Problem Analysis Agent

• Problem Analysis Agent is an agentic subsystem that transforms ambiguous inputs like execution traces and narratives into structured objects such as causal graphs, decompositions, and specifications.
• It supports downstream tasks by enabling efficient decision-making, runtime verification, and resource-aware commitment through various methodologies like AgentTrace and ACP.
• It integrates techniques from closed-loop control to collaborative debate, ensuring precise evaluation and improved scalability in complex multi-agent systems.

to=arxiv_search 彩神争霸高 日本一本道json {"query":"Problem Analysis Agent AgentTrace Agent Capability Problem Agentic Problem Frames multi-agent problem analysis", "max_results": 10, "sort_by": "submittedDate"}/Subthreshold A Problem Analysis Agent is an agentic subsystem that converts ambiguous inputs, execution traces, or domain narratives into structured objects that support downstream decision-making, diagnosis, or action. Across recent arXiv work, this role appears in several technically distinct forms: post-hoc root-cause analysis over deployed multi-agent logs, a-priori solvability prediction under resource constraints, closed-loop specification and verification for domain agents, open-ended task decomposition for mathematical modeling, collaborative debate for clinical problem detection, and tool-mediated translation of natural-language problems into executable formal models (Wang, 16 Mar 2026). Taken together, these systems treat “problem analysis” not as a single algorithmic primitive but as a family of procedures for constructing explicit representations such as causal graphs $G=(V,E)$, dynamic specifications $S_t$, task decompositions $D=\{\mathcal D_1,\ldots,\mathcal D_n\}$, or effective-cost estimates $C_{\mathrm{eff}}$.

1. Formal scope and representational foundations

The most explicit formalizations define problem analysis as a mapping from unstructured or weakly structured input into analyzable state. In AgentTrace, the input is a timestamp-ordered execution log $T$ of a deployed multi-agent workflow, with log entries corresponding to agent actions of types such as tool-call, message-send, message-receive, and decision; the output is a ranked list of candidate root causes $C_{\mathrm{ranked}}$, ideally the earliest node or nodes whose correction prevents the downstream error (Wang, 16 Mar 2026). The formal objects are $V=\{v_1,\ldots,v_n\}$, causal edges $E\subseteq V\times V$, the causal graph $G=(V,E)$, an observed error event $v_{\mathrm{error}}$, and an unknown true root-cause node $S_t$0.

In the Agent Capability Problem, the problem is cast as uncertainty over a hypothesis space $S_t$1 with goal set $S_t$2. A task is “solvable” if the true solution $S_t$3 lies in $S_t$4, and the central quantities are the total information required,

$S_t$5

the information gain per action,

$S_t$6

and the resource cost $S_t$7 (Lutati, 8 Dec 2025). Here, problem analysis is an a-priori estimation task: before substantial search, the agent estimates whether available budget is compatible with the information needed to solve the problem.

Agentic Problem Frames formulates the same role in control-theoretic terms. Intent $S_t$8, environment $S_t$9, and a fixed requirement $D=\{\mathcal D_1,\ldots,\mathcal D_n\}$0 are combined with the agent’s current knowledge base $D=\{\mathcal D_1,\ldots,\mathcal D_n\}$1 to produce a dynamic specification,

$D=\{\mathcal D_1,\ldots,\mathcal D_n\}$2

while the instantaneous error is defined as $D=\{\mathcal D_1,\ldots,\mathcal D_n\}$3 with the control objective $D=\{\mathcal D_1,\ldots,\mathcal D_n\}$4 (Park, 22 Feb 2026). In this formulation, problem analysis is not only decomposition or diagnosis; it is the repeated concretization of incomplete intent into executable specification immediately before action.

MM-Agent makes the representational turn even more explicit for open-ended mathematical modeling. The output of analysis is a detailed Problem Understanding report $D=\{\mathcal D_1,\ldots,\mathcal D_n\}$5, a decomposition $D=\{\mathcal D_1,\ldots,\mathcal D_n\}$6, a Task Dependency Graph $D=\{\mathcal D_1,\ldots,\mathcal D_n\}$7 over subtasks, and a memory placeholder $D=\{\mathcal D_1,\ldots,\mathcal D_n\}$8 that stores intermediate modeling plans, code stubs, and results (Liu et al., 20 May 2025). This suggests that, across these frameworks, the unifying property of a Problem Analysis Agent is the explicit externalization of latent problem structure into objects that can be verified, ranked, executed, or reused.

2. Closed-loop specification, verification, and jurisdiction

A major line of work treats reliable problem analysis as inseparable from runtime control and verification. Agentic Problem Frames argues that “frameless” development—ambiguous natural language without engineering blueprints—produces risks including scope creep and open-loop failures. Its proposed remedy is the Act-Verify-Refine (AVR) loop, in which the agent first generates a specification and acts, then validates the raw outcome, and finally incorporates only verified knowledge into the next cycle (Park, 22 Feb 2026).

