Tootfinder

edit_wikiquote: Wikiquote edits (2010)
A bipartite user-page network extracted from Wikiquotes. A user connects to a page if that user edited that page. Edits (edges) are timestamped. Edge weights represent counts of the number of edits.
This network has 5297 nodes and 27934 edges.
Tags: Informational, Web graph, Multigraph, Timestamps

Unhindered by Europe and the US, Israel continues its relentless war of starvation against the people of Gaza. It makes a laughing stock of the EU, letting in less than 40 aid trucks per day, while the EU's whispered demand was 160 (still too few).
In @… today.

Good grief! Phyllis Gardner has died, age only 75. She was my second US postdoc, and produced a nice single channel study in 1984: https://onemol.org.uk/?page_id=10#goc84
She was a very determined woman. We met again in 2009 in Stanford

Publications
Jump to follow-up Some of our main papers since 2003 are listed below, with commentaries and reprints. Some older papers and classics can be downloaded here too. .For more papers, follow these…

A Timed Obstruction Logic for Dynamic Game Models
David Cortes, Jean Leneutre, Vadim Malvone, James Ortiz
https://arxiv.org/abs/2510.06045 https://arxiv.or…

A Timed Obstruction Logic for Dynamic Game Models
Real-time cybersecurity and privacy applications require reliable verification methods and system design tools to ensure their correctness. Many of these reactive real-time applications embedded in various infrastructures, such as airports, hospitals, and oil pipelines, are potentially vulnerable to malicious cyber-attacks. Recently, a growing literature has recognized Timed Game Theory as a sound theoretical foundation for modeling strategic interactions between attackers and defenders. This p…

Wann immer linke, liberale und ökologische Kräfte einen Sieg erreichen, treten sie mit einem vielfältigen und charismatischen Team an. Wenn aber Pelosi, Newsom und wie sie alle heißen, ihre eigene Agenda verfolgen, geht man eben gemeinsam unter. Und wir gehen mit. | Nils Minkmar zum neuen Buch von Kamala Harris https://

Adaptive Coverage Policies in Conformal Prediction
Etienne Gauthier, Francis Bach, Michael I. Jordan
https://arxiv.org/abs/2510.04318 https://arxiv.org/pdf…

Adaptive Coverage Policies in Conformal Prediction
Traditional conformal prediction methods construct prediction sets such that the true label falls within the set with a user-specified coverage level. However, poorly chosen coverage levels can result in uninformative predictions, either producing overly conservative sets when the coverage level is too high, or empty sets when it is too low. Moreover, the fixed coverage level cannot adapt to the specific characteristics of each individual example, limiting the flexibility and efficiency of thes…

J'ai trouvé #pedantix nº1242 en 41 coups !
🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟩🟧🟧🟧🟥🟥🟥🟥
https://pedantix.certitudes.org/

pédantix - Découvrez la page Wikipédia !
Découvrez la page Wikipédia en devinant les mots cachés.

Most of the 475 people arrested at Hyundai facility in Georgia are Korean nationals
The arrested workers were being held at Ice’s Folkston, Georgia detention facility.
Local Korean media said roughly 300 people detained were South Korean nationals.
“It is our understanding that none of those detained is directly employed by Hyundai Motor Co.
We prioritize the safety and well-being of everyone working at the site and comply with all laws and regulations wherever we op…

Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective
Weixin Wang, Haoyang Zheng, Guang Lin, Wei Deng, Pan Xu
https://arxiv.org/abs/2510.05023 https:…

Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective
Most existing approximate Thompson Sampling (TS) algorithms for multi-armed bandits use Stochastic Gradient Langevin Dynamics (SGLD) or its variants in each round to sample from the posterior, relaxing the need for conjugacy assumptions between priors and reward distributions in vanilla TS. However, they often require approximating a different posterior distribution in different round of the bandit problem. This requires tricky, round-specific tuning of hyperparameters such as dynamic learning …

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[8/17]:
- ReTiDe: Real-Time Denoising for Energy-Efficient Motion Picture Processing with FPGAs
Changhong Li, Cl\'ement Bled, Rosa Fernandez, Shreejith Shanker

Ranking Week 1 fantasy football winners and losers and injury impacts from Sunday https://www.espn.com/fantasy/football/story/_/page/FFSundayHighLows-46188663/nfl-week-1-fantasy-football-winners-losers-i…

ModernBERT ColBERT: Enhancing biomedical RAG through an advanced re-ranking retriever
Eduardo Mart\'inez Rivera, Filippo Menolascina
https://arxiv.org/abs/2510.04757 https…

ModernBERT + ColBERT: Enhancing biomedical RAG through an advanced re-ranking retriever
Retrieval-Augmented Generation (RAG) is a powerful technique for enriching Large Language Models (LLMs) with external knowledge, allowing for factually grounded responses, a critical requirement in high-stakes domains such as healthcare. However, the efficacy of RAG systems is fundamentally restricted by the performance of their retrieval module, since irrelevant or semantically misaligned documents directly compromise the accuracy of the final generated response. General-purpose dense retrieve…

A limit lifting theorem for fibrations between bicategories
Patrick Nicodemus
https://arxiv.org/abs/2509.04639 https://arxiv.org/pdf/2509.04639

A limit lifting theorem for fibrations between bicategories
A standard result from the theory of Grothendieck fibrations states that if $p : E \to B$ is a fibration, then $E$ has limits of shape $\mathcal{J}$ if $B$ has limits of shape $\mathcal{J}$ the fibers of $\mathcal{E}$ have limits of shape $\mathcal{J}$, and the reindexing functors preserve these limits. Such a result is a basic tool when computing limits in a category known to be the total category of a fibration, for example, one that is defined using the Grothendieck construction. This paper …

Power-Constrained and Quantized MIMO-RSMA Systems with Imperfect CSIT: Joint Precoding, Antenna Selection, and Power Control
Jiwon Sung, Seokjun Park, Jinseok Choi
https://arxiv.org/abs/2508.05080

Power-Constrained and Quantized MIMO-RSMA Systems with Imperfect CSIT: Joint Precoding, Antenna Selection, and Power Control
To utilize the full potential of the available power at a base station (BS), we propose a joint precoding, antenna selection, and transmit power control algorithm for a total power budget at the BS. We formulate a sum spectral efficiency (SE) maximization problem for downlink multi-user multiple-input multiple-output (MIMO) rate-splitting multiple access (RSMA) systems with arbitrary-resolution digital-to-analog converters (DACs). We reformulate the problem by defining the ergodic sum SE using …

Regular black holes and boson stars in semiclassical gravity
Irfan Javed, Viqar Husain
https://arxiv.org/abs/2510.03935 https://arxiv.org/pdf/2510.03935

Regular black holes and boson stars in semiclassical gravity
We use a Hamiltonian version of the semiclassical Einstein equation to study classical gravity coupled to a quantum scalar field with potential in spherical symmetry. The system is defined by effective constraints where the matter terms are replaced by their expectation values in a quantum state. For the static case, we find numerically that the resulting equations admit asymptotically flat, de Sitter, and anti-de Sitter boson star and regular black hole solutions. We also show that the Bardeen…

PRvL: Quantifying the Capabilities and Risks of Large Language Models for PII Redaction
Leon Garza, Anantaa Kotal, Aritran Piplai, Lavanya Elluri, Prajit Das, Aman Chadha
https://arxiv.org/abs/2508.05545

