SAAG: Structured Agent Assessment and Grounding

• URL: http://arxiv.org/abs/2607.18245v1
• Date: Thu, 30 Apr 2026 19:33:58 GMT
• Title: SAAG: Structured Agent Assessment and Grounding
• Abstract summary: We propose a cascaded diagnostic framework that decomposes agent-calling evaluation into three sequential stages.<n>We evaluate this framework on a benchmark derived from Glaive's function-calling dataset across registry sizes of 5, 10, and 15 agents.
• Score: 13.62148061055744
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Exact-match evaluation of agent-calling obscures qualitatively different failure modes: a model may select the right function yet hallucinate argument values, or satisfy a schema while choosing a agent for the wrong reason. Existing benchmarks collapse these distinctions into a single binary score, leaving practitioners unable to diagnose where agent calls fail. We propose SAAG a cascaded diagnostic framework that decomposes agent-calling evaluation into three sequential stages: registry conformance, structural completeness, and argument grounding, each producing interpretable stage-specific diagnostics. These diagnostics additionally enable iterative self-repair: on prediction failure, the stage-specific signal guides targeted correction without leaking ground-truth values. We evaluate this framework on a controlled benchmark derived from Glaive's function-calling dataset across registry sizes of 5, 10, and 15 agents using three local sub-4B-parameter models. Structured feedback consistently improves argument precision and reduces value hallucination relative to single-pass inference and uninformative binary feedback, while end-to-end F1 gains are modest and model-dependent. These results suggest that stage-decomposed diagnostic evaluation is a necessary lens for understanding and improving agent-calling reliability across model families and registry scales.

