OpenRaiser/Intern-Atlas huggingface dataset:
Optimality of Simultaneous Consensus with Limited Information Exchange (Extended Abstract)
Venue: arXiv cs.DC 2025
Year: 2025

Work on the development of optimal fault-tolerant Agreement protocols using the logic of knowledge has concentrated on the "full information" approach to information exchange, which is costly with respect to message size. Alpturer, Halpern, and van der Meyden (PODC 2023) introduced the notion of optimality with respect to a limited information exchange, and studied the Eventual Agreement problem in the sending omissions failure model. The present paper studies the Simultaneous Agreement problem for the crash failures model, and a number of limited information exchanges from the literature. In particular, the paper considers information exchanges from a FloodSet protocol (Lynch, Distributed Algorithms 1996), a variant of this in which agents also count the number of failures (Castañeda et al, NETYS 2017), and a variant in which agents associate each agent with a value (Raynal, PRDC 2002). A new information exchange is also introduced that enables decisions to be made at worst one round later than the optimal protocol of Dwork and Moses (I&C 88), but with lower computation cost and space requirements. By determining implementations of a knowledge based program, protocols are derived that are optimal amongst protocols for each of these information exchanges.
Who is Afraid of Minimal Revision?
Venue: arXiv cs.AI 2025
Year: 2025

The principle of minimal change in belief revision theory requires that, when accepting new information, one keeps one's belief state as close to the initial belief state as possible. This is precisely what the method known as minimal revision does. However, unlike less conservative belief revision methods, minimal revision falls short in learning power: It cannot learn everything that can be learned by other learning methods. We begin by showing that, despite this limitation, minimal revision is still a successful learning method in a wide range of situations. Firstly, it can learn any problem that is finitely identifiable. Secondly, it can learn with positive and negative data, as long as one considers finitely many possibilities. We then characterize the prior plausibility assignments (over finitely many possibilities) that enable one to learn via minimal revision, and do the same for conditioning and lexicographic upgrade. Finally, we show that not all of our results still hold when learning from possibly erroneous information.
Complexity of Łukasiewicz Modal Probabilistic Logics
Venue: arXiv cs.LO 2025
Year: 2025

Modal probabilistic logics provide a framework for reasoning about probability in modal contexts, involving notions such as knowledge, belief, time, and action. In this paper, we study a particular family of these logics, extending the modal Łukasiewicz many-valued logic. These logics are shown to be capable of expressing nuanced probabilistic concepts, including upper and lower probabilities. Our main contribution is a PSPACE-completeness result for two variants of the local consequence problem, providing a precise computational characterisation.
Muddy Waters
Venue: arXiv cs.LO 2025
Year: 2025

In the 2013 Advent calender of the Berlin Mathematics Research Center MATH+, Gerhard Woeginger presents a novel hat problem with an uncommon initial announcement. Although the information given is insufficient for the hat bearers to learn their colour, they are informed that the colours have been chosen so that they can learn their colour. We formalize this announcement in public announcement logic and in an extension of public announcement logic with fixpoints.
Asking like Socrates: Socrates helps VLMs understand remote sensing images
Venue: arXiv cs.CV 2025
Year: 2025

Recent multimodal reasoning models, inspired by DeepSeek-R1, have significantly advanced vision-language systems. However, in remote sensing (RS) tasks, we observe widespread pseudo reasoning: models narrate the process of reasoning rather than genuinely reason toward the correct answer based on visual evidence. We attribute this to the Glance Effect, where a single, coarse perception of large-scale RS imagery results in incomplete understanding and reasoning based on linguistic self-consistency instead of visual evidence. To address this, we propose RS-EoT (Remote Sensing Evidence-of-Thought), a language-driven, iterative visual evidence-seeking paradigm. To instill this paradigm, we propose SocraticAgent, a self-play multi-agent system that synthesizes reasoning traces via alternating cycles of reasoning and visual inspection. To enhance and generalize these patterns, we propose a two-stage progressive RL strategy: first, RL on fine-grained Grounding tasks to enhance RS-EoT capabilities, followed by RL on RS VQA to generalize to broader understanding scenarios. Experiments show RS-EoT achieves state-of-the-art performance on multiple RS VQA and grounding benchmarks. Analyses reveal clear iterative cycles of reasoning and evidence seeking, confirming RS-EoT mitigates the Glance Effect and enables genuine evidence-grounded reasoning. Our code, data, and models are available at https://geox-lab.github.io/Asking_like_Socrates
GEO-Detective: Unveiling Location Privacy Risks in Images with LLM Agents
Venue: arXiv cs.CR 2025
Year: 2025

