RAIL Guard: Closing the Evaluation-to-Remediation Gap in Responsible AI for LLM Agents

• URL: http://arxiv.org/abs/2607.16215v1
• Date: Thu, 28 May 2026 21:23:49 GMT
• Title: RAIL Guard: Closing the Evaluation-to-Remediation Gap in Responsible AI for LLM Agents
• Abstract summary: RAIL Guard is a closed-loop responsible AI pipeline.<n>It iteratively remediates failing outputs through an evaluate-rewrite-reevaluate loop.<n>System is available as open-source SDKs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing guardrail systems for large language model agents operate as binary classifiers that block unsafe content, leaving organizations to discard failing outputs and retry from scratch. We introduce RAIL Guard, a closed-loop responsible AI pipeline that evaluates LLM outputs across eight measurable dimensions and iteratively remediates failing outputs through an evaluate-rewrite-reevaluate loop. We evaluate the pipeline across three experiments on four frontier LLMs and 4,276 content outputs plus 6,400 agent tool-call scenarios. Closed-loop remediation achieves 96.9% convergence versus 49.1% for block-and-retry, though the highest-convergence method reduces utility by 22.3%; feedback-driven self-repair achieves 86.6% convergence on fixable dimensions with no significant utility loss (p = 0.177). Pre-tool-call evaluation reduces unsafe agent executions by 33% (p = 0.007) with zero impact on task completion. We identify a key distinction between fixable dimensions that respond to remediation and structural dimensions (Transparency at 93.0%, Accountability at 92.8%, and Inclusivity at 82.5% failure) that require architectural rather than algorithmic solutions. The system is available as open-source SDKs.

