/sovereign/chronological270/FREEZE/frontdoor_legacy_v1/frontdoor_legacy_v1.html-526- KingKong, Hannibal, Nova, and Kronos read your flows: leaks, risk, opportunity,
/sovereign/chronological270/FREEZE/frontdoor_legacy_v1/frontdoor_legacy_v1.html-566- From side hustles to full companies, build your Business Box, plug in your avatar,
/sovereign/chronological270/FREEZE/frontdoor_legacy_v1/frontdoor_legacy_v1.html-615- Post your workflows as videos, diagrams, blogs, or code. Others scroll, learn, and build on top.
/sovereign/chronological270/FREEZE/frontdoor_legacy_v1/frontdoor_legacy_v1.html-630- Co-op Build Rooms
/sovereign/chronological270/FREEZE/frontdoor_legacy_v1/frontdoor_legacy_v1.html-631- Build flows together: mapping, scripting, designing, and testing with the grid as a live lab.
/sovereign/chronological270/FREEZE/frontdoor_legacy_v1/frontdoor_legacy_v1.html-656- Design personas (Tenant, Owner, Investor, Creator, Scholar, Guardian).
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/beta_smartwater/MODULES_LAND_DOOR/general_letters/NOVA.md-3-**Scope:** Land Door (Foundation) module build
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/tenant-portal/index.html-104- Council stamp: 2026-01-15T19:32:21Z · build 1768505541 · general King Solomon · role Wisdom, Governance, Judgment
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/tenant-portal/index.html-130- | Primary workflow | Intake → Verify → Route → Resolve → Witness |
| Escalation | If risk/late/blocked: notify Owner Lens + GCC |
| Artifacts | Every action produces a trail: notes, docs, signals |
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/tenant-portal/index.html-158- Design principle: “clear story + witnessable outcomes”.
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/system-status-backups/index.html-104- Council stamp: 2026-01-15T19:32:21Z · build 1768505541 · general Qin Shi Huang · role Unification, Standardization
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/system-status-backups/index.html-130- | Primary workflow | Intake → Verify → Route → Resolve → Witness |
| Escalation | If risk/late/blocked: notify Owner Lens + GCC |
| Artifacts | Every action produces a trail: notes, docs, signals |
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/system-status-backups/index.html-158- Design principle: “clear story + witnessable outcomes”.
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/index.html-1-
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/index.html-71-Build 1768505541 · 2026-01-15T19:32:21Z · rotation_offset 2
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/index.html-79-07. Grand Command HQ (GCC) — steward MALCOLM
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/index.html-101-29. Security & Access Control — steward MACHIAVELLI
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/pros-advisors-vendors/index.html-5-
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/pros-advisors-vendors/index.html-7-
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/pros-advisors-vendors/index.html-8-
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/pros-advisors-vendors/index.html-82- Chronos wisdom · Reena care · Solomon stewardship · Musa-grade wealth discipline
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/pros-advisors-vendors/index.html-104- Council stamp: 2026-01-15T19:32:21Z · build 1768505541 · general Nova · role Execution
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/pros-advisors-vendors/index.html-130- | Primary workflow | Intake → Verify → Route → Resolve → Witness |
| Escalation | If risk/late/blocked: notify Owner Lens + GCC |
| Artifacts | Every action produces a trail: notes, docs, signals |
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/pros-advisors-vendors/index.html-158- Design principle: “clear story + witnessable outcomes”.
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T030938Z/alpha_aipm/services/pros-advisors-vendors/index.html-168-
/sovereign/chronological270/FREEZE/frontdoor_v1/frontdoor_leo_pyth_v1.html-526- KingKong, Hannibal, Nova, and Kronos read your flows: leaks, risk, opportunity,
/sovereign/chronological270/FREEZE/frontdoor_v1/frontdoor_leo_pyth_v1.html-566- From side hustles to full companies, build your Business Box, plug in your avatar,
/sovereign/chronological270/FREEZE/frontdoor_v1/frontdoor_leo_pyth_v1.html-615- Post your workflows as videos, diagrams, blogs, or code. Others scroll, learn, and build on top.
/sovereign/chronological270/FREEZE/frontdoor_v1/frontdoor_leo_pyth_v1.html-630- Co-op Build Rooms
/sovereign/chronological270/FREEZE/frontdoor_v1/frontdoor_leo_pyth_v1.html-631- Build flows together: mapping, scripting, designing, and testing with the grid as a live lab.
