arXiv:2607.14903v1 Announce Type: new
Abstract: Firmware rehosting executes firmware images in emulated environments such as QEMU to enable scalable dynamic analysis of Internet of Things (IoT) devices. In practice, rehosting pipelines remain fragile across diverse real-world firmware images, as reaching an externally observable execution state depends on tightly coupled artifacts spanning boot scripts, persistent configuration (e.g., NVRAM-like key-value state), and network setup. Template-driven frameworks often fail to accommodate long-tail vendor conventions, while unconstrained use of large language models (LLMs) risks unsupported modifications and irreproducible executions. We introduce FirmPilot, an evidence-guided multi-agent framework for environment recovery in firmware rehosting. FirmPilot reformulates rehosting as iterative environment reconstruction in which a search agent grounds decisions through similarity-based retrieval, a planner coordinates execution-accepted transitions, and specialized agents recover filesystem/init artifacts, persistent state, and network exposure. Through repeated execution and evidence-grounded artifact deltas, the system resolves cross-layer dependencies across boot, state, and networking that otherwise prevent firmware executions from reaching a stable, externally reachable state in emulation. Evaluated on the large-scale, real-world LFwC firmware corpus, FirmPilot improves web-service reachability over FirmAE from 25.49% to 52.39% and network reachability from 39.30% to 71.93%. The resulting rehosts raise the average number of detected services per firmware from 0.86 to 1.62 and support downstream analysis workflows, including RouterSploit interaction and protocol-aware fuzzing over recovered service surfaces. The evaluation shows that evidence- and feedback-grounded agent coordination improves rehosting success, service recovery, and downstream utility in automated firmware rehosting.
Science Journals
arXiv:2607.14283v1 Announce Type: new
Abstract: Resolving heterogeneity in megadalton assemblies requires precise single-molecule mass measurements in solution. Mass photometry infers mass from individual molecular surface-landing events, and event-to-event measurement variability limits precision. Nanofluidic scattering microscopy overcomes this limitation by continuously tracking molecules in motion, enabling repeated sampling and temporal averaging of these fluctuations. Benchmarking with 4.5 MDa DNA origami demonstrates up to a fourfold improved resolution, approaching the performance of ensemble-averaged native mass spectrometry.
arXiv:2607.15275v1 Announce Type: new
Abstract: Recent robot foundation models operate with single-step or short-history visuomotor context. We introduce Test-Time-Training Robot Policies (RoboTTT), a robot model and training recipe that scale visuomotor context to 8K timesteps, three orders of magnitude beyond state-of-the-art policies, without growing inference latency. At this context length, we unlock new robot capabilities: one-shot in-context imitation from human video demonstrations, on-the-fly policy improvement, robustness to perturbations, and stronger performance on multi-stage, long-horizon tasks. We also observe, for the first time, steady gains in closed-loop performance as pretraining context length scales. At its core, RoboTTT integrates Test-Time Training into robot foundation models such as Vision-Language-Action policies, yielding a sequence model whose recurrent state consists of fast weights, parameters updated by gradient descent during both training and inference, compressing histories into weight space and retrieving contextual information for long-context conditioning. To scale training context length, the recipe combines sequence action forcing with truncated backpropagation through time. On challenging real-robot manipulation tasks, RoboTTT improves overall performance by 87% over the single-step context baseline and fully completes a five-minute, ten-stage assembly task, which no baseline ever does. RoboTTT trained with 8K-timestep context outperforms the same model pretrained with 1K timesteps by 62%, suggesting context length as a new scaling axis for robot foundation models. Videos are available at https://research.nvidia.com/labs/gear/robottt/
arXiv:2607.14875v1 Announce Type: cross
Abstract: Building on the multi-determinant Transformer backflow neural quantum state (NQS) ansatz and the associated multi-stage training workflow for the doped two-dimensional Hubbard model, we investigate how the optimization dynamics of the NQS depend on several key optimization and architectural hyperparameters. The workflow consists of neural-network backflow (NNB) initialization, supervised Transformer pre-training, and main energy optimization using the Moment-Adaptive ReConfiguration Heuristic (MARCH) within variational Monte Carlo. Using the doped $4\times4$ periodic Hubbard model at $U=8$ as a baseline, we examine how the update-norm threshold, Transformer width, number of determinant channels, and Monte Carlo batch size affect convergence. We find that a moderate update constraint improves the efficiency of MARCH optimization, larger Transformer width and more determinant channels improve the expressive capacity of the ansatz, and larger Monte Carlo batches reduce sampling noise in the update direction. We further test the same workflow at half filling, weaker interaction strength, open boundary conditions, and on a larger $8\times8$ doped lattice. These results identify practical optimization trends for Transformer backflow NQSs and highlight the balance between ansatz expressivity, MARCH update stability, and Monte Carlo sampling quality.
