Tootfinder

An Apple-funded study says the global App Store drove $1.3T in developer billings and sales in 2024, with developers paying no commission on 90% of sales (Sarah Perez/TechCrunch)
https://techcrunch.com/2025/06/05/appl

Apple says its App Store helped generate $1.3T in billings and sales, most without a commission | TechCrunch
Apple said developers generated $1.3 trillion in sales in 2024 through its App Store, of which 90% did not involve paying the company a commission.

Anyone else experiencing problems (again) with link previews (including thumbnail images) when posting from #Mastodon via #Bridgy to #Bluesky?
Have reported the issue to the team, hopefully it can be sorted b…

Fediverse => Bluesky: auto-generate embeds (with thumbnail images) for links? · Issue #1411 · snarfed/bridgy-fed
I do not know if this is a general problem between platforms or if it is strictly an issue between Mastodon -> Bluesky, but whenever I post an article (or other external link) on Mastodon the thumb...

I normally don't use ChatGPT, but I asked him to generate a poster for the "Alien v Predator II" movie I posted about recently. Here is the prompt:
"Dear CharGPT, could you please create a fake movie poster for me?
At the top, the movie title "Alien v. Predator"
In the middle, at left the head of Elon Musk in profile, facing right, grimacing, with bared fangs, drooling. 🧵‍>

Empirical Evaluation of Generalizable Automated Program Repair with Large Language Models
Viola Campos, Ridwan Shariffdeen, Adrian Ulges, Yannic Noller
https://arxiv.org/abs/2506.03283

Empirical Evaluation of Generalizable Automated Program Repair with Large Language Models
Automated Program Repair (APR) proposes bug fixes to aid developers in maintaining software. The state of the art in this domain focuses on using LLMs, leveraging their strong capabilities to comprehend specifications in natural language and to generate program code. Recent works have shown that LLMs can be used to generate repairs. However, despite the APR community's research achievements and several industry deployments in the last decade, APR still lacks the capabilities to generalize broad…

"leave the democratic party" sounds like an item on markers list of how to generate attention for your book.

GenTT: Generate Vectorized Codes for General Tensor Permutation
Yaojian Chen, Tianyu Ma, An Yang, Lin Gan, Wenlai Zhao, Guangwen Yang
https://arxiv.org/abs/2506.03686

GenTT: Generate Vectorized Codes for General Tensor Permutation
Tensor permutation is a fundamental operation widely applied in AI, tensor networks, and related fields. However, it is extremely complex, and different shapes and permutation maps can make a huge difference. SIMD permutation began to be studied in 2006, but the best method at that time was to split complex permutations into multiple simple permutations to do SIMD, which might increase the complexity for very complex permutations. Subsequently, as tensor contraction gained significant attention…

Redefining Functionality and Construction-Defining Capacity: Functions as Principles of Syntactic and Semantic Generation
Yumiko Nishiyama
https://arxiv.org/abs/2506.04278

Redefining Functionality and Construction-Defining Capacity: Functions as Principles of Syntactic and Semantic Generation
This study redefines the notion of functionality-traditionally understood as a property of mappings or structure preservation-from a more fundamental and generative perspective. Introducing the concept of a Construction-Defining function (CDF), we formalize functionality as a dual capacity to generate both syntactic terms and semantic interpretations. We provide an explicit axiomatization of CDF based on syntactic generativity and semantic compositionality, and further construct categorical mod…

This https://arxiv.org/abs/2502.11844 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

BaxBench: Can LLMs Generate Correct and Secure Backends?
Automatic program generation has long been a fundamental challenge in computer science. Recent benchmarks have shown that large language models (LLMs) can effectively generate code at the function level, make code edits, and solve algorithmic coding tasks. However, to achieve full automation, LLMs should be able to generate production-quality, self-contained application modules. To evaluate the capabilities of LLMs in solving this challenge, we introduce BaxBench, a novel evaluation benchmark c…

This https://arxiv.org/abs/2401.04003 has been replaced.
link: https://scholar.google.com/scholar?q=a

Simultaneous Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications
Research in robotic planning with temporal logic specifications, such as Linear Temporal Logic (LTL), has relied on single formulas. However, as task complexity increases, LTL formulas become lengthy, making them difficult to interpret and generate, and straining the computational capacities of planners. To address this, we introduce a hierarchical structure for a widely used specification type -- LTL on finite traces (LTL$_f$). The resulting language, termed H-LTL$_f$, is defined with both its…

AMC signs a deal with Runway to use its AI tech to generate marketing images and pre-visualize unproduced shows, the first cable player to work with Runway (Steven Zeitchik/The Hollywood Reporter)
https://www.hollywoodreporter.com/business

AMC Networks Inks Deal With AI Company Runway (Exclusive)
The ‘Mad Men’ and 'The Walking Dead' platform is the latest Hollywood company to sign a pact with the generative AI firm.

In a few days I’ll have a CSV/json of exported Cryptpad answers for some kind of census. Are there any open source softwares out there that make analysing these kind of stuff easier?
Instead of just like opening it on a fucking libreoffice spreadsheet or something.
Ideally it’d give me like the amount of people who selected each option on a question, same for written ones (being able to set aliases like “abcd = ABCD” and “Guix = GNU Guix” bc people can’t be consistent when answering polls), with the ability to understand comma-separated replies to those questions as if they were multiple selections on a checkbox question).
Maybe being able to generate some graphs too, but at least giving me a list with like “question1, reply1, amount” for each so I can easily make graphs myself.

This https://arxiv.org/abs/2501.00092 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Moments and saddles of heavy CFT correlators
We study the operator product expansion (OPE) of identical scalars in a conformal four-point correlator as a Stieltjes moment problem, and use Riemann-Liouville type fractional differential operators to generate classical moments from the correlation function. We use crossing symmetry to derive leading and subleading relations between moments in $Δ$ and $J_2 \equiv \ell(\ell+d-2)$ in the ``heavy" limit of large external scaling dimension, and combine them with constraints from unitarity to der…

Knowledge-Guided Attention-Inspired Learning for Task Offloading in Vehicle Edge Computing
Ke Ma, Junfei Xie
https://arxiv.org/abs/2506.04456 https://

Knowledge-Guided Attention-Inspired Learning for Task Offloading in Vehicle Edge Computing
Vehicle edge computing (VEC) brings abundant computing resources close to vehicles by deploying them at roadside units (RSUs) or base stations, thereby enabling diverse computation-intensive and delay sensitive applications. Existing task offloading strategies are often computationally expensive to execute or generate suboptimal solutions. In this paper, we propose a novel learning-based approach, Knowledge-guided Attention-inspired Task Offloading (KATO), designed to efficiently offload tasks …

Labeled Trees Generating Separable and Locally Finite Ultrametrics
Oleksiy Dovgoshey, Olga Rovenska
https://arxiv.org/abs/2506.03853 https://

Labeled Trees Generating Separable and Locally Finite Ultrametrics
We analyze the interplay between labeled trees and the ultrametric spaces they present. We provide characterizations of labeled trees that generate separable ultrametric spaces and those that generate locally finite ultrametric spaces. In particular, we establish an analog of König's Infinity Lemma for locally finite ultrametric spaces generated by labeled trees.

