Domain-Agnostic Reasoning

• Domain-agnostic reasoning skills are the ability of an agent to apply flexible, robust inferential strategies across varied fields without relying on subject-specific training.
• Empirical benchmarks reveal significant transfer gaps, where high domain-specific performance does not guarantee success on abstract reasoning tasks, highlighting the need for improved generalization.
• Innovative training approaches, including multi-stage curricula, RL with verifiable rewards, and symbolic reasoning layers, are key to enhancing cross-domain adaptability.

Domain-agnostic reasoning skills refer to the capacity of an artificial or biological agent to deploy flexible, robust, and generalizable inferential strategies across a broad spectrum of tasks, situations, or knowledge fields, independent of any particular subject-matter expertise. These skills encompass logical consistency, analogical mapping, multi-hop inference, bias suppression, verification, and meta-cognitive control. In AI systems, domain-agnostic reasoning is distinguished from domain-specific expertise by its transferability between contexts, resistance to superficial pattern-matching, and adaptability to unfamiliar or adversarial problem instances.

1. Characterization and Measurement of Domain-Agnostic Reasoning

Recent large-scale evaluations demonstrate that high performance on domain-specific tasks (e.g., law, mathematics) does not entail commensurate success on classical tests of abstract reasoning, such as the Wason Selection Task, conjunction fallacy, and base-rate neglect (Alsagheer et al., 16 Jun 2025). For instance, while state-of-the-art LLMs such as ChatGPT-4 achieved 64.6% accuracy on Multistate Bar Exam (law) questions and 61.3% on a suite of domain-general cognitive tasks, the absence of significant Pearson correlation (r in –0.11 to +0.29, all p > 0.05) between these scores indicates weak or null transfer. This empirical dissociation persists across a wide range of model scales (∼7B to ∼1.76T parameters), suggesting that scaling alone fails to induce genuine domain-agnostic reasoning.

Comprehensive multi-domain academic benchmarks such as AcadReason (Gui et al., 13 Oct 2025) further expose the limitations of current models: even leading LLMs (GPT-5) and agentic systems (OAgents) solve only 16–34% of hard, publication-derived research problems spanning computer science, economics, law, mathematics, and philosophy, with performance varying widely by domain.

2. Cognitive and Computational Foundations

Canonical computational models attribute human domain-agnostic reasoning to structured relational representations and analogical inference mechanisms (Doumas et al., 2019). The LISA/DORA framework demonstrates that learning explicit predicate structures and dynamically binding roles (using mechanisms such as oscillatory synchrony in neural units) enables zero-shot transfer across domains (e.g., Breakout→Pong, or visual analogy tasks). Explicit relational mapping—supported by structural alignment metrics such as

$$S(p, q) = \frac{\sum_k \min(P_p(k), P_q(k))}{\sum_k \max(P_p(k), P_q(k))}$$

for predicate feature vectors—enables the agent to re-index policies and behaviors from one environment to structurally analogous states in another, achieving both expressivity and combinatorial generalization.

Graph neural network (GNN) approaches such as EpiGNN operationalize epistemic states as node embeddings: each vector dimension encodes the agent's degree of belief over possible primitive relations. Layerwise message passing then aligns with algebraic closure methods for systematic relational inference, allowing models to generalize reasoning to previously unseen, longer inference chains and aggregate disjunctive evidence from multiple paths (Khalid et al., 24 Jul 2024).

3. Architectures, Training Protocols, and Post-Training Adaptation

Several architectural and training innovations have emerged for inducing domain-agnostic reasoning in LLMs and multimodal agents:

• Curriculum and RL Approaches: Two-stage curricula that first establish core reasoning skills in mathematically aligned domains, then adapt and refine via joint RL on mixed-domain datasets, reliably boost cross-domain performance. For instance, the Reasoning Curriculum yields 61.3% average accuracy across six reasoning domains and notably increases advanced cognitive skills such as backtracking and verification (Pang et al., 30 Oct 2025).
• RL with Verifiable Rewards (RLVR) and GRPO: Explicit reward engineering, using automatic verifiers (unit tests, symbolic normalization, checklist items) and relativistic PPO variants like GRPO, supports robust policy learning across mathematical, code, and puzzle domains. Multi-domain RL (ideally tri-domain) is crucial for maximizing cross-domain gains and minimizing mutual interference (Li et al., 23 Jul 2025).
• Symbolic Reasoning Layers: Agentic frameworks such as CoreThink augment LLMs with a symbolic head that enforces structured goal decomposition, adaptive tool orchestration, and explicit per-step verification, resulting in significant OOD generalization gains on adversarial tool-calling tasks (e.g., MAVEN). The separation of symbolic planning from execution and explicit verification is key to robust transfer (Bhat et al., 27 Oct 2025).
• Text-Only Post-Training for Multimodality: Post-training vision-LLMs solely on general-domain textual chain-of-thought traces—without direct multimodal data—confers substantial transfer gains to both out-of-domain and multimodal tasks, as evidenced by X-Reasoner's consistent improvement across text, image, and medical reasoning benchmarks (Liu et al., 6 May 2025).