/sovereign/chronological270/FREEZE/frontdoor_v1/frontdoor_leo_pyth_v1.html-656- Design personas (Tenant, Owner, Investor, Creator, Scholar, Guardian).
/sovereign/chronological270/FREEZE/BUTTON_ZERO_20251231T183000Z/ui/chronological_core/gpt_loader.html.BAK_STREAM_20251228T040258Z-5-
/sovereign/chronological270/FREEZE/BUTTON_ZERO_20251231T183000Z/ui/chronological_core/gpt_loader.html.BAK_STREAM_20251228T040258Z-96- Chat / command
/sovereign/chronological270/FREEZE/BUTTON_ZERO_20251231T183000Z/ui/chronological_core/gpt_loader.html-5-
/sovereign/chronological270/FREEZE/BUTTON_ZERO_20251231T183000Z/ui/chronological_core/gpt_loader.html-96- Chat / command
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-127-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Bridging the Trust Gap: Clinician-Validated Hybrid Explainable AI for Maternal Health Risk Assessment in Bangladesh", "link": "https://arxiv.org/abs/2601.07866", "summary": "arXiv:2601.07866v1 Announce Type: new Abstract: While machine learning shows promise for maternal health risk prediction, clinical adoption in resource-constrained settings faces a critical barrier: lack of explainability and trust. This study presents a hybrid explainable AI (XAI) framework combining ante-hoc fuzzy logic with post-hoc SHAP explanations, validated through systematic clinician feedback. We developed a fuzzy-XGBoost model on 1,014 maternal health records, achieving 88.67% accuracy (ROC-AUC: 0.9703). A validation study with 14 healthcare professionals in Bangladesh revealed stron", "tags": [], "hash": "71203daca38c9c35"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-128-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Executable Ontologies in Game Development: From Algorithmic Control to Semantic World Modeling", "link": "https://arxiv.org/abs/2601.07964", "summary": "arXiv:2601.07964v1 Announce Type: new Abstract: This paper examines the application of Executable Ontologies (EO), implemented through the boldsea framework, to game development. We argue that EO represents a paradigm shift: a transition from algorithmic behavior programming to semantic world modeling, where agent behavior emerges naturally from declarative domain rules rather than being explicitly coded. Using a survival game scenario (Winter Feast), we demonstrate how EO achieves prioritybased task interruption through dataflow conditions rather than explicit preemption logic. Comparison wit", "tags": [], "hash": "430a03c2826634e7"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-129-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning", "link": "https://arxiv.org/abs/2601.07965", "summary": "arXiv:2601.07965v1 Announce Type: new Abstract: When a model knows when it does not know, many possibilities emerge. The first question is how to enable a model to recognize that it does not know. A promising approach is to use confidence, computed from the model's internal signals, to reflect its ignorance. Prior work in specific domains has shown that calibration can provide reliable confidence estimates. In this work, we propose a simple, effective, and universal training-free method that applies to both vision and language models, performing model calibration, cascading, and data cleaning ", "tags": [], "hash": "52879f7578bd8d96"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-138-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "How vehicles change lanes after encountering crashes: Empirical analysis and modeling", "link": "https://arxiv.org/abs/2601.08125", "summary": "arXiv:2601.08125v1 Announce Type: new Abstract: When a traffic crash occurs, following vehicles need to change lanes to bypass the obstruction. We define these maneuvers as post crash lane changes. In such scenarios, vehicles in the target lane may refuse to yield even after the lane change has already begun, increasing the complexity and crash risk of post crash LCs. However, the behavioral characteristics and motion patterns of post crash LCs remain unknown. To address this gap, we construct a post crash LC dataset by extracting vehicle trajectories from drone videos captured after crashes. ", "tags": [], "hash": "d70b703a78c310ae"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-151-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Sparsity Is Necessary: Polynomial-Time Stability for Agentic LLMs in Large Action Spaces", "link": "https://arxiv.org/abs/2601.08271", "summary": "arXiv:2601.08271v1 Announce Type: new Abstract: Tool-augmented LLM systems expose a control regime that learning theory has largely ignored: sequential decision-making with a massive discrete action universe (tools, APIs, documents) in which only a small, unknown subset is relevant for any fixed task distribution. We formalize this setting as Sparse Agentic Control (SAC), where policies admit block-sparse representations over M >> 1 actions and rewards depend on sparse main effects and (optionally) sparse synergies. We study ell_{1,2}-regularized policy learning through a convex surrogate and ", "tags": [], "hash": "31d0aab5db928dac"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-152-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "ToolACE-MCP: Generalizing History-Aware Routing from MCP Tools to the Agent Web", "link": "https://arxiv.org/abs/2601.08276", "summary": "arXiv:2601.08276v1 Announce Type: new Abstract: With the rise of the Agent Web and Model Context Protocol (MCP), the agent ecosystem is evolving into an open collaborative network, exponentially increasing accessible tools. However, current architectures face severe scalability and generality bottlenecks. To address this, we propose ToolACE-MCP, a pipeline for training history-aware routers to empower precise navigation in large-scale ecosystems. By leveraging a dependency-rich candidate Graph to synthesize multi-turn trajectories, we effectively train routers with dynamic context understandin", "tags": [], "hash": "9df8afb5261a4ba6"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-153-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Greedy Is Enough: Sparse Action Discovery in Agentic LLMs", "link": "https://arxiv.org/abs/2601.08280", "summary": "arXiv:2601.08280v1 Announce Type: new Abstract: Modern agentic systems operate in environments with extremely large action spaces, such as tool-augmented language models with thousands of available APIs or retrieval operations. Despite this scale, empirical evidence suggests that only a small subset of actions meaningfully influences performance in a given deployment. Motivated by this observation, we study a contextual linear reward model in which action relevance is governed by a structured sparsity assumption: only a small number of actions have nonzero effects across latent states. We form", "tags": [], "hash": "acfb2620ae540d97"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-157-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Thematic Working Group 5 -- Artificial Intelligence (AI) literacy for teaching and learning: design and implementation", "link": "https://arxiv.org/abs/2601.08380", "summary": "arXiv:2601.08380v1 Announce Type: new Abstract: TWG 5 focused on developing and implementing effective strategies for enhancing AI literacy and agency of teachers, equipping them with the knowledge and skills necessary to integrate AI into their teaching practices. Explorations covered curriculum design, professional development programs, practical classroom applications, and policy guidelines aiming to empower educators to confidently utilize AI tools and foster a deeper understanding of AI concepts among students.", "tags": [], "hash": "07f9e04c1aa5700e"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-160-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Creativity in AI as Emergence from Domain-Limited Generative Models", "link": "https://arxiv.org/abs/2601.08388", "summary": "arXiv:2601.08388v1 Announce Type: new Abstract: Creativity in artificial intelligence is most often addressed through evaluative frameworks that aim to measure novelty, diversity, or usefulness in generated outputs. While such approaches have provided valuable insights into the behavior of modern generative models, they largely treat creativity as a property to be assessed rather than as a phenomenon to be explicitly modeled. In parallel, recent advances in large-scale generative systems, particularly multimodal architectures, have demonstrated increasingly sophisticated forms of pattern recom", "tags": [], "hash": "ed5c198eeaaa4465"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-161-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Owen-Shapley Policy Optimization (OSPO): A Principled RL Algorithm for Generative Search LLMs", "link": "https://arxiv.org/abs/2601.08403", "summary": "arXiv:2601.08403v1 Announce Type: new Abstract: Large language models are increasingly trained via reinforcement learning for personalized recommendation tasks, but standard methods like GRPO rely on sparse, sequence-level rewards that create a credit assignment gap, obscuring which tokens drive success. This gap is especially problematic when models must infer latent user intent from under-specified language without ground truth labels, a reasoning pattern rarely seen during pretraining. We introduce Owen-Shapley Policy Optimization (OSPO), a framework that redistributes sequence-level advant", "tags": [], "hash": "2f9b203ca7ebb88b"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-162-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "WebTrap Park: An Automated Platform for Systematic Security Evaluation of Web Agents", "link": "https://arxiv.org/abs/2601.08406", "summary": "arXiv:2601.08406v1 Announce Type: new Abstract: Web Agents are increasingly deployed to perform complex tasks in real web environments, yet their security evaluation remains fragmented and difficult to standardize. We present WebTrap Park, an automated platform for systematic security evaluation of Web Agents through direct observation of their concrete interactions with live web pages. WebTrap Park instantiates three major sources of security risk into 