Agentic Reasoning Framework

• Agentic reasoning frameworks are systems that model LLMs and multimodal models as decision-making agents with explicit planning, tool selection, and memory integration.
• They decompose complex tasks into modular workflows involving perception, action, and feedback, enabling robust reasoning in dynamic, open-world environments.
• Advanced optimization via supervised fine-tuning and reinforcement learning refines these frameworks for improved tool use, error recovery, and real-world applicability.

Agentic reasoning frameworks represent a paradigm in which machine learning systems—primarily LLMs and multimodal foundation models—are reinterpreted as decision-making agents embedded within interactive, partially observable environments. These frameworks decompose complex problem-solving into explicit modules for planning, perception, tool-use, search, memory, adaptation, and feedback, unifying reasoning and action over multi-step workflows. They are distinguished from static, monolithic systems by their explicit orchestration of latent “thought” processes and external tool interactions, allowing for adaptive, robust, and interpretable reasoning under open-world and dynamic deployment scenarios (Wei et al., 18 Jan 2026, Liang et al., 12 Jun 2025, Zhao et al., 25 Aug 2025).

1. Formalization and Taxonomy

Agentic reasoning is most rigorously characterized as a partially-observable Markov decision process (POMDP) or, more commonly, as a Markov decision process (MDP) with an extended state that includes internal reasoning traces, action/control history, and privileged memory (Wei et al., 18 Jan 2026). This formalism enables a two-stage policy:

• Internal reasoning selection: $z_t \sim \pi_{\mathrm{reason}}(z_t \mid h_t)$
• External action: $a_t \sim \pi_{\mathrm{exec}}(a_t \mid h_t, z_t)$

where $h_t$ summarizes observations $o_{1:t}$, thought tokens $z_{1:t-1}$, action history $a_{1:t-1}$, and episodic memory $m_t$.

Frameworks are commonly organized taxonomically along three major axes (Zhao et al., 25 Aug 2025):

Category	Description	Core Example Systems
Single-agent	Fixed LLM or model with reasoning, reflection, and memory	Reflexion, ToT
Tool-based	Agent + dynamic tool API set (search, code, perception)	ReAct, Toolformer
Multi-agent	Multiple roles/agents (e.g. Solver, Verifier, Corrector)	MarsRL, MetaGPT, GLARE

This taxonomy underpins application in domains such as scientific discovery, code generation, healthcare, law, and multimodal understanding (Shopnil et al., 20 Oct 2025, Kurpath et al., 18 Dec 2025, Yang et al., 22 Aug 2025).

2. Core Methodologies in Agentic Reasoning

2.1 In-Context Orchestration

In-context agentic frameworks operate with frozen model parameters, utilizing sophisticated prompting, modular tool selection, and memory interfaces to dynamically plan, act, and refine responses at inference time (Wei et al., 18 Jan 2026, Zhao et al., 25 Aug 2025). The system proceeds through sequences of “thought–tool–observation” interaction cycles (as in ReAct or Tree-of-Thoughts):

1. Generate a latent plan or hypothesis via CoT, ToT, or a designer policy.
2. Invoke tools (retrievers, calculators, code sandboxes, APIs) according to explicit policy or gated by action selection heads.
3. Integrate tool outputs and feedback into a working memory for subsequent reasoning or further tool invocation.
4. Continue until a termination predicate on the state/context is satisfied ($Q(\mathcal{C}_k, k)$).

Tool selection can be explicit (API call tokens) or latent (autoregressive controller, action head in SFT/RL), supporting both sequential and parallel tool utilization (Singh et al., 28 Apr 2025, Shopnil et al., 20 Oct 2025). Memory modules, structured as graphs (e.g., Mind-Map (Wu et al., 7 Feb 2025)), workflows (Wang et al., 30 Sep 2025), or simple replay buffers, allow for context persistence and intermediate feedback integration.

2.2 Post-Training Optimization

Agentic reasoning policies can also be refined by supervised fine-tuning (SFT) or reinforcement learning (RL) on proper agentic datasets. The dominant RL algorithm is Group-Relative Proximal Policy Optimization (GRPO) (Wei et al., 18 Jan 2026, Singh et al., 28 Apr 2025, Shang et al., 28 Aug 2025, Liu et al., 14 Nov 2025), with outcome-based rewards for final answer correctness, tool invocation quality, and format adherence. The reward function typically decomposes as:

$$R(y) = R_{\text{answer}}(y) + R_{\text{format}}(y) + R_{\text{tool}}(y),$$

where tool-reward terms encourage successful, succinct, and contextually valid usage. Training involves grouped rollouts and special masking of tool-output tokens in the loss, enabling stable RL for text-only and multi-turn, tool-augmented tasks (Shang et al., 28 Aug 2025, Du et al., 8 Jul 2025).

2.3 Multi-Agent and Modular Architectures

Agentic frameworks often decompose complex workflows into pipelines of specialized agents or modules. Roles may include Solver, Verifier, Corrector (Liu et al., 14 Nov 2025, Yang et al., 22 Aug 2025), or domain-specialized agents such as Dreamer/Thinker/Spotter (Zhang et al., 16 Dec 2025) or visual/veracity/retrieval/judgment agents (MIRAGE (Shopnil et al., 20 Oct 2025)). Each module communicates via structured context or formal subgraph representations, enabling agentic pipeline parallelism and precise credit assignment under RL.

In multi-agent orchestration, role communication is coordinated by centralized or decentralized policies, and agent-specific rewards are employed to align gradient signals with individual agent objectives, which substantially reduces credit noise and drives generalization across models (Liu et al., 14 Nov 2025).

