arXiv AI
Sep 10
When Agent Governance Helps
arXiv:2609.05531v1 Announce Type: cross Abstract: No specification says how a governed autotelic AI agent organization, where agents pursue self-generated goals inside guardrails, should be designed...
GPS-Bench: A Governance Policy Benchmark for Automating Policy Analysis
arXiv:2609. 03553v1 Announce Type: new Abstract: Policy analysis requires more than predicting whether a proposal will pass: it requires identifying who will be affected, how those actors respond, and what follows.
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arXiv AI
Aug 18
Governance at the Boundary: How Agent Decomposition Degrades Policy Compliance
arXiv:2608. 16055v1 Announce Type: new Abstract: Existing agent benchmarks ask whether the agent finished the task.
Hugging Face Trending Papers
Jun 17
LegalWorld: A Life-Cycle Interactive Environment for Legal Agents
Civil litigation is inherently a life-cycle process: what a lawyer drafts on day one constrains what unfolds at trial months later. Yet existing legal benchmarks evaluate isolated subtasks, and prior legal-agent simulators reinitialize each scenario from shared ground truth, leaving cross-stage causal dependencies unmodeled.
arXiv AI
Oct 2
Verify Claims, Not Scores: Evidence-Based Verification of Modular Agents
The paper proposes a claim‑specific verification audit for modular agents that replaces aggregate task scores with evidence‑based evaluations. Each agent conclusion is recorded with supporting evidence and classified as supported, unsupported, unresolved, or not evaluated, along with the boundary of validity. The audit employs three tools—oracle policies, perfect component replacements, and verifier‑score tests—to trace value changes, locate lost value, and assess verifier effectiveness, demonstrated on a portfolio‑allocation agent in a synthetic market.
arXiv AI
Jun 4
Parthenon Law: A Self-Evolving Legal-Agent Framework
arXiv:2606. 04602v1 Announce Type: new Abstract: As agents grow more capable, legal-domain LLM agents promise to turn document-heavy matters into reviewable work products -- yet reliable deployment faces three obstacles: no large-scale evidence on how today's strongest model-and-harness combinations behave on end-to-end legal matters; no agent architecture adapted to the legal vertical, only general-purpose harnesses; and, in a setting that keeps shifting with new facts, authorities, and deadlines, no mechanism for systems to learn from their own outcomes.
arXiv AI
Oct 15
Computational Reflective Equilibrium for Agentic AI: A Rawlsian Alignment Scaffold for Governance
arXiv:2610.16015v1 Announce Type: new Abstract: The rapid deployment of agentic AI has introduced complex challenges for the ethics and governance of AI systems. Agents built on large language models...
arXiv AI
Aug 10
Does Splitting a Triage Decision Across Agents Hide Bias or Help Catch It? A Multi-Agent Simulation Study of LLM-Based Resource Allocation Under Audit Capacity Constraints
arXiv:2608. 06949v1 Announce Type: new Abstract: Prior benchmarking work has shown that a single large language model (LLM), forced to make life-or-death resource-allocation decisions, exhibits measurable demographic bias.
arXiv AI
2d ago
Governance-Aware Federated Learning for Multi-Agent AI Systems: A Policy-Driven Framework for Trustworthy Collaborative AI
The paper introduces Governance-Aware Federated Learning (GAFL), a framework that enhances traditional federated learning with independent governance verification, fairness and explainability assessment, anomaly detection, policy enforcement, and tamper‑evident audit logging. In a cross‑silo prototype with 10 participants, GAFL successfully identified all falsified‑evidence participants and intervened on affected participants across various threat scenarios, achieving macro‑F1 scores between 0.7520 and 0.7733. The added governance processing time per round was 173.67 ± 26.94 ms, compared to 3.82 ± 0.79 ms for standard FedAvg, highlighting a trade‑off between fairness policy enforcement and utility.
arXiv AI
Jun 15
Regulating the Machine Contributor: Governance and Policy Alignment in Open Source
arXiv:2606. 14594v1 Announce Type: cross Abstract: AI-assisted software development has moved from line-level autocomplete to agents that can plan changes, edit files, and submit pull requests with limited human supervision.
arXiv AI
Aug 13
Governing Agentic AI in FinTech
arXiv:2608. 11344v1 Announce Type: cross Abstract: Financial institutions are delegating consequential decisions to agentic AI systems that decompose goals, coordinate models and tools, and act with little oversight.
arXiv AI
Sep 25
PAWS: Policy-driven Agentic World Simulation
PAWS is a new dataset for policy-driven agentic world simulation that covers 36 verified U.S. financial and economic policy episodes. It includes 12,727 policy-linked news records and 65,291 stakeholder actions, each linked to supporting news and represented by a multi-layer event frame with interaction mode, financial-action family, semantic attributes, and taxonomic mappings. The dataset aligns actions with daily market-return context and has been validated by AI and human reviewers, demonstrating high agreement on interaction mode and revealing challenges in detecting rare stakeholder actions.
arXiv AI
Oct 7
Confidence Reasoning Graphs: Structured Confidence Estimation for LLM Agents
The paper introduces Confidence Reasoning Graphs (CRGs), an inference-time framework that estimates the probability an LLM agent has successfully completed its task using a single trajectory without requiring privileged model access or training data. CRGs decompose the agent’s overall claim of success into contextualized sub-claims, estimate confidence for each terminal claim based on trajectory evidence, and aggregate these into an overall confidence estimate. Experiments across multiple benchmarks, models, and agent frameworks show that CRGs produce better-calibrated confidence and more effective risk-aware decision making than existing verbalized, sampling-based, and white-box surrogate methods, while also providing transparent, auditable evidence for each estimate.