Hi HN,
I\u2019m releasing minikv, a distributed key-value and object store in Rust.
What is minikv? minikv is an open-source, distributed storage engine built for learning, experimentation, and self-hosted setups. It combines a strongly-consistent key-value database (Raft), S3-compatible object storage, and basic multi-tenancy. I started minikv as a learning project about distributed systems, and it grew into something production-ready and fun to extend.
Features/highlights:
- Raft consensus with automatic failover and sharding - S3-compatible HTTP API (plus REST/gRPC APIs) - Pluggable s", "tags": [], "hash": "9aa4502e83d3f11a"} {"schema": "intel.signal.v1", "ts": "2026-01-18T10:07:11Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "
Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "bb7e01581eecbb83"} {"schema": "intel.signal.v1", "ts": "2026-01-18T10:07:11Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Data Activation Thoughts", "link": "https://galsapir.github.io/sparse-thoughts/2026/01/17/data_activation/", "summary": "
i've been working with healthcare/biobank data and keep thinking about what \"data moats\" mean now that llms can ingest anything. some a16z piece from 2019 said moats were eroding \u2014 now the question seems to be whether you can actually make your data useful to these systems, not just have it. there's some recent work (tables2traces, ehr-r1) showing you can convert structured medical data into reasoning traces that improve llm performance, but the approaches are still rough and synthetic traces don't fully hold up to scrutiny (writing this to think through it, not because i have answers)
", "tags": ["docs", "templates"], "hash": "8db2375147ab4c2a"} {"schema": "intel.signal.v1", "ts": "2026-01-18T10:37:10Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "8eb2befdfc1d421c"} {"schema": "intel.signal.v1", "ts": "2026-01-18T10:37:10Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Data Activation Thoughts", "link": "https://galsapir.github.io/sparse-thoughts/2026/01/17/data_activation/", "summary": "
i've been working with healthcare/biobank data and keep thinking about what \"data moats\" mean now that llms can ingest anything. some a16z piece from 2019 said moats were eroding \u2014 now the question seems to be whether you can actually make your data useful to these systems, not just have it. there's some recent work (tables2traces, ehr-r1) showing you can convert structured medical data into reasoning traces that improve llm performance, but the approaches are still rough and synthetic traces don't fully hold up to scrutiny (writing this to think through it, not because i have answers)
", "tags": ["docs", "templates"], "hash": "ffed22de73b5fb82"} {"schema": "intel.signal.v1", "ts": "2026-01-18T11:07:11Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "8d947b4aaa488617"} {"schema": "intel.signal.v1", "ts": "2026-01-18T11:07:11Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Data Activation Thoughts", "link": "https://galsapir.github.io/sparse-thoughts/2026/01/17/data_activation/", "summary": "
i've been working with healthcare/biobank data and keep thinking about what \"data moats\" mean now that llms can ingest anything. some a16z piece from 2019 said moats were eroding \u2014 now the question seems to be whether you can actually make your data useful to these systems, not just have it. there's some recent work (tables2traces, ehr-r1) showing you can convert structured medical data into reasoning traces that improve llm performance, but the approaches are still rough and synthetic traces don't fully hold up to scrutiny (writing this to think through it, not because i have answers)
", "tags": ["docs", "templates"], "hash": "ffed22de73b5fb82"} {"schema": "intel.signal.v1", "ts": "2026-01-18T12:22:11Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "41b8c31c9e6b4875"} {"schema": "intel.signal.v1", "ts": "2026-01-18T12:22:11Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Data Activation Thoughts", "link": "https://galsapir.github.io/sparse-thoughts/2026/01/17/data_activation/", "summary": "
i've been working with healthcare/biobank data and keep thinking about what \"data moats\" mean now that llms can ingest anything. some a16z piece from 2019 said moats were eroding \u2014 now the question seems to be whether you can actually make your data useful to these systems, not just have it. there's some recent work (tables2traces, ehr-r1) showing you can convert structured medical data into reasoning traces that improve llm performance, but the approaches are still rough and synthetic traces don't fully hold up to scrutiny (writing this to think through it, not because i have answers)
", "tags": ["docs", "templates"], "hash": "a7cb77a2ec4a0980"} {"schema": "intel.signal.v1", "ts": "2026-01-18T13:52:12Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "b6eea365a9ecb54e"} {"schema": "intel.signal.v1", "ts": "2026-01-18T13:52:12Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Data Activation Thoughts", "link": "https://galsapir.github.io/sparse-thoughts/2026/01/17/data_activation/", "summary": "