Can we reconstruct a dysarthric voice with the large speech model Parler TTS?
Ariadna Sanchez, Simon King
https://arxiv.org/abs/2506.04397 https://

Can we reconstruct a dysarthric voice with the large speech model Parler TTS?
Speech disorders can make communication hard or even impossible for those who develop them. Personalised Text-to-Speech is an attractive option as a communication aid. We attempt voice reconstruction using a large speech model, with which we generate an approximation of a dysarthric speaker's voice prior to the onset of their condition. In particular, we investigate whether a state-of-the-art large speech model, Parler TTS, can generate intelligible speech while maintaining speaker identity. We…

PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation
Chenglong Ma, Ziqi Xu, Yongli Ren, Danula Hettiachchi, Jeffrey Chan
https://arxiv.org/abs/2506.04551

PUB: An LLM-Enhanced Personality-Driven User Behaviour Simulator for Recommender System Evaluation
Traditional offline evaluation methods for recommender systems struggle to capture the complexity of modern platforms due to sparse behavioural signals, noisy data, and limited modelling of user personality traits. While simulation frameworks can generate synthetic data to address these gaps, existing methods fail to replicate behavioural diversity, limiting their effectiveness. To overcome these challenges, we propose the Personality-driven User Behaviour Simulator (PUB), an LLM-based simulati…

This https://arxiv.org/abs/2501.12227 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIT_…

Multi-terminal Strong Coordination over Noisy Channels with Encoder Co-operation
We investigate the problem of strong coordination over a multiple-access channel (MAC) with cribbing encoders. In this configuration, two encoders observe independent and identically distributed (i.i.d.) samples of a source random variable each and encode the inputs to the MAC. The decoder which observes the output of the MAC together with side-information, must generate approximately i.i.d. samples of another random variable which is jointly distributed with the two sources and the side inform…

This https://arxiv.org/abs/2505.14613 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Virtual Cells: Predict, Explain, Discover
Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from computational models that can reliably simulate patient responses, enabling researchers to generate and test large numbers of therapeutic hypotheses safely and economically before initiating costly clinical trials. Even a more specific model that predicts the functional response of cells to a wide range of perturbations would be tremendously valuable for disco…

Improving AI-generated music with user-guided training
Vishwa Mohan Singh, Sai Anirudh Aryasomayajula, Ahan Chatterjee, Beste Aydemir, Rifat Mehreen Amin
https://arxiv.org/abs/2506.04852

Improving AI-generated music with user-guided training
AI music generation has advanced rapidly, with models like diffusion and autoregressive algorithms enabling high-fidelity outputs. These tools can alter styles, mix instruments, or isolate them. Since sound can be visualized as spectrograms, image-generation algorithms can be applied to generate novel music. However, these algorithms are typically trained on fixed datasets, which makes it challenging for them to interpret and respond to user input accurately. This is especially problematic beca…

Exciton-Exciton Annihilation Mediated by Many-Body Coulomb and Phonon Interactions: An Ab Initio Study
Guy Vosco, Sivan Refaely-Abramson
https://arxiv.org/abs/2506.05223

Exciton-Exciton Annihilation Mediated by Many-Body Coulomb and Phonon Interactions: An Ab Initio Study
Exciton-exciton annihilation (EEA), in which two excitons interact to generate high-energy excitations, is an important non-radiative channel in light-induced excited-state relaxation. When efficient, this process offers an alternative route to exciton emission, potentially allowing extended energetically excited particles' lifetime and coherence. These properties are significant in designing and understanding materials-based quantum devices, particularly for low-dimensional semiconductors. Her…

How AI-assisted "vibe hacking", which often relies on jailbroken versions of mainstream AI models, is leading to a cybersecurity "arms race" (Matthew Gault/Wired)
https://www.wired.com/story/youre-not-ready-for-ai-hacker-agents/

The Rise of ‘Vibe Hacking’ Is the Next AI Nightmare
In the very near future, victory will belong to the savvy blackhat hacker who uses AI to generate code at scale.

Extensions of Schr\"odinger operators that generate $C_0$ contraction semigroups
Lawrence Frolov
https://arxiv.org/abs/2506.00231 https://

Extensions of Schrödinger operators that generate $C_0$ contraction semigroups
Consider a non-relativistic quantum particle with wave function $ψ$ in a bounded $C^2$ region $Ω\subset \mathbb{R}^n$, and suppose detectors are placed along the boundary $\partial Ω$. Assume the detection process is irreversible, its mechanism is time independent and also hard, i.e., detections occur only along the boundary $\partial Ω$. Under these conditions Tumulka argued that the dynamics of $ψ$ must be governed by a $C_0$ contraction semigroup that weakly solves the Schrödinger equa…

It's about time: a thermodynamic information criterion (TIC)
Brendan Lucas, Google Gemini 2. 5 Pro Preview 05-06
https://arxiv.org/abs/2506.04519 https…

It's about time: a thermodynamic information criterion (TIC)
Useful chemical processes often involve a desired steady state probability distribution, equilibrium or not, from which product is extracted. Given many different ways to attain the same steady state, which candidate "loses" the least in terms of time and energy? A scalar thermodynamic information criterion (TIC), inspired by AIC, assigns lower values to chemical processes with less estimated "loss" to generate the same desired steady state. As an element of thermodynamic machine learning, TIC …

Cosmology Constraints from Type Ia Supernova Simulations of the Nancy Grace Roman Space Telescope Strategy Recommended by the High Latitude Time Domain Survey Definition Committee
Richard Kessler, Rebekah Hounsell, Bhavin Joshi, David Rubin, Masao Sako, Rebecca Chen, Vivian Miranda, Benjamin. M. Rose
https://arxiv.org/abs/2506.0…

Cosmology Constraints from Type Ia Supernova Simulations of the Nancy Grace Roman Space Telescope Strategy Recommended by the High Latitude Time Domain Survey Definition Committee
Within the next few years, the upcoming Nancy Grace Roman Space Telescope will be gathering data for the High Latitude Time Domain Survey (HLTDS) that will be used to significantly improve the Type Ia supernova measurement of the dark energy equation of state parameters w0 and wa. Here we generate a catalog-level simulation of the in-guide strategy recommended by the HLTDS definition committee, and determine dark energy parameter constraints using a detailed analysis that includes light curve f…

This https://arxiv.org/abs/2504.09200 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

A Datagram Extension to DNS over QUIC: Proven Resource Conservation in the Internet of Things
In this paper, we investigate the Domain Name System (DNS) over QUIC (DoQ) and propose a non-disruptive extension, which can greatly reduce DoQ's resource consumption. This extension can benefit all DNS clients - especially Internet of Things (IoT) devices. This is important because even resource-constrained IoT devices can generate dozens of DNS requests every hour. DNS is a crucial service that correlates IP addresses and domain names. It is traditionally sent as plain-text, favoring low-late…

This https://arxiv.org/abs/2410.22038 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A Cramér-Wold theorem for mixtures
We show how a Cramér-Wold theorem for a family of multivariate probability distributions can be used to generate a similar theorem for mixtures (convex combinations) of distributions drawn from the same family. Using this abstract result, we establish a Cramér-Wold theorem for mixtures of multivariate Gaussian distributions. According to this theorem, two such mixtures can be distinguished by projecting them onto a certain predetermined finite set of lines, the number of lines depending onl…