The APF state variables are $D=\{\mathcal D_1,\ldots,\mathcal D_n\}$9, $C_{\mathrm{eff}}$0, $C_{\mathrm{eff}}$1, $C_{\mathrm{eff}}$2, and $C_{\mathrm{eff}}$3. The loop equations are organized as follows: Act computes $C_{\mathrm{eff}}$4 and $C_{\mathrm{eff}}$5; the world then produces a raw outcome $C_{\mathrm{eff}}$6; Verify computes

$C_{\mathrm{eff}}$7

Refine updates $C_{\mathrm{eff}}$8 and computes the next error signal (Park, 22 Feb 2026). The paper’s key distinction is epistemic rather than purely procedural: $C_{\mathrm{eff}}$9 is never trusted as ground truth, and only callbacks plus confirms can promote it into $T$0.

The Agentic Job Description (AJD) is the specification contract that constrains this loop. Its schema includes Mission $T$1, Workplace $T$2ContextDomain, InteractionDomain, VerificationDomain$T$3, Scope$T$4AuthorityRules, ForbiddenZones, ProfessionalIdentity$T$5, OperationalContext$T$6Contexts, Memory, Capabilities$T$7, and EvaluationMethod$T$8CallbackRules, ConfirmRules$T$9 (Park, 22 Feb 2026). For a Problem Analysis Agent, this matters because it relocates “analysis” from informal prompt reasoning to a bounded jurisdiction with explicit evaluation procedures.

The two case studies clarify the consequences of that design choice. In the delegated proxy model for business travel, callbacks include e-ticket email detection and confirm asks the user to accept the itinerary; in the autonomous supervisor model for industrial equipment management, callback rules include monitoring RPM and temperature, while confirm includes manager approval for a parts order report (Park, 22 Feb 2026). A plausible implication is that APF treats problem analysis as a control surface over business processes rather than as an isolated cognitive stage.

3. Causal diagnosis and post-hoc root-cause localization

In deployed multi-agent systems, problem analysis often appears as retrospective failure diagnosis. AgentTrace reconstructs a causal graph directly from execution logs using three edge types: sequential edges linking consecutive actions by the same agent, communication edges linking message-send to message-receive events, and data-dependency edges linking a variable producer to later consumers (Wang, 16 Mar 2026). Graph construction runs in $C_{\mathrm{ranked}}$0 time and is dominated by one pass over logs and simple lookups.

After graph construction, AgentTrace performs backward tracing from the designated error manifestation event $C_{\mathrm{ranked}}$1. The procedure collects all ancestors up to depth $C_{\mathrm{ranked}}$2, with default $C_{\mathrm{ranked}}$3, and has complexity $C_{\mathrm{ranked}}$4 (Wang, 16 Mar 2026). The resulting candidate set is then ranked by a linear weighted score over five feature groups:

• Position with weight $C_{\mathrm{ranked}}$5
• Structure with weight $C_{\mathrm{ranked}}$6
• Content with weight $C_{\mathrm{ranked}}$7
• Flow with weight $C_{\mathrm{ranked}}$8
• Confidence with weight $C_{\mathrm{ranked}}$9

The position features include normalized position, distance to error, depth ratio, and reverse position. Structure includes out-degree, in-degree, betweenness centrality, and reachability. Content includes error keywords, uncertainty keywords, length anomaly score, and keyword density. Flow includes agent-switch flag, role-criticality weight, and message flag. Confidence includes stated confidence or default $V=\{v_1,\ldots,v_n\}$0 and a hedging-language score (Wang, 16 Mar 2026). The framework is explicitly designed to avoid LLM inference at debugging time.

The evaluation uses 550 synthetic failure scenarios in 10 domains, with 8–15 steps each and five bug types: logic, comms, data, missing validation, and role confusion. The benchmark injects and verifies ground-truth root causes. On Hit@1, Hit@3, and mean reciprocal rank, AgentTrace substantially exceeds random, first-node, last-node, and GPT-4 prompt baselines: Random achieves 9.1%, 27.3%, and 0.18; First Node 3.6%, 10.9%, and 0.07; Last Node 12.7%, 38.2%, and 0.25; LLM Analysis 68.5%, 81.4%, and 0.74; AgentTrace 94.9%, 98.4%, and 0.97 (Wang, 16 Mar 2026).