Images shared on social media often expose geographic cues. While early geolocation methods required expert effort and lacked generalization, the rise of Large Vision Language Models (LVLMs) now enables accurate geolocation even for ordinary users. However, existing approaches are not optimized for this task. To explore the full potential and associated privacy risks, we present Geo-Detective, an agent that mimics human reasoning and tool use for image geolocation inference. It follows a procedure with four steps that adaptively selects strategies based on image difficulty and is equipped with specialized tools such as visual reverse search, which emulates how humans gather external geographic clues. Experimental results show that GEO-Detective outperforms baseline large vision language models (LVLMs) overall, particularly on images lacking visible geographic features. In country level geolocation tasks, it achieves an improvement of over 11.1% compared to baseline LLMs, and even at finer grained levels, it still provides around a 5.2% performance gain. Meanwhile, when equipped with external clues, GEO-Detective becomes more likely to produce accurate predictions, reducing the "unknown" prediction rate by more than 50.6%. We further explore multiple defense strategies and find that Geo-Detective exhibits stronger robustness, highlighting the need for more effective privacy safeguards.
What Is the Optimal Ranking Score Between Precision and Recall? We Can Always Find It and It Is Rarely $F_1$
Venue: arXiv cs.PF 2025
Year: 2025

Ranking methods or models based on their performance is of prime importance but is tricky because performance is fundamentally multidimensional. In the case of classification, precision and recall are scores with probabilistic interpretations that are both important to consider and complementary. The rankings induced by these two scores are often in partial contradiction. In practice, therefore, it is extremely useful to establish a compromise between the two views to obtain a single, global ranking. Over the last fifty years or so,it has been proposed to take a weighted harmonic mean, known as the F-score, F-measure, or $F_β$. Generally speaking, by averaging basic scores, we obtain a score that is intermediate in terms of values. However, there is no guarantee that these scores lead to meaningful rankings and no guarantee that the rankings are good tradeoffs between these base scores. Given the ubiquity of $F_β$ scores in the literature, some clarification is in order. Concretely: (1) We establish that $F_β$-induced rankings are meaningful and define a shortest path between precision- and recall-induced rankings. (2) We frame the problem of finding a tradeoff between two scores as an optimization problem expressed with Kendall rank correlations. We show that $F_1$ and its skew-insensitive version are far from being optimal in that regard. (3) We provide theoretical tools and a closed-form expression to find the optimal value for $β$ for any distribution or set of performances, and we illustrate their use on six case studies.
Gaussians on Fire: High-Frequency Reconstruction of Flames
Venue: arXiv cs.CV 2025
Year: 2025

We propose a method to reconstruct dynamic fire in 3D from a limited set of camera views with a Gaussian-based spatiotemporal representation. Capturing and reconstructing fire and its dynamics is highly challenging due to its volatile nature, transparent quality, and multitude of high-frequency features. Despite these challenges, we aim to reconstruct fire from only three views, which consequently requires solving for under-constrained geometry. We solve this by separating the static background from the dynamic fire region by combining dense multi-view stereo images with monocular depth priors. The fire is initialized as a 3D flow field, obtained by fusing per-view dense optical flow projections. To capture the high frequency features of fire, each 3D Gaussian encodes a lifetime and linear velocity to match the dense optical flow. To ensure sub-frame temporal alignment across cameras we employ a custom hardware synchronization pattern -- allowing us to reconstruct fire with affordable commodity hardware. Our quantitative and qualitative validations across numerous reconstruction experiments demonstrate robust performance for diverse and challenging real fire scenarios.
Rethinking Cross-Generator Image Forgery Detection through DINOv3
Venue: arXiv cs.CV 2025
Year: 2025

As generative models become increasingly diverse and powerful, cross-generator detection has emerged as a new challenge. Existing detection methods often memorize artifacts of specific generative models rather than learning transferable cues, leading to substantial failures on unseen generators. Surprisingly, this work finds that frozen visual foundation models, especially DINOv3, already exhibit strong cross-generator detection capability without any fine-tuning. Through systematic studies on frequency, spatial, and token perspectives, we observe that DINOv3 tends to rely on global, low-frequency structures as weak but transferable authenticity cues instead of high-frequency, generator-specific artifacts. Motivated by this insight, we introduce a simple, training-free token-ranking strategy followed by a lightweight linear probe to select a small subset of authenticity-relevant tokens. This token subset consistently improves detection accuracy across all evaluated datasets. Our study provides empirical evidence and a feasible hypothesis for understanding why foundation models generalize across diverse generators, offering a universal, efficient, and interpretable baseline for image forgery detection.
Exploring Performance Variations in Finetuned Translators of Ultra-Low Resource Languages: Do Linguistic Differences Matter?
Venue: arXiv cs.CL 2025
Year: 2025