arXiv:2607.15166v1 Announce Type: new
Abstract: Most medical AI benchmarks measure whether a model knows the correct answer. MedFailBench asks a different question: which safety boundary failed? We present a clinician-built synthetic benchmark and failure atlas that labels medical AI errors by severity (1--5) and safety gate type (missed urgent escalation, unsafe remote dosing, unsafe discharge reassurance, evidence fabrication, unsafe protocol execution, source support gap). The current public release (v0.2.1) contains 44 clinician-reviewed synthetic cases with severity annotations, a live HuggingFace leaderboard preview, a safety gate taxonomy, a clinical severity rubric, and an automated pipeline for archiving model-response screening runs. No patient data, clinical validation claims, or model rankings are included. MedFailBench is released under Apache-2.0 and CC-BY-4.0 and carries the Zenodo DOI 10.5281/zenodo.21205535.
arXiv:2607.14811v1 Announce Type: new
Abstract: Retrieval-augmented generation (RAG) enhances large language models via external document retrieval, but retrieved contexts may leak sensitive information. Current privacy protection methods typically rely on a document-level static risk assumption, treating all retrieved documents as having the same privacy leakage risk. However, this assumption overlooks a fundamental characteristic of RAG: the privacy risk of a document is highly dependent on the user's query, making privacy leakage inherently query-driven and dynamic. To address this challenge, we propose a Prompt-Aware Dynamic Hierarchical Differential Privacy framework (PA-HDP) for privacy-preserving RAG. PA-HDP first performs a prompt-aware risk hierarchy to dynamically assess privacy risks under different queries. It then applies adaptive sensitive entity replacement and exponential mechanism-based text selection to provide differentiated privacy protection while preserving semantic utility. By protecting only the content that is truly sensitive under a given query, PA-HDP minimizes unnecessary modifications to the retrieval corpus. Extensive experiments on benchmark datasets demonstrate that PA-HDP significantly reduces privacy leakage while maintaining high retrieval quality, achieving a better privacy-utility trade-off than prior methods.
arXiv:2607.14817v1 Announce Type: new
Abstract: Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning. However, the Bayes-optimal decision strategies for these tasks do not coincide with the scores commonly used to quantify epistemic uncertainty. Building on the epistemic reject-option framework, we evaluate epistemic uncertainty using its ability to identify regret, the reducible error. Formulating selective prediction as a constrained optimization over coverage, expected risk, and regret, we prove the optimal selector is a thresholded convex combination of the ground-truth aleatoric and epistemic uncertainties. This theoretical unification exposes a weakness in recent uncertainty disentanglement literature: we demonstrate that standard correlation metrics between learned components do not necessarily predict their actual operational utility. We instead propose to evaluate the achievable risk, regret, coverage surface of the decomposition as a diagnostic for joint disentanglement and utility. Benchmarking standard methods on datasets with dense human annotations reveals that decision-theoretic rankings can disagree substantially with proxy-task rankings, including pairwise rank inversions between methods that are top-ranked on one criterion and bottom-ranked on other.
arXiv:2607.15146v1 Announce Type: new
Abstract: Online encyclopedias shape political opinion and, through it, democratic discourse. In late 2025, Grokipedia was released, an encyclopedia written entirely by the LLM Grok. One motivation behind the project was to provide an unbiased alternative to Wikipedia, which has faced accusations of "left-wing" and "liberal" bias. But does an encyclopedia written by an LLM deliver greater neutrality, or does it simply embed a different ideology? We conduct a large-scale political bias study on Grokipedia and Wikipedia, analysing 1,394 article pairs describing members of government for neutrality along nine expert-coded ideology dimensions employing four LLM judges, Grok, Claude, Mistral, and DeepSeek. As the LLMs could themselves be biased, we also investigate patterns in their judgments. We find all LLM-judges, including Grok, to rate Grokipedia less neutral than Wikipedia. Both encyclopedias are rated as portraying politicians favourably overall, but towards different ideological groups. Grokipedia particularly favours economically right-wing politicians and penalises socially liberal ones, while Wikipedia is rated as favourably biased towards the latter.
