{"id":"c84e7175-a8d6-4d65-96e7-d0e58bf26315","arxiv_id":"2505.13516","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":5,"one_line_summary":"HALO, a three-tier hierarchical multi-agent LLM framework with MCTS workflow search and prompt refinement, reports 78.6% average accuracy on HumanEval, MMLU, and MATH, beating six baselines by 14.6 percentage points.","lead":"The paper introduces HALO, a hierarchical multi-agent system where GPT-4o agents plan, create specialized roles, and execute subtasks via Monte Carlo Tree Search. It reports large gains over baseline methods on code, reasoning, and math benchmarks, but missing details on compute and reproducibility temper the claims.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Algorithm 1 does not implement the MCTS workflow search described in §3.4, so the headline gains are not yet attributable to the claimed search mechanism.","rationale":"The reader's CONDITIONAL verdict is reasonable: the empirical claims are strong but the evidence is incomplete. My stress-test pass finds a more basic issue than the one identified by the reader. The reader assumes the Judge/Score rewards feed an MCTS search; the pseudocode suggests the search itself may be absent. Algorithm 1 is the only concrete specification of the method, and it does not contain the MCTS stages claimed in Section 3.4. This matters for the central claim because the paper repeatedly attributes HALO's gains to 'MCTS-driven workflow exploration' (Section 4.2) and lists the Workflow Search Engine as a key contribution. If the implemented method is actually a single-pass generate-and-score procedure, the comparison to ADAS and other baselines is still informative as a systems result, but the novelty and the attribution of the 14.4% improvement change materially. If the implementation does include MCTS, the manuscript is missing the core algorithmic details and is not reproducible from the text. Either way, the central claim as stated is not adequately supported. I do not call the results fraudulent; the repository may contain a genuine MCTS implementation. But the published algorithm and the narrative are inconsistent, and this should be resolved before acceptance. Because this is fixable by releasing code and pseudocode, I keep the reader's CONDITIONAL verdict rather than moving to REJECT.","tokens_in":17950,"tokens_out":6518,"duration_ms":70539,"concrete_test":"Obtain the released repository and instrument the Workflow Search Engine to log, for one MMLU or MATH problem, the full MCTS trace: per-node visit counts, UCT values, expansion events, simulation rollouts, and backpropagation updates. If no such trace can be produced, re-run the system with the MCTS loop disabled (i.e., exactly Algorithm 1's one-pass generate-and-score loop) on the same 500-problem MATH subset; if accuracy remains 58.9%, the claimed search is not the source of the gains, and if it drops, the paper must provide the missing MCTS specification before its central claim can be evaluated.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is that MCTS-based workflow search drives HALO's 14.4% improvement over baselines (Table 2). Section 3.4 defines a four-stage MCTS with UCT selection (Eq. 9), expansion, simulation (Eq. 10), and value backpropagation (Eq. 11). However, Algorithm 1, the paper's only executable-level description, contains no Selection, Expansion, or Simulation stages: lines 12–16 simply loop over the generated role agents, evaluate each output with the Judge/Score agents, and call an undefined MCTS_Backpropagate. There are no visit counts, no UCT scores, no repeated rollouts, and no explicit tree structure; the loop runs once per subtask. The final answer is an aggregation over the generated outputs. Thus the published algorithm corresponds to generate-and-score with greedy choice, not to MCTS. If the actual implementation does contain an MCTS loop, that loop is absent from the specification, making the reported 58.9% MATH and 95.2% HumanEval numbers unreproducible from the paper; if it does not, the contribution is misdescribed and the gains may come from hierarchical prompting and an increased inference budget rather than from workflow search. This concern is independent of, and more immediate than, the reward-correlation question raised by the reader: even a perfect reward signal cannot be used for UCT search if no search loop is implemented.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes HALO, a three-stage hierarchical multi-agent framework for LLM-based reasoning. The first stage refines raw user queries through four prompt-engineering agents; the second stage decomposes the task with a planning agent and dynamically instantiates role-specific agents for each subtask; the third stage is described as an MCTS-driven workflow search over agent reasoning trajectories. Experiments on HumanEval, MMLU, and MATH report large gains over six baselines, with a 78.6% average versus 64.0% for the strongest baseline ADAS, and ablations show performance drops when the prompt-refinement module or the planning layer is removed.","tokens_in":18243,"tokens_out":4178,"duration_ms":40847,"significance":"If the central claim held, HALO would be a useful contribution to adaptive multi-agent orchestration: it directly addresses the limitation of static role designs and fixed communication workflows, and it combines hierarchical decomposition with search-based workflow construction. The final evaluation is grounded in external correctness signals (HumanEval unit tests, MMLU and MATH ground-truth