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新论文 · v1arXiv:2611.16557

Game-Theoretic Analysis of RIS-Assisted Challenge-Response PLA - Part I: Perfect Channel Knowledge

Anna V. Guglielmi, Linda Senigagliesi, Marco Baldi, Stefano Tomasin

原文摘要

We consider a physical layer authentication scenario in which a legitimate user (Alice) communicates with an authen- ticator (Bob) through a reconfigurable intelligent surface (RIS), controlled by Bob. An attacker (Trudy) aims to impersonate Alice by suitably precoding her messages and still transmitting them through the RIS. This two-part study analyzes this attack-defense interaction between Bob and Trudy under perfect (Part I) and partial (Part II) channel knowledge by all the agents. Part I formulates the joint RIS selection and precoder design under perfect channel knowledge as a finite zero-sum game, establishing exact channel subspace conditions for attack indistinguishability alongside equilibrium mixed strategies. Part II extends the defense to incomplete channel information, where each agent observes only its own channel, by formulating a finite-action Bayesian game and characterizing the resulting Bayesian Nash equilibrium. Simulations in geometric and 3GPP TR 38.901 UMi-NLOS channels reveal that authentication performance is governed by available spatial degrees of freedom, channel correlation, and operating signal-to-noise ratio. The proposed equilibrium strategies effectively balance communication rate and security, outperforming conventional benchmark policies.

cs.CR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16555

Similarity-Guided Optimization Strategies for Approximate Network Symmetry Detection

David Hartman, Jaroslav Hlinka, Dominik Klepl, Anna Pidnebesna, Petra Vidnerová

原文摘要

Symmetry in complex networks is a structural property with broad implications for network function, dynamics, and robustness. While classical approaches rely on exact automorphisms and thus remain highly sensitive to small perturbations, recent work of Liu has introduced approximate formulations that relax the strict invariance condition and recast symmetry detection as an optimization problem over permutations. We investigate how node-level similarity information can be used to bias the search toward structurally plausible vertex correspondences, with network centralities serving as one interpretable instance. Building on this perspective, we adapt simulated annealing, genetic algorithms, tabu search, particle swarm optimization, differential evolution and the quadratic symmetry approximator to this problem, add similarity guidance to each, and introduce Bilinear Approximate Symmetry Inference (BASIN), which learns a soft assignment matrix via bilinear embeddings. Using a unified evaluation pipeline, we compare their guided and unguided variants, BASIN and a random baseline on four synthetic graph families, structural brain connectomes, and three empirical networks with known automorphism groups. Node-similarity guidance is decisive where a planted orbit exists that the unguided search misses, and algorithm-dependent elsewhere, on synthetic and empirical networks alike. Overall, tabu search achieves the strongest performance; its guided variant (SGTS) has the lowest median score among the guided variants in all 44 synthetic conditions, while BASIN is the better of the continuous relaxations on rewired lateral graphs. The effect of node-similarity guidance is therefore regime-dependent rather than uniformly positive.

cs.NE · cs.LG · q-bio.NC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.10613

Exact Fixed-Point Constraints in Neural-ODEs: Injecting Prior Knowledge into the Network Parameters

Feliciano Giuseppe Pacifico, Duccio Fanelli, Lorenzo Buffoni, Lorenzo Chicchi, Diego Febbe, Raffaele Marino

原文摘要

Neural ordinary differential equations (Neural-ODEs) learn a velocity field from data. Often, however, additional information on the target field is available, such as the location of some of its equilibria or, more generally, its values at given points. We introduce a method to inject this information directly into the parameters of a Neural-ODE, so that a finite set of prescribed fixed points holds exactly throughout training, with no penalty terms and no post-hoc projection. The output matrix is parametrized through the Moore-Penrose pseudoinverse, implemented by a thin QR factorization of the hidden features at the prescribed points. This splits the output matrix into a component determined by the constraints and a free one, which shapes the field away from the prescribed points without altering its values there. As a result, the constrained Neural-ODE remains a universal approximator of the continuous vector fields compatible with the constraints. We test the method on a two-dimensional Lotka-Volterra competition model with four equilibria. In vector field regression, the constrained model keeps the fixed-point residual at single-precision round-off, and its test error is up to about five times lower than that of an unconstrained network of the same architecture. A soft penalty on the equilibria lowers the residual only at the cost of accuracy, and still leaves it at least an order of magnitude above that of the constrained model. Trained to convergence on datasets of increasing size, the unconstrained and penalized networks saturate already on small datasets, while the constrained model keeps improving over a wider range. Close to the equilibria and over long time horizons, the constrained model is the most accurate at every dataset size considered. This also holds when the models are trained on observed trajectories, by backpropagation through the ODE solver.

cond-mat.dis-nn · cs.LG · physics.bio-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16551

Hardness, Approximation, and Parameterized Algorithms for Asynchronous Border Minimization