PromptCanvas: Composable Prompting Workspaces Using Dynamic Widgets for Exploration and Iteration in Creative Writing
Rifat Mehreen Amin, Oliver Hans K\"uhle, Daniel Buschek, Andreas Butz
https://arxiv.org/abs/2506.03741

PromptCanvas: Composable Prompting Workspaces Using Dynamic Widgets for Exploration and Iteration in Creative Writing
We introduce PromptCanvas, a concept that transforms prompting into a composable, widget-based experience on an infinite canvas. Users can generate, customize, and arrange interactive widgets representing various facets of their text, offering greater control over AI-generated content. PromptCanvas allows widget creation through system suggestions, user prompts, or manual input, providing a flexible environment tailored to individual needs. This enables deeper engagement with the creative proce…

IllumiCraft: Unified Geometry and Illumination Diffusion for Controllable Video Generation
Yuanze Lin, Yi-Wen Chen, Yi-Hsuan Tsai, Ronald Clark, Ming-Hsuan Yang
https://arxiv.org/abs/2506.03150

IllumiCraft: Unified Geometry and Illumination Diffusion for Controllable Video Generation
Although diffusion-based models can generate high-quality and high-resolution video sequences from textual or image inputs, they lack explicit integration of geometric cues when controlling scene lighting and visual appearance across frames. To address this limitation, we propose IllumiCraft, an end-to-end diffusion framework accepting three complementary inputs: (1) high-dynamic-range (HDR) video maps for detailed lighting control; (2) synthetically relit frames with randomized illumination ch…

Tunable spin-phonon polarons in a chiral molecular qubit framework
Aimei Zhou, Ruihao Bi, Zhenghan Zhang, Luming Yang, Xudong Tian, Denan Li, Mingshu Tan, Weibin Ni, Haozhou Sun, Jinkun Guo, Xinxing Zhao, Zhifu Shi, Wei Tong, Zhitao Zhang, Jin-Hu Dou, Feng Jin, Shi Liu, Mircea Dinca, Tijana Rajh, Jian Li, Wenjie Dou, Lei Sun
https://

Tunable spin-phonon polarons in a chiral molecular qubit framework
Chiral structures that produce asymmetric spin-phonon coupling can theoretically generate spin-phonon polarons -- quasiparticles exhibiting non-degenerate spin states with phonon displacements. However, direct experimental evidence has been lacking. Using a chiral molecular qubit framework embedding stable semiquinone-like radicals, we report spin dynamic signatures that clearly indicate the formation of spin-phonon polarons for the first time. Our non-adiabatic model reveals that these quasipa…

Reuse or Generate? Accelerating Code Editing via Edit-Oriented Speculative Decoding
Peiding Wang, Li Zhang, Fang Liu, Yinghao Zhu, Wang Xu, Lin Shi, Xiaoli Lian, Minxiao Li, Bo Shen, An Fu
https://arxiv.org/abs/2506.02780

Reuse or Generate? Accelerating Code Editing via Edit-Oriented Speculative Decoding
Large Language Models (LLMs) have demonstrated remarkable capabilities in code editing, substantially enhancing software development productivity. However, the inherent complexity of code editing tasks forces existing approaches to rely on LLMs' autoregressive end-to-end generation, where decoding speed plays a critical role in efficiency. While inference acceleration techniques like speculative decoding are applied to improve the decoding efficiency, these methods fail to account for the uniqu…

Exact operator inference with minimal data
Henrik Rosenberger, Benjamin Sanderse, Giovanni Stabile
https://arxiv.org/abs/2506.01244 https://

Exact operator inference with minimal data
This work introduces a novel method to generate snapshot data for operator inference that guarantees the exact reconstruction of intrusive projection-based reduced-order models (ROMs). To ensure exact reconstruction, the operator inference least squares matrix must have full rank, without regularization. Existing works have achieved this full rank using heuristic strategies to generate snapshot data and a-posteriori checks on full rank, but without a guarantee of success. Our novel snapshot dat…

A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging
Xuanru Zhou, Jiarun Liu, Shoujun Yu, Hao Yang, Cheng Li, Tao Tan, Shanshan Wang
https://arxiv.org/abs/2506.04116

A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging
In medical imaging, 4D MRI enables dynamic 3D visualization, yet the trade-off between spatial and temporal resolution requires prolonged scan time that can compromise temporal fidelity--especially during rapid, large-amplitude motion. Traditional approaches typically rely on registration-based interpolation to generate intermediate frames. However, these methods struggle with large deformations, resulting in misregistration, artifacts, and diminished spatial consistency. To address these chall…

quick and easy way to build a website for your r package: #rstats #package

This https://arxiv.org/abs/2501.12261 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCG_…

A Framework for the Design of Efficient Diversification Algorithms to NP-Hard Problems
There has been considerable recent interest in computing a diverse collection of solutions to a given optimization problem, both in the AI and theory communities. Given a classical optimization problem $Π$ (e.g., spanning tree, minimum cuts, maximum matching, minimum vertex cover) with input size $n$ and an integer $k\geq 1$, the goal is to generate a collection of $k$ maximally diverse solutions to $Π$. This diverse-X paradigm not only allows the user to generate very different solutions, bu…

Speculative Evolution Through 3D Cellular Automata
Amir Hossein Khazaei
https://arxiv.org/abs/2505.01692 https://arxiv.org/pdf/2505.0…

Speculative Evolution Through 3D Cellular Automata
This project explores speculative evolution through a 3D implementation of Conway's Game of Life, using procedural simulation to generate unfamiliar extraterrestrial organic forms. By applying a volumetric optimized workflow, the raw cellular structures are smoothed into unified, bone-like geometries that resemble hypothetical non-terrestrial morphologies. The resulting forms, strange yet organic, are 3D printed as fossil-like artifacts, presenting a tangible representation of generative struct…

To Grok: "Why is Neuralink so vague about their Neuralink Blindsight brain implant, describing it as technology to "generate visual perception" - even unpatterned flashes of light qualify as such?" https://x.com/i/grok/share/vI84HuktzHHh093wRbd6YzHXl …

Harnessing curvature for helical wave generation in spiral-based metamaterial structures
Mohamed Roshdy, Osama R. Bilal
https://arxiv.org/abs/2506.04903 ht…

Harnessing curvature for helical wave generation in spiral-based metamaterial structures
Linearly polarized elastic waves propagating in a linear path have been extensively explored in numerous applications from biomedical imaging to structural health monitoring. However, elastic waves propagating in a circularly polarized helical path are less explored because of the challenges in their generation and control. In this paper, we harness conventional actuation methods combined with sheets decorated by Archimedean spirals to generate elastic helical waves. We show that our metamateri…

Do not Abstain! Identify and Solve the Uncertainty
Jingyu Liu, Jingquan Peng, xiaopeng Wu, Xubin Li, Tiezheng Ge, Bo Zheng, Yong Liu
https://arxiv.org/abs/2506.00780

