{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:26Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "Advancing ESG Intelligence: An Expert-level Agent and Comprehensive Benchmark for Sustainable Finance", "link": "https://arxiv.org/abs/2601.08676", "summary": "arXiv:2601.08676v2 Announce Type: new Abstract: Environmental, social, and governance (ESG) criteria are essential for evaluating corporate sustainability and ethical performance. However, professional ESG analysis is hindered by data fragmentation across unstructured sources, and existing large language models (LLMs) often struggle with the complex, multi-step workflows required for rigorous auditing. To address these limitations, we introduce ESGAgent, a hierarchical multi-agent system empowered by a specialized toolset, including retrieval augmentation, web search and domain-specific functi", "tags": [], "hash": "a6a9b1eecccc352d"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T03:18:27Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "Analyzing GitHub Issues and Pull Requests in nf-core Pipelines: Insights into nf-core Pipeline Repositories", "link": "https://arxiv.org/abs/2601.09612", "summary": "arXiv:2601.09612v1 Announce Type: new Abstract: Scientific Workflow Management Systems (SWfMSs) such as Nextflow have become essential software frameworks for conducting reproducible, scalable, and portable computational analyses in data-intensive fields like genomics, transcriptomics, and proteomics. Building on Nextflow, the nf-core community curates standardized, peer-reviewed pipelines that follow strict testing, documentation, and governance guidelines. Despite its broad adoption, little is known about the challenges users face during the development and maintenance of these pipelines. Th", "tags": [], "hash": "6de945dc025fdab7"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T05:03:28Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T05:03:30Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "A Governance Model for IoT Data in Global Manufacturing", "link": "https://arxiv.org/abs/2601.09744", "summary": "arXiv:2601.09744v1 Announce Type: new Abstract: Industrial IoT platforms in global manufacturing environments generate continuous operational data across production assets, utilities, and connected products. While data ingestion and storage capabilities have matured significantly, enterprises continue to face systemic challenges in governing IoT data at scale. These challenges are not rooted in tooling limitations but in the absence of a governance model that aligns with the realities of distributed operational ownership, heterogeneous source systems, and continuous change at the edge. This pa", "tags": [], "hash": "e36ed1919bc425ea"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T10:03:32Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T10:18:32Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T15:33:30Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T15:48:30Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T17:33:32Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T17:48:33Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T18:48:33Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T19:03:33Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T19:33:34Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T19:48:35Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T20:03:34Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T20:18:35Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T20:48:35Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T21:18:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T21:33:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T22:03:36Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T23:03:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-16T23:18:37Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T00:03:41Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T00:18:38Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T00:33:38Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T00:48:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T03:48:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-17T04:48:39Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "PCN-Rec: Agentic Proof-Carrying Negotiation for Reliable Governance-Constrained Recommendation", "link": "https://arxiv.org/abs/2601.09771", "summary": "arXiv:2601.09771v1 Announce Type: new Abstract: Modern LLM-based recommenders can generate compelling ranked lists, but they struggle to reliably satisfy governance constraints such as minimum long-tail exposure or diversity requirements. We present PCN-Rec, a proof-carrying negotiation pipeline that separates natural-language reasoning from deterministic enforcement. A base recommender (MF/CF) produces a candidate window of size W, which is negotiated by two agents: a User Advocate optimizing relevance and a Policy Agent enforcing constraints. A mediator LLM synthesizes a top-N slate together", "tags": [], "hash": "b99b6ae91a9ebb22"}
{"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:40Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "The Stability Trap: Evaluating the Reliability of LLM-Based Instruction Adherence Auditing", "link": "https://arxiv.org/abs/2601.11783", "summary": "arXiv:2601.11783v1 Announce Type: new Abstract: The enterprise governance of Generative AI (GenAI) in regulated sectors, such as Human Resources (HR), demands scalable yet reproducible auditing mechanisms. While Large Language Model (LLM)-as-a-Judge approaches offer scalability, their reliability in evaluating adherence of different types of system instructions remains unverified. This study asks: To what extent does the instruction type of an Application Under Test (AUT) influence the stability of judge evaluations? To address this, we introduce the Scoped Instruction Decomposition Framework ", "tags": ["docs", "permits"], "hash": "fe18c7735bf61080"}
{"schema": "intel.signal.v1", "ts": "2026-01-21T05:07:40Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.SE", "title": "Governance Matters: Lessons from Restructuring the data.table OSS Project", "link": "https://arxiv.org/abs/2601.13466", "summary": "arXiv:2601.13466v1 Announce Type: new Abstract: Open source software (OSS) forms the backbone of industrial data workflows and enterprise systems. However, many OSS projects face operational risks due to informal or centralized governance. This paper presents a practical case study of data.table, a high-performance R package widely adopted in production analytics pipelines, which underwent a community-led governance reform to address scalability and sustainability concerns. Before the reform, data.table faced a growing backlog of unresolved issues and open pull requests, unclear contributor pa", "tags": ["docs", "templates"], "hash": "76d5b48b845236ab"}
{"schema": "intel.signal.v1", "ts": "2026-01-22T05:07:47Z", "source": "rss", "feed": "https://export.arxiv.org/rss/cs.AI", "title": "The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution", "link": "https://arxiv.org/abs/2601.15075", "summary": "arXiv:2601.15075v1 Announce Type: new Abstract: Large Language Model (LLM)-based agents are widely used in real-world applications such as customer service, web navigation, and software engineering. As these systems become more autonomous and are deployed at scale, understanding why an agent takes a particular action becomes increasingly important for accountability and governance. However, existing research predominantly focuses on \\textit{failure attribution} to localize explicit errors in unsuccessful trajectories, which is insufficient for explaining the reasoning behind agent behaviors. T", "tags": ["docs", "permits"], "hash": "f00379f3f6c8a908"}