Giulian Buzatu, Adrian Miclăuş, Alexandru Popa

原文摘要

In the Asynchronous Border Minimization Problem (BMP), a set of DNA probe strings is placed on a grid and synthesized using a common sequence of nucleotide-deposition steps. At each step, every cell may accept or skip the offered nucleotide. The cost is the total number of disagreements between neighboring cells over all steps. We study both free placement, where the probes may be rearranged, and fixed placement, where their positions are given. We show that free-placement BMP on a square grid is NP-complete over three symbols. For one-dimensional BMP, we prove APX-hardness over a binary alphabet, and NP-completeness even when every probe has length two. We also obtain an $8/7$-approximation for the latter restriction. For square grids, we give a randomized $O(n^{1/4}\log n)$-approximation, improving the earlier $O(n^{1/4}\log^2 n)$ guarantee. Both free- and fixed-placement BMP on rectangular grids are fixed-parameter tractable when parameterized only by the allowed border cost.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16543

To Race or Not to Race? Exploiting Performance Variability in Mixed-Integer Programming via Root-Gated Resource Allocation

Mourchid Adegbindin, Brenda Dietrich, Jamol Pender

原文摘要

Mixed-integer programming (MIP) solver runtimes vary substantially across random seeds. Parallel replication exploits this variability but can waste computational resources when applied indiscriminately. We investigate whether information from an ordinary solve can guide this allocation decision. We formulate replication under a finite deadline, establish an exact relationship between runtime dispersion and the value of independent searches, and analyze delayed replication and imperfect allocation decisions. Using 101,615 seeded SCIP runs, we examine the association between root-LP dual degeneracy and replication value. We then introduce a non-invasive root-gated policy that preserves the original solver trajectory and launches three additional seeded processes when a root dual-degeneracy indicator reaches a fixed threshold. In a prospective two-hour experiment on 130 paired instances, root-gated racing proves optimality on 77 instances (59.23%), versus 73 (56.15%) for both Single and unconditional Race-4, while reducing active worker-process occupancy by 43.00% relative to unconditional racing. These findings demonstrate the promise of selective parallel resource allocation without solver-source modifications.

math.OC · cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16541

A Finite Markov Chain Analysis of the Simplified Swarm Optimisation Update Rule

Wei-Chang Yeh

原文摘要

Simplified swarm optimisation (SSO) updates each variable by one draw that copies the global or personal best, keeps the current value or redraws uniformly, with probabilities $c_g$, $c_p$, $c_w$ and $c_r$. As a finite Markov chain under frozen memory, its coordinate kernel $c_w I + (1 - c_w)Π$ has an explicit stationary law and exact convergence rate $c_w^t$. The elitist chain has a geometric hitting-time bound, from a non-optimal start a fixed $c_r$ needs at least $\exp(nc_r/2)$ expected evaluations to reach a unique optimum, and a coupling under changing memories gives $O(n\log n)$ and $O(n^2)$ expected generations on OneMax and LeadingOnes for every population size with redraw share of order $1/n$ and no inertia, and within a $\log n$ factor for fixed inertia. With a frozen memory, inertia cannot speed up first entry into any set from stationary positions, though it can from others. Where the three reference values agree, only $c_r$ acts; where they disagree, copy branches recombine the memories, forcing a redraw only where no active branch holds the target value. Branch logging and checkpoint replay show inertia slowed the tested OneMax and LeadingOnes runs and suggest memory-mediated gains on wider gaps. On Jump functions SSO needed 3.8--8.9 times fewer evaluations than the (1+1) evolutionary algorithm with rate $1/n$, but no consistent advantage over rate-adjusted mutation or a $(μ+1)$ genetic algorithm with uniform crossover was established. The analytical redraw rule improved six of eight benchmark--setting pairs and redundancy allocation, but was slower than $c_r = 0.1$ on Jump$_2$.

cs.NE · math.OC · math.PR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16538

Pl@ntNet machine-to-machine identification service with Pl@ntNet API: enabling automated biodiversity monitoring through large-scale plant identification

Benjamin Bourel, Jean-Christophe Lombardo, Alexis Joly, Mathias Chouet, Thomas Paillot, Antoine Affouard, Vanessa Hequet, Herv{é} Go{ë}au 等 9 位作者

原文摘要

1. Monitoring plant biodiversity requires identifying large numbers of plants across space and time. Although artificial intelligence (AI) can now identify plant species from images with high accuracy, most existing systems rely on users manually submitting images. As ecological monitoring increasingly generates large image collections from cameras, drones, robots and online platforms, there is a growing need for plant identification services that can be directly integrated into automated data-processing workflows. 2. Here, we present Pl@ntNet's machine-to-machine AI plant identification service. This service uses an application programming interface (API) to provide programmatic access to Pl@ntNet's plant recognition models and the associated taxonomic processing. This service enables the implementation of automated identification workflows without human intervention and can thus directly link large image collections and automated observation systems to an AI-based plant identification system. 3. We establish that this service, which covers 85,000 species from around the world, (1) has a species prediction accuracy of 0.89, (2) process images up to 666 times faster than workflows requiring human intervention, (3) and has processed over 84 million identification requests since its launch, including more than 40 million in 2025 alone. We demonstrate how it has already enabled the implementation of a new generation of ecological workflows: identifying plants from images of insects to reconstruct the plant-pollinator interactions, analysing geolocated images from social media to study plant distribution, detecting invasive plants using images taken at the roadside, and identifying weeds using agricultural robots. Finally, we illustrate its application using a reproducible example: identifying plants from insect photos on iNaturalist. In approximately 30 min on a standard laptop, 2,424 images were processed to generate a dataset of 708 plant-pollinator interactions, with a weighted precision of 0.98 for flower detection and an accuracy of 0.88 for genus plant identification. 4. In conclusion, this machine-to-machine species identification is already operating on a considerable scale and meets a genuine need. All of this paves the way for a new paradigm in biodiversity monitoring, in which automated sensors and image collections can be directly connected to AI-based identification services.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16534