Do not Abstain! Identify and Solve the Uncertainty
Despite the widespread application of Large Language Models (LLMs) across various domains, they frequently exhibit overconfidence when encountering uncertain scenarios, yet existing solutions primarily rely on evasive responses (e.g., "I don't know") overlooks the opportunity of identifying and addressing the uncertainty to generate more satisfactory responses. To systematically investigate and improve LLMs' ability of recognizing and addressing the source of uncertainty, we introduce \textbf{C…

Learning to cluster neuronal function
Nina S. Nellen, Polina Turishcheva, Michaela Vystr\v{c}ilov\'a, Shashwat Sridhar, Tim Gollisch, Andreas S. Tolias, Alexander S. Ecker
https://arxiv.org/abs/2506.03293

Learning to cluster neuronal function
Deep neural networks trained to predict neural activity from visual input and behaviour have shown great potential to serve as digital twins of the visual cortex. Per-neuron embeddings derived from these models could potentially be used to map the functional landscape or identify cell types. However, state-of-the-art predictive models of mouse V1 do not generate functional embeddings that exhibit clear clustering patterns which would correspond to cell types. This raises the question whether th…

Macroscopic entanglement of three magnon modes in three cavities via optical parametric amplifier
Ying Zhou, Guo-Qiang Zhang
https://arxiv.org/abs/2506.01721

Macroscopic entanglement of three magnon modes in three cavities via optical parametric amplifier
We propose a scheme to generate bipartite and tripartite entanglements of three magnon modes in a three-cavity system using a nonlinear optical parametric amplifier (OPA). The three magnon modes in three YIG spheres are respectively placed inside three cavities near the maximum magnetic fields of the cavities and coupled to cavity modes via linear magnetic dipole interaction. Additionally, linear coupling interaction exists between two cavities. Using experimentally feasible parameters, we demo…

Gravothermal expansion of dwarf spheroidal galaxies heated by dark subhaloes
Jorge Pe\~narrubia, Rapha\"el Errani, Eduardo Vitral, Matthew G. Walker
https://arxiv.org/abs/2506.03904

Gravothermal expansion of dwarf spheroidal galaxies heated by dark subhaloes
We use analytical and $N$-body methods to study the evolution of dwarf spheroidal galaxies (dSphs) embedded in dark matter (DM) haloes that host a sizeable subhalo population. Dark subhaloes generate a fluctuating gravitational field that injects energy into stellar orbits, driving a gradual expansion of dSphs. Despite the overall expansion, the stellar density profile preserves its initial shape, suggesting that the evolution proceeds in a self-similar manner. Meanwhile, the velocity dispersio…

Multi-Spectral Gaussian Splatting with Neural Color Representation
Lukas Meyer, Josef Gr\"un, Maximilian Weiherer, Bernhard Egger, Marc Stamminger, Linus Franke
https://arxiv.org/abs/2506.03407

Multi-Spectral Gaussian Splatting with Neural Color Representation
We present MS-Splatting -- a multi-spectral 3D Gaussian Splatting (3DGS) framework that is able to generate multi-view consistent novel views from images of multiple, independent cameras with different spectral domains. In contrast to previous approaches, our method does not require cross-modal camera calibration and is versatile enough to model a variety of different spectra, including thermal and near-infra red, without any algorithmic changes. Unlike existing 3DGS-based frameworks that tre…

SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning
Zhongwei Wan, Zhihao Dou, Che Liu, Yu Zhang, Dongfei Cui, Qinjian Zhao, Hui Shen, Jing Xiong, Yi Xin, Yifan Jiang, Yangfan He, Mi Zhang, Shen Yan
https://arxiv.org/abs/2506.01713

SRPO: Enhancing Multimodal LLM Reasoning via Reflection-Aware Reinforcement Learning
Multimodal large language models (MLLMs) have shown promising capabilities in reasoning tasks, yet still struggle with complex problems requiring explicit self-reflection and self-correction, especially compared to their unimodal text-based counterparts. Existing reflection methods are simplistic and struggle to generate meaningful and instructive feedback, as the reasoning ability and knowledge limits of pre-trained models are largely fixed during initial training. To overcome these challenges…

A note on metapopulation models
Diepreye Ayabina, Hasan Sevil, Adam Kleczkowski, M. Gabriela M. Gomes
https://arxiv.org/abs/2506.04284 https://

A note on metapopulation models
Metapopulation models are commonly used in ecology, evolution, and epidemiology. These models usually entail homogeneity assumptions within patches and study networks of migration between patches to generate insights into conservation of species, differentiation of populations, and persistence of infectious diseases. Here, focusing on infectious disease epidemiology, we take a complementary approach and study the effects of individual variation within patches while neglecting any form of diseas…

AMC signs a deal with Runway to use its tech to generate marketing images and pre-visualize unproduced shows, the first cable player to work with Runway (Steven Zeitchik/The Hollywood Reporter)
https://www.hollywoodreporter.com/business

AMC Networks Inks Deal With AI Company Runway (Exclusive)
The ‘Mad Men’ and 'The Walking Dead' platform is the latest Hollywood company to sign a pact with the generative AI firm.

#LB Esse texto sobre LLMs do @… é bem grande, mas excelente. Vale a leitura, apesar do tamanho.
…

Optimal BESS Scheduling for Multi-Market Participation in the Nordics
Zeenat Hameed, Chresten Traeholt
https://arxiv.org/abs/2506.02837 https://

Optimal BESS Scheduling for Multi-Market Participation in the Nordics
Battery energy storage systems BESSs can provide fast frequency reserves and energy arbitrage in Nordic electricity markets but their limited energy capacity requires accurate revenue forecasts and coordinated bidding across multiple submarkets. This paper introduces a unified framework that employs generalized additive models GAMs to generate one week ahead forecasts of spot and FCRN revenues in Denmark, Finland, and Norway using three years of hourly price and volume data. Forecast outputs fe…

This https://arxiv.org/abs/2504.09304 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Black holes with electroweak hair -- the detailed derivation
We present a very detailed derivation of solutions describing hairy black holes within the gravity-coupled Weinberg-Salam theory, which were previously reported in \href{https://doi.org/10.1103/PhysRevLett.133.171402}{Phys.Rev.Lett. 133 (2024) 171402}. These black holes support a strong magnetic field that polarizes the electroweak vacuum and creates a condensate of massive fields carrying superconducting currents along the black hole horizon. The currents, in turn, generate a ``corona'' of mag…

This https://arxiv.org/abs/2506.01294 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Equation of State and First Principles Prediction of the Vibrational Matrix Shift of Solid Parahydrogen
We generate the equation of state (EOS) of solid parahydrogen using a path-integral Monte Carlo (PIMC) simulation based on a highly accurate first-principles adiabatic hindered rotor (AHR) potential energy curve for the \para dimer. The EOS curves for the fcc and hcp structures of solid parahydrogen near the equilibrium density show that the hcp structure is the more stable of the two, in agreement with experiment. To accurately reproduce the structural and energy properties of solid parahydrog…

A Transformer-Based Neural Network for Optimal Deterministic-Allocation and Anonymous Joint Auction Design
Zhen Zhang, Luowen Liu, Wanzhi Zhang, Zitian Guo, Kun Huang, Qi Qi, Qiang Liu, Xingxing Wang
https://arxiv.org/abs/2506.02435

