable, we introduce evidence-faithful counterfactual verification (EFCV), which rewards selected evidence that remains supportive, compact, and necessary under counterfactual verification. We further introduce SEEK-Bench, featuring video-level episodes with temporal interval annotations, semantic QA and event-chain stage labels. Together, SEEK-VAU and SEEK-Bench establish a strong foundation for evidence-faithful and actively verifiable VAU.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.30250

Agentic Detection of Online Conspiracies

Lior Biton, Oren Tsur

原文摘要

Conspiratorial discourse on social media is not always expressed through explicit claims or stable lexical markers. The same surface content may express endorsement, legitimate concerns, criticism, satire, or mockery. The main challenge is therefore not only recognizing conspiracy-related claims, but inferring the speaker's intent -- the utterance's illocutionary force. We argue that this can be achieved through the use of relevant social contexts and propose an agentic framework, equipped with a set of tools supporting social queries. We demonstrate the benefits of our approach on a unique dataset of Hebrew tweets, covering 80%--90% of the public Hebrew tweets published over a four-year span (late 2018-- early 2023), encompassing several election cycles as well as the COVID pandemic years and related vaccination campaigns. This extensive coverage can be used in recovering different social contexts. Evaluating our framework on a manually-annotated adversarial dataset, we find that context-aware workflows consistently outperform text-only classification and that the agentic framework performs better than other frameworks and settings, with significant gains over a non-agentic model exposed to the same contexts available to the agent. We further provide an analysis of the results, the errors and efficiency (token economy) tradeoffs. These findings support viewing the task of conspiracy detection as a socially embedded interpretation task, in which effective classification depends not only on access to contexts, but also on adaptive reasoning in which the agent uses tools on a per-case basis, asking only for evidence relevant to its current reasoning step.

cs.CL · cs.LG

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版本更新 · v2arXiv:2505.21039

Scalable and adaptive prediction bands with kernel sum-of-squares

Louis Allain, S{é}bastien da Veiga, Brian Staber

原文摘要

Conformal Prediction (CP) is a popular framework for constructing prediction bands with valid coverage in finite samples, while being free of any distributional assumption. A well-known limitation of conformal prediction is the lack of adaptivity, although several works introduced practically efficient alternate procedures. In this work, we build upon recent ideas that rely on recasting the CP problem as a statistical learning problem, directly targeting coverage and adaptivity. This statistical learning problem is based on reproducible kernel Hilbert spaces (RKHS) and kernel sum-of-squares (SoS) methods. First, we extend previous results with a general representer theorem and exhibit the dual formulation of the learning problem. Crucially, such dual formulation can be solved efficiently by accelerated gradient methods with several hundreds or thousands of samples, unlike previous strategies based on off-the-shelf semidefinite programming algorithms. Second, we introduce a new hyperparameter tuning strategy tailored specifically to target adaptivity through bounds on test-conditional coverage. This strategy, based on the Hilbert-Schmidt Independence Criterion (HSIC), is introduced here to tune kernel lengthscales in our framework, but has broader applicability since it could be used in any CP algorithm where the score function is learned. Finally, extensive experiments are conducted to show how our method compares to related work. All figures can be reproduced with the accompanying code at \href{https://www.gitlab.com/drti/ksos-bands}{gitlab.com/drti/ksos-bands}.

cs.LG · math.ST

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新论文 · v1arXiv:2611.04552

Mutual-Coupling-Aware Joint Precoding and Antenna Positioning for Movable-Antenna MU-MIMO Systems

Wei Guo, Tianyi Liao, Zixin Wang, Shenghui Song, Jun Zhang, Khaled B. Letaief

原文摘要

Movable antennas (MAs) improve wireless communications by adapting antenna positions to the propagation environment. However, antenna movement also changes the mutual coupling (MC) among elements of the transmit and receive arrays, which affects the performance of MA-enabled systems. In this paper, we investigate MC-aware joint precoding and antenna positioning for a downlink multiuser multiple-input multiple-output (MU-MIMO) system with MAs at both the base station and users. We establish an MC-aware system model by combining field-response propagation with a circuit-based representation of the transmit and receive arrays. Based on this model, we formulate a weighted sum-rate (WSR) maximization problem that jointly optimizes multi-stream precoders and antenna positions. The problem is challenging because the precoders and positions are coupled through the propagation channels and position-dependent MC matrices. To address this challenge, we reformulate the problem in the antenna-current domain to to efficiently handle the MC matrices and develop a current-domain weighted minimum mean square error (WMMSE) framework for alternating optimization. Furthermore, we handle the nonconvex position updates within the framework by using majorization--minimization (MM) and solve the resulting convex subproblems with a dedicated conic alternating direction method of multipliers (ADMM) algorithm. Simulation results demonstrate that the proposed design improves the WSR over both an MC-aware fixed array and a movable-array design that ignores MC during optimization.

cs.IT · eess.SP

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