Context-Predictive Low-Rank Adaptation for Face Presentation Attack Detection

Aminu Yusuf, Shakeel Muhammad Ibrahim, Sulaimon Oyeniyi Adebayo, Muhammad Sohail Ibrahim, Muhammad Shahid Jabbar, Shujaat Khan

原文摘要

Face presentation attack detection requires representations that distinguish physical spoofing cues across changes in acquisition devices, subjects, and attack media. Large visual encoders offer transferable representations, but conventional binary fine-tuning supervises their final decisions without explicitly constraining how local facial evidence is represented under partial observation. We present CP-JEPA, a context-predictive adaptation framework that combines low-rank tuning of a pretrained CLIP image encoder with a joint-embedding predictive objective. A student predicts complete-image teacher features at masked spatial locations, while unmasked classification, masked classification, and prediction consistency retain the connection to the live/spoof task. An exponential-moving-average teacher supplies evolving latent targets. The auxiliary components are used only during training; inference requires only the LoRA-adapted CLIP visual encoder and binary classifier. {We evaluate CP-JEPA under single-source and multi-source OCIM transfer, additional CelebA-Spoof training, transfer to CelebA-Spoof, two-source training, and unseen-attack evaluation on SiW-Mv2.} On the standard OCIM benchmark, CP-JEPA achieves 3.74\% mean HTER and 98.73\% mean AUC. {Single-source evaluation achieves 11.36\% mean HTER across twelve source--target combinations.} With CelebA-Spoof added to the three OCIM training sources, CP-JEPA reduces mean HTER from 3.45\% to 2.35\% and increases mean AUC from 98.68\% to 99.32\%. On SiW-Mv2, it achieves 3.36\% mean HTER across the held-out attack categories. Controlled loss ablations examine the contributions of latent prediction, masked classification, and prediction consistency. The results show that context-predictive adaptation can improve cross-dataset PAD performance, although the gains vary with the training sources and evaluation setting.

cs.CV · eess.IV · eess.SP · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.16733

TheoPhysEval: Assessing AI agents at autonomous theoretical physics problem solving

A. Ramesh Chandra, Aaghaz Mahajan, Ainesh Sanyal, Mianqi Wang, Shubhadeep Chakraborty, Sudip Chakraborty, Shailesh Chandrasekharan, Rama Govindarajan 等 35 位作者

原文摘要

We present a suite of 50 unpublished research-level theoretical physics problems with verifiable answers. Without AI assistance, a typical problem in our collection could be solved by a world expert in about a day or less but would take considerably longer for experts in adjacent specialties. We test the ability of off-the-shelf AI agents to tackle these problems autonomously, without human hints. When given access to tools available in a realistic setting, including the ability to search the literature and write scripts for numerical and symbolic work, the best-performing agent, GPT-6-Astra harnessed in Codex, obtains the final answers with a near-perfect accuracy of 98% at an average cost under USD 3 per problem. On average, agents show only small changes in accuracy when denied web search and access to background research papers, suggesting significant memorization of the literature. These results suggest that state-of-the-art agents can accurately perform concrete calculations required across theoretical physics research. We comment on the broader implications of these findings and discuss possible explanations for the current lack of agent-led breakthroughs in theoretical physics despite their outstanding problem-solving abilities.

hep-th · cond-mat.stat-mech · cond-mat.str-el · cs.AI · gr-qc

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16527

AI has its own style in mathematical proving

Xiaokai Li, Ziyu Zhou, Qibin Cheng, Yangcheng Wu, An Zeng, Ying Fan

原文摘要

As large language models increasingly generate mathematical proofs, whether they develop a proving style distinct from that of human mathematicians remains an open question. This question is particularly pressing as mathematicians increasingly voice concerns that proofs generated by large language models are difficult to interpret. Here we ask three large language models to independently reprove all 465 propositions of Euclid's \textit{Elements} and compare their citation choices against the human-proof baseline through Jaccard similarity and complex network analysis. We find that AI-generated proofs differ from human proofs, and this difference is stable and reproducible: mean pairwise Jaccard similarity among AI runs (~0.68) is nearly twice that between AI and human proofs (~0.37), and cross-run classification consistency is also high. By examining citation structure in detail, we identify the concrete signature of this difference: approximately 40% of AI citations point to foundational elements (definitions, common notions, and postulates), compared with only 13\% in human proofs; even among theorem-to-theorem citations, AI tends to cite more distant results. Roughly half of AI's extra citations trace to the upstream dependencies of the human proof. Together, these findings suggest that the AI proving style is a tendency to construct arguments on its own from foundational elements rather than directly citing later, more established results, which we term "building from the ground up". Our findings offer a new perspective on how AI systems organise and invoke mathematical knowledge, with implications for understanding the interpretability of AI-generated proofs.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.13018