PRvL: Quantifying the Capabilities and Risks of Large Language Models for PII Redaction
Redacting Personally Identifiable Information (PII) from unstructured text is critical for ensuring data privacy in regulated domains. While earlier approaches have relied on rule-based systems and domain-specific Named Entity Recognition (NER) models, these methods fail to generalize across formats and contexts. Recent advances in Large Language Models (LLMs) offer a promising alternative, yet the effect of architectural and training choices on redaction performance remains underexplored. LLMs…

Correcting quantum errors using a classical code and one additional qubit
Tenzan Araki, Joseph F. Goodwin, Zhenyu Cai
https://arxiv.org/abs/2510.05008 https://

Correcting quantum errors using a classical code and one additional qubit
Classical error-correcting codes are powerful but incompatible with quantum noise, which includes both bit-flips and phase-flips. We introduce Hadamard-based Virtual Error Correction (H-VEC), a protocol that empowers any classical bit-flip code to correct arbitrary Pauli noise with the addition of only a single ancilla qubit and two layers of controlled-Hadamard gates. Through classical post-processing, H-VEC virtually filters the error channel, projecting the noise into pure Y-type errors that…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[3/3]:
- Universal Adversarial Perturbation Attacks On Modern Behavior Cloning Policies
Akansha Kalra, Basavasagar Patil, Guanhong Tao, Daniel S. Brown

Apparently even NIOSH can't spell "fluoride" right consistently

What Types of Code Review Comments Do Developers Most Frequently Resolve?
Saul Goldman, Hong Yi Lin, Jirat Pasuksmit, Patanamon Thongtanunam, Kla Tantithamthavorn, Zhe Wang, Ray Zhang, Ali Behnaz, Fan Jiang, Michael Siers, Ryan Jiang, Mike Buller, Minwoo Jeong, Ming Wu
https://arxiv.org/abs/2510.05450

What Types of Code Review Comments Do Developers Most Frequently Resolve?
Large language model (LLM)-powered code review automation tools have been introduced to generate code review comments. However, not all generated comments will drive code changes. Understanding what types of generated review comments are likely to trigger code changes is crucial for identifying those that are actionable. In this paper, we set out to investigate (1) the types of review comments written by humans and LLMs, and (2) the types of generated comments that are most frequently resolved …

edit_wikinews: Wikipedia news edits (2010)
Two bipartite user-page networks extracted from Wikipedia, about news events. A user connects to a page if that user edited that page. Edits (edges) are timestamped. Edge weights represent counts of the number of edits.
This network has 61822 nodes and 686017 edges.
Tags: Informational, Web graph, Multigraph, Timestamps

Reinforce-Ada: An Adaptive Sampling Framework for Reinforce-Style LLM Training
Wei Xiong, Chenlu Ye, Baohao Liao, Hanze Dong, Xinxing Xu, Christof Monz, Jiang Bian, Nan Jiang, Tong Zhang
https://arxiv.org/abs/2510.04996

Reinforce-Ada: An Adaptive Sampling Framework for Reinforce-Style LLM Training
Reinforcement learning applied to large language models (LLMs) for reasoning tasks is often bottlenecked by unstable gradient estimates due to fixed and uniform sampling of responses across prompts. Prior work such as GVM-RAFT addresses this by dynamically allocating inference budget per prompt to minimize stochastic gradient variance under a budget constraint. Inspired by this insight, we propose Reinforce-Ada, an adaptive sampling framework for online RL post-training of LLMs that continuousl…

On Equivalent Characterizations of NP in Abstract Models of Computation
Jeremy C. Kirn, Lucas Meijer, Tillmann Miltzow, Hans L. Bodlaender
https://arxiv.org/abs/2510.05894 https…

On Equivalent Characterizations of NP in Abstract Models of Computation
We investigate machine models similar to Turing machines that are augmented by the operations of a first-order structure $\mathcal{R}$, and we show that under weak conditions on $\mathcal{R}$, the complexity class $\text{NP}(\mathcal{R})$ may be characterized in three equivalent ways: (1) by polynomial-time verification algorithms implemented on $\mathcal{R}$-machines, (2) by the $\text{NP}(\mathcal{R})$-complete problem $\text{SAT}(\mathcal{R})$, and (3) by existential second-order metafinite …

A Low-Resource Speech-Driven NLP Pipeline for Sinhala Dyslexia Assistance
Peshala Perera, Deshan Sumanathilaka
https://arxiv.org/abs/2510.04750 https://arx…

A Low-Resource Speech-Driven NLP Pipeline for Sinhala Dyslexia Assistance
Dyslexia in adults remains an under-researched and under-served area, particularly in non-English-speaking contexts, despite its significant impact on personal and professional lives. This work addresses that gap by focusing on Sinhala, a low-resource language with limited tools for linguistic accessibility. We present an assistive system explicitly designed for Sinhala-speaking adults with dyslexia. The system integrates Whisper for speech-to-text conversion, SinBERT, an open-sourced fine-tune…

Local Distance Query with Differential Privacy
Weihong Sheng, Jiajun Chen, Bin Cai, Chunqiang Hu, Meng Han, Jiguo Yu
https://arxiv.org/abs/2508.05518 https://

Local Distance Query with Differential Privacy
Differential Privacy (DP) is commonly employed to safeguard graph analysis or publishing. Distance, a critical factor in graph analysis, is typically handled using curator DP, where a trusted curator holds the complete neighbor lists of all vertices and answers queries privately. However, in many real-world scenarios, such a curator may not be present, posing a significant challenge for implementing differentially private distance queries under Local Differential Privacy (LDP). This paper propo…

Characterization of permutation gates in the third level of the Clifford hierarchy
Zhiyang He, Luke Robitaille, Xinyu Tan
https://arxiv.org/abs/2510.04993 https://

Characterization of permutation gates in the third level of the Clifford hierarchy
The Clifford hierarchy is a fundamental structure in quantum computation whose mathematical properties are not fully understood. In this work, we characterize permutation gates -- unitaries which permute the $2^n$ basis states -- in the third level of the hierarchy. We prove that any permutation gate in the third level must be a product of Toffoli gates in what we define as \emph{staircase form}, up to left and right multiplications by Clifford permutations. We then present necessary and suffic…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[2/3]:
- FreqPolicy: Frequency Autoregressive Visuomotor Policy with Continuous Tokens
Zhong, Liu, Xiao, Yang, Wang, Zhu, Shi, Sun, Zhu, Ma

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data
Efthymios Costa, Ioanna Papatsouma, Angelos Markos

Power Transform Revisited: Numerically Stable, and Federated
Xuefeng Xu, Graham Cormode
https://arxiv.org/abs/2510.04995 https://arxiv.org/pdf/2510.04995…

Power Transform Revisited: Numerically Stable, and Federated
Power transforms are popular parametric techniques for making data more Gaussian-like, and are widely used as preprocessing steps in statistical analysis and machine learning. However, we find that direct implementations of power transforms suffer from severe numerical instabilities, which can lead to incorrect results or even crashes. In this paper, we provide a comprehensive analysis of the sources of these instabilities and propose effective remedies. We further extend power transforms to th…

On the Interplay of Cube Learning and Dependency Schemes in QCDCL Proof Systems
Abhimanyu Choudhury, Meena Mahajan
https://arxiv.org/abs/2510.05876 https://