A Transformer-Based Neural Network for Optimal Deterministic-Allocation and Anonymous Joint Auction Design
With the advancement of machine learning, an increasing number of studies are employing automated mechanism design (AMD) methods for optimal auction design. However, all previous AMD architectures designed to generate optimal mechanisms that satisfy near dominant strategy incentive compatibility (DSIC) fail to achieve deterministic allocation, and some also lack anonymity, thereby impacting the efficiency and fairness of advertising allocation. This has resulted in a notable discrepancy between…

A Grammatical Calculus for the Ramanujan Polynomials
William Y. C. Chen, Amy M. Fu, Elena L. Wang
https://arxiv.org/abs/2506.01649 https://

A Grammatical Calculus for the Ramanujan Polynomials
As remarked by Berndt, no combinatorial perspective seems to be alluded in the original definition of the Ramanujan polynomials. On a different scene, a recursive algorithm to generate rooted trees has been devised independently by Shor and Dumont-Ramamonjisoa. Zeng discovered the connection between the Ramanujan polynomials and the enumeration of rooted trees by number of improper edges. We present a proper labeling scheme for rooted trees by employing an extra label. H…

Long-Term Optical Follow Up of S231206cc: Multi-Model Constraints on BBH Merger Emission in AGN Disks
P. Darc, C. R. Bom, C. D. Kilpatrick, A. Souza Santos, B. Fraga, J. C. Rodr\'iguez-Ram\'irez, D. A. Coulter, C. Mendes de Oliveira, A. Kanaan, T. Ribeiro, W. Schoenell, E. A. D. Lacerda
https://arxiv.org/abs/2506.02224…

Long-Term Optical Follow Up of S231206cc: Multi-Model Constraints on BBH Merger Emission in AGN Disks
The majority of gravitational wave events detected by the LIGO, Virgo, and KAGRA Collaboration originate from binary black hole (BBH) mergers, for which no confirmed electromagnetic counterparts have been identified to date. However, if such mergers occur within the disk of an active galactic nucleus (AGN), they may generate observable optical flares induced by relativistic jet activity and shock-heated gas. We present results from a long-term optical follow-up of the gravitational wave event S…

This https://arxiv.org/abs/2407.08438 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Symmetries of power-free integers in number fields and their shift spaces
We describe the group of $\mathbb Z$-linear automorphisms of the ring of integers of a number field $K$ that preserve the set $V_{K,k}$ of $k$th power-free integers: every such map is the composition of a field automorphism and the multiplication by a unit. We show that those maps together with translations generate the extended symmetry group of the shift space $\mathbb D_{K,k}$ associated to $V_{K,k}$. Moreover, we show that no two such dynamical systems $\mathbb D_{K,k}$ and $\mathbb D_{L,…

in case i need to ask something outside the context of the reddit-posting or video:
- generate a temporary URL (with the full posting/video transcript)
- call perplexity by URL and add the URL as context
with that, i can ask everything (full context included) that needs a search 😍
#php #laravel

I have released LLama2.c64 - an LLM running on a C64 with 2MB REU. It runs the Llama2 LLM architecture, using the tokenizer and weights from the Tinystories 260K model.
It's a storytelling model that tries its best to spin your prompt into a story, as if told by a kindergarten child. It will generate one output token about every 8 minutes.
…

This https://arxiv.org/abs/2505.18882 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Personalized Safety in LLMs: A Benchmark and A Planning-Based Agent Approach
Large language models (LLMs) typically generate identical or similar responses for all users given the same prompt, posing serious safety risks in high-stakes applications where user vulnerabilities differ widely. Existing safety evaluations primarily rely on context-independent metrics - such as factuality, bias, or toxicity - overlooking the fact that the same response may carry divergent risks depending on the user's background or condition. We introduce personalized safety to fill this gap …

This https://arxiv.org/abs/2407.00144 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SCOPE: Stochastic Cartographic Occupancy Prediction Engine for Uncertainty-Aware Dynamic Navigation
This article presents a family of Stochastic Cartographic Occupancy Prediction Engines (SCOPEs) that enable mobile robots to predict the future states of complex dynamic environments. They do this by accounting for the motion of the robot itself, the motion of dynamic objects, and the geometry of static objects in the scene, and they generate a range of possible future states of the environment. These prediction engines are software-optimized for real-time performance for navigation in crowded …

This https://arxiv.org/abs/2504.07879 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Towards Sustainable Creativity Support: An Exploratory Study on Prompt Based Image Generation
Creativity is a valuable human skill that has long been augmented through both analog and digital tools. Recent progress in generative AI, such as image generation, provides a disruptive technological solution to supporting human creativity further and helping humans generate solutions faster. While AI image generators can help to rapidly visualize ideas based on user prompts, the use of such AI systems has also been critiqued due to their considerable energy usage. In this paper, we report on …

This https://arxiv.org/abs/2505.16512 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Beyond Face Swapping: A Diffusion-Based Digital Human Benchmark for Multimodal Deepfake Detection
In recent years, the rapid development of deepfake technology has given rise to an emerging and serious threat to public security: diffusion model-based digital human generation. Unlike traditional face manipulation methods, such models can generate highly realistic videos with consistency through multimodal control signals. Their flexibility and covertness pose severe challenges to existing detection strategies. To bridge this gap, we introduce DigiFakeAV, the first large-scale multimodal digi…

This https://arxiv.org/abs/2505.24584 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

AutoChemSchematic AI: A Closed-Loop, Physics-Aware Agentic Framework for Auto-Generating Chemical Process and Instrumentation Diagrams
Recent advancements in generative AI have accelerated the discovery of novel chemicals and materials; however, transitioning these discoveries to industrial-scale production remains a critical bottleneck, as it requires the development of entirely new chemical manufacturing processes. Current AI methods cannot auto-generate PFDs or PIDs, despite their critical role in scaling chemical processes, while adhering to engineering constraints. We present a closed loop, physics aware framework for the…

This https://arxiv.org/abs/2410.02634 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDS_…

When is local search both effective and efficient?
Many combinatorial optimization problems can be represented by valued constraint satisfaction problems (VCSPs) and the fitness landscapes that they generate. Local search starts at an assignment in this landscape and successively moves assignments until no further improvement is possible among the adjacent assignments. Classic analyses of local search algorithms have focused mostly on the question of effectiveness ("did we find a good solution?") and often implicitly assumed that there are no d…

Speculative Evolution Through 3D Cellular Automata
Amir Hossein Khazaei
https://arxiv.org/abs/2505.01692 https://arxiv.org/pdf/2505.0…

Speculative Evolution Through 3D Cellular Automata
This project explores speculative evolution through a 3D implementation of Conway's Game of Life, using procedural simulation to generate unfamiliar extraterrestrial organic forms. By applying a volumetric optimized workflow, the raw cellular structures are smoothed into unified, bone-like geometries that resemble hypothetical non-terrestrial morphologies. The resulting forms, strange yet organic, are 3D printed as fossil-like artifacts, presenting a tangible representation of generative struct…