answers), which is a strength. However, the paper's significance depends on two load-bearing points that are not currently established: that the implemented algorithm is actually the MCTS search described in Section 3.4, and that the reported gains are not simply a consequence of added inference compute and self-referential internal scoring. Both issues are addressable but require substantive revision.","major_comments":[{"comment":"Algorithm 1 does not implement the MCTS workflow search described in Section 3.4. Lines 12–16 loop once over the generated role agents, evaluate each output, and call an undefined MCTS_Backpropagate; there is no selection stage using Eq. (9), no expansion, no simulation using Eq. (10), no visit-count updates, and no explicit tree structure. The only executable-level specification therefore corresponds to generate-and-score with aggregation, not to UCT-based tree search. The reported gains cannot be attributed to the claimed MCTS mechanism unless the missing search loop is specified and shown in the algorithm, or the mechanism description is revised to match the actual implementation.","section":"Algorithm 1 vs. §3.4"},{"comment":"The reward signal for the workflow search is produced by GPT-4o-based Judge and Score agents, and the paper provides no evidence that these internal scores correlate with true correctness on MMLU or MATH. Since the same model family generates the candidate answers and evaluates them, the search may be optimizing a self-referential proxy. The authors should validate the Judge/Score outputs against ground truth on a held-out subset and report agreement rates, or otherwise justify that the search reward is meaningful.","section":"§3.4, Appendix B"},{"comment":"The empirical comparison is not sufficiently controlled. Tables 2 and 3 report results \"averaged over three runs\" without error bars or standard deviations. The MMLU and MATH subsampling (13% and 500 items) is described as random but no seeds are given, and there is no statement that all baselines were evaluated on the identical question subsets. In addition, HALO uses multiple role-specific agents per subtask plus an iterative planning loop, while the baselines do not appear to be matched for inference budget; the reported improvements may therefore partly reflect additional compute rather than the proposed mechanism. Please provide seed-controlled, budget-matched comparisons or explicitly discuss computational cost.","section":"§4.1, Tables 2 and 3"},{"comment":"The ablation study does not isolate the contribution of the MCTS workflow search. It reports only the full system, the system without prompt refinement, and the system without the planning layer; there is no ablation that removes or replaces the MCTS search while keeping the hierarchical prompt-refinement and role-instantiation modules. Without this, the paper does not establish that the search-based workflow exploration, rather than the hierarchical prompting structure and increased inference budget, drives the reported gains.","section":"§4.3, Figure 4"}],"minor_comments":[{"comment":"The figure caption contains a typo: \"Adapative Prompt Refinement\" should be \"Adaptive Prompt Refinement.\"","section":"Figure 1"},{"comment":"The abstract reports a 14.4% average improvement over state-of-the-art baselines, while §4.2 states a 14.6% average improvement (78.6 vs. 64.0 in Table 2) and Table 3 reports a 14.4% gain on the abstract MMLU subjects. Please clarify which comparison the abstract refers to.","section":"Abstract and §4.2"},{"comment":"The algorithm is imprecise about how the final answer is formed: line 17 updates H_k with a value \\hat{Y}_k that is never computed inside the loop, and line 22 aggregates {y^{(i)}_k} after the loop has terminated, leaving the scope of the variable k unclear. Please make the aggregation and history-update steps explicit.","section":"Algorithm 1, lines 17 and 22"},{"comment":"References [25] and [44] are duplicates of the same work, which makes it harder for readers to identify the intended citations for the baseline comparison.","section":"References"},{"comment":"There is a grammatical error in the sentence \"some researchs apply reinforcement learning to refine cooperation strategies\"; \"researchs\" should be \"researchers\" or \"research works.\"","section":"§2.3"}],"recommendation":"major_revision","confidential_remarks":"The central obstacle is the mismatch between the prose description of MCTS and Algorithm 1; this determines whether the paper is an evaluation of a genuinely new search mechanism or of a hierarchical prompting pipeline. I did not inspect the linked repository, so I cannot determine which case actually holds. I would ask the editor to require the authors to clarify, with reference to the released code, whether the implementation contains the four MCTS stages, and to add the missing specification to the paper. The duplicate reference [25]/[44] is also worth flagging for the authors."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The thing to know: the paper reports very large gains for a multi-agent framework, but the published algorithm does not implement the MCTS search that the whole contribution rests on. That's the elephant in the room.\n\nWhat's genuinely useful: HALO combines three known ideas—hierarchical task decomposition, LLM-generated agent roles, and search over workflows—into one system. The prompt-refinement module is clearly specified in the appendix, and the benchmark numbers on GPT-4o are impressive: 95.2 on HumanEval, 81.6 on MMLU, 58.9 on MATH, about 14.6 points above the strongest baseline ADAS on average. If those numbers hold up, that's a meaningful recipe for practitioners.