On the Interplay of Cube Learning and Dependency Schemes in QCDCL Proof Systems
Quantified Conflict Driven Clause Leaning (QCDCL) is one of the main approaches to solving Quantified Boolean Formulas (QBF). Cube-learning is employed in this approach to ensure that true formulas can be verified. Dependency Schemes help to detect spurious dependencies that are implied by the variable ordering in the quantifier prefix of QBFs but are not essential for constructing (counter)models. This detection can provably shorten refutations in specific proof systems, and is expected to spe…

JSON Whisperer: Efficient JSON Editing with LLMs
Sarel Duanis, Asnat Greenstein-Messica, Eliya Habba
https://arxiv.org/abs/2510.04717 https://arxiv.org/pdf…

JSON Whisperer: Efficient JSON Editing with LLMs
Large language models (LLMs) can modify JSON documents through natural language commands, but current approaches regenerate entire structures for each edit, resulting in computational inefficiency. We present JSON Whisperer, a framework that enables LLMs to generate RFC 6902 diff patches-expressing only the necessary modifications-rather than complete documents. We identify two key challenges in patch-based editing: (1) LLMs often miss related updates when generating isolated patches, and (2) a…

Grouped k-threshold random grid-based visual cryptography scheme
Xiaoli Zhuo, Xuehu Yan, Wei Yan
https://arxiv.org/abs/2508.05394 https://arxiv.org/pdf/250…

Grouped k-threshold random grid-based visual cryptography scheme
Visual cryptography schemes (VCSs) belong to a category of secret image sharing schemes that do not require cryptographic knowledge for decryption, instead relying directly on the human visual system. Among VCSs, random grid-based VCS (RGVCS) has garnered widespread attention as it avoids pixel expansion while requiring no basic matrices design. Contrast, a core metric for RGVCS, directly determines the visual quality of recovered images, rendering its optimization a critical research objective…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/3]:
- A Benchmarking Study of Vision-Based Robotic Grasping Algorithms: A Comparative Analysis
Rameshbabu, Balakrishna, Flynn, Kapoor, Norton, Yanco, Calli

Quantum Circuit Benchmarking on IBM Brisbane: Performance Insights from Superconducting Qubit Models
J. Thirunirai Selvam, S. Saravana Veni
https://arxiv.org/abs/2508.05331 http…

Quantum Circuit Benchmarking on IBM Brisbane: Performance Insights from Superconducting Qubit Models
This paper investigates quantum communication using superconducting qubits, emphasizing the simulation and control of quantum systems via IBM Brisbane quantum processor. We focus on implementing fundamental quantum gates and analyzing the evolution of entangled states, which are essential for secure and reliable information transfer. The study highlights the role of entanglement as a critical resource in quantum communication, enabling secure connectivity across quantum networks. Simulations in…

edit_wikiquote: Wikiquote edits (2010)
A bipartite user-page network extracted from Wikiquotes. A user connects to a page if that user edited that page. Edits (edges) are timestamped. Edge weights represent counts of the number of edits.
This network has 550 nodes and 938 edges.
Tags: Informational, Web graph, Multigraph, Timestamps

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/2]:
- Mixtures of Gaussian Process Experts with SMC$^2$
Teemu H\"ark\"onen, Sara Wade, Kody Law, Lassi Roininen

Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization
Kristi Topollai, Anna Choromanska
https://arxiv.org/abs/2510.04988 https://

Adaptive Memory Momentum via a Model-Based Framework for Deep Learning Optimization
The vast majority of modern deep learning models are trained with momentum-based first-order optimizers. The momentum term governs the optimizer's memory by determining how much each past gradient contributes to the current convergence direction. Fundamental momentum methods, such as Nesterov Accelerated Gradient and the Heavy Ball method, as well as more recent optimizers such as AdamW and Lion, all rely on the momentum coefficient that is customarily set to $β= 0.9$ and kept constant during …

[2025-10-08 Wed (UTC), 3 new articles found for cs.LO Logic in Computer Science]
toXiv_bot_toot

ShikkhaChain: A Blockchain-Powered Academic Credential Verification System for Bangladesh
Ahsan Farabi, Israt Khandaker, Nusrat Jahan, Ibrahim Khalil Shanto
https://arxiv.org/abs/2508.05334

ShikkhaChain: A Blockchain-Powered Academic Credential Verification System for Bangladesh
Academic credential fraud threatens educational integrity, especially in developing countries like Bangladesh, where verification methods are primarily manual and inefficient. To address this challenge, we present ShikkhaChain, a blockchain-powered certificate management platform designed to securely issue, verify, and revoke academic credentials in a decentralized and tamper-proof manner. Built on Ethereum smart contracts and utilizing IPFS for off-chain storage, the platform offers a transpar…

Crosslisted article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/1]:
- Adaptive Cruise Control in Autonomous Vehicles: Challenges, Gaps, Comprehensive Review, and, Futu...
Shradha Bavalatti, Yash Kangralkar, Santosh Pattar, Veena P Badiger

On the convergence of the variational quantum eigensolver and quantum optimal control
Marco Wiedmann, Daniel Burgarth, Gunther Dirr, Thomas Schulte-Herbr\"uggen, Emanuel Malvetti, Christian Arenz
https://arxiv.org/abs/2509.05295

On the convergence of the variational quantum eigensolver and quantum optimal control
When does a variational quantum algorithm converge to a globally optimal problem solution? Despite the large literature around variational approaches to quantum computing, the answer is largely unknown. We address this open question by developing a convergence theory for the variational quantum eigensolver (VQE). By leveraging the terminology of quantum control landscapes, we prove a sufficient criterion that characterizes when convergence to a ground state of a Hamiltonian can be guaranteed fo…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[3/3]:
- Domain Generalization: A Tale of Two ERMs
Yilun Zhu, Naihao Deng, Naichen Shi, Aditya Gangrade, Clayton Scott

Federated Computation of ROC and PR Curves
Xuefeng Xu, Graham Cormode
https://arxiv.org/abs/2510.04979 https://arxiv.org/pdf/2510.04979

Federated Computation of ROC and PR Curves
Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are fundamental tools for evaluating machine learning classifiers, offering detailed insights into the trade-offs between true positive rate vs. false positive rate (ROC) or precision vs. recall (PR). However, in Federated Learning (FL) scenarios, where data is distributed across multiple clients, computing these curves is challenging due to privacy and communication constraints. Specifically, the server cannot access raw …

Crosslisted article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- Subvarieties of pointed Abelian l-groups
Filip Jankovec
https://

An Overview of 7726 User Reports: Uncovering SMS Scams and Scammer Strategies
Sharad Agarwal, Guillermo Suarez-Tangil, Marie Vasek
https://arxiv.org/abs/2508.05276 https://

An Overview of 7726 User Reports: Uncovering SMS Scams and Scammer Strategies
Mobile network operators implement firewalls to stop illicit messages, but scammers find ways to evade detection. Previous work has looked into SMS texts that are blocked by these firewalls. However, there is little insight into SMS texts that bypass them and reach users. To this end, we collaborate with a major mobile network operator to receive 1.35m user reports submitted over four months. We find 89.16% of user reports comprise text messages, followed by reports of suspicious calls and URLs…