Finetuning pre-trained language models with small amounts of data is a commonly-used method to create translators for ultra-low resource languages such as endangered Indigenous languages. However, previous works have reported substantially different performances with translators created using similar methodology and data. In this work we systematically explored possible causes of the performance difference, aiming to determine whether it was a product of different cleaning procedures, limitations of the pre-trained models, the size of the base model, or the size of the training dataset, studying both directions of translation. Our studies, using two Brazilian Indigenous languages, related but with significant structural linguistic characteristics, indicated none or very limited influence from those training factors, suggesting differences between languages may play a significant role in the ability to produce translators by fine-tuning pre-trained models.
AI Deception: Risks, Dynamics, and Controls
Venue: arXiv cs.AI 2025
Year: 2025

As intelligence increases, so does its shadow. AI deception, in which systems induce false beliefs to secure self-beneficial outcomes, has evolved from a speculative concern to an empirically demonstrated risk across language models, AI agents, and emerging frontier systems. This project provides a comprehensive and up-to-date overview of the AI deception field, covering its core concepts, methodologies, genesis, and potential mitigations. First, we identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception. We then review existing empirical studies and associated risks, highlighting deception as a sociotechnical safety challenge. We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment. Deception emergence reveals the mechanisms underlying AI deception: systems with sufficient capability and incentive potential inevitably engage in deceptive behaviors when triggered by external conditions. Deception treatment, in turn, focuses on detecting and addressing such behaviors. On deception emergence, we analyze incentive foundations across three hierarchical levels and identify three essential capability preconditions required for deception. We further examine contextual triggers, including supervision gaps, distributional shifts, and environmental pressures. On deception treatment, we conclude detection methods covering benchmarks and evaluation protocols in static and interactive settings. Building on the three core factors of deception emergence, we outline potential mitigation strategies and propose auditing approaches that integrate technical, community, and governance efforts to address sociotechnical challenges and future AI risks. To support ongoing work in this area, we release a living resource at www.deceptionsurvey.com.
Optimized Agent Shift Scheduling Using Multi-Phase Allocation Approach
Venue: arXiv cs.AI 2025
Year: 2025

Effective agent shift scheduling is crucial for businesses, especially in the Contact Center as a Service (CCaaS) industry, to ensure seamless operations and fulfill employee needs. Most studies utilizing mathematical model-based solutions approach the problem as a single-step process, often resulting in inefficiencies and high computational demands. In contrast, we present a multi-phase allocation method that addresses scalability and accuracy by dividing the problem into smaller sub-problems of day and shift allocation, which significantly reduces number of computational variables and allows for targeted objective functions, ultimately enhancing both efficiency and accuracy. Each subproblem is modeled as a Integer Programming Problem (IPP), with solutions sequentially feeding into the subsequent subproblem. We then apply the proposed method, using a multi-objective framework, to address the difficulties posed by peak demand scenarios such as holiday rushes, where maintaining service levels is essential despite having limited number of employees
Spatially Aware Dictionary-Free Eigenfunction Identification for Modeling and Control of Nonlinear Dynamical Systems
Venue: arXiv cs.LG 2025
Year: 2025

A new approach to data-driven discovery of Koopman eigenfunctions without a pre-defined set of basis functions is proposed. The approach is based on a reference trajectory, for which the Koopman mode amplitudes are first identified, and the Koopman mode decomposition is transformed to a new basis, which contains fundamental functions of eigenvalues and time. The initial values of the eigenfunctions are obtained by projecting trajectories onto this basis via a regularized least-squares fit. A global optimizer was employed to optimize the eigenvalues. Mapping initial-state values to eigenfunction values reveals their spatial structure, enabling the numerical computation of their gradients. Thus, deviations from the Koopman partial differential equation are penalized, leading to more robust solutions. The approach was successfully tested on several benchmark nonlinear dynamical systems, including the FitzHugh-Nagumo system with inputs, van der Pol and Duffing oscillators, and a 2-spool turbojet engine with control. The study demonstrates that incorporating principal eigenvalues and spatial structure integrity promotion significantly improves the accuracy of Koopman predictors. The approach effectively discovers Koopman spectral components even with sparse state-space sampling and reveals geometric features of the state space, such as invariant partitions. Finally, the numerical approximation of the eigenfunction gradient can be used for input dynamics modeling and control design. The results support the practicality of the approach for use with various dynamical systems.
CacheTrap: Injecting Trojans in LLMs without Leaving any Traces in Inputs or Weights
Venue: arXiv cs.CR 2025
Year: 2025

