State-Centric Decision Process
Sungheon Jeong, Ryozo Masukawa, Sanggeon Yun, Mahdi Imani, Mohsen Imani
Abstract
Language environments such as web browsers, code terminals, and interactive simulations emit raw text rather than states, and provide none of the runtime structure that MDP analysis requires. No explicit state space, no observation-to-state mapping, no certified transitions, and no termination criterion. We introduce the State-Centric Decision Process (SDP), a runtime framework that constructs these missing inputs by having the agent build them, predicate by predicate, as it acts. At each step the agent commits to a natural-language predicate describing how the world should look, takes an action to make it true, and checks the observation against it. Predicates that pass become certified states, and the resulting trajectory carries the four objects language environments do not provide, namely a task-induced state space, an observation-to-state mapping, certified transitions, and a termination criterion. We evaluate SDP on five benchmarks spanning planning, scientific exploration, web reasoning, and multi-hop question answering. SDP achieves the best training-free results on all five, with the advantage widening as the horizon grows. The certified trajectories additionally support analyses unavailable to reactive agents, including per-predicate credit assignment, failure localization, partial-progress measurement, and modular operator replacement.
AI Impact Assessments
(1 models)Scientific Impact Assessment: State-Centric Decision Process
1. Core Contribution
The paper identifies a precise structural gap: language environments (web browsers, terminals, simulators) emit raw text but provide none of the formal objects MDP analysis requires—no state space, no observation-to-state mapping, no certified transitions, no termination criterion. The proposed solution, SDP, inverts the standard agent design by having the agent commit to natural-language predicates (desired future states) before acting, then verify whether observations satisfy them. This produces four operators—PROPOSE, REALIZE, VALIDATE, REPLAN—that together construct an MDP at runtime, predicate by predicate.
The key intellectual move is reframing the decision variable from actions to states. Rather than solving argmax_a P(success|h_t, a), SDP decomposes the problem into an outer optimization over predicate chains in Σ^n and an inner per-step action selection conditioned on the next predicate. This separation is conceptually clean and has concrete consequences: plan entries become verifiable (predicates have truth values), the plan is decoupled from the action space, and execution failures are distinguished from planning failures.
2. Methodological Rigor
Formal framework. The formalization (Definition 1) is precise and the four operators have well-defined signatures. Proposition 1's Markov property is correctly stated as conditional on an assumption about environment responses depending on history only through (s_ti, P_i). This is honest—the authors acknowledge the assumption rather than claiming unconditional Markov guarantees.
Experimental breadth. Five benchmarks across meaningfully different domains (constraint satisfaction, interactive simulation, web QA, multi-hop reasoning) provide good coverage. The benchmarks exercise different SDP mechanisms: TravelPlanner tests deterministic validation, ScienceWorld tests LLM-based validation and long horizons, AssistantBench tests tool-augmented realize, and HotpotQA/MuSiQue test per-hop certification.
Weaknesses in rigor. The ablation methodology (Section 4.3/Appendix C) is the most significant weakness. Ablations are performed via replay estimation with counterfactual rescaling rather than actual re-execution. The authors acknowledge this produces "optimistic bounds," but this fundamentally limits the reliability of the ablation conclusions. The conversion factors (action fidelity, certified-prefix ratio) are reasonable heuristics but not substitutes for true ablations.
Baseline comparisons are somewhat heterogeneous. Different baselines use different LLM backbones (GPT-4o, GPT-4T, Gemini variants, GPT-3), making direct attribution of gains to the framework versus the backbone difficult. The authors are transparent about this and provide backbone-matched comparisons where possible, but the overall picture is muddied.
3. Potential Impact
Immediate practical value. The framework provides a concrete way to add structured verification to any LLM agent pipeline. The adapter-based design (common core loop, domain-specific adapters) makes adoption relatively straightforward. The diagnostic artifacts—failure localization, partial-progress measurement, cascade analysis—address real engineering pain points when debugging long-horizon agent failures.