4. Robustness, Biases, and Failure Modes

Robustness to domain-agnostic perturbations—such as lexical (typos), lexico-semantic (synonyms), or structural (redundancy, semantic hints)—is necessary for practical deployment in noisy, unpredictable environments (Zheng et al., 2023). LLMs are most sensitive to synonym replacements, with observable drops in accuracy (ΔAcc ≈ –0.12 for GSM8K reasoning under synonym perturbations). Inserting noisy exemplars into few-shot prompts (increasing up to 8 perturbed examples) yields measurable robustness gains (ΔGain up to +0.114) in multi-hop reasoning settings.

Notable systematic biases persist: confirmation and representativeness errors (Wason Selection, conjunction fallacy), base-rate neglect, and inconsistent self-correction even under identical API settings. These biases are robust across both narrow (law) and broad (reasoning) tasks (Alsagheer et al., 16 Jun 2025), and persist in diverse LLMs regardless of pretraining scale or API determinism.

5. Meta-Reasoning and Open-World Adaptation

Open-world agents must recognize and respond to events outside their design scope using domain-general meta-knowledge (Wray et al., 16 Apr 2025). Key appraisal functions—novelty, urgency, goal-conduciveness, and degree of control—compose meta-level state annotations that are rapidly computable and domain-independent:

$A = \langle n, u, g, c, \ldots \rangle$

where, for example,

$n(S_e) = 1 - \max_a P_{pol}(S_e, a)$

quantifies model uncertainty or novelty, and

$$u(S_e) = \sigma \left( \alpha (t_{deadline} - t)^{-1} \right)$$

measures urgency. A metareasoning module then selects among fast heuristics, deliberative planning, or fallback policies, guided by this compact appraisal vector. Under time pressure and epistemic risk, this mechanism allows bounded-loss adaptation, formalized as minimizing expected loss subject to decision time constraints.

6. Curricula, Benchmarks, and Evaluation Protocols

Rigorous assessment of domain-agnostic reasoning requires benchmarks that span both domain-specific and domain-general axes. Key protocols include:

Benchmark	Coverage	Metrics	Notable Findings
AcadReason (Gui et al., 13 Oct 2025)	Math, CS, Econ, Law, Philosophy	Pass rate (Rₚ), checklist (Rⱼ)	Even SOTA agents score <40%; LLMs <20%
Multistate Bar (MBE), cognitive suite (Alsagheer et al., 16 Jun 2025)	Legal, classical psychology	Accuracy, Pearson r between domains	No significant narrow-to-broad transfer
CLUTRR, RCC-8, GraphLog (Khalid et al., 24 Jul 2024)	Family tree, spatial, temporal reasoning	Path length-specific accuracy	EpiGNN generalizes to unseen lengths, others fail
MAVEN (Bhat et al., 27 Oct 2025)	Math, Physics (adversarial, OOD)	Subgoal, tool, verification, trace fidelity	CoreThink halves generalization gap vs. raw LLM
OPT-R (AlKhamissi et al., 2023)	26 atomic reasoning skills	Accuracy per skill, ΔAcc rationales	Numerical, analogical skills benefit most
Noisy Exemplars (Zheng et al., 2023)	Math/logic (GSM8K, StrategyQA)	ΔAcc, ΔGain (robustness drop/gain)	Chain-of-thought + noisy exemplars increases robustness

Effective evaluation strategies incorporate both all-or-nothing correctness and fine-grained checklists, along with OOD generalization gaps and robustness analyses under prompt perturbations. Incorporating human expert review, dynamic hints, and tool-use traces is critical for auditing claimed reasoning competence.

7. Limitations, Open Challenges, and Research Trajectories

Current LLMs and multi-modal agents exhibit strong compartmentalization across domains, limited meta-cognitive coherence, and brittleness outside training distributions. Recognition and adaptation to out-of-scope events remain emergent properties, with explicit meta-reasoning and appraisal-driven modes a promising but partially implemented solution (Wray et al., 16 Apr 2025).

Theoretical and empirical evidence suggests that domain-agnostic reasoning hinges on:

• Learning explicit, reusable relational structures and decomposition heuristics (as in EpiGNN, LISA/DORA, CoreThink).
• Careful reward design and validation (RLVR, checklists, multi-path verification).
• Cross-domain curricula for both pretraining and RL (mathematical “anchor” domains, multi-domain SFT).
• Modular, auditable architectures that separate planning, execution, and verification, with domain-independence baked into symbolic layers.

Progress requires expanding benchmarks (AcadReason, MAVEN), integrating modular toolkits for symbolic, economic, and legal inference, and deepening the theoretical understanding of analogical transfer and meta-reasoning in high-capacity models. Closing the gap between surface “task competence” and deep, reliable domain-agnostic reasoning remains a frontier challenge in artificial general intelligence research.