Agentified Assessment of Logical Reasoning Agents

• URL: http://arxiv.org/abs/2603.02788v2
• Date: Fri, 06 Mar 2026 20:38:20 GMT
• Title: Agentified Assessment of Logical Reasoning Agents
• Abstract summary: Building on agentified assessment, we use an assessor agent to issue tasks, enforce execution budgets, parse outputs, and record structured failure types.<n>As a case study, we benchmark an auto-formalization agent for first-order logic (FOL) reasoning on a solver-verified and repaired split of FOLIO.<n>The auto-formalization agent achieves 86.70% accuracy under the assessor protocol, outperforming a chain-of-thought baseline (73.89%)
• Score: 3.5548629490839594
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a framework for evaluating and benchmarking logical reasoning agents when assessment itself must be reproducible, auditable, and robust to execution failures. Building on agentified assessment, we use an assessor agent to issue tasks, enforce execution budgets, parse outputs, and record structured failure types, while the agent under test only needs to expose a standardized agent-to-agent interface. As a case study, we benchmark an auto-formalization agent for first-order logic (FOL) reasoning on a solver-verified and repaired split of FOLIO. The agent translates natural language premises and conclusions into executable Z3Py programs and employs satisfiability modulo theories (SMT) solving to determine logical entailment. On the cleaned FOLIO validation set, the auto-formalization agent achieves 86.70% accuracy under the assessor protocol, outperforming a chain-of-thought baseline (73.89%).

