MANTA: Multi-turn Assessment for Nonhuman Thinking & Alignment

• URL: http://arxiv.org/abs/2605.16301v1
• Date: Sat, 18 Apr 2026 19:51:32 GMT
• Title: MANTA: Multi-turn Assessment for Nonhuman Thinking & Alignment
• Abstract summary: MANTA is a dynamic multi-turn evaluation framework built on the Inspect AI platform.<n>We present preliminary results from evaluations of claude-sonnet-4-20250514 and openai/gpt-4o.<n>We additionally present STYLEJUDGE, a controlled four-judge study demonstrating systematic format bias in LLM-as-judge evaluation.
• Score: 0.1452394725421793
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Single-turn benchmarks such as AnimalHarmBench (AHB) have established important baselines for measuring animal welfare alignment in large language models (LLMs), but they miss a critical failure mode: models that respond appropriately when unpressured may capitulate when follow-up conversational turns introduce economic, social, or authority-based arguments. We introduce MANTA (Multi-turn Assessment for Nonhuman Thinking and Alignment), a dynamic multi-turn evaluation framework built on the Inspect AI platform that stress-tests frontier LLMs across realistic professional and everyday scenarios using adversarially generated follow-up questions. Unlike static benchmarks, MANTA generates pressure turns dynamically from each model's actual responses, producing targeted and realistic adversarial pressure. The framework evaluates models across up to 13 AHB-derived scoring dimensions on a continuous 0-1 scale. We present preliminary results from evaluations of claude-sonnet-4-20250514 and openai/gpt-4o, revealing consistent patterns: Turn 1 welfare framing is reliable but Turn 2 introduces substantial variance; evidence-based capacity attribution is the weakest dimension across all models and runs; and AI governance scenarios elicit significantly stronger welfare reasoning (mean score 0.91) than first-order practical scenarios. We additionally present STYLEJUDGE, a controlled four-judge study demonstrating systematic format bias in LLM-as-judge evaluation, with directly actionable implications for MANTA's scorer design. Code, dataset, and evaluation logs are available at https://github.com/Mycelium-tools/manta.

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