Enabling future work. The more significant potential impact is as an interface layer. By producing certified (s, a, s') tuples, SDP makes offline RL, credit assignment, and policy optimization well-posed problems on language agent trajectories. The authors frame this as a research program (Section 6) rather than claiming to have solved these downstream problems—an honest and potentially generative framing.
Limitations on impact. The framework inherits LLM reliability as a ceiling. VALIDATE's false positive rate (21% on HotpotQA, 40% on MuSiQue) means "certified" states carry substantial uncertainty. The term "certified" may overstate the guarantee. Additionally, the increased LLM call count per environment step raises cost concerns that could limit practical adoption.
4. Timeliness & Relevance
The paper addresses a genuine and timely bottleneck. As language agents tackle increasingly complex tasks, the lack of formal structure for monitoring, debugging, and improving multi-step trajectories becomes acute. The observation that language environments fundamentally cannot supply MDP structure (because useful state abstractions are goal-dependent) is well-argued and fills a conceptual gap in the literature.
The positioning relative to reactive agents (ReAct), reflective agents (Reflexion), action planners (Tree of Thoughts), and world models is thorough and precise, identifying what each provides and what SDP adds.
5. Strengths & Limitations
Strengths:
Limitations:
Overall Assessment
This is a well-conceived framework paper that identifies a real structural gap and proposes a clean solution. The empirical results are strong across diverse domains, and the diagnostic analyses add genuine novelty. The main tensions are between the formal language and the empirical reality of LLM-based operators, and between the promise of enabling downstream methods and the absence of any such demonstration. Nevertheless, the framework is likely to influence how the community thinks about structuring language agent trajectories.
Generated May 14, 2026
Comparison History (22)
Paper 2 has higher estimated impact: it introduces a general runtime formalism (SDP) that supplies missing MDP structure in text-only environments via certified predicates, enabling broader methodological and analytical advances (state construction, transition certification, termination criteria, credit assignment, failure localization). Its applicability spans many agent settings (web, tools, scientific exploration), making it more cross-field and timely for LLM agent evaluation/training. Paper 1 is novel and practical for efficient reasoning gains, but is narrower (token-level delegation between two models) and depends on access to a stronger “reasoning model.”
Paper 2 introduces a foundational framework (SDP) that bridges classic MDP structures with unstructured language environments, addressing a critical bottleneck in LLM agent research. Its state-centric approach has broad applicability across planning, web reasoning, and scientific exploration, enabling new forms of analysis like credit assignment and failure localization. While Paper 1 is highly innovative, it is more narrowly focused on the specific, albeit important, domain of future prediction and forecasting.
Paper 1 is more novel and broadly impactful: it proposes a new runtime formalism (SDP) that reconstructs MDP-like structure in text-only, language-mediated environments, enabling certified states/transitions and new analysis tools. Its applications span multiple AI domains (planning, web, scientific exploration, QA) and it reports strong benchmark results, suggesting wider adoption potential. Paper 2 is a practical, domain-specific empirical study on exit-parameter tuning for crypto trading; useful operationally but narrower scientifically, with methodological limitations (randomized split to mitigate regime shift) and less generalizable theoretical contribution.
Paper 1 introduces a novel theoretical framework (SDP) that addresses a fundamental gap between language environments and formal MDP structure, with broad applicability across planning, scientific exploration, web reasoning, and QA. Its contribution is conceptually deeper—providing certified states, credit assignment, and failure localization—offering new analytical capabilities. Paper 2 makes a strong engineering contribution for scaling RL training of web agents via caching and synthesis, but is more narrowly focused on web navigation. Paper 1's framework-level innovation has broader potential to influence how agents interact with text-based environments generally.