ResMimic: From General Motion Tracking to Humanoid Whole-body Loco-Manipulation via Residual Learning
Siheng Zhao, Yanjie Ze, Yue Wang, C. Karen Liu, Pieter Abbeel, Guanya Shi, Rocky Duan
https://arxiv.org/abs/2510.05070

ResMimic: From General Motion Tracking to Humanoid Whole-body Loco-Manipulation via Residual Learning
Humanoid whole-body loco-manipulation promises transformative capabilities for daily service and warehouse tasks. While recent advances in general motion tracking (GMT) have enabled humanoids to reproduce diverse human motions, these policies lack the precision and object awareness required for loco-manipulation. To this end, we introduce ResMimic, a two-stage residual learning framework for precise and expressive humanoid control from human motion data. First, a GMT policy, trained on large-sc…

Excitable quantum systems: the bosonic avalanche laser
Louis Garbe, Peter Rabl
https://arxiv.org/abs/2509.05290 https://arxiv.org/pdf/2509.05290

Excitable quantum systems: the bosonic avalanche laser
We investigate the dynamics of a lasing system that is driven by a current of bosonic (quasi-) particles via a dissipative three-mode mixing process. A semi-classical analysis of this system predicts distinct dynamical regimes, where both the cavity mode and the gain medium can undergo lasing transitions. Of particular interest is an intermediate self-pulsing phase that exhibits the characteristics of an excitable system and converts random input signals into separated, quasi-periodic pulses at…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/3]:
- Group Policy Gradient
Junhua Chen, Zixi Zhang, Hantao Zhong, Rika Antonova

StructuralDecompose: A Modular Framework for Robust Time Series Decomposition in R
Allen Daniel Sunny
https://arxiv.org/abs/2510.04974 https://arxiv.org/pd…

StructuralDecompose: A Modular Framework for Robust Time Series Decomposition in R
We present StructuralDecompose, an R package for modular and interpretable time series decomposition. Unlike existing approaches that treat decomposition as a monolithic process, StructuralDecompose separates the analysis into distinct components: changepoint detection, anomaly detection, smoothing, and decomposition. This design provides flexibility and robust- ness, allowing users to tailor methods to specific time series characteristics. We demonstrate the package on simulated and real-world…

Higher order differential calculus in mathlib
S\'ebastien Gou\"ezel (IRMAR)
https://arxiv.org/abs/2509.04922 https://arxiv.org/pdf/2509.04922

Higher order differential calculus in mathlib
We report on the higher-order differential calculus library developed inside the Lean mathematical library mathlib. To support a broad range of applications, we depart in several ways from standard textbook definitions: we allow arbitrary fields of scalars, we work with functions defined on domains rather than full spaces, and we integrate analytic functions in the broader scale of smooth functions. These generalizations introduce significant challenges, which we address from both the mathemati…

Incident Response Planning Using a Lightweight Large Language Model with Reduced Hallucination
Kim Hammar, Tansu Alpcan, Emil C. Lupu
https://arxiv.org/abs/2508.05188 https://…

Incident Response Planning Using a Lightweight Large Language Model with Reduced Hallucination
Timely and effective incident response is key to managing the growing frequency of cyberattacks. However, identifying the right response actions for complex systems is a major technical challenge. A promising approach to mitigate this challenge is to use the security knowledge embedded in large language models (LLMs) to assist security operators during incident handling. Recent research has demonstrated the potential of this approach, but current methods are mainly based on prompt engineering o…

Automaton Constrained Q-Learning
Anastasios Manganaris, Vittorio Giammarino, Ahmed H. Qureshi
https://arxiv.org/abs/2510.05061 https://arxiv.org/pdf/2510.0…

Automaton Constrained Q-Learning
Real-world robotic tasks often require agents to achieve sequences of goals while respecting time-varying safety constraints. However, standard Reinforcement Learning (RL) paradigms are fundamentally limited in these settings. A natural approach to these problems is to combine RL with Linear-time Temporal Logic (LTL), a formal language for specifying complex, temporally extended tasks and safety constraints. Yet, existing RL methods for LTL objectives exhibit poor empirical performance in compl…

KVCompose: Efficient Structured KV Cache Compression with Composite Tokens
Dmitry Akulov, Mohamed Sana, Antonio De Domenico, Tareq Si Salem, Nicola Piovesan, Fadhel Ayed
https://arxiv.org/abs/2509.05165

KVCompose: Efficient Structured KV Cache Compression with Composite Tokens
Large language models (LLMs) rely on key-value (KV) caches for efficient autoregressive decoding; however, cache size grows linearly with context length and model depth, becoming a major bottleneck in long-context inference. Prior KV cache compression methods either enforce rigid heuristics, disrupt tensor layouts with per-attention-head variability, or require specialized compute kernels. We propose a simple, yet effective, KV cache compression framework based on attention-guided, layer-adap…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/3]:
- Machine Learning Workflows in Climate Modeling: Design Patterns and Insights from Case Studies
Zheng, Venkatasubramanian, Li, Braverman, Ke, Hou, Jin, Agrawal

Forall-Exists Relational Verification by Filtering to Forall-Forall
Ramana Nagasamudram, Anindya Banerjee, David A. Naumann
https://arxiv.org/abs/2509.04777 https://

Forall-Exists Relational Verification by Filtering to Forall-Forall
Relational verification encompasses research directions such as reasoning about data abstraction, reasoning about security and privacy, secure compilation, and functional specificaton of tensor programs, among others. Several relational Hoare logics exist, with accompanying tool support for compositional reasoning of $\forall\forall$ (2-safety) properties and, generally, k-safety properties of product programs. In contrast, few logics and tools exist for reasoning about $\forall\exists$ propert…

Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights
Cosmin-Andrei Hatfaludi, Alex Serban
https://arxiv.org/abs/2509.05142 https://

Foundational Models and Federated Learning: Survey, Taxonomy, Challenges and Practical Insights
Federated learning has the potential to unlock siloed data and distributed resources by enabling collaborative model training without sharing private data. As more complex foundational models gain widespread use, the need to expand training resources and integrate privately owned data grows as well. In this article, we explore the intersection of federated learning and foundational models, aiming to identify, categorize, and characterize technical methods that integrate the two paradigms. As a …

On the Classical Hardness of the Semidirect Discrete Logarithm Problem in Finite Groups
Mohammad Ferry Husnil Arif, Muhammad Imran
https://arxiv.org/abs/2508.05048 https://

On the Classical Hardness of the Semidirect Discrete Logarithm Problem in Finite Groups
The semidirect discrete logarithm problem (SDLP) in finite groups was proposed as a foundation for post-quantum cryptographic protocols, based on the belief that its non-abelian structure would resist quantum attacks. However, recent results have shown that SDLP in finite groups admits efficient quantum algorithms, undermining its quantum resistance. This raises a fundamental question: does the SDLP offer any computational advantages over the standard discrete logarithm problem (DLP) against cl…

StaMo: Unsupervised Learning of Generalizable Robot Motion from Compact State Representation
Mingyu Liu, Jiuhe Shu, Hui Chen, Zeju Li, Canyu Zhao, Jiange Yang, Shenyuan Gao, Hao Chen, Chunhua Shen
https://arxiv.org/abs/2510.05057