The performance profile is equally central to the design. End-to-end average latency is 0.12 s per trace versus 8.3 s for GPT-4, described as an approximately 69× speedup. The mean component times are 15.2 ms for graph construction, 8.4 ms for backward tracing, 62.3 ms for feature extraction, and 28.6 ms for node ranking, for a total of 114.5 ms. The scaling relation is reported as $V=\{v_1,\ldots,v_n\}$1 ms, and traces up to 25 steps remain below 0.3 s (Wang, 16 Mar 2026). In the software-development example, AgentTrace traces from the test failure back through reviewer approval to the bug injection and ranks the bug-injection step highest, whereas GPT-4 often selects the terminal error step itself. This directly addresses a common misconception in debugging: the nearest visible failure event is not necessarily the root cause.

4. Information-theoretic solvability prediction and budget-aware commitment

Where AgentTrace analyzes failures after execution, the Agent Capability Problem analyzes feasibility before search. ACP formalizes the decision of whether an autonomous agent should commit resources to a task by treating problem solving as information acquisition (Lutati, 8 Dec 2025). The optimal action policy is defined as

$V=\{v_1,\ldots,v_n\}$2

that is, maximize expected bits gained per unit cost.

Under the optimal policy, if $V=\{v_1,\ldots,v_n\}$3 is average information gain per step and $V=\{v_1,\ldots,v_n\}$4 is average cost per step, the effective cost bound is

$V=\{v_1,\ldots,v_n\}$5

The interpretation given in the paper is operational: $V=\{v_1,\ldots,v_n\}$6 estimates the number of steps needed to acquire the necessary information, and multiplication by $V=\{v_1,\ldots,v_n\}$7 converts this into a predicted total resource cost (Lutati, 8 Dec 2025).

The theory provides both expected-cost and high-probability guarantees. If the per-step information gains $V=\{v_1,\ldots,v_n\}$8 are conditionally independent, with $V=\{v_1,\ldots,v_n\}$9, $E\subseteq V\times V$0, and $E\subseteq V\times V$1, and if $E\subseteq V\times V$2 with actual cost $E\subseteq V\times V$3, then the expected cost satisfies

$E\subseteq V\times V$4

and

$E\subseteq V\times V$5

If additionally $E\subseteq V\times V$6 almost surely, then with probability at least $E\subseteq V\times V$7,

$E\subseteq V\times V$8

These results allow the agent to reserve a margin proportional to $E\subseteq V\times V$9 for mission-critical settings (Lutati, 8 Dec 2025).

The implementation recipe for a Problem Analysis Agent under ACP is explicit: specify a prior over $G=(V,E)$0 and the indicator $G=(V,E)$1; model or approximate $G=(V,E)$2; estimate $G=(V,E)$3 for each action, for example via Gaussian process posterior or Monte Carlo; measure or assign $G=(V,E)$4; compute $G=(V,E)$5; and, if $G=(V,E)$6 exceeds budget $G=(V,E)$7, abort or defer, otherwise commit and execute the optimal action-selection policy (Lutati, 8 Dec 2025). In the noisy slope-identification and random graph 3-coloring studies, ACP’s predicted effective cost remains a lower bound on actual effort, and ACP-guided backtracking outperforms greedy and random strategies. This suggests a different meaning of “problem analysis”: not explanation after the fact, but principled refusal or commitment before expenditure.

5. Decomposition, dependency analysis, and debate-mediated interpretation

A separate family of systems equates problem analysis with decomposition and structured interpretation. MM-Agent formalizes open-ended problem analysis as the transformation of a loosely defined real-world scenario into a structured modeling program (Liu et al., 20 May 2025). The first stage produces a Problem Understanding report $G=(V,E)$8, then a decomposition

$G=(V,E)$9

and then a Task Dependency Graph

$v_{\mathrm{error}}$0

A memory structure $v_{\mathrm{error}}$1 stores, for each subtask, a tuple $v_{\mathrm{error}}$2 comprising model plan, code stub, and outputs (Liu et al., 20 May 2025).

The analysis workflow comprises three sequential steps. Problem understanding is written as

$v_{\mathrm{error}}$3

problem decomposition as

$v_{\mathrm{error}}$4

and dependency analysis as

$v_{\mathrm{error}}$5

where $v_{\mathrm{error}}$6, $v_{\mathrm{error}}$7, and $v_{\mathrm{error}}$8 are specialized prompt contexts (Liu et al., 20 May 2025). The framework also emphasizes critique and improvement prompts, meaning that analysis is not a single forward pass but a self-reflective loop.

