Tootfinder

Thermodynamic Signature of Logical Depth in Quantum Circuits
Issam Ibnouhsein
https://arxiv.org/abs/2508.03203 https://arxiv.org/pdf/2508.03203

Thermodynamic Signature of Logical Depth in Quantum Circuits
We demonstrate that the internal logical structure of a quantum circuit can leave a distinct thermodynamic signature under progressive decoherence. By comparing deep, conditionally branching circuits with shallow, uniform counterparts-while controlling for overall halting probability and physical resources-we show that branching architectures induce greater entropy flow into the environment. This effect is captured by a logical depth factor $L_d$, which quantifies entropy accumulation during en…

Ontological differentiation as a measure of semantic accuracy
Pablo Garcia-Cuadrillero, Fabio Revuelta, Jose Angel Capitan
https://arxiv.org/abs/2507.06208

Ontological differentiation as a measure of semantic accuracy
Understanding semantic relationships within complex networks derived from lexical resources is fundamental for network science and language modeling. While network embedding methods capture contextual similarity, quantifying semantic distance based directly on explicit definitional structure remains challenging. Accurate measures of semantic similarity allow for navigation on lexical networks based on maximizing semantic similarity in each navigation jump (Semantic Navigation, SN). This work in…

MultiRAG: A Knowledge-guided Framework for Mitigating Hallucination in Multi-source Retrieval Augmented Generation
Wenlong Wu, Haofen Wang, Bohan Li, Peixuan Huang, Xinzhe Zhao, Lei Liang
https://arxiv.org/abs/2508.03553

MultiRAG: A Knowledge-guided Framework for Mitigating Hallucination in Multi-source Retrieval Augmented Generation
Retrieval Augmented Generation (RAG) has emerged as a promising solution to address hallucination issues in Large Language Models (LLMs). However, the integration of multiple retrieval sources, while potentially more informative, introduces new challenges that can paradoxically exacerbate hallucination problems. These challenges manifest primarily in two aspects: the sparse distribution of multi-source data that hinders the capture of logical relationships and the inherent inconsistencies among…

Who Wins the Multi-Structural Game?
Ronald Fagin, Neil Immerman, Phokion Kolaitis, Jonathan Lenchner, Rik Sengupta
https://arxiv.org/abs/2507.18718 https://

Who Wins the Multi-Structural Game?
Combinatorial games played between two players, called Spoiler and Duplicator, have often been used to capture syntactic properties of formal logical languages. For instance, the widely used Ehrenfeucht-Fraïssé (EF) game captures the syntactic measure of quantifier rank of first-order formulas. For every such game, there is an associated natural decision problem: "given an instance of the game, does Spoiler win the game on that instance?" For EF games, this problem was shown to be PSPACE-comp…

CCRS: A Zero-Shot LLM-as-a-Judge Framework for Comprehensive RAG Evaluation
Aashiq Muhamed
https://arxiv.org/abs/2506.20128 https://a…

CCRS: A Zero-Shot LLM-as-a-Judge Framework for Comprehensive RAG Evaluation
RAG systems enhance LLMs by incorporating external knowledge, which is crucial for domains that demand factual accuracy and up-to-date information. However, evaluating the multifaceted quality of RAG outputs, spanning aspects such as contextual coherence, query relevance, factual correctness, and informational completeness, poses significant challenges. Existing evaluation methods often rely on simple lexical overlap metrics, which are inadequate for capturing these nuances, or involve complex …

WebGuard :Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT
Ye Tian, Zhang Yumin, Yifan Jia, Jianguo Sun, Yanbin Wang
https://arxiv.org/abs/2506.19356

WebGuard++:Interpretable Malicious URL Detection via Bidirectional Fusion of HTML Subgraphs and Multi-Scale Convolutional BERT
URL+HTML feature fusion shows promise for robust malicious URL detection, since attacker artifacts persist in DOM structures. However, prior work suffers from four critical shortcomings: (1) incomplete URL modeling, failing to jointly capture lexical patterns and semantic context; (2) HTML graph sparsity, where threat-indicative nodes (e.g., obfuscated scripts) are isolated amid benign content, causing signal dilution during graph aggregation; (3) unidirectional analysis, ignoring URL-HTML feat…

"In Ohio, oil and gas industry is steering new carbon capture bill"
#US #USA #America #FossilFuels

In Ohio, oil and gas industry is steering new carbon capture bill
Newly obtained public records show that the industry has worked hard to ensure the legislation prioritizes their interests over climate change concerns.

IMALL with a Mixed-State Modality: A Logical Approach to Quantum Computation
Kinnari Dave, Alejandro D\'iaz-Caro, Vladimir Zamdzhiev
https://arxiv.org/abs/2506.09545

IMALL with a Mixed-State Modality: A Logical Approach to Quantum Computation
We introduce a proof language for Intuitionistic Multiplicative Additive Linear Logic (IMALL), extended with a modality B to capture mixed-state quantum computation. The language supports algebraic constructs such as linear combinations, and embeds pure quantum computations within a mixed-state framework via B, interpreted categorically as a functor from a category of Hilbert Spaces to a category of finite-dimensional C*-algebras. Measurement arises as a definable term, not as a constant, and t…

MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers
Jushaan Singh Kalra, Xinran Zhao, To Eun Kim, Fengyu Cai, Fernando Diaz, Tongshuang Wu
https://arxiv.org/abs/2506.15862

MoR: Better Handling Diverse Queries with a Mixture of Sparse, Dense, and Human Retrievers
Retrieval-augmented Generation (RAG) is powerful, but its effectiveness hinges on which retrievers we use and how. Different retrievers offer distinct, often complementary signals: BM25 captures lexical matches; dense retrievers, semantic similarity. Yet in practice, we typically fix a single retriever based on heuristics, which fails to generalize across diverse information needs. Can we dynamically select and integrate multiple retrievers for each individual query, without the need for manual…

The Flaws of Others: An LLM-driven Framework for Scientific Knowledge Production
Juan B. Guti\'errez
https://arxiv.org/abs/2507.06565 https://

The Flaws of Others: An LLM-driven Framework for Scientific Knowledge Production
Large-language models turn writing into a live exchange between humans and software. We capture this new medium with a discursive-network model that treats people and LLMs as equal nodes and tracks how their statements circulate. Broadening the focus from isolated hallucinations, we define invalidation (any factual, logical, or structural breach) and show it follows four hazards: drift from truth, self-repair, fresh fabrication, and external detection. A general mathematical model of discursive…