{"id":"5edb0fa6-d5cc-4e08-91e6-518082bc06b3","arxiv_id":"2411.10496","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":3.0,"correctness_risk":"high","formal_verification":"none","parameter_count":2,"one_line_summary":"Adding intermediate guide losses to an end-to-end portfolio model improves backtested Sharpe and Calmar ratios versus stage-wise and unguided end-to-end baselines.","lead":"This paper proposes Guided Learning, a training method that adds intermediate guide losses to end-to-end neural networks so that intermediate layers are pulled toward stage-level goals. Applied to quantitative investment, the guided model reports higher Sharpe and Calmar ratios than stage-wise and unguided end-to-end baselines on a Chinese A-share backtest.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The claim that guided learning 'significantly outperforms' end-to-end baselines is not statistically established and rests on post hoc guide selection; the paper's own Table 4 and Figure 5 show guide choice can collapse or non-monotonically alter performance.","rationale":"The paper proposes guided learning as a framework that adds intermediate guide losses to an end-to-end model, with the promise of improving training stability and final performance. The central claim is empirical: guided learning significantly outperforms both stage-wise and end-to-end baselines. I read the experiments in good faith; the architecture is reasonable, and the comparison of guide types, positions, and coefficients is a genuine attempt to characterize the method. However, the load-bearing support for the headline claim is weak in a specific, checkable way. The paper's own evidence shows that guide selection is consequential and fragile: the Rank guide in Table 4 produces a degenerate solution with identical metrics across runs, and Figure 5 shows all four portfolio metrics changing non-monotonically with the two guide coefficients. The Discussion section explicitly says the outcome depends on the problem, architecture, and guidance choices and requires empirical study. This is an honest limitation, but it undercuts the general claim. The comparison between IC-Guide and Guide-free in Table 1 is the key evidence, yet the paper does not report significance tests, uses only four runs, and does not disclose how many configurations were tried before choosing the reported one. The difference in Sharpe ratio is small relative to the reported standard deviation, and the Calmar difference, while larger, is not tested. Because no code or data are released, the reported results cannot be independently checked, and the reader's conditional verdict is appropriate. I agree with the reader's weakest-assumption analysis: the absence of a principled selection rule for guides, losses, and coefficients is the central soft spot. My concrete test would settle whether the effect is real or an artifact of configuration search and small-sample noise. If the effect survives a pre-registered public-data replication with proper significance testing, the conditional can be lifted; if not, the claim of significant improvement should be withdrawn or substantially qualified. No change to the reader's verdict is needed.","tokens_in":11537,"tokens_out":4210,"duration_ms":52035,"concrete_test":"Pre-specify one guide configuration a priori (e.g., IC guide on the temporal embedding with a fixed coefficient) and all other hyperparameters, then run an out-of-sample comparison of Guide-free versus IC-Guide on a public dataset (for example, Qlib's CSI300 or CSI500 universe with standard alpha features) across at least two non-overlapping test years and at least 10 random seeds. Report paired bootstrap or paired t-tests over daily excess returns and over seeds for Sharpe and Calmar ratios. If the improvement is not significant at p<0.05 or does not replicate across test years, the abstract's claim of significant and general improvement is not supported. If code and data were released, a direct rerun of the exact Table 1 pipeline would also settle whether the reported numbers are reproducible.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central empirical claim is Table 1: IC-Guide improves Sharpe from 0.94 to 1.05 and Calmar from 2.58 to 3.27 over the guide-free baseline. For this improvement to support the abstract's 'significantly outperforms' claim, it must be attributable to the guide mechanism rather than to favorable configuration search, and it must be statistically distinguishable from run-to-run noise. Neither condition is met. No significance tests or confidence intervals are reported; with four repeated runs, the Sharpe gap (0.11) is comparable to the reported run-to-run standard deviation of IC-Guide (0.09), so a paired test over seeds would likely not reject. The paper compares many guide positions and types (Tables 3 and 4) and grid-searches guide coefficients (Figure 5), then reports only the best configuration, without a pre-registration or multiple-testing protocol. The Rank guide collapsing to 0.00 standard deviation across all metrics (Table 4) and the non-monotonic sensitivity of every metric to both coefficients (Figure 5) demonstrate that the benefit is configuration-dependent, not a generic property of the framework. The authors themselves state in the Conclusions that current guide design relies on 'ad-hoc manual design with domain expertise' and that analyzing guide effectiveness is future work. Thus the evidence supports at most that one tuned IC-guide configuration improved one backtest, not that guided learning significantly outperforms existing methods as a framework.