3. Representative Frameworks and Benchmarks

3.1 MIRAGE: Multimodal Misinformation Detection

MIRAGE demonstrates an inference-time, model-pluggable agentic framework with a sequential four-module pipeline—visual veracity assessment, cross-modal consistency, retrieval-augmented fact-checking, and calibrated judgment. The system achieves $81.65$ F1 and $75.1\%$ accuracy on MMFakeBench, outperforming zero-shot baselines and demonstrating superior generalization without domain-specific training (Shopnil et al., 20 Oct 2025).

3.2 DyFlow: Dynamic Workflow Generation

DyFlow exemplifies dynamic designer–executor separation, where the designer decomposes problems into feedback-driven, stage-wise operator subgraphs, and the executor (an arbitrary LLM or tool chain) realizes each subgoal. DyFlow achieves substantial improvement across diverse domains, outperforming prior static and template-based workflows (Wang et al., 30 Sep 2025).

3.3 SAGE-32B: Inverse Reasoning and Meta-Cognitive Forecasting

SAGE-32B introduces a meta-cognitive (“inverse reasoning”) head for failure forecast, paired with iterative distillation training. This enables hybrid-mode inference, toggling between fast autoregression and expensive look-ahead simulation. On agentic benchmarks, the model attains high accuracy and recovery rates in multi-tool, long-range planning scenarios (Jha et al., 4 Jan 2026).

3.4 MarsRL: Multi-Agent Pipeline-Parallel RL

MarsRL advances multi-agent systems by factorizing reward signals across Solver, Verifier, and Corrector roles and orchestrating pipeline-parallel training. This approach achieves state-of-the-art results on mathematical and beyond-math benchmarks (AIME2025, BeyondAIME), surpassing larger open-source models (Liu et al., 14 Nov 2025).

3.5 GLARE: Legal Judgment Prediction

GLARE orchestrates charge expansion, precedent retrieval, and legal search modules under LLM control with dynamic knowledge acquisition, yielding interpretable, syllogistic reasoning chains and improving legal judgment prediction accuracy and interpretability (Yang et al., 22 Aug 2025).

4. Empirical Patterns and Methods Assessment

Agentic frameworks excel where reasoning tasks demand multi-step planning, external knowledge integration, adaptive tool use, and robust recovery from errors or environmental uncertainty (Du et al., 8 Jul 2025, Liu et al., 7 May 2025). Extensive ablation studies show:

• Agentic RL outperforms static prompting and script-based tool use, delivering consistent gains in pass@1, F1, and human-rubric metrics (Singh et al., 28 Apr 2025, Du et al., 8 Jul 2025, Shang et al., 28 Aug 2025).
• Modular agentic pipelines distributing reward and context updates by role enable not only accuracy gains but also interpretable and auditable reasoning (Liu et al., 14 Nov 2025, Shopnil et al., 20 Oct 2025, Wang et al., 30 Sep 2025).
• Toolset size and quality must be carefully curated: excessive tool proliferation degrades performance, while focused core agent sets (e.g., Web-Search, Code, Mind-Map) yield synergistic gains (Wu et al., 7 Feb 2025).

5. Open Challenges and Trends

Agentic reasoning frameworks face several open challenges (Wei et al., 18 Jan 2026, Liang et al., 12 Jun 2025, Liu et al., 7 May 2025):

• Long-horizon memory and credit assignment: Mitigating error accumulation and ensuring coherent context across extended interactions.
• Tool and API integration: Richer tool schemas (filters, arguments), multi-modal and dynamic tool orchestration.
• Generalization and efficiency: Handling unseen tools, shifting environments, search loop avoidance, and adaptive stopping.
• Multi-agent coordination: Discovering optimal collaboration and communication hierarchies, trust, and reward allocation.
• Interpretability and governance: Structured logging, rationale tracing, uncertainty exposure, and human-in-the-loop review.
• Safety: Auditable policies and fine-grained control of autonomous agent actions in high-stakes domains.

Emerging research incorporates meta-reasoning heads, explicit memory modules, dual-strategy distillation, and cross-disciplinary insights from neuroscience for more robust and cognitively aligned agentic reasoning (Liu et al., 7 May 2025, Jha et al., 4 Jan 2026).

6. Impact and Future Directions

Agentic reasoning frameworks unlock robust, adaptive, and interpretable reasoning in open-ended and multi-modal environments, closing the gap between static model inference and real-world interactive autonomy (Wei et al., 18 Jan 2026, Shopnil et al., 20 Oct 2025, Kurpath et al., 18 Dec 2025, Zhu et al., 26 Sep 2025). Current trends point toward:

• Increased deployment of modular, feedback-driven, and multi-agent architectures.
• Expansion of benchmarks and evaluation strategies to capture agentic patterns (explore/exploit/revisit), memory management, and grounded tool use (Zhu et al., 26 Sep 2025, Zhang et al., 16 Dec 2025).
• Integration of cognitive neuroscience principles and hierarchical, hybrid memory systems for continual adaptation and transfer.
• Emphasis on scalable, test-time orchestration, and hybrid in-context/post-training optimization for efficient, safe deployment.

Agentic reasoning thus constitutes the unifying foundation for next-generation intelligent systems capable of autonomous, long-horizon problem-solving across domains ranging from science and law to embodied multimodal AI (Wei et al., 18 Jan 2026, Zhao et al., 25 Aug 2025, Liu et al., 7 May 2025).