i've been working with healthcare/biobank data and keep thinking about what \"data moats\" mean now that llms can ingest anything. some a16z piece from 2019 said moats were eroding \u2014 now the question seems to be whether you can actually make your data useful to these systems, not just have it. there's some recent work (tables2traces, ehr-r1) showing you can convert structured medical data into reasoning traces that improve llm performance, but the approaches are still rough and synthetic traces don't fully hold up to scrutiny (writing this to think through it, not because i have answers)
", "tags": ["docs", "templates"], "hash": "2fc88ec0e77a908b"} {"schema": "intel.signal.v1", "ts": "2026-01-18T14:52:12Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "0929d3c5c243bc8d"} {"schema": "intel.signal.v1", "ts": "2026-01-18T15:07:12Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "
Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "21e7c7b3329757f8"} {"schema": "intel.signal.v1", "ts": "2026-01-18T15:22:12Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "
Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "eb58b99f03d6653c"} {"schema": "intel.signal.v1", "ts": "2026-01-18T15:37:13Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "
Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "28dd5656ac2216f8"} {"schema": "intel.signal.v1", "ts": "2026-01-18T15:52:12Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "
Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "419b4d41f65150bd"} {"schema": "intel.signal.v1", "ts": "2026-01-18T16:07:13Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "
Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "337262dec383370b"} {"schema": "intel.signal.v1", "ts": "2026-01-18T16:22:12Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "
Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "a0ed939958dd196f"} {"schema": "intel.signal.v1", "ts": "2026-01-18T16:37:13Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "
Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "a0ed939958dd196f"} {"schema": "intel.signal.v1", "ts": "2026-01-18T16:52:14Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "
Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "bbd8bb572c131945"} {"schema": "intel.signal.v1", "ts": "2026-01-18T17:22:13Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "
Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "4a0cbaebe7c906a9"} {"schema": "intel.signal.v1", "ts": "2026-01-18T17:37:14Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "
Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "454d5e8250208405"} {"schema": "intel.signal.v1", "ts": "2026-01-18T18:07:13Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "
Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "c51d64f5abb203d7"} {"schema": "intel.signal.v1", "ts": "2026-01-18T18:22:13Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Show HN: GibRAM an in-memory ephemeral GraphRAG runtime for retrieval", "link": "https://github.com/gibram-io/gibram", "summary": "
Hi HN,
I have been working with regulation-heavy documents lately, and one thing kept bothering me. Flat RAG pipelines often fail to retrieve related articles together, even when they are clearly connected through references, definitions, or clauses.
After trying several RAG setups, I subjectively felt that GraphRAG was a better mental model for this kind of data. The Microsoft GraphRAG paper and reference implementation were helpful starting points. However, in practice, I found one recurring friction point: graph storage and vector indexing are usually handled by separate systems, whi", "tags": ["law", "policy"], "hash": "c51d64f5abb203d7"} {"schema": "intel.signal.v1", "ts": "2026-01-19T05:07:18Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Do You Trust Me? Cognitive-Affective Signatures of Trustworthiness in Large Language Models", "link": "https://arxiv.org/abs/2601.10719", "summary": "arXiv:2601.10719v1 Announce Type: new Abstract: Perceived trustworthiness underpins how users navigate online information, yet it remains unclear whether large language models (LLMs),increasingly embedded in search, recommendation, and conversational systems, represent this construct in psychologically coherent ways. We analyze how instruction-tuned LLMs (Llama 3.1 8B, Qwen 2.5 7B, Mistral 7B) encode perceived trustworthiness in web-like narratives using the PEACE-Reviews dataset annotated for cognitive appraisals, emotions, and behavioral intentions. Across models, systematic layer- and head-", "tags": ["docs", "templates"], "hash": "621c4f3c57c143fc"} {"schema": "intel.signal.v1", "ts": "2026-01-19T05:07:18Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "CTHA: Constrained Temporal Hierarchical Architecture for Stable Multi-Agent LLM Systems", "link": "https://arxiv.org/abs/2601.10738", "summary": "arXiv:2601.10738v1 Announce Type: new Abstract: Recently, multi-time-scale agent architectures have