arXiv subjects

Radu State

Publications and source records attributed to Radu State.

At least 19 recordsLinked to original sources

arXiv2026

SoK: Cryptographic Key Recovery for Cryptoasset Custody and Financial Technologies

Cryptoasset systems often bind cryptographic key control to financial control: losing a wallet seed, custody share, hardware device, or smart-account credential can remove spend authority, while compromised recovery can enable theft. Existing work treats recovery through separate vocabularies--key backup, secret sharing, account recovery, credential re-issuance, social recovery, and asset migration--making mechanisms and tradeoffs difficult to compare. This paper presents a Systematization of Knowledge (SoK) on cryptographic key recovery for cryptoasset custody and financial technologies. Starting from a 118-paper systematic-review discovery corpus, we derive a 77-paper synthesis corpus and code each retained system in a master matrix covering recovered objects, recovery semantics, mechanisms, enrollment and storage, authorization, trust placement, failure events, post-recovery state, validation evidence, deployment status, privacy, usability, and limitations. The matrix supports an axis-first taxonomy that separates secret-restoring, hybrid, control-restoring, forensic/extractive, and framework-oriented recovery. Our central observation is that recovery is not a single operation: systems may reconstruct an original secret, regenerate a seed, restore a share, reissue a credential, migrate signing authority, restore account control, move assets, or extract forensic artifacts. We derive a generalized construction model, check it against production-facing designs, and identify six findings: recovery semantics are heterogeneous; recovery shifts trust; liveness improvements create abuse paths; post-recovery lifecycle management is uneven; protocol evidence outpaces user evidence; and recovery metadata remains underprotected. These gaps motivate a research agenda for recovery-aware financial technologies.

cs.CR↗

arXiv2026

Geometry-Guided Layerwise FFN Width Allocation in Transformers

Feed-forward networks (FFNs) account for a large fraction of Transformer parameters, yet their hidden width is usually constant across depth. We ask whether this capacity can instead be allocated from a forward-pass measurement of layer behavior. We view each FFN as transporting a cloud of token representations and quantify the induced geometric change using correspondence-preserving shift, Gromov-Wasserstein distortion, and degree-one persistent homology under raw and scale-normalized metrics. A layerwise approximation surrogate yields an exact fixed-budget optimizer. Across seven pretrained language models, raw Euclidean work largely tracks residual-norm growth, whereas normalized work is predominantly front-loaded. Gromov-Wasserstein work is more consistently associated with perturbation-based layer sensitivity than the finite-sample topological estimate. In paired 128M and 256M training runs, several normalized-work schedules reduce mean validation loss relative to both uniform width and a hand-designed cosine taper. With the amplified paired differences at 440M, the best geometry-based allocations improve over uniform substantially larger than the cosine taper, while the anti-topological raw control is worse than uniform.

cs.LG↗

arXiv2026

Feed-Forward Steering in Transformer Residual Dynamics

Attention-only dynamical theories model Transformer residual directions as particles aggregating on a sphere. We extend this framework by incorporating the feed-forward network (FFN) term as a local steering field acting on each token state. The resulting theory predicts that the tangential component of the FFN field is necessary for motion in residual-direction space, that critical residual directions correspond to nonlinear projective equilibria, and that a commutator defect determines when a finite attention--FFN block can be accurately approximated by a parallel, additive flow. Across GPT-2, Pythia, Mistral, and Llama models, the extended theory improves one-step angular prediction relative to an attention-only baseline, with the contribution of the FFN increasing from GPT-2 to Llama-3-8B. Intervention experiments show that retaining only the tangential FFN component preserves most model quality, whereas retaining only the radial component causes performance to collapse. The tangential component also preserves output diversity under aggregation pressure. As a practical application, layers with small commutator defects can be approximately parallelized with only a modest increase in loss, whereas layers with large defects degrade rapidly. These findings support the interpretation of FFN layers as directional steering fields that shape Transformer residual geometry and govern the feasibility of block-level interventions.