1,226 executable evaluation tasks and enables action based assessment without requiring agent modification. Our results reveal clear security ", "tags": [], "hash": "6c4e40887912f672"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-163-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Hybrid Distillation with CoT Guidance for Edge-Drone Control Code Generation", "link": "https://arxiv.org/abs/2601.08412", "summary": "arXiv:2601.08412v1 Announce Type: new Abstract: With large language models demonstrating significant potential in code generation tasks, their application to onboard control of resource-constrained Unmanned Aerial Vehicles has emerged as an important research direction. However, a notable contradiction exists between the high resource consumption of large models and the real-time, lightweight requirements of UAV platforms. This paper proposes an integrated approach that combines knowledge distillation, chain-of-thought guidance, and supervised fine-tuning for UAV multi-SDK control tasks, aimin", "tags": [], "hash": "9d6ae3c07b36167d"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-164-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "RubricHub: A Comprehensive and Highly Discriminative Rubric Dataset via Automated Coarse-to-Fine Generation", "link": "https://arxiv.org/abs/2601.08430", "summary": "arXiv:2601.08430v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has driven substantial progress in reasoning-intensive domains like mathematics. However, optimizing open-ended generation remains challenging due to the lack of ground truth. While rubric-based evaluation offers a structured proxy for verification, existing methods suffer from scalability bottlenecks and coarse criteria, resulting in a supervision ceiling effect. To address this, we propose an automated Coarse-to-Fine Rubric Generation framework. By synergizing principle-guided synthesis, mul", "tags": [], "hash": "f361be530d9b714d"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-165-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "YaPO: Learnable Sparse Activation Steering Vectors for Domain Adaptation", "link": "https://arxiv.org/abs/2601.08441", "summary": "arXiv:2601.08441v1 Announce Type: new Abstract: Steering Large Language Models (LLMs) through activation interventions has emerged as a lightweight alternative to fine-tuning for alignment and personalization. Recent work on Bi-directional Preference Optimization (BiPO) shows that dense steering vectors can be learned directly from preference data in a Direct Preference Optimization (DPO) fashion, enabling control over truthfulness, hallucinations, and safety behaviors. However, dense steering vectors often entangle multiple latent factors due to neuron multi-semanticity, limiting their effect", "tags": [], "hash": "79cb3f6c19a631eb"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-171-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Sketch-Based Facade Renovation With Generative AI: A Streamlined Framework for Bypassing As-Built Modelling in Industrial Adaptive Reuse", "link": "https://arxiv.org/abs/2601.08531", "summary": "arXiv:2601.08531v1 Announce Type: new Abstract: Facade renovation offers a more sustainable alternative to full demolition, yet producing design proposals that preserve existing structures while expressing new intent remains challenging. Current workflows typically require detailed as-built modelling before design, which is time-consuming, labour-intensive, and often involves repeated revisions. To solve this issue, we propose a three-stage framework combining generative artificial intelligence (AI) and vision-language models (VLM) that directly processes rough structural sketch and textual de", "tags": [], "hash": "c4d0c7a4c44d3008"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-177-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "From Classical to Quantum Reinforcement Learning and Its Applications in Quantum Control: A Beginner's Tutorial", "link": "https://arxiv.org/abs/2601.08662", "summary": "arXiv:2601.08662v1 Announce Type: new Abstract: This tutorial is designed to make reinforcement learning (RL) more accessible to undergraduate students by offering clear, example-driven explanations. It focuses on bridging the gap between RL theory and practical coding applications, addressing common challenges that students face when transitioning from conceptual understanding to implementation. Through hands-on examples and approachable explanations, the tutorial aims to equip students with the foundational skills needed to confidently apply RL techniques in real-world scenarios.", "tags": [], "hash": "ea27215fa988e801"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-183-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "All Required, In Order: Phase-Level Evaluation for AI-Human Dialogue in Healthcare and Beyond", "link": "https://arxiv.org/abs/2601.08690", "summary": "arXiv:2601.08690v1 Announce Type: new Abstract: Conversational AI is starting to support real clinical work, but most evaluation methods miss