extended the ubiquitous single-loop paradigm by introducing temporal hierarchies with distinct cognitive layers. While yielding substantial performance gains, this diversification fundamentally compromises the coordination stability intrinsic to unified agent systems, which causes severe inter-layer conflicts, unbounded error propagation, and restricted scalability. To address these challenges, we propose Constrained Temporal Hierarchical Architecture (CTHA), a general framework that projects th", "tags": ["contracts", "docs", "templates"], "hash": "deab4b143242b706"} {"schema": "intel.signal.v1", "ts": "2026-01-19T05:07:18Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Bridging Cognitive Neuroscience and Graph Intelligence: Hippocampus-Inspired Multi-View Hypergraph Learning for Web Finance Fraud", "link": "https://arxiv.org/abs/2601.11073", "summary": "arXiv:2601.11073v1 Announce Type: cross Abstract: Online financial services constitute an essential component of contemporary web ecosystems, yet their openness introduces substantial exposure to fraud that harms vulnerable users and weakens trust in digital finance. Such threats have become a significant web harm that erodes societal fairness and affects the well being of online communities. However, existing detection methods based on graph neural networks (GNNs) struggle with two persistent challenges: (1) fraud camouflage, where malicious transactions mimic benign behaviors to evade detect", "tags": [], "hash": "769f3c55ad915935"} {"schema": "intel.signal.v1", "ts": "2026-01-19T05:07:18Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Epistemic Control and the Normativity of Machine Learning-Based Science", "link": "https://arxiv.org/abs/2601.11202", "summary": "arXiv:2601.11202v1 Announce Type: cross Abstract: The past few years have witnessed an increasing use of machine learning (ML) systems in science. Paul Humphreys has argued that, because of specific characteristics of ML systems, human scientists are pushed out of the loop of science. In this chapter, I investigate to what extent this is true. First, I express these concerns in terms of what I call epistemic control. I identify two conditions for epistemic control, called tracking and tracing, drawing on works in philosophy of technology. With this new understanding of the problem, I then argu", "tags": [], "hash": "4559571491e5fbe2"} {"schema": "intel.signal.v1", "ts": "2026-01-19T05:07:18Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "SecMLOps: A Comprehensive Framework for Integrating Security Throughout the MLOps Lifecycle", "link": "https://arxiv.org/abs/2601.10848", "summary": "arXiv:2601.10848v1 Announce Type: new Abstract: Machine Learning (ML) has emerged as a pivotal technology in the operation of large and complex systems, driving advancements in fields such as autonomous vehicles, healthcare diagnostics, and financial fraud detection. Despite its benefits, the deployment of ML models brings significant security challenges, such as adversarial attacks, which can compromise the integrity and reliability of these systems. To address these challenges, this paper builds upon the concept of Secure Machine Learning Operations (SecMLOps), providing a comprehensive fram", "tags": ["docs", "permits", "templates"], "hash": "5a7efe50127423d9"} {"schema": "intel.signal.v1", "ts": "2026-01-19T05:07:18Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "Towards Quantum-Resistant Trusted Computing: Architectures for Post-Quantum Integrity Verification Techniques", "link": "https://arxiv.org/abs/2601.11095", "summary": "arXiv:2601.11095v1 Announce Type: new Abstract: Trust is the core building block of secure systems, and it is enforced through methods to ensure that a specific system is properly configured and works as expected. In this context, a Root of Trust (RoT) establishes a trusted environment, where both data and code are authenticated via a digital signature based on asymmetric cryptography, which is vulnerable to the threat posed by Quantum Computers (QCs). Firmware, being the first layer of trusted software, faces unique risks due to its longevity and difficult update. The transition of firmware p", "tags": [], "hash": "087984083fc0e362"} {"schema": "intel.signal.v1", "ts": "2026-01-19T05:07:19Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "SecMLOps: A Comprehensive Framework for Integrating Security Throughout the MLOps Lifecycle", "link": "https://arxiv.org/abs/2601.10848", "summary": "arXiv:2601.10848v1 Announce Type: cross Abstract: Machine Learning (ML) has emerged as a pivotal technology in the operation of large and complex systems, driving advancements in fields such as autonomous vehicles, healthcare diagnostics, and financial fraud detection. Despite its benefits, the deployment of ML models brings significant security challenges, such as adversarial attacks, which can compromise the integrity and reliability of these systems. To address these challenges, this paper builds upon the concept of Secure Machine Learning Operations (SecMLOps), providing a comprehensive fr", "tags": ["docs", "permits", "templates"], "hash": "399d65957602f376"} {"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:38Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "FutureX-Pro: Extending Future Prediction to High-Value Vertical Domains", "link": "https://arxiv.org/abs/2601.12259", "summary": "arXiv:2601.12259v1 Announce Type: new Abstract: Building upon FutureX, which established a live benchmark for general-purpose future prediction, this report introduces FutureX-Pro, including FutureX-Finance, FutureX-Retail, FutureX-PublicHealth, FutureX-NaturalDisaster, and FutureX-Search. These together form a specialized framework extending agentic future prediction to high-value vertical domains. While generalist agents demonstrate proficiency in open-domain search, their reliability in capital-intensive and safety-critical sectors remains under-explored. FutureX-Pro targets four economical", "tags": ["docs", "permits", "templates"], "hash": "fc3ae1145e2ad800"} {"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:38Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "SL-CBM: Enhancing Concept Bottleneck Models with Semantic Locality for Better Interpretability", "link": "https://arxiv.org/abs/2601.12804", "summary": "arXiv:2601.12804v1 Announce Type: new Abstract: Explainable AI (XAI) is crucial for building transparent and trustworthy machine learning systems, especially in high-stakes domains. Concept Bottleneck Models (CBMs) have emerged as a promising ante-hoc approach that provides interpretable, concept-level explanations by explicitly modeling human-understandable concepts. However, existing CBMs often suffer from poor locality faithfulness, failing to spatially align concepts with meaningful image regions, which limits their interpretability and reliability. In this work, we propose SL-CBM (CBM wit", "tags": [], "hash": "5bd91709cddac71a"} {"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "A Survey on Mapping Digital Systems with Bill of Materials: Development, Practices, and Challenges", "link": "https://arxiv.org/abs/2601.11678", "summary": "arXiv:2601.11678v1 Announce Type: new Abstract: Modern digital ecosystems, spanning software, hardware, learning models, datasets, and cryptographic products, continue to grow in complexity, making it difficult for organizations to understand and manage component dependencies. Bills of Materials (BOMs) have emerged as a structured way to document product components, their interrelationships, and key metadata, improving visibility and security across digital supply chains. This survey provides the first comprehensive cross-domain review of BOM developments and practices. We start by examining t", "tags": [], "hash": "a5d46bc010451404"} {"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "Taming Various Privilege Escalation in LLM-Based Agent Systems: A Mandatory Access Control Framework", "link": "https://arxiv.org/abs/2601.11893", "summary": "arXiv:2601.11893v1 Announce Type: new Abstract: Large Language Model (LLM)-based agent systems are increasingly deployed for complex real-world tasks but remain vulnerable to natural language-based attacks that exploit over-privileged tool use. This paper aims to understand and mitigate such attacks through the lens of privilege escalation, defined as agent actions exceeding the least privilege required for a user's intended task. Based on a formal model of LLM agent systems, we identify novel privilege escalation scenarios, particularly in multi-agent systems, including a variant akin to the ", "tags": ["docs", "templates"], "hash": "6a3985c358cdd51a"} {"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "MongoDB Injection Query Classification Model using MongoDB Log files as Training Data", "link": "https://arxiv.org/abs/2601.11996", "summary": "arXiv:2601.11996v1 Announce Type: new Abstract: NoSQL Injection attacks are a class of cybersecurity attacks where an attacker sends a specifically engineered query to a NoSQL database which then performs an unauthorized operation. To defend against such attacks, rule based systems were initially developed but then were found to be ineffective to innovative injection attacks hence a model based approach was developed. Most model based detection systems, during testing gave exponentially positive results but were trained only on the query statement sent to the server. However due to the scarcit", "tags": ["docs", "templates"], "hash": "19949b7f70c5b593"} {"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "Efficient Privacy-Preserving Retrieval Augmented Generation with Distance-Preserving Encryption", "link": "https://arxiv.org/abs/2601.12331", "summary": "arXiv:2601.12331v1 Announce Type: new Abstract: RAG has emerged as a key technique for enhancing response quality of LLMs without high computational cost. In traditional architectures, RAG services are provided by a single entity that hosts the dataset within a trusted local environment. However, individuals or small