cs.LG↗

arXiv2026

Agents Don't Paginate: First-Chunk Selection for LLM Tool Responses

Coding agents built on large language models (LLMs), such as Claude Code, Cursor, OpenAI Codex, GitHub Copilot, and Aider, receive tool responses that routinely exceed the agent's per-turn token budget. The standard remedy, pagination, is available in every protocol that produced these responses; yet across the corpus of session logs from a public Model Context Protocol middleware we observed no agent-initiated requests for a second chunk. The first chunk is what the agent reads, so we ask how often the gold item (the one the agent needs) is placed first in it: the precision-at-1 rate $p_1$. In a controlled offline benchmark we treat first-chunk selection as a 0/1 knapsack and compare six value functions on 500 SWE-bench Verified tasks, then test whether $p_1$ matters with a single-turn file-localisation probe on five language models (4,800 LLM calls; not an end-to-end resolve-rate test). Two pre-registered hypotheses did not hold and are our main findings. The central one is negative: raising $p_1$ does not systematically raise downstream accuracy. Per-model deltas stay under three percentage points (p.p.), are not consistently signed, and no model is significant; the agent recovers the gold from anywhere in the chunk, so what reaches its answer is first-chunk inclusion, not the gold's rank within it. The second: adding four file-metadata signals to a keyword scorer hurts $p_1$ by 4.8 p.p. (paired significance test, $p = 0.001$). A parameter-free keyword scorer does raise $p_1$, from a 24.2% baseline to 35.0% (+10.8 p.p., far beyond chance; $p = 3.9 \times 10^{-8}$), and to 35.8% with a fallback to the tool's native ordering when no keyword matches. But by our central finding this is a rank-1 gain, and rank-1 is the part that does not reach the agent's answer: downstream accuracy does not move.

cs.CL↗

arXiv2026

QeHDC: Hyperdimensional Computing based on Quantum-enhanced binding and SuperClass Construction

Hyperdimensional Computing (HDC) is a robust computational framework inspired by human cognition characterized by simple and efficient operations within high-dimensional vector spaces. Quantum-enhanced Hyperdimensional Computing (QeHDC) extends classical HDC by leveraging quantum mechanical properties to enhance computational efficiency. In this paper, we propose a novel Quantum HDC framework featuring a one-pass training method, leveraging sinusoidal and quantum encoding to project classical data into quantum amplitude states efficiently. Our framework introduces an innovative reference-state-based quantum binding operation realized via quantum circuits. Furthermore, we propose a density-matrix-based superclass generation strategy employing eigenvalue decomposition to extract critical quantum state features effectively, enabling a more accurate and robust class representation. Experimental evaluations conducted on standard benchmark datasets demonstrate our approach's superior performance, robustness to noise, and computational feasibility compared to traditional classical and existing quantum-enhanced approaches. The results highlight the practical benefits and potential of Quantum HDC for quantum-enhanced classification tasks and pave the way for future advancements in quantum-inspired computational paradigms.

cs.LG↗

arXiv2026

The Last Visible Pixel: Probing Fine-Scale Perception in Vision-Language Models

Recent vision-language models (VLMs) excel at multimodal understanding and reasoning, yet their fine-grained visual perception remains underexplored. A natural extension of ``How many r are there in Strawberry?'' asks: how small a visual pattern can a VLM reliably perceive? As such, we introduce FineSightBench, a new benchmark that systematically probes this limit by separating perception tasks (pixel-level recognition of letters, shapes, objects) from reasoning tasks (spatial reasoning, counting, ordering over small targets) across controlled scales of 4--48px. Through comprehensive experiments and detailed failure mode analysis on state-of-the-art models, we reveal a sharp dissociation: perception saturates around 12px, while reasoning remains limited even at larger scales, with persistent numeracy and sequence errors. These findings expose fundamental deficiencies in VLMs' fine-scale visual reasoning that demand more rigorous evaluation.

cs.CV↗

arXiv2026

Friction-Augmented Drifting Models for Resource-Efficient Domain Translation

Single-step generators promise high-fidelity synthesis at a fraction of the inference and training cost of ordinary differential equation (ODE)-based flow models, a central concern when compute is limited. Drifting Models (DMs) train a one-step generator by evolving samples under a kernel-based drift field, avoiding ODE integration entirely, but a two-particle surrogate of their iteration admits a \emph{locally repulsive} regime in which repulsion can dominate the attraction to the target. We introduce DMF (Drifting Model with Friction), which scales the drift field by a linearly-scheduled coefficient $1-\gamma(i)$. A closed-form analysis of the surrogate gives a per-step contraction threshold and a finite-horizon bound on the error trajectory, suggesting why friction can halt the iteration before it relaxes to a spurious force-balance fixed point. On FFHQ latent-space domain translation, DMF significantly improves on the frictionless DM it extends in both Fr\'echet Inception Distance (FID; paired $p=0.019$, Cohen's $d=1.71$) and CLIP-MMD (CMMD; $p=0.005$, $d=2.55$) with no additional forward passes or parameters, and on a 2D task it sharply improves DM's Fr\'echet (moment-matching) error while remaining on par under the 2-Wasserstein distance. DMF also achieves FID and CMMD comparable to the far more expensive Optimal Flow Matching (OFM) in our runs, at roughly $29\times$ lower training wall-clock on identical hardware. DMF thus delivers these gains with a single scheduled scalar.