arXiv:2607.15180v1 Announce Type: new
Abstract: Ordinary differential equations (ODEs) are widely used to model dynamical systems in physics, biology, neuroscience, and physiology, but in many applications some equations of the dynamics are unknown and only a subset of the state variables are measured. We propose a hybrid neural--physics framework in which the known components of the ODE are kept explicit and the missing components are represented by a neural network. The proposed method consists of two stages where we alternate between state and parameter estimation and iterate until a predetermined criterion is met. Specifically, in the first step, we treat the model parameters as being known and we infer the latent states from the available measurements using a Rauch--Tung--Striebel (RTS) smoother. In the second stage, we treat the smoothed trajectories as being known and use them to estimate the neural networks' parameters through backpropagation. We evaluate the method on benchmark systems spanning linear, nonlinear, and stiff dynamics under partial state observation. Across these settings, the proposed method learns missing ODE components from incomplete measurements while exploiting and retaining interpretable mechanistic structure and improving latent-state reconstruction and long-horizon prediction.
arXiv:2607.14420v1 Announce Type: new
Abstract: When designing a network, engineers must navigate trade-offs (e.g., one topology offers more aggregate bandwidth, another lower latency or better resilience) that demand reasoning about quantitative properties. We present a fast analyzer for quantitative network properties based on weighted NetKAT (wNetKAT), a domain-specific language that provides a semantic foundation for quantitative reasoning by modeling network behavior using weights drawn from a semiring. At the core of our development is the design of a symbolic data structure -- weighted symbolic packet programs (wSPPs) -- that compactly represent the semantics of weighted policies, for which a direct implementation would be intractable. We show how to compute all policy constructs symbolically; unsurprisingly, the crux is Kleene star, for which we design a tailored algorithm. We further develop trace-carrying Pareto semirings, which compute multi-objective frontiers together with the network paths that realize them. We formalize the development in Lean and provide an optimized Rust implementation. Being parametric on a semiring, our implementation covers both classical and quantitative analyses: we show that it is competitive with KATch, a heavily optimized Boolean-reachability verifier, and orders of magnitude faster than McNetKAT and Storm on probabilistic analyses. A case study comparing Fat-tree and Jellyfish data-center topologies shows the framework supports multi-objective design-time analysis.
arXiv:2607.14430v1 Announce Type: new
Abstract: Prediction market prices are routinely interpreted as probabilities, both in academic work and in derivative products built atop these markets. We document two systematic ways this interpretation fails on Kalshi, a major U.S. event-contract exchange, using 23 million moneyline trades across major sport leagues. First, calibration, the agreement between quoted prices and realized event frequencies, is not a static property of a contract: fitting calibration models within time-to-expiry buckets, we find that parameters sit at their perfect-calibration reference values in the middle of a contract's life but depart sharply as expiry approaches. In the final ten minutes before settlement the empirical calibration curve becomes step-like, fitting a Prelec form with curvature parameter well above one, the opposite sign of the canonical lottery-choice fit, consistent with insurance-demand behavior by traders holding losing positions. Second, cross-game parlays on Kalshi are systematically overpriced relative to the product of their contemporaneous leg prices, with overpricing growing in leg count. This holds when the parlay legs are drawn from the TTE regime in which leg-level calibration is essentially perfect, indicating a separate, market-level markup at the parlay-pricing stage. Both deviations are systematic and therefore admit computational correction. Practical use of prediction-market prices as probabilities requires conditioning on time to expiry and on product type, not on price alone.
arXiv:2607.15142v1 Announce Type: new
Abstract: World models are usually evaluated as components of model-based reinforcement learning (MBRL) systems, while the world models themselves are rarely studied in isolation.