Adversarial weight perturbation has emerged as a concerning threat to LLMs that either use training privileges or system-level access to inject adversarial corruption in model weights. With the emergence of innovative defensive solutions that place system- and algorithm-level checks and corrections in the input and weight spaces, these perturbations are increasingly susceptible to defenses. This work develops a novel perspective on Trojan attacks that generates an attacker-designed model output while leaving no attack traces on the inputs or weights. Such an attack space can be unlocked through corruption of the key-value (KV) cache. In this paper, we introduce CacheTrap, a novel Trojan attack that corrupts the value vectors stored in the KV cache. These vectors capture the dynamic activations for specific token positions and therefore constitute a natural surface for transient, inference-time trigger insertion. The transient nature of these KV values and their dependence on victim input imply additional constraints on our attack, such as a lack of knowledge of the victim's data or domain application, and, consequently, a lack of gradient information. The objective of the proposed CacheTrap is to develop a vulnerable KV bit-searching algorithm so that, once the attack employs the identified bit-flip as a trigger, the model generates targeted behavior, e.g., classifying inputs towards the target class. Moreover, CacheTrap is a data- and gradient-free attack which also has no impact on the model's utility. Our evaluation demonstrates that the proposed attack enables the first successful Trojan attack on LLMs with a single bit flip in the KV cache. In addition, the data-independent nature of the attack ensures that once the attacker identifies the vulnerable bit index, the location remains constant and can be transferred to a wide range of victim tasks/datasets/queries with no overhead.
Emergent Extreme-View Geometry in 3D Foundation Models
Venue: arXiv cs.CV 2025
Year: 2025

3D foundation models (3DFMs) have recently transformed 3D vision, enabling joint prediction of depths, poses, and point maps directly from images. Yet their ability to reason under extreme, non-overlapping views remains largely unexplored. In this work, we study their internal representations and find that 3DFMs exhibit an emergent understanding of extreme-view geometry, despite never being trained for such conditions. To further enhance these capabilities, we introduce a lightweight alignment scheme that refines their internal 3D representation by tuning only a small subset of backbone bias terms, leaving all decoder heads frozen. This targeted adaptation substantially improves relative pose estimation under extreme viewpoints without degrading per-image depth or point quality. Additionally, we contribute MegaUnScene, a new benchmark of Internet scenes unseen by existing 3DFMs, with dedicated test splits for both relative pose estimation and dense 3D reconstruction. All code and data will be released.
A Synthetic Reconstruction of Multiparty Session Types (with Appendix)
Venue: arXiv cs.PL 2025
Year: 2025

Multiparty session types (MPST) provide a rigorous foundation for verifying the safety and liveness of concurrent systems. However, existing approaches often force a difficult trade-off: classical, projection-based techniques are compositional but limited in expressiveness, while more recent techniques achieve higher expressiveness by relying on non-compositional, whole-system model checking, which scales poorly. This paper introduces a new approach to MPST that delivers both expressiveness and compositionality, called the synthetic approach. Our key innovation is a type system that verifies each process directly against a global protocol specification, represented as a labelled transition system (LTS) in general, with global types as a special case. This approach uniquely avoids the need for intermediate local types and projection. We demonstrate that our approach, while conceptually simpler, supports a benchmark of challenging protocols that were previously beyond the reach of compositional techniques in the MPST literature. We generalise our type system, showing that it can validate processes against any specification that constitutes a "well-behaved" LTS, supporting protocols not expressible with the standard global type syntax. The entire framework, including all theorems and many examples, has been formalised and mechanised in Agda, and we have developed a prototype implementation as an extension to VS Code.
Ghosting Your LLM: Without The Knowledge of Your Gradient and Data
Venue: arXiv cs.CR 2025
Year: 2025

In recent years, large language models (LLMs) have achieved substantial advancements and are increasingly integrated into critical applications across various domains. This growing adoption underscores the need to ensure their security and robustness. In this work, we focus on the impact of Bit Flip Attacks (BFAs) on LLMs, which exploits hardware faults to corrupt model parameters, posing a significant threat to model integrity and performance. Existing studies on BFA against LLMs adopt a progressive bit-search strategy that predominantly relies on gradient-based techniques to identify sensitive layers or weights. However, computing gradients comes with two specific challenges: First, in the context of LLMs, it increases computational and memory costs exponentially, and Second, it requires access to a sample victim dataset or knowledge of the victim domain to compute the gradient. In this work, we investigate beyond the scope of attack efficacy and aim to develop an efficient, practical Gradient-Data-free Bit-Flip Attack. The challenge lies in the core principle of adversarial attacks, which relies heavily on computing gradients from sample test/train data and manipulating model weights based on gradient information. To overcome this, we propose novel vulnerability index metrics that can identify vulnerable weight bits in LLMs independent of any gradient or data knowledge. By removing the dependency on gradient computation, our approach drastically reduces memory requirements and scales efficiently across multiple tasks with constant complexity. Experimental results demonstrate the efficiency of our method, requiring as few as a single bit flip to achieve adversarial objectives for five open-source LLMs.