Support is Search

Alexander V. Gheorghiu

原文摘要

For intuitionistic propositional logic, Sandqvist's base-extension semantics defines a support relation parametrised by atomic bases, with validity identified as support in every base. Sandqvist's completeness theorem answers the global question: which formulae are valid? This paper addresses the local question: given a fixed base, what does support in that base correspond to? We show that support in a fixed base coincides with proof-search in a hereditary Harrop logic program with a quantifier over atoms, via an encoding of formulae as logic programming goals. The encoding is a variant of the Russell-Prawitz translation of the connectives, with quantification restricted to atoms, and it is obtained by reading the semantic clauses in continuation-passing style. So read, the universal quantifiers over base extensions and atoms in those clauses need not range over a completed totality: they are eigenvariables under a standard freshness discipline. Base-extension semantics thereby acquires an explicit computational content: support is proof-search. The result complements Sandqvist's global theorem with a local correspondence and makes the constructive character of the framework explicit.

cs.LO · math.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16526

Lost in Delegation: Studying the Effectiveness of Task Orchestration in Generative Search

Tamara Stojanovska, Elisabeth Kirsten, Leon Swazinna, Muhammad Bilal Zafar

原文摘要

LLMs have quickly evolved from models used in isolation to agentic systems that delegate tasks to external tools and other LLMs to solve complex assignments. This agentic augmentation has led to impressive capability gains. However, when and how delegation improves performance remains poorly understood. For example, it is unclear whether delegation helps when tasks involve post-cutoff information that the orchestrating LLM cannot reliably resolve from its own knowledge. In this work, we use purpose-built datasets with pre- and post-cutoff splits to compare various delegation modes. We find that delegation provides no consistent accuracy improvement over non-delegated setups, with effects varying across models and tasks. When tasks involve knowledge unavailable to the orchestrator, delegation can substantially degrade accuracy, while direct tool use with the same LLM performs much better. Our analysis identifies distorted delegated queries, inefficient tool use, and failures to preserve and use information between agents as recurring failure modes. These findings suggest that effective orchestration requires both well-calibrated delegation decisions and effective exchange of information between agents.

cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16525

ZONOS3-Mini Technical Report

Gabriel Clark, Sofian Mejjoute, George Close, Beren Millidge

原文摘要

ZONOS3-Mini is an ultra-compact, ultra-fast, zero-shot voice-cloning TTS model. The model can run in real time on a laptop or smartphone. For its latency and size, it achieves state-of-the-art accuracy and cloning fidelity. To achieve this we propose a latent MoE backbone with only 886M parameters (100M active per token) and a new audio codec, Zyphra Audio Codec 1 (ZAC-1), which represents 44.1 kHz audio at a 15 Hz frame rate. On the new ZTTS2-Eval benchmark, ZONOS3-Mini reaches a 6.71% macro-average WER over 55 language sets (35 clean and 20 in the wild), which is competitive with larger and slower state-of-the-art models. We release the model weights, inference, and training code.

cs.SD · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16524

A Joint LLM and Optimization Agent Solution for Drone Scheduling in Victim Search Missions

Yinghao Zhang, Yalin Liu, Hong-Ning Dai, Yan Zhang, Qubeijian Wang, Bishenghui Tao, Yaohan Li

原文摘要

Victim search in wilderness areas is time-critical. Drones enable rapid area search, but scheduling them is challenging because victims may continue moving, detection results are uncertain, and effective decisions must combine structured mission information with unstructured contextual clues and domain knowledge. To address this, we propose an agent-aided drone scheduling system. The system employs an agent at the ground control station (GCS) to coordinate multiple drones over successive search cycles. In each cycle, the agent applies a scheduling policy that combines a large language model (LLM) for reasoning about where to search with an optimizer for determining how to dispatch the drones under energy constraints. Simulation results show that the proposed system consistently completes the victim-search mission, whereas none of the six non-LLM baselines does so and direct LLM scheduling is considerably less reliable. The source code is publicly available at https://github.com/1643204431/llm-uav-victim-search.

eess.SY · cs.AI · cs.ET

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16523

RESYNC: Residual Latent-State Synchronization for Deadline-Bound Streaming Inference

Eljas Linna, Hao Dong, Juho Kanniainen

原文摘要

Predicting every observation in a high-rate stream requires a model to finish before the next arrival. A large model may offer better predictions but miss that deadline. We propose RESYNC: the large model runs asynchronously, while a fast path updates its latest exact but stale latent state using the observations received since that state was computed and applies its unchanged prediction head. The updater learns from the frozen model's representations without task labels. Across one real and two synthetic streams and five architecture families, RESYNC beats the best compact or distilled alternative with a paired 95% interval above zero in all twelve dataset--architecture pairs with a material teacher advantage at anchor ages of 4--12 observations, and in eleven pairs at older tested ages up to 32. The share of the teacher's gain preserved remains complete on the real stream but is lower and generally declines with anchor age on the synthetic streams. In live replay with 1 ms arrivals, the synchronous large models cannot deliver any predictions on time while RESYNC serves at least 99.4% of arrivals within the interval, with 0.35--0.62 ms median latency on one CPU thread: around the same speed as or faster than the compact model, and 7.9--17.7$\times$ faster than synchronous large-model inference. RESYNC targets settings with background compute available but a strict per-observation response deadline.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16522