cs.LG↗

arXiv2026

Geometric Entropy and Retrieval Phase Transitions in Continuous Thermal Dense Associative Memory

We study the thermodynamic memory capacity of modern Hopfield networks (Dense Associative Memory models) with continuous states under geometric constraints, extending classical analyses of pairwise associative memory. We derive thermodynamic phase boundaries for Dense Associative Memory networks with exponential capacity $M = e^{\alpha N}$, comparing Gaussian (LSE) and Epanechnikov (LSR) kernels. For continuous neurons on an $N$-sphere, the geometric entropy depends solely on the spherical geometry, not the kernel. In the sharp-kernel regime, the maximum theoretical capacity $\alpha = 0.5$ is achieved at zero temperature; below this threshold, a critical line separates retrieval from non-retrieval. The two kernels differ qualitatively in their phase boundary structure: for LSE, a critical line exists at all loads $\alpha > 0$. For LSR, the finite support introduces a threshold $\alpha_{\text{th}}$ below which no spurious patterns contribute to the noise floor, and no critical line exists -- retrieval is perfect at any temperature. These results advance the theory of high-capacity associative memory and clarify fundamental limits of retrieval robustness in modern attention-like memory architectures.

cond-mat.dis-nn↗

arXiv2026

Low-Complexity Algorithm for Stackelberg Prediction Games with Global Optimality

Stackelberg prediction games (SPGs) model strategic data manipulation in adversarial learning via a leader--follower interaction between a learner and a self-interested data provider, leading to challenging bilevel optimization problems. Focusing on the least-squares setting (SPG-LS), recent work shows that the bilevel program admits an equivalent spherically constrained least-squares (SCLS) reformulation, which avoids costly conic programming and enables scalable algorithms. In this paper, we develop a simple and efficient alternating direction method of multiplier (ADMM) based solver for the SCLS problem. By introducing a consensus splitting that separates the quadratic objective from the spherical constraint, we obtain an augmented Lagrangian formulation with closed-form updates: the primal quadratic step reduces to solving a fixed shifted linear system, the constraint step is a projection onto the unit sphere, and the dual step is a lightweight scaled ascent. The resulting method has low per-iteration complexity and allows pre-factorization of the constant system matrix for substantial speedups. Experiments demonstrate that the proposed ADMM approach achieves competitive solution quality with significantly improved computational efficiency compared with existing global solvers for SCLS, particularly in sparse and high-dimensional regimes.

eess.SP↗

arXiv2026

The Necessity of Setting Temperature in LLM-as-a-Judge

Using large language models (LLMs) as judges for evaluating model outputs has emerged as an important paradigm for automated evaluation. However, the choice of decoding temperature in LLM-as-a-judge settings is still largely chosen empirically, with limited systematic evidence on its impact. To address this gap, we conduct a systematic study of how temperature affects judgment behavior across different LLM judge models, prompting strategies, and evaluation paradigms. Our results show that higher temperatures generally decrease judgment consistency and increase formatting errors, while also exposing latent uncertainty that tends to remain suppressed under low-temperature decoding, particularly in ambiguous cases. Further analysis suggests that higher temperatures can serve as an exploratory mechanism and may improve judging performance in complex or uncertain evaluation scenarios. Overall, low-temperature settings are better suited to tasks that prioritize stability and reproducibility, whereas higher-temperature settings are more appropriate for scenarios involving substantial ambiguity or complexity, where exploration of the judge's decision space is beneficial. These findings suggest that, in LLM-as-a-judge systems, temperature should be treated not as a fixed hyperparameter, but as a controllable, task-dependent design choice that mediates the trade-off between reliability and exploration.

cs.CL↗

arXiv2026

How Much Does Persuasion Strategy Matter? LLM-Annotated Evidence from Charitable Donation Dialogues