\n\nThe soft spots are not minor. Section 3.4 describes a standard four-stage MCTS with UCT selection, expansion, simulation, and backpropagation, with equations 9-11. Algorithm 1, the only executable-level specification, contains none of that. It just loops over the generated role agents, evaluates each with the Judge/Score agents, and calls an undefined MCTS_Backpropagate. There are no visit counts, no UCT scores, no rollouts, no tree. The final answer is an aggregation over the generated outputs. So the contribution as written is generate-and-score with a greedy pick, not MCTS. The reported gains cannot be attributed to the claimed search mechanism. This isn't a presentation quibble; it's a load-bearing inconsistency.\n\nAlso: no error bars despite \"averaged over three runs,\" no seeds for the MMLU/MATH subsampling, no matched compute comparison, and no ablation of the MCTS component. The reward comes from GPT-4o Judge/Score agents, so search is self-referential to the same model producing the answers, but that's a secondary issue.\n\nWho this is for: people working on multi-agent orchestration and automated workflow search. The paper could be a useful practical recipe if the code ships and the algorithm is reconciled with the description. Right now it's a promising systems paper with a critical gap between narrative and executable specification.\n\nMy recommendation: send it to peer review, but expect heavy revision. The reviewers need to push for a real algorithm, code, seeds, error bars, and an ablation of the search component. If the MCTS claim collapses, the remaining contribution is hierarchical prompting with dynamic roles—less novel, but not worthless.","headline":"A multi-agent framework with huge headline gains, but the published algorithm doesn't actually implement the MCTS search that the whole story rests on.","tokens_in":18768,"tokens_out":3247,"would_cite":false,"duration_ms":30866,"reading_group":"maybe","serious_thinker":"no","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Treating LLM multi-agent cooperation as a search over dynamic roles yields 14.4% average gains.","keywords":["multi-agent systems","large language models","Monte Carlo tree search","hierarchical reasoning","adaptive prompt refinement","workflow search","code generation","mathematical reasoning"],"falsifier":"Run HALO with the Judge/Score-guided MCTS replaced by a fixed random or greedy expansion of the same number of role-specific agents, keeping the hierarchical stack and prompt refinement unchanged; if accuracy on HumanEval, MMLU, and MATH does not drop, the reported gains come from added compute or prompt engineering, not from search. A complementary check is to feed the Judge known-wrong but fluent answers and see whether its scores still rise with apparent confidence.","tokens_in":17723,"feed_emoji":"🤖","tokens_out":8248,"duration_ms":79050,"temperature":0.7,"pith_summary":"HALO is a three-stage multi-agent architecture that replaces fixed agent roles and static communication graphs with dynamically generated roles and search over reasoning workflows. The paper claims that a high-level planner, mid-level role designers, and low-level inference agents, coordinated by Monte Carlo Tree Search, beat six established baselines by 14.4% on average across HumanEval, MMLU, and MATH when every method runs on GPT-4o. A four-agent prompt-refinement stage first converts raw user queries into structured prompts, and ablations show that removing either prompt refinement or task decomposition costs 5.3 and 11.3 percentage points of average accuracy respectively. If the results hold, multi-agent LLM systems can be organized on the fly rather than handcrafted for each domain.","feed_headline":"LLM agents that search their own workflow score 14.4% higher","feed_subtitle":"A dynamic planner plus Monte Carlo search over agent roles beats fixed multi-agent pipelines on three benchmarks.","key_machinery":"The central mechanism is the Workflow Search Engine layered on a Hierarchical Reasoning Stack. Each node in the search tree is a role-specific agent's intermediate output for a subtask, and edges are transitions between reasoning states; selection uses UCT with a quality score, expansion instantiates a new agent, simulation runs hypothetical future agents, and backpropagation updates node values using a reward signal adjusted by the terminal status label. The hierarchical stack supplies the structure that makes the search space meaningful: a high-level planner decomposes the task, mid-level role-design agents instantiate specialized roles, and low-level inference agents execute each subtask. The Adaptive Prompt Refinement module is the entry point that turns raw user queries into structured prompts for both planning and inference.","core_discovery":"The paper's central claim is that subtask execution in a multi-agent LLM system should be treated as a search problem, not a fixed pipeline. HALO's high-level planning agent emits one subtask at a time based on the execution history; mid-level role-design agents generate specialist roles and role-specific system prompts for each subtask; low-level inference agents carry out the subtask. A Monte Carlo Tree Search over the agent action space scores every intermediate output with a Judge agent (success/fail/continue) and a Score agent (0-1), then backpropagates a status-adjusted value to select the