PISim: A Unified Photonic Inference Simulator for Hardware-Aware Cross-Layer Design-Space Exploration

Andreas Falkenberg

原文摘要

Photonic neural accelerators offer ultra-low latency linear algebra, yet their usable numerical precision, sustainable throughput, and application accuracy remain tightly bounded by surrounding electro-optical interfaces and physical non-idealities. Pre-fabrication design-space exploration (DSE) requires a cross-layer electronic design automation (EDA) framework capable of evaluating disparate optical topologies under identical neural workloads and physical assumptions. We present \textbf{PISim}, a unified Photonic Inference Simulator that couples pretrained deep neural network execution with physics-based models of six accelerator architecture families, six electro-optic modulator technologies, four optical source organizations, and direct or coherent readout schemes. PISim embeds stage-resolved impairment operators---covering control and analog-to-digital quantization, finite extinction ratio, shot noise, correlated relative-intensity noise (RIN), thermal drift, propagation loss, and channel crosstalk---alongside an analytical cycle-level interface timing model to expose electronic-optical bottlenecks. Using simulated MNIST multilayer perceptron (MLP) benchmarks, we demonstrate how PISim identifies optimal precision operating points under multi-impairment stress (peaking at 6 bits for Mach--Zehnder and microring modulators), quantifies a 52.5 percentage-point accuracy gap between VCSEL-array and parallel-fanout laser architectures arising, under the modeled conditions, from power partitioning and correlated source noise, and models interface-limited throughput scaling across emitter-array widths from 32 to 128 channels. This comprehensive framework establishes an extensible, reproducible foundation for accuracy-constrained Pareto optimization prior to photonic tape-out.

physics.optics · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16521

Online Survivable Network Design with Arbitrarily Small Connectivity Loss

MohammadTaghi Hajiaghayi, Saeed Seddighin

原文摘要

In the online survivable network design problem, a graph with edge costs is known in advance, and requests arrive one at a time. Each request is a pair of vertices $(s,t)$ with a connectivity requirement $k$, and the algorithm must irrevocably buy edges so that $s$ and $t$ are joined by $k$ edge-disjoint paths. The goal is to keep the total cost close to that of an optimal offline solution. Gupta, Krishnaswamy, and Ravi~\cite{GKR12} obtain an $O(k\log^3 n)$-competitive algorithm, where $k$ is the maximum requirement, without relaxing the connectivity requirement. Dehghani, Ehsani, Hajiaghayi, Liaghat, and Seddighin~\cite{DEHLS18} obtain a polylogarithmic competitive ratio when the connectivity requirement is allowed to be relaxed by a factor of $2+\varepsilon$. We show that relaxing the connectivity requirement by a factor of only $1+\varepsilon$ suffices to obtain a polylogarithmic competitive ratio.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.31399

World-Model Collapse as a Phase Transition

Xinyuan Song, Zekun Cai

原文摘要

Water looks unchanged as it warms, then at a critical point it boils. We ask whether long-horizon language agents show an analogous transition in their ability to maintain an accurate representation of the evolving world state. In some parameter settings, a small change in a task parameter, or adding a single step to the task, leaves behavior nearly unchanged; near a critical boundary, the same small change causes a sudden collapse. We study this effect in a deterministic task family where the true world state is known at every step. A large grid search over state size, state dependency, horizon, number of available actions, observation mode, and frequency of state changes reveals a phase diagram: a low-stress region where the agent consistently succeeds, a narrow region where success drops sharply, and a high-stress region where the agent consistently fails. Step-by-step traces show the mechanism: the agent loses track of the true world before its actions become invalid, so the agent is not merely choosing a bad action; it is acting from a corrupted world. Stronger models reach the critical boundary later but do not eliminate the sharp transition. These results make world-model collapse a measurable bottleneck for long-horizon agents. Code will be made publicly available soon.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16517

UniSpark: Exploring Hybrid Attention for Multimodal Understanding and Generation

Jiancheng Pan, Yuzheng Liu, Hengyuan Cao, Qiyuan Zhang, Yangyan Li, Biao Gong, Zheng Zhang, Ka Long Cheng 等 10 位作者

原文摘要

We present UniSpark-9B, a unified multimodal model that investigates hybrid attention for visual understanding and generation. Built on a Mixture-of-Transformer-Experts backbone with approximately 9B parameters in each branch, UniSpark combines full attention for explicit token interactions with GatedDeltaNet linear attention for compact context propagation. Autoregressive text prediction and visual flow matching jointly support visual question answering, text-to-image generation, instruction-guided editing, and interleaved text-image prediction. In interleaved generation, noisy visual targets are isolated from subsequent predictions, while clean visual representations provide context for later outputs. A blockwise protocol enforces this distinction through attention masks in full-attention layers and recurrent-state retention rules in linear-attention layers. The selected training sources contain 159.7M generation samples, 213.4M understanding samples, and 624.8K interleaved sequences. A four-stage curriculum jointly trains multimodal understanding and visual generation, refining answer accuracy and instruction following while extending image synthesis to 2K. UniSpark scores 2470.3 on MME-S and 0.83 on GenEval. Qualitative examples further illustrate its capabilities in interleaved image generation, video synthesis, and video editing. Together, these results demonstrate that hybrid attention provides an effective backbone for unified multimodal understanding and generation.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.16516