organizations often lack the resources to maintain data storage servers, leading them to rely on outsourced cloud storage. This dependence on untrusted third-party services introduces privacy risks. Embedding-based retrieval mechanisms, commonly used in RAG systems, are vulnerable", "tags": [], "hash": "7dbabf60d0a47562"} {"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "Abusing the Internet of Medical Things: Evaluating Threat Models and Forensic Readiness for Multi-Vector Attacks on Connected Healthcare Devices", "link": "https://arxiv.org/abs/2601.12593", "summary": "arXiv:2601.12593v1 Announce Type: new Abstract: Individuals experiencing interpersonal violence (IPV), who depend on medical devices, represent a uniquely vulnerable population as healthcare technologies become increasingly connected. Despite rapid growth in MedTech innovation and \"health-at-home\" ecosystems, the intersection of MedTech cybersecurity and technology-facilitated abuse remains critically under-examined. IPV survivors who rely on therapeutic devices encounter a qualitatively different threat environment from the external, technically sophisticated adversaries typically modeled in ", "tags": [], "hash": "dbaa5aaff8a661a0"} {"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "Quantum Encryption Resilience Score (QERS) for MQTT, HTTP, and HTTPS under Post-Quantum Cryptography in Computer, IoT, and IIoT Systems", "link": "https://arxiv.org/abs/2601.13423", "summary": "arXiv:2601.13423v1 Announce Type: new Abstract: Post-quantum cryptography (PQC) introduces significant computational and communication overhead, which poses challenges for resource-constrained computer systems, Internet of Things (IoT), and Industrial IoT (IIoT) devices. This paper presents an experimental evaluation of the Quantum Encryption Resilience Score (QERS) applied to MQTT, HTTP, and HTTPS communication protocols operating under PQC. Using an ESP32-C6 client and an ARM-based Raspberry Pi CM4 server, latency, CPU utilization, RSSI, energy consumption, key size, and TLS handshake overhe", "tags": ["docs", "templates"], "hash": "40fd41b99d7f760c"} {"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "AttackMate: Realistic Emulation and Automation of Cyber Attack Scenarios Across the Kill Chain", "link": "https://arxiv.org/abs/2601.14108", "summary": "arXiv:2601.14108v1 Announce Type: new Abstract: Adversary emulation tools facilitate scripting and automated execution of cyber attack chains, thereby reducing costs and manual expert effort required for security testing, cyber exercises, and intrusion detection research. However, due to the fact that existing tools typically rely on agents installed on target systems, they leave suspicious traces that make it easy to distinguish their activities from those of real human attackers. Moreover, these tools often lack relevant capabilities, such as handling of interactive prompts, and are unsuitab", "tags": ["docs", "templates"], "hash": "8038c40d9fae02a0"} {"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:40Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "Software Testing in the Quantum World", "link": "https://arxiv.org/abs/2601.13996", "summary": "arXiv:2601.13996v1 Announce Type: new Abstract: Quantum computing offers significant speedups for simulating physical, chemical, and biological systems, and for optimization and machine learning. As quantum software grows in complexity, the classical simulation of quantum computers, which has long been essential for quality assurance, becomes infeasible. This shift requires new quality-assurance methods that operate directly on real quantum computers. This paper presents the key challenges in testing large-scale quantum software and offers software engineering perspectives for addressing them.", "tags": [], "hash": "1bd5f027f6110f2c"} {"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:40Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "Analyzing the Availability of E-Mail Addresses for PyPI Libraries", "link": "https://arxiv.org/abs/2601.14034", "summary": "arXiv:2601.14034v1 Announce Type: new Abstract: Open Source Software (OSS) libraries form the backbone of modern software systems, yet their long-term sustainability often depends on maintainers being reachable for support, coordination, and security reporting. In this paper, we empirically analyze the availability of contact information - specifically e-mail addresses - across 686,034 Python libraries on the Python Package Index (PyPI) and their associated GitHub repositories. We examine how and where maintainers provide this information, assess its validity, and explore coverage across indiv", "tags": ["docs", "templates"], "hash": "b70a958c3b75b0a6"} {"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:40Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "Feature-Aware Test Generation for Deep Learning Models", "link": "https://arxiv.org/abs/2601.14081", "summary": "arXiv:2601.14081v1 Announce Type: new Abstract: As deep learning models are