Evaluation in MM-Agent includes Analysis Evaluation (AE), which scores clarity of problem restatement, identification of key components, and coherence of subtasks. An LLM evaluator scores AE on a 1–10 scale, and two human experts independently rate AE, with inter-annotator agreement reported as Pearson approximately 0.75 (Liu et al., 20 May 2025). In the Tennis Momentum example, the analysis decomposes the task into Momentum Quantification, Distinguish Momentum vs Randomness, Momentum Prediction, and Cross-Match Generalization, with dependency structure Task 1 $v_{\mathrm{error}}$9 {Task 2, Task 3} and Tasks 2 and 3 $S_t$00 Task 4; two human modelers each award AE at least 9 (Liu et al., 20 May 2025).

Clinical problem detection introduces a different analytical mechanism: collaborative interpretation through debate. The system in Lee et al. uses a Manager agent and dynamically assigned Specialist agents operating over only the Subjective and Objective sections of SOAP notes (Lee et al., 29 Aug 2025). The Manager performs role discovery, spawns $S_t$01 Specialist agents in parallel, checks for consensus at threshold $S_t$02, allows up to $S_t$03 rounds, and, if needed, dissolves and reassembles the team up to $S_t$04 times before fallback aggregation. Consensus is defined by

$S_t$05

All messages are JSON-validated via Pydantic/lm-format-enforcer, and discussion history is summarized once it exceeds 70% of the context window (Lee et al., 29 Aug 2025).

On 420 MIMIC-III notes, the dynamic specialist MAS improves macro-average F1 from 0.493 to 0.502 relative to a single zero-shot chain-of-thought baseline, with class-specific gains reported for congestive heart failure, acute kidney injury, and sepsis (Lee et al., 29 Aug 2025). Yet the paper also documents “groupthink” failures, and proposes explicit devil’s advocate agents, weighted voting, and heterogeneous LLM teams as future mitigations. This is important because it corrects another common misconception: additional agents do not automatically imply more reliable analysis; diversity, moderation, and fallback design remain necessary.

6. Tool-mediated implementations, performance characteristics, and design trade-offs

Problem analysis can also be instantiated as an execution-centered coding agent rather than a static reasoning pipeline. CP-Agent adopts a pure agentic strategy without any fixed pipeline, implemented in a few hundred lines of code around a ReAct loop, a persistent IPython kernel, a tool suite, and prompt hierarchy comprising system prompt, project prompt, and task prompt (Szeider, 10 Aug 2025). The tool suite includes read_file, write_file, list_files, delete_file, todo_write, and python_exec, while the kernel maintains Python state across calls and returns stdout, result, and error fields. In this design, domain expertise is injected solely through the project prompt rather than specialized architecture.

The project prompt encodes a “Mandatory Workflow,” “The Playbook,” “Verification Requirement,” common pitfalls, debugging tips, and performance optimizations (Szeider, 10 Aug 2025). For constraint programming, the mandatory workflow is: deconstruct the problem, model with CPMpy, solve and verify, and finalize for JSON output. The verification requirement includes independent Python checks, structural and logical correctness, and a final 12-item checklist. The system reports 100% success on all 101 problems in the CP-Bench benchmark set, with average approximately 10 python_exec calls per problem, input tokens approximately 180k, and output tokens approximately 6k (Szeider, 10 Aug 2025).

MM-Agent provides a complementary performance profile in mathematical modeling. On MM-Bench, a benchmark of 111 problems from MCM/ICM spanning 2000 to 2025 across ten domains, MM-Agent decomposes work into four stages: open-ended problem analysis, structured model formulation, computational problem solving, and report generation (Liu et al., 20 May 2025). The paper reports an 11.88% improvement over human expert solutions while requiring only 15 minutes and $0.88 per task using GPT-4o, and further reports that MM-Agent assisted two undergraduate teams in winning the Finalist Award, described as top 2.0% among 27,456 teams, in MCM/ICM 2025 (Liu et al., 20 May 2025). Within the narrower scope of its problem-analysis stage, the emphasis is on modular prompts, self-reflection loops, graph-based task management, memory capture, and early evaluation.

Across these systems, several trade-offs recur. AgentTrace emphasizes sub-second log-based analysis and explicitly avoids LLM calls at debug time (Wang, 16 Mar 2026). ACP emphasizes theory-grounded feasibility screening before action (Lutati, 8 Dec 2025). APF emphasizes runtime verification and bounded authority (Park, 22 Feb 2026). Clinical MAS emphasizes interpretability through debate but exposes susceptibility to groupthink (Lee et al., 29 Aug 2025). CP-Agent emphasizes general tooling and prompt-encoded expertise rather than fixed workflows (Szeider, 10 Aug 2025). MM-Agent emphasizes decomposition and dependency structure for open-ended modeling tasks (Liu et al., 20 May 2025). A plausible implication is that “Problem Analysis Agent” names a design space whose axes are not only accuracy but also timing of analysis, degree of structure, verification regime, and dependence on tool feedback versus deliberative consensus.