Authenticated Approximate Nearest Neighbor Search in Outsourced Vector Databases

Yuanhang Zhou, Xinyi Zhang, Cheng Xu, Jianliang Xu

原文摘要

Approximate Nearest Neighbor (ANN) search has become a fundamental query primitive in modern vector databases for managing large-scale embedding data. To support growing vector collections, organizations increasingly outsource database management and query processing to third-party clouds. However, untrusted service providers may return suboptimal or fabricated results, posing a serious threat to ANN search integrity. Existing authenticated ANN approaches face substantial scalability challenges. They typically require transmitting high-dimensional vectors for distance verification and rely on topology-agnostic authentication structures, resulting in excessive communication and verification overhead. In this paper, we propose VIDA, a novel and efficient scheme for authenticated graph-based ANN search. VIDA replaces raw vector transmission with compact vector commitments and employs an aggregated Inner Product Argument to efficiently verify multiple distance computations. Furthermore, we introduce MeST, a topology-aware authenticated data structure that aligns authentication paths with graph traversal, significantly reducing structural proof overhead. Additionally, we design a quantization-aware authentication strategy that exploits distance bounds for pruning, reducing the number of exact distances requiring cryptographic authentication. Extensive experiments demonstrate that the proposed schemes substantially improve the efficiency of authenticated ANN search, achieving up to a $6.6\times$ speedup in end-to-end latency and reducing data transfer time by up to 99.2% compared with state-of-the-art methods.

cs.DB · cs.CR

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新论文 · v1arXiv:2611.16515

PiLLVD: A Real-World Low-Light Video Dataset for Dynamic Scenes Captured with Off-the-Shelf Cameras

Yaser Abu Awwad, Paul L. Rosin, Oktay Karakuş

原文摘要

Low-light image and video enhancement has received increasing attention in recent years. However, most existing enhancement methods are developed and evaluated on still images, while progress in low-light video enhancement remains limited by the lack of real-world paired video datasets. Acquiring paired low-light and normal-light video sequences is challenging because it requires spatially and temporally aligned captures while avoiding motion blur, exposure mismatch, and correspondence errors in dynamic scenes. To address this gap, we introduce PiLLVD, the Pi-based Low-Light Video Dataset, a paired low-light video dataset captured using an accessible Raspberry Pi-based multi-camera system with off-the-shelf cameras. PiLLVD provides paired low-light and aligned normal-light reference sequences for dynamic indoor and outdoor scenes, including diverse human and object motion under varying illumination conditions. Unlike existing datasets that are often image-based, static, synthetic, or limited to simple motion patterns, PiLLVD focuses on motion-rich scenes where frame-to-frame stability and motion-related artefacts are important for evaluation. In addition to presenting the dataset and acquisition pipeline, we benchmark representative pre-trained low-light video enhancement methods on PiLLVD under a zero-shot setting. The results show that existing methods do not generalise uniformly across the proposed sequences, highlighting the challenges posed by the proposed dataset and its value for both benchmarking existing methods and supporting the development of future low-light video enhancement algorithms.

cs.CV · eess.IV

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新论文 · v1arXiv:2611.16514

Learning Temporal Dynamics with Derivative Representations for Time Series Forecasting

Shujie Duan, Jia Hong Puah, Haocheng Wu, Yifei Mao, Hongting Zhou, Chenxuan Li, Zitao Fang, Yang Lu 等 9 位作者

原文摘要

Time series forecasting aims to predict the future evolution of complex systems by learning their underlying dynamics from past observations. Despite recent advances in deep learning, Transformers in particular still struggle to capture the temporal evolution of system dynamics. Existing approaches have been largely driven by architectural innovation, with comparatively little attention paid to training strategies that explicitly exploit the temporal structure governing system dynamics. In this work, we introduce DeltaTSF, a dynamics-aware forecasting framework based on discrete derivative representations of time series. Specifically, DeltaTSF jointly learns the series and their discrete temporal derivatives across both input and forecast horizons within a unified, model-agnostic framework. By coupling these representations over time, the model is encouraged to maintain temporal consistency, a property we show to induce a form of discrete Taylor consistency during training. This formulation enables the model to better reflect the underlying system dynamics, resulting in more stable and physically coherent forecasts. Notably, DeltaTSF does not require explicit derivative computation and introduces no additional computational overhead at inference time. Extensive experiments across diverse benchmarks and backbone architectures demonstrate that DeltaTSF consistently improves forecasting performance and yields more dynamically coherent predictions than strong SOTA methods. These results highlight the effectiveness of enforcing dynamic coherence through derivative representations as a model-agnostic paradigm that operates alongside and complements architectural design. Source code will be made publicly available upon acceptance.