StaMo: Unsupervised Learning of Generalizable Robot Motion from Compact State Representation
A fundamental challenge in embodied intelligence is developing expressive and compact state representations for efficient world modeling and decision making. However, existing methods often fail to achieve this balance, yielding representations that are either overly redundant or lacking in task-critical information. We propose an unsupervised approach that learns a highly compressed two-token state representation using a lightweight encoder and a pre-trained Diffusion Transformer (DiT) decoder…

Causal Abstractions, Categorically Unified
Markus Englberger, Devendra Singh Dhami
https://arxiv.org/abs/2510.05033 https://arxiv.org/pdf/2510.05033…

Causal Abstractions, Categorically Unified
We present a categorical framework for relating causal models that represent the same system at different levels of abstraction. We define a causal abstraction as natural transformations between appropriate Markov functors, which concisely consolidate desirable properties a causal abstraction should exhibit. Our approach unifies and generalizes previously considered causal abstractions, and we obtain categorical proofs and generalizations of existing results on causal abstractions. Using string…

[2025-09-08 Mon (UTC), 2 new articles found for cs.LO Logic in Computer Science]
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Adversarial Attacks and Defenses on Graph-aware Large Language Models (LLMs)
Iyiola E. Olatunji, Franziska Boenisch, Jing Xu, Adam Dziedzic
https://arxiv.org/abs/2508.04894 http…

Adversarial Attacks and Defenses on Graph-aware Large Language Models (LLMs)
Large Language Models (LLMs) are increasingly integrated with graph-structured data for tasks like node classification, a domain traditionally dominated by Graph Neural Networks (GNNs). While this integration leverages rich relational information to improve task performance, their robustness against adversarial attacks remains unexplored. We take the first step to explore the vulnerabilities of graph-aware LLMs by leveraging existing adversarial attack methods tailored for graph-based models, i…

Walking, Rolling, and Beyond: First-Principles and RL Locomotion on a TARS-Inspired Robot
Aditya Sripada, Abhishek Warrier
https://arxiv.org/abs/2510.05001 https://

Walking, Rolling, and Beyond: First-Principles and RL Locomotion on a TARS-Inspired Robot
Robotic locomotion research typically draws from biologically inspired leg designs, yet many human-engineered settings can benefit from non-anthropomorphic forms. TARS3D translates the block-shaped 'TARS' robot from Interstellar into a 0.25 m, 0.99 kg research platform with seven actuated degrees of freedom. The film shows two primary gaits: a bipedal-like walk and a high-speed rolling mode. For TARS3D, we build reduced-order models for each, derive closed-form limit-cycle conditions, and valid…

Curiosity-Driven Co-Development of Action and Language in Robots Through Self-Exploration
Theodore Jerome Tinker, Kenji Doya, Jun Tani
https://arxiv.org/abs/2510.05013 https://

Curiosity-Driven Co-Development of Action and Language in Robots Through Self-Exploration
Human infants acquire language and action co-developmentally, achieving remarkable generalization capabilities from only a minimal number of learning examples. In contrast, recent large language models require exposure to billions of training tokens to achieve such generalization. What mechanisms underlie such efficient developmental learning in humans? This study addresses this question through simulation experiments in which robots learn to perform various actions corresponding to imperative …

[2025-08-08 Fri (UTC), 8 new articles found for cs.CR Cryptography and Security]
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Efficient Navigation in Unknown Indoor Environments with Vision-Language Models
D. Schwartz, K. Kondo, J. P. How
https://arxiv.org/abs/2510.04991 https://a…

Efficient Navigation in Unknown Indoor Environments with Vision-Language Models
We present a novel high-level planning framework that leverages vision-language models (VLMs) to improve autonomous navigation in unknown indoor environments with many dead ends. Traditional exploration methods often take inefficient routes due to limited global reasoning and reliance on local heuristics. In contrast, our approach enables a VLM to reason directly about an occupancy map in a zero-shot manner, selecting subgoals that are likely to lead to more efficient paths. At each planning st…

Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning
Marcel Wien\"obst, Leonard Henckel, Sebastian Weichwald
https://arxiv.org/abs/2510.04970 https…

Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning
We present FLOP (Fast Learning of Order and Parents), a score-based causal discovery algorithm for linear models. It pairs fast parent selection with iterative Cholesky-based score updates, cutting run-times over prior algorithms. This makes it feasible to fully embrace discrete search, enabling iterated local search with principled order initialization to find graphs with scores at or close to the global optimum. The resulting structures are highly accurate across benchmarks, with near-perfect…

Crosslisted article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/1]:
- A High-Capacity and Secure Disambiguation Algorithm for Neural Linguistic Steganography
Yapei Feng, Feng Jiang, Shanhao Wu, Hua Zhong

HyperVLA: Efficient Inference in Vision-Language-Action Models via Hypernetworks
Zheng Xiong, Kang Li, Zilin Wang, Matthew Jackson, Jakob Foerster, Shimon Whiteson
https://arxiv.org/abs/2510.04898

HyperVLA: Efficient Inference in Vision-Language-Action Models via Hypernetworks
Built upon language and vision foundation models with strong generalization ability and trained on large-scale robotic data, Vision-Language-Action (VLA) models have recently emerged as a promising approach to learning generalist robotic policies. However, a key drawback of existing VLAs is their extremely high inference costs. In this paper, we propose HyperVLA to address this problem. Unlike existing monolithic VLAs that activate the whole model during both training and inference, HyperVLA us…

PhishSSL: Self-Supervised Contrastive Learning for Phishing Website Detection
Wenhao Li, Selvakumar Manickam, Yung-Wey Chong, Shankar Karuppayah, Priyadarsi Nanda, Binyong Li
https://arxiv.org/abs/2510.05900

PhishSSL: Self-Supervised Contrastive Learning for Phishing Website Detection
Phishing websites remain a persistent cybersecurity threat by mimicking legitimate sites to steal sensitive user information. Existing machine learning-based detection methods often rely on supervised learning with labeled data, which not only incurs substantial annotation costs but also limits adaptability to novel attack patterns. To address these challenges, we propose PhishSSL, a self-supervised contrastive learning framework that eliminates the need for labeled phishing data during trainin…

CLEAR-IR: Clarity-Enhanced Active Reconstruction of Infrared Imagery
Nathan Shankar, Pawel Ladosz, Hujun Yin
https://arxiv.org/abs/2510.04883 https://arxiv…

CLEAR-IR: Clarity-Enhanced Active Reconstruction of Infrared Imagery
This paper presents a novel approach for enabling robust robotic perception in dark environments using infrared (IR) stream. IR stream is less susceptible to noise than RGB in low-light conditions. However, it is dominated by active emitter patterns that hinder high-level tasks such as object detection, tracking and localisation. To address this, a U-Net-based architecture is proposed that reconstructs clean IR images from emitter-populated input, improving both image quality and downstream rob…

TAG-K: Tail-Averaged Greedy Kaczmarz for Computationally Efficient and Performant Online Inertial Parameter Estimation
Shuo Sha, Anupam Bhakta, Zhenyuan Jiang, Kevin Qiu, Ishaan Mahajan, Gabriel Bravo, Brian Plancher
https://arxiv.org/abs/2510.04839

TAG-K: Tail-Averaged Greedy Kaczmarz for Computationally Efficient and Performant Online Inertial Parameter Estimation
Accurate online inertial parameter estimation is essential for adaptive robotic control, enabling real-time adjustment to payload changes, environmental interactions, and system wear. Traditional methods such as Recursive Least Squares (RLS) and the Kalman Filter (KF) often struggle to track abrupt parameter shifts or incur high computational costs, limiting their effectiveness in dynamic environments and for computationally constrained robotic systems. As such, we introduce TAG-K, a lightweigh…