Which persuasion strategies, if any, are associated with donation compliance? Answering this requires fine-grained strategy labels across a full corpus and statistical tests corrected for multiple comparisons. We annotate all 10,600 persuader turns in the 1,017-dialogue PersuasionForGood corpus (Wang et al., 2019), where donation outcomes are directly observable, with a taxonomy of 41 strategies in 11 categories, using three open-source large language models (LLMs; Qwen3:30b, Mistral-Small-3.2, Phi-4). Strategy categories alone explain little variance in donation outcome (pseudo $R^2 \approx 0.015$, consistent across all three annotators). Guilt Induction is the only strategy significantly associated with lower donation rates ($\Delta \approx -23$ percentage points), an effect that replicates across all three models despite only moderate inter-model agreement. Reciprocity is the most robust positive correlate. Target sentiment and interest predict whether a donation occurs but show at most a weak correlation with donation amount. These findings suggest that strategy identification alone is insufficient to explain persuasion effectiveness, and that guilt-based appeals may be counterproductive in prosocial settings. We release the fully annotated corpus as a public resource.

cs.CL↗

arXiv2026

Agent Skill Framework: Perspectives on the Potential of Small to Medium Language Models in Industrial Environments

Agent skills are widely supported by major agentic frameworks and perform well with proprietary models, yet their effectiveness for small and medium-sized open source language models (270 M-80B) remains underexplored. We systematically study the Skill paradigm in resource-constrained industrial settings, where reliance on proprietary APIs is impractical due to data security and budget constraints. Across two open-source tasks and a real-world insurance claims classification task, we find that very small models struggle with reliable skill selection, while models around 30B-80B benefit substantially. Thinking variants do not show major levels of improvement from skills, also considering GPU usage increases due to overthinking. These findings reveal a trade-off between GPU cost and agent performance, and provide actionable insights for effective Skill configuration and SLM deployment in real world settings.

cs.AI↗

arXiv2026

FedRandom: Sampling Consistent and Accurate Contribution Values in Federated Learning

Federated Learning is a privacy-preserving decentralized approach for Machine Learning tasks. In industry deployments characterized by a limited number of entities possessing abundant data, the significance of a participant's role in shaping the global model becomes pivotal given that participation in a federation incurs costs, and participants may expect compensation for their involvement. Additionally, the contributions of participants serve as a crucial means to identify and address potential malicious actors and free-riders. However, fairly assessing individual contributions remains a significant hurdle. Recent works have demonstrated a considerable inherent instability in contribution estimations across aggregation strategies. While employing a different strategy may offer convergence benefits, this instability can have potentially harming effects on the willingness of participants in engaging in the federation. In this work, we introduce FedRandom, a novel mitigation technique to the contribution instability problem. Tackling the instability as a statistical estimation problem, FedRandom allows us to generate more samples than when using regular FL strategies. We show that these additional samples provide a more consistent and reliable evaluation of participant contributions. We demonstrate our approach using different data distributions across CIFAR-10, MNIST, CIFAR-100 and FMNIST and show that FedRandom reduces the overall distance to the ground truth by more than a third in half of all evaluated scenarios, and improves stability in more than 90% of cases.

cs.LG↗

arXiv2026

Geometric Analysis of Token Selection in Multi-Head Attention

We present a geometric framework for analysing multi-head attention in large language models (LLMs). Without altering the mechanism, we view standard attention through a top-N selection lens and study its behaviour directly in value-state space. We define geometric metrics - Precision, Recall, and F-score - to quantify separability between selected and non-selected tokens, and derive non-asymptotic bounds with explicit dependence on dimension and margin under empirically motivated assumptions (stable value norms with a compressed sink token, exponential similarity decay, and piecewise attention weight profiles). The theory predicts a small-N operating regime of strongest non-trivial separability and clarifies how sequence length and sink similarity shape the metrics. Empirically, across LLaMA-2-7B, Gemma-7B, and Mistral-7B, measurements closely track the theoretical envelopes: top-N selection sharpens separability, sink similarity correlates with Recall. We also found that in LLaMA-2-7B heads specialize into three regimes - Retriever, Mixer, Reset - with distinct geometric signatures. Overall, attention behaves as a structured geometric classifier with measurable criteria for token selection, offering head level interpretability and informing geometry-aware sparsification and design of attention in LLMs.

cs.AI↗

arXiv2025

Temporal-Spatial Tubelet Embedding for Cloud-Robust MSI Reconstruction using MSI-SAR Fusion: A Multi-Head Self-Attention Video Vision Transformer Approach

