Tootfinder

DualResearch: Entropy-Gated Dual-Graph Retrieval for Answer Reconstruction
Jinxin Shi, Zongsheng Cao, Runmin Ma, Yusong Hu, Jie Zhou, Xin Li, Lei Bai, Liang He, Bo Zhang
https://arxiv.org/abs/2510.08959

DualResearch: Entropy-Gated Dual-Graph Retrieval for Answer Reconstruction
The deep-research framework orchestrates external tools to perform complex, multi-step scientific reasoning that exceeds the native limits of a single large language model. However, it still suffers from context pollution, weak evidentiary support, and brittle execution paths. To address these issues, we propose DualResearch, a retrieval and fusion framework that matches the epistemic structure of tool-intensive reasoning by jointly modeling two complementary graphs: a breadth semantic graph th…

Verifying Chain-of-Thought Reasoning via Its Computational Graph
Zheng Zhao, Yeskendir Koishekenov, Xianjun Yang, Naila Murray, Nicola Cancedda
https://arxiv.org/abs/2510.09312 …

Verifying Chain-of-Thought Reasoning via Its Computational Graph
Current Chain-of-Thought (CoT) verification methods predict reasoning correctness based on outputs (black-box) or activations (gray-box), but offer limited insight into why a computation fails. We introduce a white-box method: Circuit-based Reasoning Verification (CRV). We hypothesize that attribution graphs of correct CoT steps, viewed as execution traces of the model's latent reasoning circuits, possess distinct structural fingerprints from those of incorrect steps. By training a classifier o…

Deterministic Legal Retrieval: An Action API for Querying the SAT-Graph RAG
Hudson de Martim
https://arxiv.org/abs/2510.06002 https://arxiv.org/pdf/2510.06…

Deterministic Legal Retrieval: An Action API for Querying the SAT-Graph RAG
The Structure-Aware Temporal Graph RAG (SAT-Graph RAG) addresses core limitations of standard Retrieval-Augmented Generation in the legal domain by providing a verifiable knowledge graph that models hierarchical structure, temporal evolution, and causal events of legal norms. However, a critical gap remains: how to reliably query this structured knowledge without sacrificing its deterministic properties. This paper introduces the SAT-Graph API, a formal query execution layer centered on canonic…

SAGE: Scene Graph-Aware Guidance and Execution for Long-Horizon Manipulation Tasks
Jialiang Li, Wenzheng Wu, Gaojing Zhang, Yifan Han, Wenzhao Lian
https://arxiv.org/abs/2509.21928

SAGE: Scene Graph-Aware Guidance and Execution for Long-Horizon Manipulation Tasks
Successfully solving long-horizon manipulation tasks remains a fundamental challenge. These tasks involve extended action sequences and complex object interactions, presenting a critical gap between high-level symbolic planning and low-level continuous control. To bridge this gap, two essential capabilities are required: robust long-horizon task planning and effective goal-conditioned manipulation. Existing task planning methods, including traditional and LLM-based approaches, often exhibit lim…

Automated Repeatable Adversary Threat Emulation with Effects Language (EL)
Suresh K. Damodaran, Paul D. Rowe
https://arxiv.org/abs/2510.06420 https://arxiv…

Automated Repeatable Adversary Threat Emulation with Effects Language (EL)
The emulation of multi-step attacks attributed to advanced persistent threats is valuable for training defenders and evaluating defense tools. In this paper, we discuss the numerous challenges and desired attributes associated with such automation. Additionally, we introduce the use of Effects Language (EL), a visual programming language with graph-based operational semantics, as a solution to address many of these challenges and requirements. We formally define the execution semantics of EL, a…

Compiling by Proving: Language-Agnostic Automatic Optimization from Formal Semantics
Jianhong Zhao, Everett Hildenbrandt, Juan Conejero, Yongwang Zhao
https://arxiv.org/abs/2509.21793

Compiling by Proving: Language-Agnostic Automatic Optimization from Formal Semantics
Verification proofs encode complete program behavior, yet we discard them after checking correctness. We present compiling by proving, a paradigm that transforms these proofs into optimized execution rules. By constructing All-Path Reachability Proofs through symbolic execution and compiling their graph structure, we consolidate many semantic rewrites into single rules while preserving correctness by construction. We implement this as a language-agnostic extension to the K framework. Evaluation…

Less is More: Faster Maximum Clique Search by Work-Avoidance
Hans Vandierendonck
https://arxiv.org/abs/2509.22245 https://arxiv.org/pdf/2509.22245

Less is More: Faster Maximum Clique Search by Work-Avoidance
The maximum clique (MC) problem is a challenging graph mining problem which, due to its NP-hard nature, can take a substantial amount of execution time. The MC problem is dominated by set intersection operations similar to Maximal Clique Enumeration, however it differs in requiring to find only a clique of maximum size. As such, key to the problem is to demonstrate efficiently that a particular part of the search space does not contain a maximum clique, allowing to skip over major parts of the …

Compose by Focus: Scene Graph-based Atomic Skills
Han Qi, Changhe Chen, Heng Yang
https://arxiv.org/abs/2509.16053 https://arxiv.org/pdf/2509.16053

Compose by Focus: Scene Graph-based Atomic Skills
A key requirement for generalist robots is compositional generalization - the ability to combine atomic skills to solve complex, long-horizon tasks. While prior work has primarily focused on synthesizing a planner that sequences pre-learned skills, robust execution of the individual skills themselves remains challenging, as visuomotor policies often fail under distribution shifts induced by scene composition. To address this, we introduce a scene graph-based representation that focuses on task-…