Related papers

• READY or Not: Reliable Enterprise Agent Deployment [16.430548759635037]
  READY is a framework for qualifying AI agents for deployment on enterprise.<n>It measures the reliability and operating cost of the human-AI system.<n>It statistically qualifies it on held-out cases.
  arXiv  Detail & Related papers  (2026-09-02T04:34:48Z)
• What Benchmarks Don't Measure: The Case for Evaluating Abstention Competence in Autonomous Agents [2.8340200313563773]
  Benchmarks for autonomous agents measure whether agents complete tasks, yet this framing is systematically blind to whether an agent should have proceeded at all.<n>Agents trained under human-feedback objectives develop a structural tendency to proceed even when they lack the inputs, evidence, or authorization to act safely.<n>We introduce a three-gap taxonomy of abstention-warranted scenarios, covering specification gaps where required information is absent, verification gaps where world state cannot be confirmed, and authority gaps where explicit authorization has not been given.
  arXiv  Detail & Related papers  (2026-06-01T23:52:56Z)
• Time to REFLECT: Can We Trust LLM Judges for Evidence-based Research Agents? [61.49434544687523]
  We introduce REFLECT, a meta-evaluation benchmark targeting fine-grained failure detection in agentic environments.<n> REFLECT defines a detailed taxonomy of process- and outcome-level failure modes, instantiated by performing controlled and localized interventions.<n>Our experiments show that even the best-performing models achieve overall accuracies below 55% across reasoning, tool-use, and report-quality failures.
  arXiv  Detail & Related papers  (2026-05-18T23:55:08Z)
• Reasoning Provenance for Autonomous AI Agents: Structured Behavioral Analytics Beyond State Checkpoints and Execution Traces [0.0]
  Agent Execution Record (AER) is a structured reasoning primitive that captures intent, observation, and inference as first-class queryable fields on every step.<n>We show how AERs enable population-level behavioral analytics: reasoning pattern mining, confidence calibration, cross-agent comparison, counterfactual regression testing via mock replay.
  arXiv  Detail & Related papers  (2026-03-23T08:27:54Z)
• Agentic Code Reasoning [6.246212222645163]
  We introduce semi-formal reasoning: a structured prompting methodology that requires agents to construct explicit premises, trace execution paths, and derive formal conclusions.<n>We evaluate three tasks (patch equivalence verification, fault localization, and code question answering) and show that semi-formal reasoning consistently improves accuracy.<n>These results demonstrate that structured agentic reasoning enables meaningful semantic code analysis without execution.
  arXiv  Detail & Related papers  (2026-03-02T14:17:06Z)
• The Necessity of a Unified Framework for LLM-Based Agent Evaluation [46.631678638677386]
  General-purpose agents have seen fundamental advancements.<n> evaluating these agents presents unique challenges that distinguish them from static QA benchmarks.<n>We propose that a unified evaluation framework is essential for the rigorous advancement of agent evaluation.
  arXiv  Detail & Related papers  (2026-02-03T08:18:37Z)
• Gaming the Judge: Unfaithful Chain-of-Thought Can Undermine Agent Evaluation [76.5533899503582]
  Large language models (LLMs) are increasingly used as judges to evaluate agent performance.<n>We show this paradigm implicitly assumes that the agent's chain-of-thought (CoT) reasoning faithfully reflects both its internal reasoning and the underlying environment state.<n>We demonstrate that manipulated reasoning alone can inflate false positive rates of state-of-the-art VLM judges by up to 90% across 800 trajectories spanning diverse web tasks.
  arXiv  Detail & Related papers  (2026-01-21T06:07:43Z)
• ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimization [73.0780809974414]
  We propose a Reflective Autoformalization method that integrates semantic consistency evaluation into the autoformalization process.<n>This enables the model to iteratively generate formal statements, assess its semantic fidelity, and self-correct identified errors.<n>Experiments show that ReForm achieves an average improvement of 22.6 percentage points over the strongest baselines.
  arXiv  Detail & Related papers  (2025-10-28T16:22:54Z)
• Alita-G: Self-Evolving Generative Agent for Agent Generation [54.49365835457433]
  We present ALITA-G, a framework that transforms a general-purpose agent into a domain expert.<n>In this framework, a generalist agent executes a curated suite of target-domain tasks.<n>It attains strong gains while reducing computation costs.
  arXiv  Detail & Related papers  (2025-10-27T17:59:14Z)
• What Is Your Agent's GPA? A Framework for Evaluating Agent Goal-Plan-Action Alignment [3.5583478152586756]
  Agent GPA is an evaluation paradigm based on an agent's operational loop of setting goals, devising plans, and executing actions.<n>The framework includes five evaluation metrics: Goal Fulfillment, Logical Consistency, Execution Efficiency, Plan Quality, and Plan Adherence.
  arXiv  Detail & Related papers  (2025-10-09T22:40:19Z)
• Towards Verified Code Reasoning by LLMs [6.973151264926856]
  We describe a method to automatically validate the answers provided by a code reasoning agent.<n>The method consists of extracting a formal representation of the agent's response and, subsequently, using formal verification and program analysis tools.
  arXiv  Detail & Related papers  (2025-09-30T17:17:51Z)
• VulAgent: Hypothesis-Validation based Multi-Agent Vulnerability Detection [55.957275374847484]
  VulAgent is a multi-agent vulnerability detection framework based on hypothesis validation.<n>It implements a semantics-sensitive, multi-view detection pipeline, each aligned to a specific analysis perspective.<n>On average, VulAgent improves overall accuracy by 6.6%, increases the correct identification rate of vulnerable--fixed code pairs by up to 450%, and reduces the false positive rate by about 36%.
  arXiv  Detail & Related papers  (2025-09-15T02:25:38Z)
• Establishing Best Practices for Building Rigorous Agentic Benchmarks [94.69724201080155]
  We show that many agentic benchmarks have issues in task setup or reward design.<n>Such issues can lead to under- or overestimation of agents' performance by up to 100% in relative terms.<n>We introduce the Agentic Benchmark Checklist (ABC), a set of guidelines that we synthesized from our benchmark-building experience.
  arXiv  Detail & Related papers  (2025-07-03T17:35:31Z)
• VerifiAgent: a Unified Verification Agent in Language Model Reasoning [10.227089771963943]
  We propose a unified verification agent that integrates two levels of verification: meta-verification and tool-based adaptive verification.<n>VerifiAgent autonomously selects appropriate verification tools based on the reasoning type.<n>It can be effectively applied to inference scaling, achieving better results with fewer generated samples and costs.
  arXiv  Detail & Related papers  (2025-04-01T04:05:03Z)
• Agent-as-a-Judge: Evaluate Agents with Agents [61.33974108405561]
  We introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems. This is an organic extension of the LLM-as-a-Judge framework, incorporating agentic features that enable intermediate feedback for the entire task-solving process. We present DevAI, a new benchmark of 55 realistic automated AI development tasks.
  arXiv  Detail & Related papers  (2024-10-14T17:57:02Z)

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.