Related papers

• From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix [55.08143827481755]
  We consolidate traffic from over 200 internal applications onto a single model.<n>We train a separate GRPO expert per axis and merge them via two-stage SLERP.<n>The model absorbs 50% of platform traffic, 116M requests per month, at a fraction of the serving cost.
  arXiv  Detail & Related papers  (2026-09-01T17:39:26Z)
• AgentDV: Closed-Loop Agentic AI for Hardware Design Verification [1.5477055061671623]
  AgentDV is a closed-loop agentic AI framework for automated RTL verification environment generation.<n>It transforms single-shot LLM testbench generation into a tool-grounded verification pipeline.<n>AgentDV achieves 100% pass rate on four DUTs and an average of 80.9% pass rate on all DUTs using Claude Sonnet 4.6.
  arXiv  Detail & Related papers  (2026-08-27T14:00:16Z)
• A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination [51.06539604709775]
  We propose A-SR, a self-evolving agentic framework that shifts the control unit from expression edits to role-conditioned evidence views.<n>A-SR coordinates formula discovery through routing among coordination protocols, an online evaluator-reward role policy, and state-routed process memory.
  arXiv  Detail & Related papers  (2026-08-05T14:01:10Z)
• GoGoTB: Agentic RTL Verification with Specification-Grounded Coverage Closure [14.71922402654502]
  GoGoTB is an agentic framework that achieves end-to-end verification closure.<n>It is tested on 8 register transfer level (RTL) designs without any human intervention.
  arXiv  Detail & Related papers  (2026-07-28T18:40:35Z)
• LLM-as-a-Verifier: A General-Purpose Verification Framework [74.40111651545979]
  We introduce LLM-as-a-Verifier, a general-purpose verification framework.<n>It provides fine-grained feedback for agentic tasks without requiring additional training.<n>It achieves state-of-the-art performance on several benchmarks.
  arXiv  Detail & Related papers  (2026-07-06T17:59:35Z)
• Efficient Visual Pointing for Embodied AI:Agent-Driven Data Synthesis, Cross-Block Attention, and Iterative Correction [55.11480729304395]
  We describe our PointArena 2026 solution, which achieves 77.2% overall accuracy and ranks second on the benchmark.<n>The ap proach targets three failure modes. First, agent-driven syn thesis builds large semantic and anchor-relative candidate pools.<n>Second, a determinis tic steerable-data pipeline creates a verified 10,000-sample main set, plus reserve samples, using masks, templates, and path verification.
  arXiv  Detail & Related papers  (2026-06-29T06:39:03Z)
• Willing but Unable: Separating Refusal from Capability in Code LLMs via Abliteration [3.9458538778648964]
  This paper is a preliminary study of abliteration, a low-rank weight edit that replicates out the refusal stream as a tool to remove this barrier.<n> Vulnerability verdicts are produced by a three-tool detector ensemble (QLCode, Sem, Bandit) followed by manual adjudication.
  arXiv  Detail & Related papers  (2026-06-03T20:06:52Z)
• Converted, Not Equivalent: Benchmarking Codebase Conversion via Observational Equivalence [56.25095230687242]
  Coding agents often over-trust their own local validation routines and declare success on artifacts that satisfy surface checks.<n>This problem is especially acute in conversion, where prior evaluation is outcome-driven.<n>Across blind conversion attempts, the best system reaches 26.7--28.9% overall pass rate despite Spec pass rates up to 91.1%.<n>This suggests that failures stem more from contract-misaligned self-validation than from limited budget or backbone strength.
  arXiv  Detail & Related papers  (2026-05-27T19:57:15Z)
• 2.5-D Decomposition for LLM-Based Spatial Construction [0.0]
  Large language models (LLMs) make systematic coordinate errors when generating three-dimensional block placements.<n>We present a neuro-symbolic pipeline based on emph2.5-D decomposition.<n>A deterministic executor computes all vertical placement from column occupancy, eliminating an entire class of errors.
  arXiv  Detail & Related papers  (2026-05-08T00:17:33Z)
• AuditRepairBench: A Paired-Execution Trace Corpus for Evaluator-Channel Ranking Instability in Agent Repair [21.974153439592317]
  AuditBench is a paired-execution trace corpus of 576,000 registered cells (96,000 executed)<n>It operationalizes evaluator-channel-blocking ranking instability within a declared observability boundary.<n>It is supported by mechanism-anchored validation on an 80-case source-level channel-surgery subset.
  arXiv  Detail & Related papers  (2026-05-06T08:12:09Z)
• Measuring the Permission Gate: A Stress-Test Evaluation of Claude Code's Auto Mode [9.116800340266066]
  Claude Code's auto mode is the first deployed permission system for AI coding agents.<n>Anthropic reports a 0.4% false positive rate and 17% false negative rate on production traffic.<n>We evaluate 253 state-changing actions at the individual action level against oracle ground truth.
  arXiv  Detail & Related papers  (2026-04-04T17:56:30Z)
• Solver-in-the-Loop: MDP-Based Benchmarks for Self-Correction and Behavioral Rationality in Operations Research [19.31559944205485]
  Operations Research practitioners routinely debug infeasible models through an iterative process.<n>We introduce two benchmarks that place the textbfsolver in the evaluation loop<n>We find that domain-specific RLVR training enables an 8B model to surpass frontier APIs.
  arXiv  Detail & Related papers  (2026-01-28T20:02:44Z)
• Structured Uncertainty guided Clarification for LLM Agents [126.26213027785813]
  LLM agents extend large language models with tool-calling capabilities, but ambiguous user instructions often lead to incorrect invocations and task failures.<n>We introduce a principled formulation of structured uncertainty over tool-call parameters, modeling joint tool-argument clarification as a POMDP with Expected Value of Perfect Information (EVPI) objective for optimal question selection and aspect-based cost modeling to prevent redundancy.<n>Our SAGE-Agent leverages this structured uncertainty to achieve superior efficiency: increasing coverage on ambiguous tasks by 7-39% while reducing clarification questions by 1.5-2.7$times$ compared to strong prompting and uncertainty-based baselines.
  arXiv  Detail & Related papers  (2025-11-11T21:50:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.