This https://arxiv.org/abs/2502.15806 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Mousetrap: Fooling Large Reasoning Models for Jailbreak with Chain of Iterative Chaos
Large Reasoning Models (LRMs) have significantly advanced beyond traditional Large Language Models (LLMs) with their exceptional logical reasoning capabilities, yet these improvements introduce heightened safety risks. When subjected to jailbreak attacks, their ability to generate more targeted and organized content can lead to greater harm. Although some studies claim that reasoning enables safer LRMs against existing LLM attacks, they overlook the inherent flaws within the reasoning process i…

This https://arxiv.org/abs/2506.01705 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

SPOT-Trip: Dual-Preference Driven Out-of-Town Trip Recommendation
Out-of-town trip recommendation aims to generate a sequence of Points of Interest (POIs) for users traveling from their hometowns to previously unvisited regions based on personalized itineraries, e.g., origin, destination, and trip duration. Modeling the complex user preferences--which often exhibit a two-fold nature of static and dynamic interests--is critical for effective recommendations. However, the sparsity of out-of-town check-in data presents significant challenges in capturing such us…

This https://arxiv.org/abs/2505.16978 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

HyGenar: An LLM-Driven Hybrid Genetic Algorithm for Few-Shot Grammar Generation
Grammar plays a critical role in natural language processing and text/code generation by enabling the definition of syntax, the creation of parsers, and guiding structured outputs. Although large language models (LLMs) demonstrate impressive capabilities across domains, their ability to infer and generate grammars has not yet been thoroughly explored. In this paper, we aim to study and improve the ability of LLMs for few-shot grammar generation, where grammars are inferred from sets of a small …

This https://arxiv.org/abs/2412.03293 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Diffusion-VLA: Generalizable and Interpretable Robot Foundation Model via Self-Generated Reasoning
In this paper, we present DiffusionVLA, a novel framework that seamlessly combines the autoregression model with the diffusion model for learning visuomotor policy. Central to our approach is a next-token prediction objective, enabling the model to reason effectively over the user's query in the context of current observations. Subsequently, a diffusion model is attached to generate robust action outputs. To enhance policy learning through self-reasoning, we introduce a novel reasoning injectio…

This https://arxiv.org/abs/2506.02943 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

A Multi-agent LLM-based JUit Test Generation with Strong Oracles
Unit testing plays a critical role in ensuring software correctness. However, writing unit tests manually is laborious, especially for strong typed languages like Java, motivating the need for automated approaches. Traditional methods primarily rely on search-based or randomized algorithms to generate tests that achieve high code coverage and produce regression oracles, which are derived from the program's current behavior rather than its intended functionality. Recent advances in large languag…

This https://arxiv.org/abs/2505.18924 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

LLM-Guided Taxonomy and Hierarchical Uncertainty for 3D Point CLoud Active Learning
We present a novel active learning framework for 3D point cloud semantic segmentation that, for the first time, integrates large language models (LLMs) to construct hierarchical label structures and guide uncertainty-based sample selection. Unlike prior methods that treat labels as flat and independent, our approach leverages LLM prompting to automatically generate multi-level semantic taxonomies and introduces a recursive uncertainty projection mechanism that propagates uncertainty across hier…

This https://arxiv.org/abs/2505.17155 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

TrimR: Verifier-based Training-Free Thinking Compression for Efficient Test-Time Scaling
Large Reasoning Models (LRMs) demonstrate exceptional capability in tackling complex mathematical, logical, and coding tasks by leveraging extended Chain-of-Thought (CoT) reasoning. Test-time scaling methods, such as prolonging CoT with explicit token-level exploration, can push LRMs' accuracy boundaries, but they incur significant decoding overhead. A key inefficiency source is LRMs often generate redundant thinking CoTs, which demonstrate clear structured overthinking and underthinking patter…

This https://arxiv.org/abs/2505.15380 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

Accelerating Autoregressive Speech Synthesis Inference With Speech Speculative Decoding
Modern autoregressive speech synthesis models leveraging language models have demonstrated remarkable performance. However, the sequential nature of next token prediction in these models leads to significant latency, hindering their deployment in scenarios where inference speed is critical. In this work, we propose Speech Speculative Decoding (SSD), a novel framework for autoregressive speech synthesis acceleration. Specifically, our method employs a lightweight draft model to generate candidat…

This https://arxiv.org/abs/2505.11414 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A Multigraph Characterization of Permutiple Strings
A permutiple is a natural number whose representation in some base is an integer multiple of a number whose representation has the same collection of digits. A previous paper utilizes a finite-state-machine construction and its state graph to recognize permutiples and to generate new examples. Permutiples are associated with walks on the state graph which necessarily satisfy certain conditions. However, the above effort does not provide conditions sufficient for the existence of permutiples. In…

This https://arxiv.org/abs/2412.03318 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Domain-Agnostic Stroke Lesion Segmentation Using Physics-Constrained Synthetic Data
Segmenting stroke lesions in MRI is challenging due to diverse acquisition protocols that limit model generalisability. In this work, we introduce two physics-constrained approaches to generate synthetic quantitative MRI (qMRI) images that improve segmentation robustness across heterogeneous domains. Our first method, $\texttt{qATLAS}$, trains a neural network to estimate qMRI maps from standard MPRAGE images, enabling the simulation of varied MRI sequences with realistic tissue contrasts. The …

This https://arxiv.org/abs/2505.03117 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Do ATCOs Need Explanations, and Why? Towards ATCO-Centered Explainable AI for Conflict Resolution Advisories
Interest in explainable artificial intelligence (XAI) is surging. Prior research has primarily focused on systems' ability to generate explanations, often guided by researchers' intuitions rather than end-users' needs. Unfortunately, such approaches have not yielded favorable outcomes when compared to a black-box baseline (i.e., no explanation). To address this gap, this paper advocates a human-centered approach that shifts focus to air traffic controllers (ATCOs) by asking a fundamental yet ov…

This https://arxiv.org/abs/2404.16873 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs
Large Language Models (LLMs) are vulnerable to jailbreaking attacks that lead to generation of inappropriate or harmful content. Manual red-teaming requires a time-consuming search for adversarial prompts, whereas automatic adversarial prompt generation often leads to semantically meaningless attacks that do not scale well. In this paper, we present a novel method that uses another LLM, called AdvPrompter, to generate human-readable adversarial prompts in seconds. AdvPrompter, which is trained …

This https://arxiv.org/abs/2505.08459 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Strategy-Augmented Planning for Large Language Models via Opponent Exploitation
Efficiently modeling and exploiting opponents is a long-standing challenge in adversarial domains. Large Language Models (LLMs) trained on extensive textual data have recently demonstrated outstanding performance in general tasks, introducing new research directions for opponent modeling. Some studies primarily focus on directly using LLMs to generate decisions based on the elaborate prompt context that incorporates opponent descriptions, while these approaches are limited to scenarios where LL…

This https://arxiv.org/abs/2505.24139 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Modelwith Spatio-Temporal Visual Representation
The latest advancements in multi-modal large language models (MLLMs) have spurred a strong renewed interest in end-to-end motion planning approaches for autonomous driving. Many end-to-end approaches rely on human annotations to learn intermediate perception and prediction tasks, while purely self-supervised approaches--which directly learn from sensor inputs to generate planning trajectories without human annotations often underperform the state of the art. We observe a key gap in the input re…

