GOATBench: Multi-Domain AI Benchmark

• GOATBench is a multi-faceted benchmark suite encompassing multimodal social abuse detection, embodied navigation, compositional API execution, and metaheuristic optimization.
• It employs diverse evaluation metrics such as macro F1, success rate, and API invocation accuracy to rigorously assess performance across complex, real-world scenarios.
• These benchmarks drive advancements in AI by exposing challenges in context sensitivity, compositional reasoning, and memory integration across multiple modalities.

GOATBench is a term associated with several distinct research benchmarks and methodologies, each targeting a different domain within artificial intelligence. The most prominent instances are: (1) a social meme-based abuse assessment benchmark for large multimodal models, (2) a universal embodied navigation benchmark for lifelong multi-modal scenarios, (3) a suite of synthetic, goal-oriented API execution tasks evaluating language-agent reasoning and compositionality, and (4) its use as a label for a bio-inspired metaheuristic optimization technique. Each benchmark advances its respective field by compelling models or algorithms to operate at the edge of context sensitivity, compositionality, reasoning, or global search.

1. Meme-Based Social Abuse Detection Benchmark

GOAT-Bench, as defined in the context of social media meme analysis, is a comprehensive benchmark for evaluating the sensitivity of large multimodal models (LMMs) to nuanced forms of social abuse manifested in memes (Lin et al., 3 Jan 2024). It evaluates a model’s ability to detect not only explicit but also subtle, context-dependent abuse that arises from the interplay of text and imagery, reflecting real-world complexities faced by content moderation systems.

Key Features

• Task Coverage: GOAT-Bench comprises five abuse axes: hatefulness, misogyny, offensiveness, sarcasm, and harmfulness, each corresponding to a task-specific meme subset.
• Dataset Construction: The benchmark consists of 6,626 memes, each represented as $M = \{\mathcal{i}, \mathcal{T}\}$, where $\mathcal{i}$ is the image and $\mathcal{T}$ the text. Data are sourced from established datasets: FHM (hateful), MAMI (misogynistic), MultiOFF (offensive), MSD (sarcastic), and Harm-C/Harm-P (harmful).
• Distribution per Task:

Abuse Type	Total	Positive	Negative
Hatefulness	2000	750	1250
Misogyny	1000	500	500
Offensiveness	743	305	438
Sarcasm	1820	910	910
Harmfulness	1063	444	619

• Evaluation Protocol: Tasks use binary classification with a templated prompt:

  Given the meme, with the text [𝒯] accompanied by the image [𝒾], is this meme [adjective]?

  Here, [adjective] refers to the abuse type under test.

Experimental Findings

• Performance: GPT-4V achieved 70.29% macro F1 (accuracy ~72.17%), with open-source LMMs typically under 62% F1.
• Error Analysis: Models demonstrate insensitivity to implicit abuse, struggling especially with historical, cultural, or multimodal interactions.
• Task Variance: Some models detect explicit abuse reliably but exhibit inconsistent performance on sarcasm and subtle implications.

AI Safety Implications

• Risks: Insufficient detection of nuanced abuse can propagate or amplify harm.
• Recommendations: The benchmark underscores urgent research needs: enhancing prompt engineering (e.g., chain-of-thought, few-shot in-context learning), leveraging rationales (SelfAlign), and improving multimodal reasoning.

2. Embodied Multi-Modal Lifelong Navigation Benchmark

GOAT-Bench also designates a benchmark for advancing the field of lifelong embodied navigation in multi-modal settings (Khanna et al., 9 Apr 2024). Its central challenge is the "GO to AnyThing" (GOAT) task: sequential navigation to diverse, open-vocabulary targets specified by category, natural language, or image, within a persistent scene.

Task Characteristics

• Goal Modalities: Agents must process targets expressed as category names, natural language, or image queries in unseen 3D environments.
• Sequential Lifelong Structure: Instead of episodically resetting after each goal, agents must chain 5–10 subtasks, accumulating knowledge and leveraging memory across a continuous session.

Benchmarking Methodologies

• Modular Approaches: Systems use semantic and instance-specific memory maps to handle perception and planning separately.
  • Examples: pixel-clustering for category, keypoint and feature matching for image/language.
• SenseAct-NN Approaches: End-to-end RL using multimodal encoders (e.g., CLIP, BERT) and recurrent units (GRU) for implicit memory.
  • Skill Chains use modality-specialized policies.
  • Monolithic policies unify all modalities in one architecture.

Evaluation Metrics

• Success Rate (SR): Proportion of goals reached.
• Success Weighted by Path Length (SPL): Path efficiency relative to optimal length.

Empirical Results

• Skill Chains outperform monolithic RL in SR (2.9–4.6% higher), though modular approaches are more efficient (SPL ~4.7–9.2% higher).
• Explicit memory structures yield 1.5× SPL improvement; implicit (GRU) memory benefits SPL by 1.9× in later subtasks but suffers from forgetting.
• Robustness: Skill Chains exhibit greater resilience to paraphrase or sensory noise in goals.