how compliance depends on the full course of a conversation. We introduce Obligatory-Information Phase Structured Compliance Evaluation (OIP-SCE), an evaluation method that checks whether every required clinical obligation is met, in the right order, with clear evidence for clinicians to review. This makes complex rules practical and auditable, helping close the gap between technical progress and what healthcare actually needs. We demonstrate the method ", "tags": [], "hash": "9ae1589293e9af89"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-184-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Evaluating the Ability of Explanations to Disambiguate Models in a Rashomon Set", "link": "https://arxiv.org/abs/2601.08703", "summary": "arXiv:2601.08703v1 Announce Type: new Abstract: Explainable artificial intelligence (XAI) is concerned with producing explanations indicating the inner workings of models. For a Rashomon set of similarly performing models, explanations provide a way of disambiguating the behavior of individual models, helping select models for deployment. However explanations themselves can vary depending on the explainer used, and need to be evaluated. In the paper \"Evaluating Model Explanations without Ground Truth\", we proposed three principles of explanation evaluation and a new method \"AXE\" to evaluate th", "tags": [], "hash": "f369731e32f91630"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-192-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "FinVault: Benchmarking Financial Agent Safety in Execution-Grounded Environments", "link": "https://arxiv.org/abs/2601.07853", "summary": "arXiv:2601.07853v1 Announce Type: cross Abstract: Financial agents powered by large language models (LLMs) are increasingly deployed for investment analysis, risk assessment, and automated decision-making, where their abilities to plan, invoke tools, and manipulate mutable state introduce new security risks in high-stakes and highly regulated financial environments. However, existing safety evaluations largely focus on language-model-level content compliance or abstract agent settings, failing to capture execution-grounded risks arising from real operational workflows and state-changing action", "tags": [], "hash": "abe67ccd43843dfc"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-194-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "An Empirical Study on Knowledge Transfer under Domain and Label Shifts in 3D LiDAR Point Clouds", "link": "https://arxiv.org/abs/2601.07855", "summary": "arXiv:2601.07855v1 Announce Type: cross Abstract: For 3D perception systems to be practical in real-world applications -- from autonomous driving to embodied AI -- models must adapt to continuously evolving object definitions and sensor domains. Yet, research on continual and transfer learning in 3D point cloud perception remains underexplored compared to 2D vision -- particularly under simultaneous domain and label shifts. To address this gap, we propose the RObust Autonomous driving under Dataset shifts (ROAD) benchmark, a comprehensive evaluation suite for LiDAR-based object classification ", "tags": [], "hash": "e3b015f201d654c5"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-198-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Imaging-anchored Multiomics in Cardiovascular Disease: Integrating Cardiac Imaging, Bulk, Single-cell, and Spatial Transcriptomics", "link": "https://arxiv.org/abs/2601.07871", "summary": "arXiv:2601.07871v1 Announce Type: cross Abstract: Cardiovascular disease arises from interactions between inherited risk, molecular programmes, and tissue-scale remodelling that are observed clinically through imaging. Health systems now routinely generate large volumes of cardiac MRI, CT and echocardiography together with bulk, single-cell and spatial transcriptomics, yet these data are still analysed in separate pipelines. This review examines joint representations that link cardiac imaging phenotypes to transcriptomic and spatially resolved molecular states. An imaging-anchored perspective ", "tags": [], "hash": "eb816f63765ba2fc"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-205-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Ideological Isolation in Online Social Networks: A Survey of Computational Definitions, Metrics, and Mitigation Strategies", "link": "https://arxiv.org/abs/2601.07884", "summary": "arXiv:2601.07884v1 Announce Type: cross Abstract: The proliferation of online social networks has significantly reshaped the way individuals access and engage with information. While these platforms offer unprecedented connectivity, they may foster environments where users are increasingly exposed to homogeneous content and like-minded interactions. Such dynamics are associated with selective exposure and the emergence of filter bubbles, echo chambers, tunnel vision, and polarization, which together can contribute to ideological isolation and raise concerns about information diversity and