widely used in software systems, test generation plays a crucial role in assessing the quality of such models before deployment. To date, the most advanced test generators rely on generative AI to synthesize inputs; however, these approaches remain limited in providing semantic insight into the causes of misbehaviours and in offering fine-grained semantic controllability over the generated inputs. In this paper, we introduce Detect, a feature-aware test generation framework for vision-based deep learning (DL) models th", "tags": ["docs", "templates"], "hash": "18dcc65ce2e8b5c6"} {"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:40Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "Toward self-coding information systems", "link": "https://arxiv.org/abs/2601.14132", "summary": "arXiv:2601.14132v1 Announce Type: new Abstract: In this extended abstract, we propose a novel research topic in the field of agentic AI, which we refer to as self-coding information systems. These systems will be able to dynamically adapt their structure or behavior by evaluating potential adaptation decisions, generate source code, test, and (re)deploy their source code autonomously, at runtime, reducing the time to market of new features. Here we motivate the topic, provide a formal definition of self-coding information systems, discuss some expected impacts of the new technology, and indica", "tags": ["docs", "templates"], "hash": "440764f01e79a99c"} {"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:40Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "A Survey on Mapping Digital Systems with Bill of Materials: Development, Practices, and Challenges", "link": "https://arxiv.org/abs/2601.11678", "summary": "arXiv:2601.11678v1 Announce Type: cross Abstract: Modern digital ecosystems, spanning software, hardware, learning models, datasets, and cryptographic products, continue to grow in complexity, making it difficult for organizations to understand and manage component dependencies. Bills of Materials (BOMs) have emerged as a structured way to document product components, their interrelationships, and key metadata, improving visibility and security across digital supply chains. This survey provides the first comprehensive cross-domain review of BOM developments and practices. We start by examining", "tags": [], "hash": "d799e9ebf1d7f761"} {"schema": "intel.signal.v1", "ts": "2026-01-22T05:07:47Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "An XAI View on Explainable ASP: Methods, Systems, and Perspectives", "link": "https://arxiv.org/abs/2601.14764", "summary": "arXiv:2601.14764v1 Announce Type: new Abstract: Answer Set Programming (ASP) is a popular declarative reasoning and problem solving approach in symbolic AI. Its rule-based formalism makes it inherently attractive for explainable and interpretive reasoning, which is gaining importance with the surge of Explainable AI (XAI). A number of explanation approaches and tools for ASP have been developed, which often tackle specific explanatory settings and may not cover all scenarios that ASP users encounter. In this survey, we provide, guided by an XAI perspective, an overview of types of ASP explanat", "tags": ["docs", "templates"], "hash": "7cdf4322ce9eb8c5"} {"schema": "intel.signal.v1", "ts": "2026-01-22T05:07:47Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Just aware enough: Evaluating awareness across artificial systems", "link": "https://arxiv.org/abs/2601.14901", "summary": "arXiv:2601.14901v1 Announce Type: new Abstract: Recent debates on artificial intelligence increasingly emphasise questions of AI consciousness and moral status, yet there remains little agreement on how such properties should be evaluated. In this paper, we argue that awareness offers a more productive and methodologically tractable alternative. We introduce a practical method for evaluating awareness across diverse systems, where awareness is understood as encompassing a system's abilities to process, store and use information in the service of goal-directed action. Central to this approach i", "tags": ["docs", "templates"], "hash": "702e29838ed8ece6"} {"schema": "intel.signal.v1", "ts": "2026-01-22T05:07:47Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "The Slow Drift of Support: Boundary Failures in Multi-Turn Mental Health LLM Dialogues", "link": "https://arxiv.org/abs/2601.14269", "summary": "arXiv:2601.14269v1 Announce Type: cross Abstract: Large language models (LLMs) have been widely used for mental health support. However, current safety evaluations in this field are mostly limited to detecting whether LLMs output prohibited words in single-turn conversations, neglecting the gradual erosion of safety boundaries in long dialogues. Examples include making definitive guarantees, assuming responsibility, and playing professional roles. We believe that with the evolution of mainstream LLMs, words with obvious safety risks are easily filtered by their underlying systems, while the