cs.LG

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新论文 · v1arXiv:2611.16509

Sound Automatic Lock Placement for Concurrent Programs with Pointers

Nicolas Waldburger, Florian Faissole, Ryo Okabe, Denis Cousineau

原文摘要

We propose a lightweight method for automatic lock placement in concurrent POSIX programs with explicit pointer manipulation, such as C programs. Given a program that lacks sufficient user-placed mutexes, or contains none at all, our method introduces new mutexes to prevent data races. The method is formally proven to guarantee the absence of data races and mainly consists of three steps. The first step performs lockset analysis and may-happen-in-parallel analysis. The second step conducts a concurrent points-to analysis derived from a Steensgaard-like procedural analysis, where per-thread points-to graphs are unified using a special completion rule. The final step places locks to prevent data races while respecting a lock ordering to avoid deadlocks. In addition, user-annotated critical sections are protected through insertion of dedicated locks. Since lock placement is a complex and error-prone task, our method aims at helping developers write correct concurrent programs.

cs.LO

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版本更新 · v2arXiv:2611.13636

An Interpretable Stylometric Framework for Early Chinese Buddhist Translator Styles: A Case Study on the Attribution of Disputed Works of An Shigao

Yating Pan, Xinyue Zhang, Jun Wang

原文摘要

Attributing early Chinese Buddhist translations requires evidence that scholars can trace back to the texts. We develop an interpretable stylometric framework that uses features defined or reviewed by Buddhist philologists, including function characters, sentence formulas, sentence-final characters, sentence length, and part-of-speech n-grams. We construct stylistic profiles from 31 authenticated translations by seven translators from the Eastern Han through the Western Jin, then compare eight disputed texts with An Shigao's profile. Two texts, T1557 and T0101, show the strongest stylistic similarity to the authenticated An Shigao corpus, with scores of 0.783 and 0.687, respectively. The attributions agree with those reported by a published BERT-based approach on the same set of disputed texts. The framework requires no task-specific training and allows scholars to inspect which linguistic features support each proposed attribution.

cs.CL

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新论文 · v1arXiv:2611.16507

Input-output characterization of robotic grasping: Connecting design parameters to grasp behavior through the admittance

Julien Vanderheyden, Guillaume Drion, Pierre Sacré, Fulvio Forni

原文摘要

Low-impedance grippers make the dynamics of a grasp matter. Current grasp metrics, however, are static: they describe how a held object resists constant loads, not how it responds to time-varying ones. To quantify this, we rely on small-signal analysis to characterize a grasp by its admittance, the transfer matrix relating load perturbations to object displacements around an equilibrium. This transfer matrix recovers the stiffness and stability of the grasp at zero frequency and captures its natural frequencies and peak gain under time-varying loads. We apply this characterization to a planar two-finger gripper under proportional-derivative control. We show that two grasps of the same object in the same position and with the same contact forces can differ in stiffness by two orders of magnitude. Likewise, two grasps that share the same stiffness can exhibit very different resonances. Nonlinear simulations confirm the effectiveness of our small-signal analysis in capturing the dynamic behavior of grasping.

cs.RO · eess.SY

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版本更新 · v2arXiv:2508.17349

2-Layer Fan-Planarity in Polynomial Time

Yasuaki Kobayashi, Yuto Okada

原文摘要

In this paper, we give a polynomial-time algorithm for deciding whether a bipartite graph admits a 2-layer fan-planar drawing. This resolves an open problem posed in several papers since 2015. We also obtain a linear-time algorithm for the one-sided case.

cs.CG · cs.DS

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新论文 · v1arXiv:2611.16502

SignRot: LLM Quantization with Massive Outlier-Aware Sign-Adjusted Rotation

Jaehun Gim, Jaewoo Kim, Gyuwan Kim, Seo Yeon Park

原文摘要

While quantization compresses LLMs and accelerates inference, outlier activations impede efficient low-bit representation. In particular, massive outliers cause substantial performance degradation. Although rotation-based methods that rely on randomized Hadamard transforms for online inference aim to mitigate this, they can produce fragmented distributions with empty bins when a massive outlier dominates a token's activation. This fragmentation severely undermines uniform quantization performance. Moreover, massive outliers concentrate in the down-projection input, where the preceding nonlinearity requires the rotation to be applied at inference time. This constraint prevents training-based approaches such as SpinQuant from learning the online rotation at this position, as a learned dense matrix cannot use the Fast Walsh-Hadamard Transform and would require a full matrix multiplication during inference. To address this, we propose SignRot (Sign-adjusted Rotation), which identifies massive outlier channels and constructs a sign adjustment vector that unifies the signs of the corresponding Hadamard row, collapsing the bimodal distribution into a unimodal profile amenable to quantization. Since the aligned outlier shifts the activation mean away from zero, we pair SignRot with asymmetric activation quantization, whose zero-point absorbs this shift. As a lightweight, drop-in improvement compatible with any Hadamard-based quantization method, SignRot requires only a single calibration forward pass and adds only element-wise sign flips at inference, without an additional matrix multiplication. Applying SignRot to QuaRot achieves a WikiText2 perplexity of 5.88 and an average zero-shot reasoning accuracy of 72.73, surpassing SpinQuant on the LLaMA3-70B model. Code is available at https://github.com/HYU-ARK-Lab/SignRot.

cs.AI

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新论文 · v1arXiv:2611.16500

EvenVLM: A Text and Multimodal Dataset for the Even Language with Vision-Language Fine-Tuning

