Tootfinder

KNARsack: Teaching Neural Algorithmic Reasoners to Solve Pseudo-Polynomial Problems
Stjepan Po\v{z}gaj, Dobrik Georgiev, Marin \v{S}ili\'c, Petar Veli\v{c}kovi\'c
https://arxiv.org/abs/2509.15239

KNARsack: Teaching Neural Algorithmic Reasoners to Solve Pseudo-Polynomial Problems
Neural algorithmic reasoning (NAR) is a growing field that aims to embed algorithmic logic into neural networks by imitating classical algorithms. In this extended abstract, we detail our attempt to build a neural algorithmic reasoner that can solve Knapsack, a pseudo-polynomial problem bridging classical algorithms and combinatorial optimisation, but omitted in standard NAR benchmarks. Our neural algorithmic reasoner is designed to closely follow the two-phase pipeline for the Knapsack problem…

Think, Verbalize, then Speak: Bridging Complex Thoughts and Comprehensible Speech
Sang Hoon Woo, Sehun Lee, Kang-wook Kim, Gunhee Kim
https://arxiv.org/abs/2509.16028 https://…

Think, Verbalize, then Speak: Bridging Complex Thoughts and Comprehensible Speech
Spoken dialogue systems increasingly employ large language models (LLMs) to leverage their advanced reasoning capabilities. However, direct application of LLMs in spoken communication often yield suboptimal results due to mismatches between optimal textual and verbal delivery. While existing approaches adapt LLMs to produce speech-friendly outputs, their impact on reasoning performance remains underexplored. In this work, we propose Think-Verbalize-Speak, a framework that decouples reasoning fr…

Bridging the Analog and the Probabilistic Computing Divide: Configuring Oscillator Ising Machines as P-bit Engines
E. M. H. E. B. Ekanayake, Nikhat Khan, Nikhil Shukla
https://arxiv.org/abs/2508.15234 …

Bridging the Analog and the Probabilistic Computing Divide: Configuring Oscillator Ising Machines as P-bit Engines
Oscillator Ising Machines (OIMs) and probabilistic bit (p-bit)-based computing platforms have emerged as promising paradigms for tackling complex combinatorial optimization problems. Although traditionally viewed as distinct approaches, this work presents a theoretically grounded framework for configuring OIMs as p-bit engines. We demonstrate that this functionality can be enabled through a novel interplay between first- and second harmonic injection to the oscillators. Our work identifies new …

Bridging Batch and Streaming Estimations to System Identification under Adversarial Attacks
Jihun Kim, Javad Lavaei
https://arxiv.org/abs/2509.15794 https://

Bridging Batch and Streaming Estimations to System Identification under Adversarial Attacks
System identification in modern engineering systems faces emerging challenges from unanticipated adversarial attacks beyond existing detection mechanisms. In this work, we obtain a provably accurate estimate of the Markov parameter matrix of order $k$ to identify partially observed linear systems, in which the probability of having an attack at each time is $O(1/k)$. We show that given the batch data accumulated up to time $T^*$, the $\ell_2$-norm estimator achieves an error decaying exponentia…

Text2Weight: Bridging Natural Language and Neural Network Weight Spaces
Bowen Tian, Wenshuo Chen, Zexi Li, Songning Lai, Jiemin Wu, Yutao Yue
https://arxiv.org/abs/2508.13633 ht…

Text2Weight: Bridging Natural Language and Neural Network Weight Spaces
How far are we really from automatically generating neural networks? While neural network weight generation shows promise, current approaches struggle with generalization to unseen tasks and practical application exploration. To address this, we propose T2W, a diffusion transformer framework that generates task-specific weights conditioned on natural language descriptions. T2W hierarchically processes network parameters into uniform blocks, integrates text embeddings from CLIP via a prior atten…

Bridging Molecular Simulation and Process Modeling for Predictive Multicomponent Adsorption
Sunghyun Yoon, Jui Tu, Li-Chiang Lin, Yongchul G. Chung
https://arxiv.org/abs/2508.12200

Bridging Molecular Simulation and Process Modeling for Predictive Multicomponent Adsorption
Accurate and efficient prediction of multicomponent adsorption equilibria across pressures, temperatures, and compositions remain a central challenge for designing energy-efficient adsorption-based separation processes. Traditional approaches, including model fitting and ideal adsorbed solution theory (IAST), often fail to balance accuracy, computational efficiency, and transferability under process-relevant conditions. Here, we introduce a material-to-process modeling framework that integrates…

It Takes a Village: Bridging the Gaps between Current and Formal Specifications for Protocols
David Basin, Nate Foster, Kenneth L. McMillan, Kedar S. Namjoshi, Cristina Nita-Rotaru, Jonathan M. Smith, Pamela Zave, Lenore D. Zuck
https://arxiv.org/abs/2509.13208

It Takes a Village: Bridging the Gaps between Current and Formal Specifications for Protocols
Formal specifications have numerous benefits for both designers and users of network protocols. They provide clear, unambiguous representations, which are useful as documentation and for testing. They can help reveal disagreements about what a protocol "is" and identify areas where further work is needed to resolve ambiguities or internal inconsistencies. They also provide a foundation for formal reasoning, making it possible to establish important security and correctness guarantees on all inp…

Py maidr: Bridging Visual and Non-Visual Data Experiences Through a Unified Python Framework
JooYoung Seo, Saairam Venkatesh, Daksh Pokar, Sanchita Kamath, Krishna Anandan Ganesan
https://arxiv.org/abs/2509.13532

Py maidr: Bridging Visual and Non-Visual Data Experiences Through a Unified Python Framework
Although recent efforts have developed accessible data visualization tools for blind and low-vision (BLV) users, most follow a "design for them" approach that creates an unintentional divide between sighted creators and BLV consumers. This unidirectional paradigm perpetuates a power dynamic where sighted creators produce non-visual content boundaries for BLV consumers to access. This paper proposes a bidirectional approach, "design for us," where both sighted and BLV collaborators can employ th…

Bridging Perception and Planning: Towards End-to-End Planning for Signal Temporal Logic Tasks
Bowen Ye, Junyue Huang, Yang Liu, Xiaozhen Qiao, Xiang Yin
https://arxiv.org/abs/2509.12813

Bridging Perception and Planning: Towards End-to-End Planning for Signal Temporal Logic Tasks
We investigate the task and motion planning problem for Signal Temporal Logic (STL) specifications in robotics. Existing STL methods rely on pre-defined maps or mobility representations, which are ineffective in unstructured real-world environments. We propose the \emph{Structured-MoE STL Planner} (\textbf{S-MSP}), a differentiable framework that maps synchronized multi-view camera observations and an STL specification directly to a feasible trajectory. S-MSP integrates STL constraints within a…

Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
Yifan Hu, Jie Yang, Tian Zhou, Peiyuan Liu, Yujin Tang, Rong Jin, Liang Sun
https://arxiv.org/abs/2509.14181

Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
Representation learning techniques like contrastive learning have long been explored in time series forecasting, mirroring their success in computer vision and natural language processing. Yet recent state-of-the-art (SOTA) forecasters seldom adopt these representation approaches because they have shown little performance advantage. We challenge this view and demonstrate that explicit representation alignment can supply critical information that bridges the distributional gap between input hist…