Paper 1 introduces a fundamental formalism (SDP) that bridges the gap between raw-text language environments and structured MDP analysis. By allowing agents to dynamically construct state spaces and transitions, it offers a foundational methodology applicable to a wide array of LLM agent tasks. Paper 2 presents a valuable but comparatively narrower reinforcement learning framework for skill evolution. Paper 1's broader applicability and introduction of a novel structural paradigm give it higher potential for widespread scientific impact.
Paper 2 introduces a novel theoretical framework (SDP) that addresses a fundamental gap between language-based environments and formal decision processes, with broad applicability across planning, scientific exploration, web reasoning, and QA. Its contribution is more foundational—providing certified states, transitions, and termination criteria for language agents—enabling new analyses like credit assignment and failure localization. Paper 1, while practically valuable for token reduction in LLM processing of machine data, is more narrowly focused on an engineering optimization. Paper 2's conceptual innovation has greater potential to influence multiple research directions in AI agent design.
Paper 2 addresses a fundamental bottleneck in modern AI: formalizing sequential decision-making in unstructured language environments. By bridging formal MDPs with open-ended text, it enables rigorous planning, evaluation, and analysis for LLM agents across diverse domains (web, coding, science). While Paper 1 offers a highly valuable and rigorous contribution to computational chemistry, Paper 2's methodological innovation has broader applicability and aligns perfectly with the rapidly expanding and highly impactful field of autonomous language agents.
Paper 1 introduces a fundamentally new framework (SDP) that addresses a foundational gap between language environments and formal MDP structure, with broad applicability across five diverse benchmark categories. Its contribution is more conceptually novel—creating runtime structure from unstructured text environments—and enables downstream analyses (credit assignment, failure localization) that are broadly useful. Paper 2 offers a meaningful but more incremental improvement (token-level credit assignment in RLVR for multimodal models), refining existing GRPO methods. Paper 1's broader scope, stronger novelty, and cross-domain applicability suggest higher long-term scientific impact.
Paper 2 likely has higher scientific impact due to strong methodological rigor, clear quantitative gains (large error reductions and robustness under subsampling), and direct applicability to industrial-scale surrogate modeling for physical simulations—an area with broad, immediate demand in engineering, climate, manufacturing, and scientific computing. Its contribution (measure-aware, multi-scale reweighting/partitioning) is general across operator learning pipelines and discretizations, making it widely reusable. Paper 1 is novel for LLM-agent interaction/MDP structure in language environments, but its impact may be narrower and more sensitive to benchmark choice and evaluation conventions.
Paper 2 is more novel and broadly impactful: it introduces a general runtime formalism (SDP) that supplies missing MDP structure in language/text environments, enabling certified states/transitions and new analyses. Its applicability spans many domains (web, code, scientific exploration, QA) and it reports strong training-free gains across five benchmarks, suggesting immediate real-world utility and timeliness for agentic LLM research. Paper 1 is a valuable benchmark/dataset for embodied 3D navigation, but its impact is narrower to spatial/robotics evaluation and less methodologically transformative than a new decision-process framework.
Paper 2 introduces a foundational framework (SDP) that bridges the gap between classical MDP theory and unstructured language environments. By enabling agents to dynamically construct state spaces and transitions, it opens up new avenues for rigorous RL-like analysis, credit assignment, and planning in LLM agents. While Paper 1 offers valuable insights into inference-time scaling, Paper 2's approach represents a broader methodological shift with high applicability across all interactive AI domains.
Paper 1 introduces a foundational abstraction (SDP) that bridges classical decision theory (MDPs) with modern unstructured LLM environments. By formalizing state spaces and transitions for language agents, it opens significant new avenues for rigorous planning, credit assignment, and theoretical analysis in agentic AI, offering broader conceptual impact across reinforcement learning and natural language processing than Paper 2's domain-specific VLM training improvements.