Related papers

• The RAT: A Unified Bayesian Model for RAG Evaluation [8.650407684623714]
  We introduce a Bayesian evaluation framework that jointly models retrieval success, abstention behavior, and answer correctness.<n>We apply the framework to 27 RAG configurations across three datasets, three retrievers, and three generators.<n>We extend the model to incorporate LLM-as-a-judge annotations as calibrated noisy observations.
  arXiv  Detail & Related papers  (2026-08-25T15:54:30Z)
• Beyond Relevance: Bayesian Evidence Acquisition for Agentic Whole-Slide Image Reasoning [72.86819994769634]
  Whole-slide image (WSI) reasoning requires an agent to sequentially acquire visual evidence before answering a diagnostic question.<n>We propose BEACON, a plug-and-play agentic framework that reformulates WSI reasoning as a Bayesian evidence acquisition problem.
  arXiv  Detail & Related papers  (2026-08-06T08:40:57Z)
• DiagLoop: A Counterfactual Data Flywheel with Stage-Localized Reinforcement for Diagnostic LLMs [5.241146164724621]
  We present DiagLoop, a counterfactual data flywheel that converts codified physical relations or clinical guidelines into training supervision.<n>A training-only teacher proposes counterfactual worlds by varying causes, contexts, and observations, while an independent hybrid checker admits only valid worlds.<n>For nonterminal failures, a bounded repair probes downstream competence, and the resulting weakness profile guides subsequent data generation.
  arXiv  Detail & Related papers  (2026-08-04T13:49:41Z)
• Position: Evaluation Scores Are Perishable Knowledge Claims [0.0]
  We argue that evaluation scores should be treated as claims with three properties: formality, scope, and validity windows.<n>We illustrate the cost of mean aggregation on the public HELM leaderboard.
  arXiv  Detail & Related papers  (2026-07-28T18:50:39Z)
• Fault Detection and Explainable Classification in Automotive HIL Validation via Denoising Autoencoders and In-Context Large Language Models [1.9435397960631864]
  Machine and deep learning have advanced fault diagnosis, yet most supervised models require large labelled datasets.<n>We propose a generalisable and explainable two-phase framework for fault detection and classification during real-time validation.
  arXiv  Detail & Related papers  (2026-07-04T06:43:43Z)
• DriftScope: Measuring The Hidden Effects of Diffusion Model Adaptation [50.584834483403675]
  Adapting pre-trained text-to-image diffusion models, whether to learn new visual concepts or erase unwanted ones, is routinely evaluated on its intended effects alone.<n>We demonstrate that adaptation systematically damages semantically unrelated concepts in ways that aggregate metrics structurally cannot surface.<n>We introduce DriftScope, a prompt-level diagnostic tool that takes any two model checkpoints and returns a ranked list of tokens whose visual concepts have shifted most between them.
  arXiv  Detail & Related papers  (2026-06-30T20:57:51Z)
• A Multi-Agent Audit Framework for High-Stakes Reasoning: Evaluation and Interpretability in Clinical Mental Health Screening [0.8758828205574085]
  We propose a Multi-Agent Audit Framework that simulates a collaborative, multi-step verification process.<n>We empirically validate this architecture in the sensitive domain of clinical mental health screening.<n>Our framework decomposes the reasoning process into a Perception Agent, Knowledge Retrieval-Augmented Generation (RAG), Chain-of-Thought (CoT) clinical inference, and a critical Audit verification stage.
  arXiv  Detail & Related papers  (2026-06-19T05:51:03Z)
• Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications [17.69274632062373]
  Open-ended conversational agents remain prone to two critical failure modes: premature diagnostic handoff and silent clinical hallucinations.<n>We propose a multi-agent framework that addresses both issues by replacing LLM-as-a-judge'' routing with deterministic orchestration constraints.<n>We evaluate the system using simulated patient agents powered by the llama-3.1-70b-instruct model on 150 test cases.
  arXiv  Detail & Related papers  (2026-06-16T15:39:19Z)
• AuthTrace: Diagnosing Evidence Construction in Thematically Dense Single-Author Corpora [6.956097396264084]
  We introduce AuthTrace, a diagnostic benchmark built on thematically dense single-author corpora.<n>AuthTrace provides explicit quoted evidence, exact fan-in annotation, and a unified pack-level protocol measuring evidence recall, evidence precision, and answer correctness.
  arXiv  Detail & Related papers  (2026-05-25T03:10:52Z)
• LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation [75.05397479715576]
  Large language models (LLMs) and agents have shown promising progress, but their true capabilities and failure modes remain unclear.<n>We present the first systematic and contamination-aware study of LLM- and agent-based formal specification generation for C programs.
  arXiv  Detail & Related papers  (2026-05-02T11:31:33Z)
• TRAJEVAL: Decomposing Code Agent Trajectories for Fine-Grained Diagnosis [23.834704102474927]
  Code agents can resolve GitHub issues, yet when they fail, current evaluation provides no visibility into where or why.<n>We introduce TRAJEVAL, a diagnostic framework that decomposes agent trajectories into three interpretable stages.<n>We validate that these diagnostics are predictive, achieving model-level Pass@1 prediction within 0.87-2.1% MAE.
  arXiv  Detail & Related papers  (2026-03-25T05:27:03Z)
• Decoding the Critique Mechanism in Large Reasoning Models [50.821607345799386]
  Large Reasoning Models (LRMs) exhibit backtracking and self-verification mechanisms that enable them to revise intermediate steps and reach correct solutions.<n>We investigate how current LRMs recover from errors by inserting arithmetic mistakes in their intermediate reasoning steps.<n>We find that despite the error propagating through the chain-of-thought, the model still reaches the correct final answer.
  arXiv  Detail & Related papers  (2026-03-17T10:03:30Z)
• Guideline-Grounded Evidence Accumulation for High-Stakes Agent Verification [60.18369393468405]
  Existing verifiers usually underperform owing to a lack of domain knowledge and limited calibration.<n>GLEAN compiles expert-curated protocols into trajectory-informed, well-calibrated correctness signals.<n>We empirically validate GLEAN with agentic clinical diagnosis across three diseases from the MIMIC-IV dataset.
  arXiv  Detail & Related papers  (2026-03-03T09:36:43Z)
• Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization [58.390885294401066]
  Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>We propose AlignRAG, a novel iterative framework grounded in Critique-Driven Alignment (CDA)<n>We introduce AlignRAG-auto, an autonomous variant that dynamically terminates refinement, removing the need to pre-specify the number of critique iterations.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)

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.