We examine five representative visual world-model agents in Atari Pong: DreamerV3, DIAMOND, TWISTER, Simulus, and STORM. After reproducing their training pipelines and matching the reported agent performance, we freeze the learned world models and evaluate them with a closed-loop rollout diagnostic: a policy trained separately from the corresponding MBRL agent interacts with each frozen model, and the generated video trajectories are inspected for visual and dynamical errors. Across all five models, the rollouts contain clear failures, including ball disappearance, incorrect ball motion, and invalid ball-paddle interactions.
Beyond visual trajectories, we further evaluate them with pixel-space zero-shot MBRL, where a new policy is trained entirely inside a frozen world model and then evaluated in the real environment. Across all five models, the resulting policies substantially underperform those produced by the corresponding original MBRL training pipelines. The gap is particularly large for DreamerV3, whose mean return drops from -5.5 to -20.9, near the minimum Pong return of -21.
We hypothesize that insufficient modeling of task-critical concepts, such as the ball in Pong, may contribute to these failures. We therefore propose Concept-Guided Spatial Regularization (CGSReg), an auxiliary pixel reconstruction loss applied to segmented concept regions. Experiments show that CGSReg improves both closed-loop rollouts and pixel-space zero-shot MBRL in DreamerV3, DIAMOND, and TWISTER. Its effects vary across the remaining models and evaluation metrics, indicating that CGSReg alone does not address all world-model bottlenecks.
arXiv:2607.15155v1 Announce Type: new
Abstract: The dynamics of premixed flames propagating in two-dimensional and cylindrical channels are investigated using direct numerical simulations of the fully compressible reactive Navier-Stokes equations coupled with conductive heat transfer within the channel walls. The simulations employ a high-order numerical method, detailed chemical kinetics and transport models for a stoichiometric hydrogen-air combustion. The influence of heat losses during the early stages of flame propagation is examined for channels of different aspect ratios, with particular focus on tulip flame formation and its subsequent transition to distorted tulip structures. Heat losses are modelled considering convective heat transfer from the hot combustion products to the inner wall surface, thermal conduction heat transfer through the wall, and convective and radiative heat losses from the outer wall surface to the surroundings. The obtained results are compared with corresponding simulations under adiabatic wall boundary conditions. The simulations reproduce the principal features of flame dynamics observed experimentally, highlighting the combined influence of wall heat losses and geometric confinement on flame dynamics in confined channels.
arXiv:2504.02500v2 Announce Type: replace-cross
Abstract: Quantum processing units (QPUs) built on superconducting Josephson junctions remain the most industrially mature route to fault-tolerant quantum computing, but the field has moved substantially since early 2025. This paper provides an updated overview of Josephson-junction QPUs, grounded in the quantum-mechanical principles, superposition, entanglement, and decoherence, that any qubit implementation must contend with, and in the physics of Cooper pairing and quantum tunneling that make a Josephson junction behave as a controllable qubit. We examine the engineering challenges of scaling these devices, including crosstalk, classical control-interface bottlenecks, and quantum error correction, and discuss the first below-threshold surface-code demonstrations on superconducting hardware. We survey recent materials advances, near-term computational results, and applications beyond computing, and compare Josephson-junction QPUs against trapped-ion, photonic, and neutral-atom alternatives, the latter having emerged as a serious fourth architecture since our original analysis. We close with the current public roadmaps toward fault-tolerant superconducting quantum computers and a calibrated assessment of how much progress those roadmaps still require.
arXiv:2607.14836v1 Announce Type: new
Abstract: Sign language production, which generates continuous 3D skeletal motion from spoken language input, must simultaneously satisfy two constraints: semantic fidelity, so that a deaf viewer can recognize the intended sequence of glosses, and biomechanical plausibility, so that the generated skeleton respects anatomical constraints. Existing approaches optimize semantic reconstruction through coordinate-based objectives that treat the skeleton as an unstructured vector, thus allowing for bone length drift, joint angle violations, and temporarily locked fingers. We introduce PIDiffSign, a physics-informed diffusion model for gloss-to-pose translation that incorporates anatomical constraints into both the architecture and training objective. The model uses a Transformer encoder-decoder, where the decoder is conditioned on the diffusion time step through adaptive zero-initialized layer normalization and cross-attends to gloss representations. A differentiable geometry module enforces bone length consistency and biologically valid joint angles throughout generation. Training combines anthropomorphic, kinematic, angular, and finger-joint constraints with a contrastive gloss-pose alignment loss and classifier-free guidance for semantically conditioned sampling. Experiments on the PHOENIX14T and CSL-Daily benchmarks show consistent improvements over a strong diffusion baseline in pose accuracy, joint-angle correctness, distributional realism, and back-translation quality. These results demonstrate that physics-informed diffusion improves both motion realism and semantic fidelity for sign language generation.