SPOT-Trip: Dual-Preference Driven Out-of-Town Trip Recommendation
Yinghui Liu, Hao Miao, Guojiang Shen, Yan Zhao, Xiangjie Kong, Ivan Lee
https://arxiv.org/abs/2506.01705

SPOT-Trip: Dual-Preference Driven Out-of-Town Trip Recommendation
Out-of-town trip recommendation aims to generate a sequence of Points of Interest (POIs) for users traveling from their hometowns to previously unvisited regions based on personalized itineraries, e.g., origin, destination, and trip duration. Modeling the complex user preferences--which often exhibit a two-fold nature of static and dynamic interests--is critical for effective recommendations. However, the sparsity of out-of-town check-in data presents significant challenges in capturing such us…

This https://arxiv.org/abs/2501.14147 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

HAMMER: Heterogeneous, Multi-Robot Semantic Gaussian Splatting
3D Gaussian Splatting offers expressive scene reconstruction, modeling a broad range of visual, geometric, and semantic information. However, efficient real-time map reconstruction with data streamed from multiple robots and devices remains a challenge. To that end, we propose HAMMER, a server-based collaborative Gaussian Splatting method that leverages widely available ROS communication infrastructure to generate 3D, metric-semantic maps from asynchronous robot data-streams with no prior knowl…

A Multi-agent LLM-based JUit Test Generation with Strong Oracles
Qinghua Xu, Guancheng Wang, Lionel Briand, Kui Liu
https://arxiv.org/abs/2506.02943 https:…

A Multi-agent LLM-based JUit Test Generation with Strong Oracles
Unit testing plays a critical role in ensuring software correctness. However, writing unit tests manually is laborious, especially for strong typed languages like Java, motivating the need for automated approaches. Traditional methods primarily rely on search-based or randomized algorithms to generate tests that achieve high code coverage and produce regression oracles, which are derived from the program's current behavior rather than its intended functionality. Recent advances in large languag…

Few-step Adversarial Schr\"{o}dinger Bridge for Generative Speech Enhancement
Seungu Han, Sungho Lee, Juheon Lee, Kyogu Lee
https://arxiv.org/abs/2506.01460

Few-step Adversarial Schrödinger Bridge for Generative Speech Enhancement
Deep generative models have recently been employed for speech enhancement to generate perceptually valid clean speech on large-scale datasets. Several diffusion models have been proposed, and more recently, a tractable Schrödinger Bridge has been introduced to transport between the clean and noisy speech distributions. However, these models often suffer from an iterative reverse process and require a large number of sampling steps -- more than 50. Our investigation reveals that the performance…

This https://arxiv.org/abs/2501.15056 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Feedback-Aware Monte Carlo Tree Search for Efficient Information Seeking in Goal-Oriented Conversations
Effective decision-making and problem-solving in conversational systems require the ability to identify and acquire missing information through targeted questioning. A key challenge lies in efficiently narrowing down a large space of possible outcomes by posing questions that minimize uncertainty. To address this, we introduce a novel framework that leverages Large Language Models (LLMs) to generate information-seeking questions, with Monte Carlo Tree Search (MCTS) to strategically select quest…

This https://arxiv.org/abs/2501.18626 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

The TIP of the Iceberg: Revealing a Hidden Class of Task-in-Prompt Adversarial Attacks on LLMs
We present a novel class of jailbreak adversarial attacks on LLMs, termed Task-in-Prompt (TIP) attacks. Our approach embeds sequence-to-sequence tasks (e.g., cipher decoding, riddles, code execution) into the model's prompt to indirectly generate prohibited inputs. To systematically assess the effectiveness of these attacks, we introduce the PHRYGE benchmark. We demonstrate that our techniques successfully circumvent safeguards in six state-of-the-art language models, including GPT-4o and LLaMA…

This https://arxiv.org/abs/2504.14822 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Completing A Systematic Review in Hours instead of Months with Interactive AI Agents
Systematic reviews (SRs) are vital for evidence-based practice in high stakes disciplines, such as healthcare, but are often impeded by intensive labors and lengthy processes that can take months to complete. Due to the high demand for domain expertise, existing automatic summarization methods fail to accurately identify relevant studies and generate high-quality summaries. To that end, we introduce InsightAgent, a human-centered interactive AI agent powered by large language models that revolu…

This https://arxiv.org/abs/2505.18492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Enumerate-Conjecture-Prove: Formally Solving Answer-Construction Problems in Math Competitions
Mathematical reasoning lies at the heart of artificial intelligence, underpinning applications in education, program verification, and research-level mathematical discovery. Mathematical competitions, in particular, present two challenging problem types: theorem-proving, requiring rigorous proofs of stated conclusions, and answer-construction, involving hypothesizing and formally verifying mathematical objects. Large Language Models (LLMs) effectively generate creative candidate answers but str…

Hierarchical Intention-Aware Expressive Motion Generation for Humanoid Robots
Lingfan Bao, Yan Pan, Tianhu Peng, Chengxu Zhou
https://arxiv.org/abs/2506.01563

Hierarchical Intention-Aware Expressive Motion Generation for Humanoid Robots
Effective human-robot interaction requires robots to identify human intentions and generate expressive, socially appropriate motions in real-time. Existing approaches often rely on fixed motion libraries or computationally expensive generative models. We propose a hierarchical framework that combines intention-aware reasoning via in-context learning (ICL) with real-time motion generation using diffusion models. Our system introduces structured prompting with confidence scoring, fallback behavio…

This https://arxiv.org/abs/2505.23387 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization
Large Language Models (LLMs) generate functionally correct solutions but often fall short in code efficiency, a critical bottleneck for real-world deployment. In this paper, we introduce a novel test-time iterative optimization framework to address this, employing a closed-loop system where LLMs iteratively refine code based on empirical performance feedback from an execution sandbox. We explore three training strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and G…

This https://arxiv.org/abs/2411.10918 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection
Cyber-Physical Systems (CPS) are vulnerable to cyber-physical attacks that violate physical laws. While invariant-based anomaly detection is effective, existing methods are limited: data-driven approaches lack semantic context, and physics-based models require extensive manual work. We propose INVARLLM, a hybrid framework that uses large language models (LLMs) to extract semantic information from CPS documentation and generate physical invariants, then validates these against real system data u…

WoMAP: World Models For Embodied Open-Vocabulary Object Localization
Tenny Yin, Zhiting Mei, Tao Sun, Lihan Zha, Emily Zhou, Jeremy Bao, Miyu Yamane, Ola Shorinwa, Anirudha Majumdar
https://arxiv.org/abs/2506.01600

WoMAP: World Models For Embodied Open-Vocabulary Object Localization
Language-instructed active object localization is a critical challenge for robots, requiring efficient exploration of partially observable environments. However, state-of-the-art approaches either struggle to generalize beyond demonstration datasets (e.g., imitation learning methods) or fail to generate physically grounded actions (e.g., VLMs). To address these limitations, we introduce WoMAP (World Models for Active Perception): a recipe for training open-vocabulary object localization policie…