Cloud cover in multispectral imagery (MSI) significantly hinders early-season crop mapping by corrupting spectral information. Existing Vision Transformer(ViT)-based time-series reconstruction methods, like SMTS-ViT, often employ coarse temporal embeddings that aggregate entire sequences, causing substantial information loss and reducing reconstruction accuracy. To address these limitations, a Video Vision Transformer (ViViT)-based framework with temporal-spatial fusion embedding for MSI reconstruction in cloud-covered regions is proposed in this study. Non-overlapping tubelets are extracted via 3D convolution with constrained temporal span $(t=2)$, ensuring local temporal coherence while reducing cross-day information degradation. Both MSI-only and SAR-MSI fusion scenarios are considered during the experiments. Comprehensive experiments on 2020 Traill County data demonstrate notable performance improvements: MTS-ViViT achieves a 2.23\% reduction in MSE compared to the MTS-ViT baseline, while SMTS-ViViT achieves a 10.33\% improvement with SAR integration over the SMTS-ViT baseline. The proposed framework effectively enhances spectral reconstruction quality for robust agricultural monitoring.

cs.CV↗

arXiv2025

NegBLEURT Forest: Leveraging Inconsistencies for Detecting Jailbreak Attacks

Jailbreak attacks designed to bypass safety mechanisms pose a serious threat by prompting LLMs to generate harmful or inappropriate content, despite alignment with ethical guidelines. Crafting universal filtering rules remains difficult due to their inherent dependence on specific contexts. To address these challenges without relying on threshold calibration or model fine-tuning, this work introduces a semantic consistency analysis between successful and unsuccessful responses, demonstrating that a negation-aware scoring approach captures meaningful patterns. Building on this insight, a novel detection framework called NegBLEURT Forest is proposed to evaluate the degree of alignment between outputs elicited by adversarial prompts and expected safe behaviors. It identifies anomalous responses using the Isolation Forest algorithm, enabling reliable jailbreak detection. Experimental results show that the proposed method consistently achieves top-tier performance, ranking first or second in accuracy across diverse models using the crafted dataset, while competing approaches exhibit notable sensitivity to model and data variations.

cs.CR↗

arXiv2025

Blockly2Hooks: Smart Contracts for Everyone with the XRP Ledger and Google Blockly

Recent technologies such as inter-ledger payments, non-fungible tokens, and smart contracts are all fruited from the ongoing development of Distributed Ledger Technologies. The foreseen trend is that they will play an increasingly visible role in daily life, which will have to be backed by appropriate operational resources. For example, due to increasing demand, smart contracts could soon face a shortage of knowledgeable users and tools to handle them in practice. Widespread smart contract adoption is currently limited by security, usability and costs aspects. Because of a steep learning curve, the handling of smart contracts is currently performed by specialised developers mainly, and most of the research effort is focusing on smart contract security, while other aspects like usability being somewhat neglected. Specific tools would lower the entry barrier, enabling interested non-experts to create smart contracts. In this paper we designed, developed and tested Blockly2Hooks, a solution towards filling this gap even in challenging scenarios such as when the smart contracts are written in an advanced language like C. With the XRP Ledger as a concrete working case, Blockly2Hooks helps interested non-experts from the community to learn smart contracts easily and adopt the technology, through leveraging well-proven teaching methodologies like Visual Programming Languages, and more specifically, the Blockly Visual Programming library from Google. The platform was developed and tested and the results are promising to make learning smart contract development smoother.

cs.CR↗

arXiv2025

To Squelch or not to Squelch: Enabling Improved Message Dissemination on the XRP Ledger

With the large increase in the adoption of blockchain technologies, their underlying peer-to-peer networks must also scale with the demand. In this context, previous works highlighted the importance of ensuring efficient and resilient communication for the underlying consensus and replication mechanisms. However, they were mainly focused on mainstream, Proof-of-Work-based Distributed Ledger Technologies like Bitcoin or Ethereum. In this paper, the problem is investigated in the context of consensus-validation based blockchains, like the XRP Ledger. The latter relies on a Federated Byzantine Agreement (FBA) consensus mechanism which is proven to have a good scalability in regards to transaction throughput. However, it is known that significant increases in the size of the XRP Ledger network would be challenging to achieve. The main reason is the flooding mechanism used to disseminate the messages related to the consensus protocol, which creates many duplicates in the network. Squelching is a recent solution proposed for limiting this duplication, however, it was never evaluated quantitatively in real-life scenarios involving the XRPL production network. In this paper, our aim is to assess this mechanism using a real-life controllable testbed and the XRPL production network, to assess its benefit and compare it to alternative solutions relying on Named Data Networking and on a gossip-based approach.

cs.NI↗