Daniil Makovetskii, Ivan Bondarenko

原文摘要

We present EvenVLM, the first text and multimodal (VQA) dataset for Even (Lamut), an endangered Northern Tungusic language spoken by approximately 6,800 people in Siberia. Our dataset comprises 50,521 structured Even-Russian entries extracted from dictionary and phrasebook sources via GPT-4o Mini-based parsing, from which we synthetically generate 107,543 training examples spanning translation, grammar, reading comprehension, and semantic classification. To address tokenization inefficiency on Even's agglutinative morphology, we propose a three-step tokenizer expansion algorithm that adds Even-specific graphemes (eng, oe, o-umlaut), whitelisted vocabulary, and BPE merges, reducing average tokens per word by 27% (4.99 to 3.64). Using LoRA fine-tuning of Qwen3-VL-4B, we adapt the model for text tasks (0.53% trainable parameters, rank 32) and separately for visual question answering (rank 64, frozen vision encoder). Our best configuration achieves 11.4% exact match and a 13.6 weighted score on text benchmarks, 0.1283 chrF for sentence translation, and 65% accuracy on VQA (up from 0% for the base model and text-only fine-tuning). We release our datasets, evaluation benchmarks, and training pipeline to support further research on Even and other low-resource languages.

cs.CL

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新论文 · v1arXiv:2611.16499

Recalibrating a Typed-Decision Model in an Afternoon: A Hands-On Recipe for Laya's Confidence Scores

Mohammed Nayeem Khan, Aliyas Ahmad Misri

原文摘要

A typed-decision model such as Laya reads a text and a list of options that the caller defines, for example ticket categories, and returns a probability for each option. Code around it compares the top probability with a threshold to answer, abstain or escalate to a person. Earlier work reports, for a Turkish model, that temperature scaling can raise held-out calibration error (ECE). This tutorial gives a five-step recipe (measure, diagnose, fit, validate, deploy) in 130 lines of numpy with standard methods, none new. On 1,065 synthetic, LLM-labelled items the released Laya checkpoint has a pooled ECE of 0.073 but a bucket-macro ECE of 0.143 (0.127-0.184), the mean over buckets (questions grouped by type and option count): the 12-option bucket is over-confident by +0.22, the three-to-five-option bucket under-confident by 0.05, and they cancel. One global temperature makes pooled ECE worse (0.173); a cross-fitted temperature per bucket brings macro-ECE to 0.073 (0.078 over 10 fold assignments) and reaches 0.089 with 10 labelled items per bucket, but holding out one of seven evaluation sets gives 0.099. After the refit, a gate aimed at 10% error answers 46.3% of items (10.5% wrong) against 6.9% on the published confidences, but one cut-off per bucket on the published confidences answers 45.4%, so most of the gain comes from separating buckets. Confidence-only maps (Platt, histogram, isotonic) need 20 or more labelled items per bucket to approach the temperature; with 320 per bucket the lowest macro-ECE belongs to Platt. In these files the retrained checkpoints store no per-bucket table and the stored 12-option value 0.1006 is clamped to 0.5 at runtime; the refit helps 31 of 33 checkpoints on random folds and 21 with a set held out. Fit and test share one generator and LLM labels, so these are findings about these checkpoints and this data.

cs.AI · cs.SE

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新论文 · v1arXiv:2611.16498

How many LLM-labelled rows does a decision encoder need, and how wrong can the labels be?

Mohammed Nayeem Khan, Aliyas Ahmad Misri

原文摘要

A decision encoder such as Laya reads a question with named options (for example, which department should handle a ticket) and a text, and picks an option. It reads both in one forward pass and returns a probability for every option, without writing any text. We ask how many LLM-written training rows such an encoder needs and how wrong those labels may be. We ran 19 fine-tunes of Laya (ModernBERT-large) on nested subsets of a 2,818-row pool and scored them on 1,065 held-out items from the same generator, so every test item is in-distribution and no out-of-family test was run. Accuracy rises from 69.4% before fine-tuning to 74.3% with 5% of the rows, 84.8% with 50% and 86.9% with all rows. The curve flattens, and a saturating fit with bootstrap intervals cannot pin down its plateau. Seed spread at 20% of the rows (s.d. 1.6 points) exceeds some of the steps between sizes. Giving small subsets as many updates as the full pool gains 1.9 points at 5% and 1.9 points at 20% of the rows (one seed; p=0.14 and 0.13), and at 20% that is below the 2.3-point difference between two ordinary seeds. A single run with 10% symmetric label noise shows no detectable change (-0.2 points, 95% interval -1.7 to +1.4); 20% costs 3.4 points and 40% costs 8.7 points. Confusable-pair noise at 20% costs 7.4 points, and under noise the model moves from over-confident to under-confident. An independent labeller disagrees with the gold labels on 3.5% of held-out items, and about half of its choice disagreements (16 of 33, leaving each item out of the partner map) name the partner option that the confusable-pair noise uses. Laya is +0.9 points ahead of a TF-IDF classifier trained on the same rows at full size (the interval includes zero) and +21.1 points ahead at 5% of the rows. Everything is synthetic, LLM-labelled and from one backbone, one trainer and mostly one seed.

cs.AI

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