highest-scoring reasoning trajectory. In the paper's experiments, HALO reports 95.2% pass@1 on HumanEval, 81.6% accuracy on MMLU, and 58.9% accuracy on MATH, with the largest improvements on abstract MMLU subjects and computationally intensive MATH subareas.","pith_inferences":["The Judge and Score agents are never validated against ground truth, so a natural ablation would replace MCTS with a fixed-depth parallel rollout of the same agents to separate gains from search and gains from extra compute.","A testable extension is to take a workflow optimized on one subject and run it on a held-out subject, which would show whether the search discovers reusable orchestration patterns or merely overfits an internal reward signal.","The 66% consistency threshold is borrowed from Byzantine fault tolerance as a heuristic; sweeping that threshold would reveal whether the early stop is well calibrated or too aggressive.","The same planner-role-inference stack with search could extend to tool-use or retrieval action spaces, where node values can be grounded in task feedback instead of a language-model judge."],"forward_implications":["If HALO's results are right, multi-agent systems can be assembled without hand-designing agent roles or communication workflows for each new domain.","The largest gains on expert-level subjects imply that hierarchical decomposition plus search helps most exactly where fixed-role teams struggle.","The ablations indicate that both query refinement and iterative task decomposition are needed, not optional extras, for the reported accuracy.","Because all baselines run on the same LLM, the reported improvements are attributable to the orchestration architecture rather than to a stronger model.","The early-stopping rule could make the framework cheaper than exhaustive search by halting when 66% of completed subtasks agree on an answer."],"supporting_citations":[{"why":"Supplies the single-agent ReAct baseline that HALO improves on by 26.6 points on average.","marker":"[11]"},{"why":"Supplies the standard four-stage Monte Carlo tree search procedure used for workflow search.","marker":"[13]"},{"why":"Supplies the 164-problem code-generation benchmark and unit tests used for evaluation.","marker":"[14]"},{"why":"Supplies the 57-subject general knowledge benchmark used for evaluation.","marker":"[15]"},{"why":"Supplies the 12,500-problem mathematics benchmark used for evaluation.","marker":"[16]"},{"why":"Supplies the DAG-plus-feedback dynamic multi-agent baseline compared in Table 2.","marker":"[21]"},{"why":"Supplies the hierarchical tree multi-agent baseline compared in Table 2.","marker":"[33]"},{"why":"Supplies the UCT selection formula used to pick which agent to expand during search.","marker":"[41]"},{"why":"Supplies the value aggregation strategy that the backpropagation update adapts, with status-dependent reward adjustment.","marker":"[42]"},{"why":"Supplies the strongest search-based baseline; HALO reports a 14.6-point average gain over it.","marker":"[44]"}],"fun_headline_variants":["Searching agent workflows with tree search lifts LLM scores 14.4%","Monte Carlo search over agent roles boosts LLM skills 14.4%","Agent teams that search their own steps beat static pipelines","Adaptive agent design via tree search yields 14.4% LLM gain","Hierarchical AI agents search for optimal reasoning paths, +14.4%"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the internal Judge and Score agents give reward signals that track true correctness, so the search selects genuinely better reasoning trajectories rather than merely more fluent-looking ones.","fun_headline_variants_meta":{"raw":{"variants":["Searching agent workflows with tree search lifts LLM scores 14.4%","Monte Carlo search over agent roles boosts LLM skills 14.4%","Agent teams that search their own steps beat static pipelines","Adaptive agent design via tree search yields 14.4% LLM gain","Hierarchical AI agents search for optimal reasoning paths, +14.4%"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000894,"raw_usage":{"total_tokens":3898,"prompt_tokens":1033,"completion_tokens":2865,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":649,"completion_tokens_details":{"reasoning_tokens":2766}},"tokens_in":649,"tokens_out":2865,"duration_ms":19640,"temperature":1.0,"reasoning_tokens":2766,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T20:46:32.288254+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run HALO with the Judge/Score-guided MCTS replaced by a fixed random or greedy expansion of the same number of role-specific agents, keeping the hierarchical stack and prompt refinement unchanged; if accuracy on HumanEval, MMLU, and MATH does not drop, the reported gains come from added compute or prompt engineering, not from search. A complementary check is to feed the Judge known-wrong but fluent answers and see whether its scores still rise with apparent confidence.","supporting_citations":[{"cited_title":"A survey of monte carlo tree search methods,","cited_arxiv_id":null,"evidence_quote":"Supplies the standard four-stage Monte Carlo tree search procedure used for workflow search."},{"cited_title":"A dynamic llm-powered agent network for task-oriented agent collaboration,","cited_arxiv_id":null,"evidence_quote":"Supplies the DAG-plus-feedback dynamic multi-agent baseline compared in Table 2."},{"cited_title":"Bandit based monte-carlo planning,","cited_arxiv_id":null,"evidence_quote":"Supplies the UCT selection formula used to pick which agent to expand during search."}],"review_version":1}