Online automatic code generation for robot swarms: LLMs and self-organizing hierarchy
Weixu Zhu, Marco Dorigo, Mary Katherine Heinrich
https://arxiv.org/abs/2510.04774 https://

Online automatic code generation for robot swarms: LLMs and self-organizing hierarchy
Our recently introduced self-organizing nervous system (SoNS) provides robot swarms with 1) ease of behavior design and 2) global estimation of the swarm configuration and its collective environment, facilitating the implementation of online automatic code generation for robot swarms. In a demonstration with 6 real robots and simulation trials with >30 robots, we show that when a SoNS-enhanced robot swarm gets stuck, it can automatically solicit and run code generated by an external LLM on the …

Performance-guided Task-specific Optimization for Multirotor Design
Etor Arza, Welf Rehberg, Philipp Weiss, Mihir Kulkarni, Kostas Alexis
https://arxiv.org/abs/2510.04724 https:…

Performance-guided Task-specific Optimization for Multirotor Design
This paper introduces a methodology for task-specific design optimization of multirotor Micro Aerial Vehicles. By leveraging reinforcement learning, Bayesian optimization, and covariance matrix adaptation evolution strategy, we optimize aerial robot designs guided exclusively by their closed-loop performance in a considered task. Our approach systematically explores the design space of motor pose configurations while ensuring manufacturability constraints and minimal aerodynamic interference. R…

Building Gradient by Gradient: Decentralised Energy Functions for Bimanual Robot Assembly
Alexander L. Mitchell, Joe Watson, Ingmar Posner
https://arxiv.org/abs/2510.04696 https…

Building Gradient by Gradient: Decentralised Energy Functions for Bimanual Robot Assembly
There are many challenges in bimanual assembly, including high-level sequencing, multi-robot coordination, and low-level, contact-rich operations such as component mating. Task and motion planning (TAMP) methods, while effective in this domain, may be prohibitively slow to converge when adapting to disturbances that require new task sequencing and optimisation. These events are common during tight-tolerance assembly, where difficult-to-model dynamics such as friction or deformation require rapi…

Bio-Inspired Robotic Houbara: From Development to Field Deployment for Behavioral Studies
Lyes Saad Saoud, Irfan Hussain
https://arxiv.org/abs/2510.04692 https://

Bio-Inspired Robotic Houbara: From Development to Field Deployment for Behavioral Studies
Biomimetic intelligence and robotics are transforming field ecology by enabling lifelike robotic surrogates that interact naturally with animals under real world conditions. Studying avian behavior in the wild remains challenging due to the need for highly realistic morphology, durable outdoor operation, and intelligent perception that can adapt to uncontrolled environments. We present a next generation bio inspired robotic platform that replicates the morphology and visual appearance of the fe…

OKVIS2-X: Open Keyframe-based Visual-Inertial SLAM Configurable with Dense Depth or LiDAR, and GNSS
Simon Boche, Jaehyung Jung, Sebasti\'an Barbas Laina, Stefan Leutenegger
https://arxiv.org/abs/2510.04612

OKVIS2-X: Open Keyframe-based Visual-Inertial SLAM Configurable with Dense Depth or LiDAR, and GNSS
To empower mobile robots with usable maps as well as highest state estimation accuracy and robustness, we present OKVIS2-X: a state-of-the-art multi-sensor Simultaneous Localization and Mapping (SLAM) system building dense volumetric occupancy maps, while scalable to large environments and operating in realtime. Our unified SLAM framework seamlessly integrates different sensor modalities: visual, inertial, measured or learned depth, LiDAR and Global Navigation Satellite System (GNSS) measuremen…

MobRT: A Digital Twin-Based Framework for Scalable Learning in Mobile Manipulation
Yilin Mei, Peng Qiu, Wei Zhang, WenChao Zhang, Wenjie Song
https://arxiv.org/abs/2510.04592 ht…

MobRT: A Digital Twin-Based Framework for Scalable Learning in Mobile Manipulation
Recent advances in robotics have been largely driven by imitation learning, which depends critically on large-scale, high-quality demonstration data. However, collecting such data remains a significant challenge-particularly for mobile manipulators, which must coordinate base locomotion and arm manipulation in high-dimensional, dynamic, and partially observable environments. Consequently, most existing research remains focused on simpler tabletop scenarios, leaving mobile manipulation relativel…

Everything-Grasping (EG) Gripper: A Universal Gripper with Synergistic Suction-Grasping Capabilities for Cross-Scale and Cross-State Manipulation
Jianshu Zhou, Jing Shu, Tianle Pan, Puchen Zhu, Jiajun An, Huayu Zhang, Junda Huang, Upinder Kaur, Xin Ma, Masayoshi Tomizuka
https://arxiv.org/abs/2510.04585

Everything-Grasping (EG) Gripper: A Universal Gripper with Synergistic Suction-Grasping Capabilities for Cross-Scale and Cross-State Manipulation
Grasping objects across vastly different sizes and physical states-including both solids and liquids-with a single robotic gripper remains a fundamental challenge in soft robotics. We present the Everything-Grasping (EG) Gripper, a soft end-effector that synergistically integrates distributed surface suction with internal granular jamming, enabling cross-scale and cross-state manipulation without requiring airtight sealing at the contact interface with target objects. The EG Gripper can handle …

Velocity-Form Data-Enabled Predictive Control of Soft Robots under Unknown External Payloads
Huanqing Wang, Kaixiang Zhang, Kyungjoon Lee, Yu Mei, Vaibhav Srivastava, Jun Sheng, Ziyou Song, Zhaojian Li
https://arxiv.org/abs/2510.04509

Velocity-Form Data-Enabled Predictive Control of Soft Robots under Unknown External Payloads
Data-driven control methods such as data-enabled predictive control (DeePC) have shown strong potential in efficient control of soft robots without explicit parametric models. However, in object manipulation tasks, unknown external payloads and disturbances can significantly alter the system dynamics and behavior, leading to offset error and degraded control performance. In this paper, we present a novel velocity-form DeePC framework that achieves robust and optimal control of soft robots under…

PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization
Jushan Chen, Santiago Paternain
https://arxiv.org/abs/2510.04436 https://arxiv.o…

PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization
Recently, diffusion models have gained popularity and attention in trajectory optimization due to their capability of modeling multi-modal probability distributions. However, addressing nonlinear equality constraints, i.e, dynamic feasi- bility, remains a great challenge in diffusion-based trajectory optimization. Recent diffusion-based trajectory optimization frameworks rely on a single-shooting style approach where the denoised control sequence is applied to forward propagate the dynamical sy…

Reliable and Scalable Robot Policy Evaluation with Imperfect Simulators
Apurva Badithela, David Snyder, Lihan Zha, Joseph Mikhail, Matthew O'Kelly, Anushri Dixit, Anirudha Majumdar
https://arxiv.org/abs/2510.04354

Reliable and Scalable Robot Policy Evaluation with Imperfect Simulators
Rapid progress in imitation learning, foundation models, and large-scale datasets has led to robot manipulation policies that generalize to a wide-range of tasks and environments. However, rigorous evaluation of these policies remains a challenge. Typically in practice, robot policies are often evaluated on a small number of hardware trials without any statistical assurances. We present SureSim, a framework to augment large-scale simulation with relatively small-scale real-world testing to prov…