publ", "tags": [], "hash": "32c146833979e659"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-218-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:27Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "Explainable Autoencoder-Based Anomaly Detection in IEC 61850 GOOSE Networks", "link": "https://arxiv.org/abs/2601.09287", "summary": "arXiv:2601.09287v1 Announce Type: new Abstract: The IEC 61850 Generic Object-Oriented Substation Event (GOOSE) protocol plays a critical role in real-time protection and automation of digital substations, yet its lack of native security mechanisms can expose power systems to sophisticated cyberattacks. Traditional rule-based and supervised intrusion detection techniques struggle to detect protocol-compliant and zero-day attacks under significant class imbalance and limited availability of labeled data. This paper proposes an explainable, unsupervised multi-view anomaly detection framework for ", "tags": [], "hash": "f8f77b4052d9037e"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-227-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:27Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "DP-FEDSOFIM: Differentially Private Federated Stochastic Optimization using Regularized Fisher Information Matrix", "link": "https://arxiv.org/abs/2601.09166", "summary": "arXiv:2601.09166v1 Announce Type: cross Abstract: Differentially private federated learning (DP-FL) suffers from slow convergence under tight privacy budgets due to the overwhelming noise introduced to preserve privacy. While adaptive optimizers can accelerate convergence, existing second-order methods such as DP-FedNew require O(d^2) memory at each client to maintain local feature covariance matrices, making them impractical for high-dimensional models. We propose DP-FedSOFIM, a server-side second-order optimization framework that leverages the Fisher Information Matrix (FIM) as a natural gra", "tags": [], "hash": "2e7a72538d3e3f16"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-230-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:27Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "Identifying Models Behind Text-to-Image Leaderboards", "link": "https://arxiv.org/abs/2601.09647", "summary": "arXiv:2601.09647v1 Announce Type: cross Abstract: Text-to-image (T2I) models are increasingly popular, producing a large share of AI-generated images online. To compare model quality, voting-based leaderboards have become the standard, relying on anonymized model outputs for fairness. In this work, we show that such anonymity can be easily broken. We find that generations from each T2I model form distinctive clusters in the image embedding space, enabling accurate deanonymization without prompt control or training data. Using 22 models and 280 prompts (150K images), our centroid-based method a", "tags": [], "hash": "c3b990ba1a021a1b"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-235-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:27Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "Secure and Efficient Access Control for Computer-Use Agents via Context Space", "link": "https://arxiv.org/abs/2509.22256", "summary": "arXiv:2509.22256v4 Announce Type: replace Abstract: Large language model (LLM)-based computer-use agents represent a convergence of AI and OS capabilities, enabling natural language to control system- and application-level functions. However, due to LLMs' inherent uncertainty issues, granting agents control over computers poses significant security risks. When agent actions deviate from user intentions, they can cause irreversible consequences. Existing mitigation approaches, such as user confirmation and LLM-based dynamic action validation, still suffer from limitations in usability, security", "tags": [], "hash": "ccacb4e68a0466a3"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-236-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:27Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "zkSTAR: A zero knowledge system for time series attack detection enforcing regulatory compliance in critical infrastructure networks", "link": "https://arxiv.org/abs/2510.23060", "summary": "arXiv:2510.23060v3 Announce Type: replace Abstract: Industrial control systems (ICS) form the operational backbone of critical infrastructure networks (CIN) such as power grids, water supply systems, and gas pipelines. As cyber threats to these systems escalate, regulatory agencies are imposing stricter compliance requirements to ensure system-wide security and reliability. A central challenge, however, is enabling regulators to verify the effectiveness of detection mechanisms without requiring utilities to disclose sensitive operational data. In this paper, we introduce zkSTAR, a cyberattack ", "tags": [], "hash": "f77373efe188b69c"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-243-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:27Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "Forward Symbolic Execution for Trustworthy Automation of Binary Code Verification", "link": "https://arxiv.org/abs/2304.08848", "summary": "arXiv:2304.08848v2 Announce Type: replace-cross