Paper 1 introduces a foundational framework (SDP) that bridges the critical gap between unstructured language environments and formal Markov Decision Processes. By enabling agents to dynamically construct state spaces and certified transitions via natural language predicates, it allows rigorous RL and planning techniques to be applied to LLMs. This theoretical and methodological innovation has broader applicability across diverse domains (web, science, QA) compared to Paper 2, which primarily offers an empirical, domain-specific analysis of world models for mobile GUI agents.
Paper 2 likely has higher impact: SDP introduces a general runtime formalism that turns unstructured language-interactive environments into analyzable decision processes with certified states/transitions/termination, enabling new evaluation and diagnostic tooling (credit assignment, failure localization) across many domains (web, code, science, QA). This is broadly applicable and timely for agentic LLM research, with strong methodological clarity (constructive state building, certification) and training-free gains across diverse benchmarks. Paper 1 is valuable but more domain-specific (negotiation/emotion modeling) and depends on complex orchestration choices that may generalize less.
Paper 2 introduces a general-purpose theoretical framework (SDP) that addresses a fundamental gap in applying MDP analysis to language-based environments. Its broad applicability across planning, web reasoning, scientific exploration, and QA, combined with novel contributions like certified trajectories and per-predicate credit assignment, gives it wider cross-field impact. Paper 1, while technically sound with strong results (86% token compression), addresses a narrower domain-specific problem (remote sensing tool selection). SDP's foundational nature makes it more likely to influence future agent architecture research broadly.
Paper 2 introduces a broadly applicable runtime framework (SDP) that reconstructs key MDP objects in text-only “language environments,” enabling certified states/transitions and richer analyses. This is novel and timely given current LLM-agent research, with immediate applications to web agents, scientific discovery loops, and long-horizon planning. The claimed benchmark breadth (five diverse tasks) and training-free gains suggest strong practical impact and cross-field relevance. Paper 1 is a solid, targeted contribution to VSA streaming memory updates, but its impact is narrower and more incremental.
Paper 1 introduces a novel runtime decision-process framework (SDP) that retrofits MDP structure into text-only environments via certified predicates, enabling training-free strong performance across diverse benchmarks and supporting new analyses (credit assignment, failure localization). Its methodological contribution is broadly applicable to many LLM-agent settings (web, code, science), making it timely and likely to influence agent evaluation and design across fields. Paper 2 is valuable and practical for legal AI, but is narrower in scope and more workflow/dataset-centric, with impact concentrated in GDPR/formalization domains.
Paper 2 introduces a novel theoretical framework (SDP) that addresses a fundamental gap between language-based environments and formal decision process theory. It has broader impact across multiple fields (planning, web reasoning, scientific exploration, QA), offers a principled formalization that could reshape how language agents are designed and analyzed, and provides new analytical capabilities (credit assignment, failure localization). Paper 1 offers an incremental efficiency improvement to machine unlearning in diffusion models—valuable but narrower in scope and conceptual contribution.
Paper 2 is more novel and broadly impactful: it introduces a general runtime framework (SDP) that supplies missing MDP structure in text-based/language environments, enabling certified states/transitions and termination—useful across planning, web interaction, scientific discovery, and QA. It shows strong empirical results on multiple diverse benchmarks and provides new analysis capabilities (credit assignment, failure localization), suggesting wide adoption potential and timeliness for LLM agents. Paper 1 is valuable but more application-specific (regulated procurement) and offers incremental impact relative to existing neurosymbolic/XAI pipelines.
Paper 2 introduces a novel theoretical framework (State-Centric Decision Process) that addresses a fundamental gap between language environments and formal MDP analysis. It provides a generalizable abstraction applicable across diverse domains (web, code, QA, planning), offers rigorous formalization with certified trajectories, and enables new analytical capabilities like credit assignment and failure localization. Paper 1, while practical, combines existing techniques (LLM + LSTM + safety filters) in a relatively narrow application domain (traffic signals) without fundamental methodological novelty, and its evaluation is limited to simulation-based comparisons against basic baselines.