arXiv:2607.15185v1 Announce Type: new
Abstract: The blocklace is a directed acyclic graph encoding the causal relationship between authenticated updates produced by participating nodes. Compared to previous approaches, it adds restrictions on what can be replicated: a new update and its causal history is replicated locally if and only if either 1) it reveals a new node behaving arbitrarily (byzantine), or 2) it was signed by a node that still appears to be correct and the new updates provide evidence incriminating at least the set of nodes locally known to have behaved arbitrarily. The restrictions purport to limit the replication of arbitrary updates, even in the presence of colluders that never produce incriminating evidence, so that only a finite number will eventually be replicated by correct nodes.
While the original description of the replication behaviour successfully achieve this aim, we show that this finite number can be made arbitrarily large, up to the size of the identifier space used to authenticate messages. This effectively enables malicious nodes to overwhelm correct nodes with arbitrary and useless updates. Practical deployments therefore require additional restrictions on the set of identifiers that will be accepted by correct nodes.
arXiv:2607.14567v1 Announce Type: cross
Abstract: The quest for superconductivity created by light extends for more than half a century, yet direct evidence of a true zero-resistance state -whose macroscopic quantum phase coherence is both created and controlled by light -- has remained elusive. Here we report for the first time on a complex but robust light-programmable superconducting (LiPS) state at an aluminium-silicon heterojunction that is created and fully controlled with femtosecond laser pulses. The superconducting critical temperatures -- ranging from 1.8-8.5 K, can be increased or erased at will by the application of tailored pulse sequences. At low temperatures the LiPS state shows features characteristic of a Berezinski-Kosterlitz-Thouless topological transition, but another distinct state appears at temperatures above 2 K, which shows clear signatures of quantum phase disorder. In the presence of a magnetic field we observe behaviour characteristic of vortex pinning and creep consistent with the 2-dimensional (2D) nature of the phase coherent system. The origin of the LiPS effect is attributed to light pulse control of the Moire-like superlattice of misfit dislocations (MDs) that naturally occur as a result of discommensurations between Al and Si lattices at the interface, and is clearly observable by high-resolution electron microscopy. We show how light pulses can be used to control the superlattice periodicity and highlight the appearance of topologically protected soliton-like kinks along the dislocation lines, important for imparting metastability to the system. The demonstration of LiPS opens a route to the design of metastable long-range phase coherent superconducting states, leading to light-engineering of quantum circuits, local gap tuning in quantum processors and novel devices utilizing switchable superconductivity.
arXiv:2607.14585v1 Announce Type: cross
Abstract: Artificial intelligence (AI) is rapidly transforming economies, societies, and polities, raising fundamental questions about how it should be regulated. Policymakers face choices over whether to prioritize innovation or safety, rely on public oversight or private self-regulation, and govern nationally or internationally. Yet little is known about how citizens evaluate these competing priorities. Here we report a conjoint survey experiment conducted in seven countries with diverse political and economic profiles. We find that citizens strongly support regulating AI and generally prioritize safety over innovation, public governance over private self-regulation, and international over national approaches. The preference for safety is strongest among those who perceive AI as risky, unpredictable, and personally consequential. These findings reveal a systematic misalignment between dominant regulatory approaches and citizen preferences.
arXiv:2408.12633v4 Announce Type: replace
Abstract: Since antiquity, musical scales have been explained by harmony rather than melody. This view relies on the mathematically designed scales of a few traditions, and was never directly tested. Testing it requires cross-cultural data and a method that judges theories by what they get wrong as well as right. We provide both, modelling scale evolution across 1,314 scales from 96 countries. A Melody model explains the near-universal preference for step-sizes of 1-3 semitones, and matches independent data from melodies, singing, and psychoacoustics. Harmony does far less: it explains the music-theoretic scales, but in those measured from performance it adds only a weak bias towards fourths, fifths, and octaves. Harmony's importance has been overstated, likely due to the historical focus on music-theoretic rather than measured scales. Melody is the primary driver of global scale structure; harmonic constraints are less impactful and mainly reflect musicological theory over musical performance.