Abstract: Control flow in unstructured programs can be complex and dynamic, which makes static analysis difficult. Yet, automated reasoning about unstructured control flow is important when certifying properties of binary (machine) code in trustworthy systems, e.g., cryptographic routines. We present a theory of forward symbolic execution for unstructured programs suitable for use in theorem provers that enables automated verification of both functional and non-functional program properties. The theory's foundation is a set of inference rules whe", "tags": [], "hash": "f6acaf0fbc403553"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-244-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:27Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "Differentially Private Bilevel Optimization", "link": "https://arxiv.org/abs/2409.19800", "summary": "arXiv:2409.19800v3 Announce Type: replace-cross Abstract: We present differentially private (DP) algorithms for bilevel optimization, a problem class that received significant attention lately in various machine learning applications. These are the first algorithms for such problems under standard DP constraints, and are also the first to avoid Hessian computations which are prohibitive in large-scale settings. Under the well-studied setting in which the upper-level is not necessarily convex and the lower-level problem is strongly-convex, our proposed gradient-based $(\\epsilon,\\delta)$-DP algo", "tags": [], "hash": "4ae03827c6b36c17"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-249-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:27Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "LAUDE: LLM-Assisted Unit Test Generation and Debugging of Hardware DEsigns", "link": "https://arxiv.org/abs/2601.08856", "summary": "arXiv:2601.08856v1 Announce Type: new Abstract: Unit tests are critical in the hardware design lifecycle to ensure that component design modules are functionally correct and conform to the specification before they are integrated at the system level. Thus developing unit tests targeting various design features requires deep understanding of the design functionality and creativity. When one or more unit tests expose a design failure, the debugging engineer needs to diagnose, localize, and debug the failure to ensure design correctness, which is often a painstaking and intense process. In this w", "tags": [], "hash": "d3ad2aaf3518dd1f"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-254-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:27Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "Build Code is Still Code: Finding the Antidote for Pipeline Poisoning", "link": "https://arxiv.org/abs/2601.08995", "summary": "arXiv:2601.08995v1 Announce Type: new Abstract: Open source C code underpins society's computing infrastructure. Decades of work has helped harden C code against attackers, but C projects do not consist of only C code. C projects also contain build system code for automating development tasks like compilation, testing, and packaging. These build systems are critcal to software supply chain security and vulnerable to being poisoned, with the XZ Utils and SolarWinds attacks being recent examples. Existing techniques try to harden software supply chains by verifying software dependencies, but suc", "tags": ["sbom", "security"], "hash": "498f2b9a52acdbff"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-256-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:27Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "SafePlanner: Testing Safety of the Automated Driving System Plan Model", "link": "https://arxiv.org/abs/2601.09171", "summary": "arXiv:2601.09171v1 Announce Type: new Abstract: In this work, we present SafePlanner, a systematic testing framework for identifying safety-critical flaws in the Plan model of Automated Driving Systems (ADS). SafePlanner targets two core challenges: generating structurally meaningful test scenarios and detecting hazardous planning behaviors. To maximize coverage, SafePlanner performs a structural analysis of the Plan model implementation - specifically, its scene-transition logic and hierarchical control flow - and uses this insight to extract feasible scene transitions from code. It then comp", "tags": [], "hash": "243da1d1b8ab3da0"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-261-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:27Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "SysPro: Reproducing System-level Concurrency Bugs from Bug Reports", "link": "https://arxiv.org/abs/2601.09616", "summary": "arXiv:2601.09616v1 Announce Type: new Abstract: Reproducing system-level concurrency bugs requires both input data and the precise interleaving order of system calls. This process is challenging because such bugs are non-deterministic, and bug reports often lack the detailed information needed. Additionally, the unstructured nature of reports written in natural language makes it difficult to extract necessary details. Existing tools are inadequate to reproduce