Stability-Aware Retargeting for Humanoid Multi-Contact Teleoperation
Stephen McCrory, Romeo Orsolino, Dhruv Thanki, Luigi Penco, Robert Griffin
https://arxiv.org/abs/2510.04353 …

Stability-Aware Retargeting for Humanoid Multi-Contact Teleoperation
Teleoperation is a powerful method to generate reference motions and enable humanoid robots to perform a broad range of tasks. However, teleoperation becomes challenging when using hand contacts and non-coplanar surfaces, often leading to motor torque saturation or loss of stability through slipping. We propose a centroidal stability-based retargeting method that dynamically adjusts contact points and posture during teleoperation to enhance stability in these difficult scenarios. Central to our…

Integrated Planning and Control on Manifolds: Factor Graph Representation and Toolkit
Peiwen Yang, Weisong Wen, Runqiu Yang, Yuanyuan Zhang, Jiahao Hu, Yingming Chen, Naigui Xiao, Jiaqi Zhao
https://arxiv.org/abs/2510.04278

Integrated Planning and Control on Manifolds: Factor Graph Representation and Toolkit
Model predictive control (MPC) faces significant limitations when applied to systems evolving on nonlinear manifolds, such as robotic attitude dynamics and constrained motion planning, where traditional Euclidean formulations struggle with singularities, over-parameterization, and poor convergence. To overcome these challenges, this paper introduces FactorMPC, a factor-graph based MPC toolkit that unifies system dynamics, constraints, and objectives into a modular, user-friendly, and efficient …

ContextVLA: Vision-Language-Action Model with Amortized Multi-Frame Context
Huiwon Jang, Sihyun Yu, Heeseung Kwon, Hojin Jeon, Younggyo Seo, Jinwoo Shin
https://arxiv.org/abs/2510.04246

ContextVLA: Vision-Language-Action Model with Amortized Multi-Frame Context
Leveraging temporal context is crucial for success in partially observable robotic tasks. However, prior work in behavior cloning has demonstrated inconsistent performance gains when using multi-frame observations. In this paper, we introduce ContextVLA, a policy model that robustly improves robotic task performance by effectively leveraging multi-frame observations. Our approach is motivated by the key observation that Vision-Language-Action models (VLA), i.e., policy models built upon a Visio…

Flexible Locomotion Learning with Diffusion Model Predictive Control
Runhan Huang, Haldun Balim, Heng Yang, Yilun Du
https://arxiv.org/abs/2510.04234 https://

Flexible Locomotion Learning with Diffusion Model Predictive Control
Legged locomotion demands controllers that are both robust and adaptable, while remaining compatible with task and safety considerations. However, model-free reinforcement learning (RL) methods often yield a fixed policy that can be difficult to adapt to new behaviors at test time. In contrast, Model Predictive Control (MPC) provides a natural approach to flexible behavior synthesis by incorporating different objectives and constraints directly into its optimization process. However, classical …

Zenbo Patrol: A Social Assistive Robot Based on Multimodal Deep Learning for Real-time Illegal Parking Recognition and Notification
Jian-jie Zheng, Chih-kai Yang, Po-han Chen, Lyn Chao-ling Chen
https://arxiv.org/abs/2510.04190

Zenbo Patrol: A Social Assistive Robot Based on Multimodal Deep Learning for Real-time Illegal Parking Recognition and Notification
In the study, the social robot act as a patrol to recognize and notify illegal parking in real-time. Dual-model pipeline method and large multimodal model were compared, and the GPT-4o multimodal model was adopted in license plate recognition without preprocessing. For moving smoothly on a flat ground, the robot navigated in a simulated parking lot in the experiments. The robot changes angle view of the camera automatically to capture the images around with the format of license plate number. F…

Using Robotics to Improve Transcatheter Edge-to-Edge Repair of the Mitral Valve
L\'ea Pistorius, Namrata U. Nayar, Phillip Tran, Sammy Elmariah, Pierre E. Dupont
https://arxiv.org/abs/2510.04178

Using Robotics to Improve Transcatheter Edge-to-Edge Repair of the Mitral Valve
Transcatheter valve repair presents significant challenges due to the mechanical limitations and steep learning curve associated with manual catheter systems. This paper investigates the use of robotics to facilitate transcatheter procedures in the context of mitral valve edge-to-edge repair. The complex handle-based control of a clinical repair device is replaced by intuitive robotic joint-based control via a game controller. Manual versus robotic performance is analyzed by decomposing the ove…

VBM-NET: Visual Base Pose Learning for Mobile Manipulation using Equivariant TransporterNet and GNNs
Lakshadeep Naik, Adam Fischer, Daniel Duberg, Danica Kragic
https://arxiv.org/abs/2510.04171

VBM-NET: Visual Base Pose Learning for Mobile Manipulation using Equivariant TransporterNet and GNNs
In Mobile Manipulation, selecting an optimal mobile base pose is essential for successful object grasping. Previous works have addressed this problem either through classical planning methods or by learning state-based policies. They assume access to reliable state information, such as the precise object poses and environment models. In this work, we study base pose planning directly from top-down orthographic projections of the scene, which provide a global overview of the scene while preservi…

Learning to Capture Rocks using an Excavator: A Reinforcement Learning Approach with Guiding Reward Formulation
Amirmasoud Molaei, Reza Ghabcheloo
https://arxiv.org/abs/2510.04168

Learning to Capture Rocks using an Excavator: A Reinforcement Learning Approach with Guiding Reward Formulation
Rock capturing with standard excavator buckets is a challenging task typically requiring the expertise of skilled operators. Unlike soil digging, it involves manipulating large, irregular rocks in unstructured environments where complex contact interactions with granular material make model-based control impractical. Existing autonomous excavation methods focus mainly on continuous media or rely on specialized grippers, limiting their applicability to real-world construction sites. This paper i…

HEHA: Hierarchical Planning for Heterogeneous Multi-Robot Exploration of Unknown Environments
Longrui Yang, Yiyu Wang, Jingfan Tang, Yunpeng Lv, Shizhe Zhao, Chao Cao, Zhongqiang Ren
https://arxiv.org/abs/2510.04161

HEHA: Hierarchical Planning for Heterogeneous Multi-Robot Exploration of Unknown Environments
This paper considers the path planning problem for autonomous exploration of an unknown environment using multiple heterogeneous robots such as drones, wheeled, and legged robots, which have different capabilities to traverse complex terrains. A key challenge there is to intelligently allocate the robots to the unknown areas to be explored and determine the visiting order of those spaces subject to traversablity constraints, which leads to a large scale constrained optimization problem that nee…

From Shadow to Light: Toward Safe and Efficient Policy Learning Across MPC, DeePC, RL, and LLM Agents
Amin Vahidi-Moghaddam, Sayed Pedram Haeri Boroujeni, Iman Jebellat, Ehsan Jebellat, Niloufar Mehrabi, Zhaojian Li
https://arxiv.org/abs/2510.04076