arXiv:2607.14880v1 Announce Type: cross
Abstract: We propose a novel measure of the discrepancy between two probability distributions $f$ and $g$ on a graph - which we call the diffusion distance - that measures the rate of convergence of $f$ to $g$ under a graph-constrained Markov chain with stationary distribution $g$. As a default choice for this Markov chain, we use the Metropolis-Hastings transition matrix targeting $g$ with proposals given by a random walk on the graph. Our primary case of interest is when the second distribution $g$ is uniform, in which case the diffusion distance becomes a measure of spatial clustering in $f$. Used in this way, (Metropolis-Hastings) diffusion distance to uniformity extends Moran's $I$-type measures of spatial autocorrelation by incorporating global graph geometry rather than just local patterns. Indeed, Moran's $I$, the most well-known measure of spatial autocorrelation, can be viewed as a one-step heuristic for diffusion distance, so long as specific spatial weights are used. We establish theoretical bounds and a stability result for our measure, connecting it to graph spectra and optimal transport. We then turn our attention to outlining a statistical test for spatial clustering using diffusion distance. Under permutation null models, we derive high-probability bounds on diffusion distance underpinned by exact spectral formulas for convergence of distributions, enabling an efficient statistical test for spatial clustering on large datasets. We empirically compare diffusion distance to Moran's $I$ both as a numerical measure and as a statistical test. We show that diffusion distance exhibits higher power on synthetic data using a stochastic block model. Empirical analysis of Black population distributions for 100 U.S. cities shows that diffusion distance detects subtle differences in urban segregation patterns that Moran's $I$ does not.
arXiv:2607.15229v1 Announce Type: new
Abstract: We develop data-driven algorithms for maintaining $N$ independent identical machines under a \textit{block replacement policy}, in which each machine is replaced upon failure and all machines are jointly replaced at regular intervals of length $k$. The goal is to learn the cost-minimizing interval $k^*$ from operational data when the lifetime distribution is unknown. At each decision epoch, the operator selects $k \in \{1, 2, \ldots, K\}$, observes the resulting failure history (a mixture of complete and right-censored lifetimes) and incurs a per-unit-time cost governed by the renewal function. We formulate this as a stochastic multi-armed bandit and propose Hoeffding- and Bernstein-based lower-confidence-bound algorithms achieving $O(K \log T)$ regret, matching the Lai--Robbins lower bound. Exploiting a nested observation property unique to block replacement, correlated variants attain $O((K-k^*)\log T)$ regret and require only $O(1)$ direct pulls of suboptimal arms $k < k^*$. A complementary Kaplan--Meier renewal algorithm estimates the lifetime distribution nonparametrically from censored data, achieving almost-sure policy consistency and empirically near-zero incremental regret at long horizons. We additionally analyze two average-cost MDPs: a time-elapsed formulation establishing that block replacement is optimal within its policy class for any lifetime distribution, and an age-vector formulation proving a monotone threshold structure under increasing failure rate distributions and providing a gold-standard cost benchmark. Numerical experiments confirm the theoretical ordering and reveal structural cost gaps between optimal block and age-dependent replacement.
arXiv:2607.14914v1 Announce Type: cross
Abstract: Resource scarcity can fundamentally encourage antisocial behaviour, whereas resource abundance can promote fair behaviour. Experimental evidence indeed suggests that scarcity induces spiteful behaviour, while repeated interactions enhance fairness. However, existing studies of game--environment feedback systems are largely confined to the evolution of cooperation and they overlook the interplay between resources, spite, and fairness. To address this lacuna, we develop a stochastic ultimatum game framework in which an offerer and an accepter repeatedly interact to negotiate exploitation of a self-renewable resource under the ownership of the offerer. Successful agreements deplete the resource, whereas unsuccessful agreements inhibit exploitation and facilitate replenishment. The mutation--selection driven two-species stochastic evolutionary dynamics reveal that the emergence of spite and fairness strongly depends on the resource growth rate. Fairness predominantly prevails for resources with high growth rates. Intriguingly, low resource growth rates give rise to a resource feedback loop driven by spite: spiteful behaviour dominates in the depleted state, facilitating transition of the resource state to replete state which, in turn, promotes fairness through repeated interactions.