re", "tags": [], "hash": "e246caf85b4c2362"} {"schema": "intel.signal.v1", "ts": "2026-01-22T05:07:47Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "A Survey of Security Challenges and Solutions for Advanced Air Mobility and eVTOL Aircraft", "link": "https://arxiv.org/abs/2601.14415", "summary": "arXiv:2601.14415v1 Announce Type: new Abstract: This survey reviews the existing and envisioned security vulnerabilities and defense mechanisms relevant to Advanced Air Mobility (AAM) systems, with a focus on electric vertical takeoff and landing (eVTOL) aircraft. Drawing from vulnerabilities in the avionics in commercial aviation and the automated unmanned aerial systems (UAS), the paper presents a taxonomy of attacks, analyzes mitigation strategies, and proposes a secure system architecture tailored to the future AAM ecosystem. The paper also highlights key threat vectors, including Global P", "tags": [], "hash": "703191cebf50627d"} {"schema": "intel.signal.v1", "ts": "2026-01-22T05:07:47Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "WebAssembly Based Portable and Secure Sensor Interface for Internet of Things", "link": "https://arxiv.org/abs/2601.14555", "summary": "arXiv:2601.14555v1 Announce Type: new Abstract: As the expansion of IoT connectivity continues to provide quality-of-life improvements around the world, they simultaneously introduce increasing privacy and security concerns. The lack of a clear definition in managing shared and protected access to IoT sensors offer channels by which devices can be compromised and sensitive data can be leaked. In recent years, WebAssembly has received considerable attention for its efficient application sandboxing suitable for embedded systems, making it a prime candidate for exploring a secure and portable sen", "tags": ["docs", "permits"], "hash": "e147b0d13ad53bba"} {"schema": "intel.signal.v1", "ts": "2026-01-22T05:07:47Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.CR", "title": "Automatically Tightening Access Control Policies with Restricter", "link": "https://arxiv.org/abs/2601.14582", "summary": "arXiv:2601.14582v1 Announce Type: new Abstract: Robust access control is a cornerstone of secure software, systems, and networks. An access control mechanism is as effective as the policy it enforces. However, authoring effective policies that satisfy desired properties such as the principle of least privilege is a challenging task even for experienced administrators, as evidenced by many real instances of policy misconfiguration. In this paper, we set out to address this pain point by proposing Restricter, which automatically tightens each (permit) policy rule of a policy with respect to an a", "tags": ["compliance", "permits"], "hash": "889b5368dc134a7b"} {"schema": "intel.signal.v1", "ts": "2026-01-22T05:07:48Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "AlertGuardian: Intelligent Alert Life-Cycle Management for Large-scale Cloud Systems", "link": "https://arxiv.org/abs/2601.14912", "summary": "arXiv:2601.14912v1 Announce Type: cross Abstract: Alerts are critical for detecting anomalies in large-scale cloud systems, ensuring reliability and user experience. However, current systems generate overwhelming volumes of alerts, degrading operational efficiency due to ineffective alert life-cycle management. This paper details the efforts of Company-X to optimize alert life-cycle management, addressing alert fatigue in cloud systems. We propose AlertGuardian, a framework collaborating large language models (LLMs) and lightweight graph models to optimize the alert life-cycle through three ph", "tags": [], "hash": "2fc323605a30837c"} {"schema": "intel.signal.v1", "ts": "2026-01-22T05:07:48Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "Software Testing in the Quantum World", "link": "https://arxiv.org/abs/2601.13996", "summary": "arXiv:2601.13996v2 Announce Type: replace Abstract: Quantum computing offers significant speedups for simulating physical, chemical, and biological systems, and for optimization and machine learning. As quantum software grows in complexity, the classical simulation of quantum computers, which has long been essential for quality assurance, becomes infeasible. This shift requires new quality-assurance methods that operate directly on real quantum computers. This paper presents the key challenges in testing large-scale quantum software and offers software engineering perspectives for addressing t", "tags": [], "hash": "66cbca33f25b9961"} {"schema": "intel.signal.v1", "ts": "2026-01-22T23:38:05Z", "source": "rss", "feed": "https://hnrss.org/frontpage", "title": "Capital One to acquire Brex for $5.15B", "link": "https://www.reuters.com/legal/transactional/capital-one-buy-fintech-firm-brex-515-billion-deal-2026-01-22/", "summary": "
Archive link: https://archive.md/vk8ov, Capitol One statement: https://investor.capitalone.com/news-releases/news-release-d..., Brex statement: https://www.brex.com/journal/brex-and-capital-one-join-force...
Comments URL: https://news.ycombinator.com/item?id=46", "tags": [], "hash": "1e85500bdec7a988"}