This https://arxiv.org/abs/2504.11711 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

The Hitchhiker's Guide to Program Analysis, Part II: Deep Thoughts by LLMs
Static analysis plays a crucial role in software vulnerability detection, yet faces a persistent precision-scalability tradeoff. In large codebases like the Linux kernel, traditional static analysis tools often generate excessive false positives due to simplified vulnerability modeling and overapproximation of path and data constraints. While large language models (LLMs) demonstrate promising code understanding capabilities, their direct application to program analysis remains unreliable due to…

This https://arxiv.org/abs/2411.14681 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

TrojanEdit: Multimodal Backdoor Attack Against Image Editing Model
Multimodal diffusion models for image editing generate outputs conditioned on both textual instructions and visual inputs, aiming to modify target regions while preserving the rest of the image. Although diffusion models have been shown to be vulnerable to backdoor attacks, existing efforts mainly focus on unimodal generative models and fail to address the unique challenges in multimodal image editing. In this paper, we present the first study of backdoor attacks on multimodal diffusion-based i…

Generating Diverse Challenging Terrains for Legged Robots Using Quality-Diversity Algorithm
Arthur Esquerre-Pourt\`ere, Minsoo Kim, Jaeheung Park
https://arxiv.org/abs/2506.01362 …

Generating Diverse Challenging Terrains for Legged Robots Using Quality-Diversity Algorithm
While legged robots have achieved significant advancements in recent years, ensuring the robustness of their controllers on unstructured terrains remains challenging. It requires generating diverse and challenging unstructured terrains to test the robot and discover its vulnerabilities. This topic remains underexplored in the literature. This paper presents a Quality-Diversity framework to generate diverse and challenging terrains that uncover weaknesses in legged robot controllers. Our method,…

This https://arxiv.org/abs/2405.04860 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Quantum Concolic Testing
This paper presents the first concolic testing framework explicitly designed for quantum programs. The framework introduces quantum constraint generation methods for quantum control statements that quantify quantum states and offers a symbolization method for quantum variables. Based on this framework, we generate path constraints for each concrete execution path of a quantum program. These constraints guide the exploration of new paths, with a quantum constraint solver determining outcomes to …

Spa-VLM: Stealthy Poisoning Attacks on RAG-based VLM
Lei Yu, Yechao Zhang, Ziqi Zhou, Yang Wu, Wei Wan, Minghui Li, Shengshan Hu, Pei Xiaobing, Jing Wang
https://arxiv.org/abs/2505.23828

Spa-VLM: Stealthy Poisoning Attacks on RAG-based VLM
With the rapid development of the Vision-Language Model (VLM), significant progress has been made in Visual Question Answering (VQA) tasks. However, existing VLM often generate inaccurate answers due to a lack of up-to-date knowledge. To address this issue, recent research has introduced Retrieval-Augmented Generation (RAG) techniques, commonly used in Large Language Models (LLM), into VLM, incorporating external multi-modal knowledge to enhance the accuracy and practicality of VLM systems. Nev…

This https://arxiv.org/abs/2404.19318 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Calibration of Large Language Models on Code Summarization
A brief, fluent, and relevant summary can be helpful during program comprehension; however, such a summary does require significant human effort to produce. Often, good summaries are unavailable in software projects, which makes maintenance more difficult. There has been a considerable body of research into automated AI-based methods, using Large Language models (LLMs), to generate summaries of code; there also has been quite a bit of work on ways to measure the performance of such summarizatio…

RealDrive: Retrieval-Augmented Driving with Diffusion Models
Wenhao Ding, Sushant Veer, Yuxiao Chen, Yulong Cao, Chaowei Xiao, Marco Pavone
https://arxiv.org/abs/2505.24808

RealDrive: Retrieval-Augmented Driving with Diffusion Models
Learning-based planners generate natural human-like driving behaviors by learning to reason about nuanced interactions from data, overcoming the rigid behaviors that arise from rule-based planners. Nonetheless, data-driven approaches often struggle with rare, safety-critical scenarios and offer limited controllability over the generated trajectories. To address these challenges, we propose RealDrive, a Retrieval-Augmented Generation (RAG) framework that initializes a diffusion-based planning po…

This https://arxiv.org/abs/2404.10304 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs
Detecting tricky bugs in plausible programs, those that pass existing test suites yet still contain bugs, remains a significant challenge in software testing. To address this problem, we propose TrickCatcher, an LLM-powered approach to generating test cases for uncovering bugs in plausible programs. TrickCatcher operates in three stages: First, it uses an LLM to generate program variants based on the program under test (PUT) and its specification. Second, it employs an LLM to construct an input…

Bi-Manual Joint Camera Calibration and Scene Representation
Haozhan Tang, Tianyi Zhang, Matthew Johnson-Roberson, Weiming Zhi
https://arxiv.org/abs/2505.24819

Bi-Manual Joint Camera Calibration and Scene Representation
Robot manipulation, especially bimanual manipulation, often requires setting up multiple cameras on multiple robot manipulators. Before robot manipulators can generate motion or even build representations of their environments, the cameras rigidly mounted to the robot need to be calibrated. Camera calibration is a cumbersome process involving collecting a set of images, with each capturing a pre-determined marker. In this work, we introduce the Bi-Manual Joint Calibration and Representation Fra…

Legal Compliance Evaluation of Smart Contracts Generated By Large Language Models
Chanuka Wijayakoon, Hai Dong, H. M. N. Dilum Bandara, Zahir Tari, Anurag Soin
https://arxiv.org/abs/2506.00943

Legal Compliance Evaluation of Smart Contracts Generated By Large Language Models
Smart contracts can implement and automate parts of legal contracts, but ensuring their legal compliance remains challenging. Existing approaches such as formal specification, verification, and model-based development require expertise in both legal and software development domains, as well as extensive manual effort. Given the recent advances of Large Language Models (LLMs) in code generation, we investigate their ability to generate legally compliant smart contracts directly from natural lang…

EL-AGHF: Extended Lagrangian Affine Geometric Heat Flow
Sangmin Kim, Hae-Won Park
https://arxiv.org/abs/2505.24751 https://arxiv.org/…

EL-AGHF: Extended Lagrangian Affine Geometric Heat Flow
We propose a constrained Affine Geometric Heat Flow (AGHF) method that evolves so as to suppress the dynamics gaps associated with inadmissible control directions. AGHF provides a unified framework applicable to a wide range of motion planning problems, including both holonomic and non-holonomic systems. However, to generate admissible trajectories, it requires assigning infinite penalties to inadmissible control directions. This design choice, while theoretically valid, often leads to high com…

This https://arxiv.org/abs/2411.01707 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Large-Scale Multi-Robot Coverage Path Planning on Grids with Path Deconfliction
We study Multi-Robot Coverage Path Planning (MCPP) on a 4-neighbor 2D grid G, which aims to compute paths for multiple robots to cover all cells of G. Traditional approaches are limited as they first compute coverage trees on a quadrant coarsened grid H and then employ the Spanning Tree Coverage (STC) paradigm to generate paths on G, making them inapplicable to grids with partially obstructed 2x2 blocks. To address this limitation, we reformulate the problem directly on G, revolutionizing grid-…