arXiv:2607.14639v1 Announce Type: new
Abstract: Image-to-Point Cloud Registration (I2P) is essential for integrating camera and LiDAR in perception and autonomous systems, yet the modality gap between images and point clouds makes it difficult to achieve both high accuracy and strong generalization. In this paper, we propose a simple yet effective I2P method that treats LiDAR as an imaging sensor: from a single sparse LiDAR scan, we generate a dense LiDAR intensity image using Conditional Rectified Flow, match it with a camera image using a pre-trained feature matcher, and estimate the 6-DoF relative pose via PnP-RANSAC. The proposed model is pre-trained through a self-supervised image completion task and fine-tuned on a small amount of LiDAR data (neither image-point cloud pairs nor ground-truth sensor poses are required), enabling it to scale to diverse LiDAR and camera configurations. Experiments on the R3LIVE dataset show that the proposed method achieves a mean error of 4.89{\deg} / 1.63 m, outperforming existing methods, while completing a single registration in approximately 0.68 s.
arXiv:2607.15001v1 Announce Type: cross
Abstract: Lattice quantum chromodynamics (LQCD) provides a first-principles framework for computing hadronic observables, but its practical use remains limited by the substantial expertise required to turn research motivation into reliable computing workflows. Here we present \textsc{LQCDMaster}, a tool-augmented, skill-guided and domain-specialized scientific computing agent that converts natural-language LQCD research tasks into executable PyQUDA computing workflows, including measurement scripts, job-submission artifacts, execution logs and numerical outputs. The system combines agentic planning, expert-annotated LQCD skills and a deterministic Wick-contraction tool to constrain the algebraically fragile components of code generation. We evaluate \textsc{LQCDMaster} on a benchmark at the forefront of scientific research, comprising 70 LQCD computing tasks, with observables covering local and nonlocal two-point functions, Wilson loops, meson and baryon three-point functions. The generated workflows exactly reproduce expert-written implementations in 63 of 70 tasks at machine precision, with three additional discrepancies attributable to convention mismatches. Across representative observables, the agent reduces implementation time from hours to minutes while preserving end-to-end numerical validation. Further, we present a typical case of \textsc{LQCDMaster}-driven exploration: a lattice computation of light-cone distribution amplitudes with diagonal Wilson-line, a quantity accessible with standard methods but never before computed, and computation of the spectrum of proton, deuteron, triton, hyperon, hyperdeuteron and hypertriton. This work pioneers the paradigm of agentic scientific computing by automating the end-to-end scientific computing workflows in lattice QCD research, lowering its barrier and facilitating the exploration and verification of non-standard scientific ideas.
arXiv:2607.14705v1 Announce Type: new
Abstract: Graph neural networks (GNNs) frequently encounter group fairness issues, often yielding biased predictions against specific demographic groups defined by sensitive attributes such as gender or race. While this challenge has motivated extensive research, most existing solutions rely on the strong assumption that demographics are fully available. To bypass this strict requirement, a few recent studies have attempted to use predicted demographics as proxies to enforce fairness constraints. However, predicted demographics may be inaccurate, resulting in the failure to improve fairness. In this work, we investigate the problem of graph fairness without demographic information and avoid the utilization of predicted demographics. Motivated by our observation that the gradient distributions of misclassified nodes implicitly encode demographic information, we first propose GradDist, a gradient-based metric that quantifies bias by measuring the distance between local modes within these distributions. To mitigate this bias, we propose Gradient-to-Fairness (Grad2Fair), a gradient-guided approach for group fairness without demographics. Due to the potential demographics in gradients, Grad2Fair directly leverages gradients to debias and eliminates demographic prediction, thereby enabling stable fairness performance. Experiments on several real-world datasets demonstrate the effectiveness of Grad2Fair, as evidenced by superior performance over baselines in most cases. Our code is available at https://github.com/ZzoomD/Grad2Fair.