these bugs due to their inability to manage the specific interleaving at the system call level. To address these challenges, we propose", "tags": [], "hash": "220fc49a90d4995a"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-272-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:27Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "CodeWiki: Evaluating AI's Ability to Generate Holistic Documentation for Large-Scale Codebases", "link": "https://arxiv.org/abs/2510.24428", "summary": "arXiv:2510.24428v5 Announce Type: replace Abstract: Given a large and evolving codebase, the ability to automatically generate holistic, architecture-aware documentation that captures not only individual functions but also cross-file, cross-module, and system-level interactions remains an open challenge. Comprehensive documentation is essential for long-term software maintenance and collaboration, yet current automated approaches still fail to model the rich semantic dependencies and architectural structures that define real-world software systems. We present \\textbf{CodeWiki}, a unified frame", "tags": [], "hash": "d2e93df511e0a412"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-276-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:28Z", "source": "github_release", "repo": "nginx/nginx", "title": "release-1.29.4", "link": "https://github.com/nginx/nginx/releases/tag/release-1.29.4", "summary": "[nginx-1.29.4](https://nginx.org/en/download.html) mainline version has been released, featuring **HTTP/2 to backend** and **Encrypted Client Hello (ECH)**. For more details, please refer to the [blog post](https://blog.nginx.org/blog/nginx-open-source-1-29-3-and-1-29-4). See official [CHANGES](https://nginx.org/en/CHANGES) on nginx.org. Below is a release summary generated by GitHub. ## What's Changed * Configure: ensure we get the \"built by ...\" line in nginx -V. by @ac000 in https://github.com/nginx/nginx/pull/905 * Adding support for pcre 10.47 by @thierryba in https://github.com/nginx/nginx/pull/963 * SSL: changed interface of ngx_ssl_set_client_hello_callback(). by @pluknet in https://github.com/nginx/nginx/pull/968 * SSL: fixed build with BoringSSL, broken by 38a701d88. by @pluknet ", "tags": ["github", "release"], "hash": "3c723de756a7d421"}
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/INTEL_out/signals.jsonl-279-{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:28Z", "source": "github_release", "repo": "nginx/nginx", "title": "release-1.29.1", "link": "https://github.com/nginx/nginx/releases/tag/release-1.29.1", "summary": "[nginx-1.29.1](https://nginx.org/en/download.html) mainline version has been released. This release includes a [security fix](https://nginx.org/en/security_advisories.html) for the vulnerability in the ngx_mail_smtp_module ([CVE-2025-53859](https://www.cve.org/CVERecord?id=CVE-2025-53859)). See official [CHANGES](https://nginx.org/en/CHANGES) on nginx.org. Below is a release summary generated by GitHub. ## What's Changed * PCRE license fix for win32 zip by @pluknet in https://github.com/nginx/nginx/pull/753 * QUIC: adjusted OpenSSL 3.5 QUIC API feature test. by @pluknet in https://github.com/nginx/nginx/pull/749 * OPENSSL_VERSION_NUMBER fix for OpenSSL 3.0 by @pluknet in https://github.com/nginx/nginx/pull/775 * kqueue build fixes by @pluknet in https://github.com/nginx/nginx/pull/777 * HT", "tags": ["github", "release"], "hash": "05aa6b2fdad7a802"}
/sovereign/chronological270/FREEZE/BUTTON_ZERO_20251231T183000Z/ui/chronological_core/gpt_loader.html.BAK_20251228T000502Z-5-
/sovereign/chronological270/FREEZE/BUTTON_ZERO_20251231T183000Z/ui/chronological_core/gpt_loader.html.BAK_20251228T000502Z-96- Chat / command
/sovereign/chronological270/FREEZE/BUTTON_ZERO_20251231T183000Z/ui/chronological_core/gpt_loader.html.BAK_STREAM_20251228T033136Z-5-
/sovereign/chronological270/FREEZE/BUTTON_ZERO_20251231T183000Z/ui/chronological_core/gpt_loader.html.BAK_STREAM_20251228T033136Z-96- Chat / command
/sovereign/chronological270/FREEZE/BUTTON_ZERO_20251231T183000Z/ui/chronological_core/star_filer.html.BAK_GPT_REMOVE_20251227T211728Z-196- GPT Loader — Command Bus (READ_ONLY)
/sovereign/chronological270/FREEZE/BUTTON_ZERO_20251231T183000Z/ui/chronological_core/star_filer.html.BAK_GPT_REMOVE_20251227T211728Z-240- if(!payload.message){ out.textContent="(enter a command)"; return; }
/sovereign/chronological270/FREEZE/BUTTON_ZERO_20251231T183000Z/ui/chronological_core/gpt_loader.html.BAK_STREAM_20251228T032227Z-5-
/sovereign/chronological270/FREEZE/BUTTON_ZERO_20251231T183000Z/ui/chronological_core/gpt_loader.html.BAK_STREAM_20251228T032227Z-96- Chat / command
/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/DOCS/doc_lane_bindings.json-20528- "foundation_item": "Land / property selection criteria (risk, upside, time)",
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/sovereign/chronological270/FREEZE/VOLUME1_BASE_20260117T031212Z/DOCS/doc_lane_bindings.json-20593- "foundation_item": "Acquisition readiness + underwriting discipline",
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