From Shadow to Light: Toward Safe and Efficient Policy Learning Across MPC, DeePC, RL, and LLM Agents
One of the main challenges in modern control applications, particularly in robot and vehicle motion control, is achieving accurate, fast, and safe movement. To address this, optimal control policies have been developed to enforce safety while ensuring high performance. Since basic first-principles models of real systems are often available, model-based controllers are widely used. Model predictive control (MPC) is a leading approach that optimizes performance while explicitly handling safety co…

Feedback Matters: Augmenting Autonomous Dissection with Visual and Topological Feedback
Chung-Pang Wang, Changwei Chen, Xiao Liang, Soofiyan Atar, Florian Richter, Michael Yip
https://arxiv.org/abs/2510.04074

Feedback Matters: Augmenting Autonomous Dissection with Visual and Topological Feedback
Autonomous surgical systems must adapt to highly dynamic environments where tissue properties and visual cues evolve rapidly. Central to such adaptability is feedback: the ability to sense, interpret, and respond to changes during execution. While feedback mechanisms have been explored in surgical robotics, ranging from tool and tissue tracking to error detection, existing methods remain limited in handling the topological and perceptual challenges of tissue dissection. In this work, we propose…

SITCOM: Scaling Inference-Time COMpute for VLAs
Ayudh Saxena, Harsh Shah, Sandeep Routray, Rishi Rajesh Shah, Esha Pahwa
https://arxiv.org/abs/2510.04041 https://

SITCOM: Scaling Inference-Time COMpute for VLAs
Learning robust robotic control policies remains a major challenge due to the high cost of collecting labeled data, limited generalization to unseen environments, and difficulties in planning over long horizons. While Vision-Language-Action (VLA) models offer a promising solution by grounding natural language instructions into single-step control commands, they often lack mechanisms for lookahead and struggle with compounding errors in dynamic tasks. In this project, we introduce Scaling Infere…

A Real-Time Framework for Intermediate Map Construction and Kinematically Feasible Off-Road Planning Without OSM
Otobong Jerome, Geesara Prathap Kulathunga, Devitt Dmitry, Eugene Murawjow, Alexandr Klimchik
https://arxiv.org/abs/2510.03948

A Real-Time Framework for Intermediate Map Construction and Kinematically Feasible Off-Road Planning Without OSM
Off-road environments present unique challenges for autonomous navigation due to their complex and unstructured nature. Traditional global path-planning methods, which typically aim to minimize path length and travel time, perform poorly on large-scale maps and fail to account for critical factors such as real-time performance, kinematic feasibility, and memory efficiency. This paper introduces a novel global path-planning method specifically designed for off-road environments, addressing these…

TCB-VIO: Tightly-Coupled Focal-Plane Binary-Enhanced Visual Inertial Odometry
Matthew Lisondra, Junseo Kim, Glenn Takashi Shimoda, Kourosh Zareinia, Sajad Saeedi
https://arxiv.org/abs/2510.03919

TCB-VIO: Tightly-Coupled Focal-Plane Binary-Enhanced Visual Inertial Odometry
Vision algorithms can be executed directly on the image sensor when implemented on the next-generation sensors known as focal-plane sensor-processor arrays (FPSP)s, where every pixel has a processor. FPSPs greatly improve latency, reducing the problems associated with the bottleneck of data transfer from a vision sensor to a processor. FPSPs accelerate vision-based algorithms such as visual-inertial odometry (VIO). However, VIO frameworks suffer from spatial drift due to the vision-based pose e…

WAFFLE: A Wearable Approach to Bite Timing Estimation in Robot-Assisted Feeding
Akhil Padmanabha, Jessie Yuan, Tanisha Mehta, Rajat Kumar Jenamani, Eric Hu, Victoria de Le\'on, Anthony Wertz, Janavi Gupta, Ben Dodson, Yunting Yan, Carmel Majidi, Tapomayukh Bhattacharjee, Zackory Erickson
https://arxiv.org/abs/2510.03910

WAFFLE: A Wearable Approach to Bite Timing Estimation in Robot-Assisted Feeding
Millions of people around the world need assistance with feeding. Robotic feeding systems offer the potential to enhance autonomy and quality of life for individuals with impairments and reduce caregiver workload. However, their widespread adoption has been limited by technical challenges such as estimating bite timing, the appropriate moment for the robot to transfer food to a user's mouth. In this work, we introduce WAFFLE: Wearable Approach For Feeding with LEarned bite timing, a system that…

NoTVLA: Narrowing of Dense Action Trajectories for Generalizable Robot Manipulation
Zheng Huang, Mingyu Liu, Xiaoyi Lin, Muzhi Zhu, Canyu Zhao, Zongze Du, Xiaoman Li, Yiduo Jia, Hao Zhong, Hao Chen, Chunhua Shen
https://arxiv.org/abs/2510.03895

NoTVLA: Narrowing of Dense Action Trajectories for Generalizable Robot Manipulation
Vision-Language-Action (VLA) models represent a pivotal advance in embodied intelligence, yet they confront critical barriers to real-world deployment, most notably catastrophic forgetting. This issue stems from their overreliance on continuous action sequences or action chunks, which inadvertently create isolated data silos that disrupt knowledge retention across tasks. To tackle these challenges, we propose the Narrowing of Trajectory VLA (NoTVLA) framework: a novel approach that narrows its …

Seeing the Bigger Picture: 3D Latent Mapping for Mobile Manipulation Policy Learning
Sunghwan Kim, Woojeh Chung, Zhirui Dai, Dwait Bhatt, Arth Shukla, Hao Su, Yulun Tian, Nikolay Atanasov
https://arxiv.org/abs/2510.03885

Seeing the Bigger Picture: 3D Latent Mapping for Mobile Manipulation Policy Learning
In this paper, we demonstrate that mobile manipulation policies utilizing a 3D latent map achieve stronger spatial and temporal reasoning than policies relying solely on images. We introduce Seeing the Bigger Picture (SBP), an end-to-end policy learning approach that operates directly on a 3D map of latent features. In SBP, the map extends perception beyond the robot's current field of view and aggregates observations over long horizons. Our mapping approach incrementally fuses multiview observ…

COVER:COverage-VErified Roadmaps for Fixed-time Motion Planning in Continuous Semi-Static Environments
Niranjan Kumar Ilampooranan, Constantinos Chamzas
https://arxiv.org/abs/2510.03875

COVER:COverage-VErified Roadmaps for Fixed-time Motion Planning in Continuous Semi-Static Environments
Having the ability to answer motion-planning queries within a fixed time budget is critical for the widespread deployment of robotic systems. Semi-static environments, where most obstacles remain static but a limited set can vary across queries, exhibit structured variability that can be systematically exploited to provide stronger guarantees than in general motion-planning problems. However, prior approaches in this setting either lack formal guarantees or rely on restrictive discretizations o…

Verifier-free Test-Time Sampling for Vision Language Action Models
Suhyeok Jang, Dongyoung Kim, Changyeon Kim, Youngsuk Kim, Jinwoo Shin
https://arxiv.org/abs/2510.05681 https:/…

Verifier-free Test-Time Sampling for Vision Language Action Models
Vision-Language-Action models (VLAs) have demonstrated remarkable performance in robot control. However, they remain fundamentally limited in tasks that require high precision due to their single-inference paradigm. While test-time scaling approaches using external verifiers have shown promise, they require additional training and fail to generalize to unseen conditions. We propose Masking Distribution Guided Selection (MG-Select), a novel test-time scaling framework for VLAs that leverages the…