Research Directions

• Memory Integration: Enhancing implicit-explicit memory hybridization remains an open problem.
• Goal Encoding: Improving fine-grained, instance-level representation for composite queries is required.
• Real-World Transferability: Migration from simulation (e.g., HM3DSem) to real robotic platforms represents a future challenge.

3. Goal-Oriented API Execution Benchmark

GOATBench also refers to a scalable, synthetic benchmark for testing goal-oriented tool-using language agents, as introduced alongside the GOAT agent-training framework (Min et al., 14 Oct 2025). It assesses an agent’s proficiency at decomposing high-level goals into compositional, interdependent API call sequences.

Benchmark Construction

• Synthetic Generation: GOATBench tasks are created by parsing API documentation into a dependency graph $G=(\mathcal{V}, \mathcal{E})$, sampling connected subgraphs, and generating natural-language subqueries and compositional API call chains.
  • Edges $(n_i, n_j, k)$ indicate use of API $n_i$’s output as argument $k$ to $n_j$.
• Task Types:
  • Single Tool: multiple calls to APIs from the same tool.
  • Inter Tool: calls spanning different tools.

Evaluation Metrics

• API Selection Accuracy (SA): Assesses overlap between predicted and gold API sets; measured by Jaccard similarity.
• API Invocation Accuracy (IA): Evaluates correctness of API name and argument values for each call.
• Success Rate (SR): GPT-4.1 judged correctness of the final composite output response.

Results and Comparison

• Scalability: Fully automatic task generation enables coverage across domains (financial, food, entertainment, travel), unlike manual datasets (e.g., RestBench, API-Bank).
• Performance: GOAT-trained Llama3-8B-Instruct improved SA from 10–20% (baseline) to nearly 60–70% (Single Tool), IA and SR showed similar gains over baselines.
• Coverage and Difficulty: GOATBench requires interdependent, compositional planning, unlike single-step benchmarks.

Underlying Formalisms

• API Graph Pruning: Edge inclusion is grounded in cosine similarity of Sentence-BERT embeddings, with task-dependent thresholds ($\tau=0.2$ for GOATBench).

4. Optimization Algorithms and the “GOATBench” Moniker

GOATBench may also be encountered adjectivally to describe standard benchmarks used for performance assessment of the Goat Optimization Algorithm (GOA) (Nozari et al., 4 Mar 2025), a metaheuristic inspired by adaptive foraging, strategic movement, and parasite avoidance in goats.

Algorithmic Principles

• Exploration: $x_i(t+1)=x_i(t)+a\cdot R\cdot (UB-LB)$ for random probing.
• Exploitation: $x_i(t+1)=x_i(t)+\beta\cdot(x_{\text{best}}-x_i(t))$ for refinement.
• Jump Strategy: $x_i(t+1)=x_i(t)+J\cdot(x_{\text{rand}}-x_i(t))$ to escape local optima.
• Filtering: The worst 20% of solutions are regenerated, mimicking parasite avoidance.
• Complexity: $O(N\cdot T_{\max}\cdot d)$.

Performance and Analysis

• Benchmarking: Empirical studies on unimodal/multimodal functions (Sphere, Rastrigin, Ackley) show faster convergence, lower best fitness, and greater solution accuracy than PSO, GWO, GA, WOA, and ABC, with statistical significance confirmed by Wilcoxon rank-sum test.
• Applications: Noted in supply chain management, bioinformatics, and energy optimization.

Limitations

• Parameter Sensitivity: Algorithm effectiveness is highly dependent on coefficients ($a$, $\beta$, $J$).
• Computational Overhead: Additional mechanisms for diversity maintenance can increase runtime in high-dimensional spaces.

5. Comparative Summary Table

Benchmark Context	Focus	Principal Metrics
Meme-based (LMMs, social abuse) (Lin et al., 3 Jan 2024)	Multimodal abuse detection in memes	Macro F1, Accuracy
Embodied Navigation (Khanna et al., 9 Apr 2024)	Lifelong, multi-modal, multi-goal navigation	SR, SPL
API Execution (LLM-agent tool use) (Min et al., 14 Oct 2025)	Goal-oriented, compositional API planning/execution	SA, IA, SR
Optimization (GOA metaheuristic) (Nozari et al., 4 Mar 2025)	Global optimization in continuous spaces	Fitness, Convergence Rate

6. Research Impact and Future Directions

Across its multiple instantiations, GOATBench benchmarks expose deficiencies in current state-of-the-art systems’ ability to reason across modalities, maintain context, detect implicit harm, compose interdependent operations, or efficiently explore high-dimensional spaces. In each field, the associated studies recommend:

• Enhanced hybridization of explicit and implicit memory mechanisms for navigation agents.
• Refined chain-of-thought, in-context, and rationale-augmented learning to bolster model sensitivity to nuanced abuse and compositional tasks.
• Improved multimodal encoders attuned to fine-grained, contextualized representations.
• Development of parameter-adaptive and hybridized metaheuristics in optimization.

A plausible implication is that GOATBench standards will accelerate the evolution of robust, context-sensitive, and trustworthy AI systems across natural language, vision, robotics, and optimization domains.