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes \"Guided Learning\" (GL), a training framework for multi-stage end-to-end decision-making in which intermediate representations receive auxiliary \"guided\" losses in addition to the final task utility (Eq. (4)). The authors define the components of a guide (phased output, phased goal, guided loss), discuss relations to multi-task learning, deep supervision, and reinforcement learning, and report a portfolio-optimization backtest on Chinese A-share data. The headline empirical result (Table 1) is that an IC-Guide configuration improves the Sharpe ratio from 0.94 (guide-free end-to-end) to 1.05 and the Calmar ratio from 2.58 to 3.27, while also outperforming stage-wise baselines. Additional experiments vary the temporal encoder (Table 2), guide placement (Table 3), and guide type (Table 4), and a sensitivity analysis of guide coefficients is presented in Figure 5.","tokens_in":11817,"tokens_out":6404,"duration_ms":62912,"significance":"If validated, Guided Learning would offer a useful compromise between stage-wise pipelines and monolithic end-to-end optimization: it is a simple, modular way to inject domain knowledge at intermediate stages while keeping a single differentiable objective. The formalization in Eq. (4) is clean, and the connections to deep supervision, multi-task learning, and RL provide useful context. However, the paper's significance hinges on the empirical claim of \"significantly outperforms,\" and that claim is not currently established: there are no significance tests, only four runs per configuration, no control for the multiple guide configurations explored, and no code or data release. The authors themselves concede in the Conclusions that guide design is \"ad-hoc manual design with domain expertise\" and that understanding guide effectiveness is future work. The framework is potentially valuable, but the evidence in this manuscript supports only a tentative, configuration-specific demonstration, not the strong claim in the abstract.","major_comments":[{"comment":"The central claim that guided learning \"significantly outperforms\" baselines is not statistically supported. The main comparison (IC-Guide vs. Guide-free) shows a Sharpe gap of 0.11 (1.05 vs. 0.94) with per-configuration standard deviations of 0.09 and 0.03 over only four runs; such a gap can easily arise from run-to-run noise. No paired significance tests, confidence intervals, or per-run values are reported anywhere in the paper. Because this improvement is the basis for the abstract's \"significantly outperforms,\" the paper needs either proper statistical testing (e.g., paired tests over seeds with correction), many more seeds, or a softened claim.","section":"Experiment, Table 1 and Abstract"},{"comment":"The paper reports the best of many configurations, creating a multiple-comparison problem. The authors select among guide positions (Table 3), guide types (Table 4), and grid-searched coefficients (Figure 5), then present the chosen configuration's metrics as the method's performance. The Rank guide's complete collapse (Table 4: 0.00 standard deviation across all metrics) and the strongly non-monotonic dependence on both coefficients (Figure 5) show that guide choices can destroy performance or have arbitrary effects. Without a pre-registered or at least validation-only selection rule followed by a test-set evaluation, the headline improvement cannot be attributed to the guide mechanism rather than to selection among many alternatives.","section":"Experiment, Tables 3-4 and Figure 5"},{"comment":"The \"guide placement\" comparison omits the guide-free baseline, so the text's conclusion that \"Temporal embedding guidance yields the best overall performance\" is uninformative about whether any placement actually helps; the reader cannot tell whether the best placement's Sharpe of 0.88 exceeds the guide-free value of 0.94 from Table 1 (it does not, on the point estimate). Table 4 likewise omits both the guide-free row and the IC guide used in Table 1, and its text says \"MSE guidance yields the best overall performance\" even though the MSE row's Sharpe (0.78) is below Table 1's guide-free value (0.94). These inconsistencies make the empirical story internally confusing and need to be resolved.","section":"Experiment, Tables 3 and 4"},{"comment":"The claimed generalization across architectures is overstated. With four runs, LSTM Sharpe is 0.56±0.11 vs. 0.54±0.33, TCN is 0.84±0.23 vs. 0.79±0.13, and PatchTST is 0.34±0.10 vs. 0.34±0.00; all differences are well within one standard deviation. The \"Improve\" percentages are also computed inconsistently (e.g., LSTM Calmar reported as 12.30% but (1.58−1.41)/1.41 = 12.06%; TCN Calmar reported as 11.84% but (2.47−2.21)/2.21 = 11.76%). This table cannot support the claim that \"guidance generalizes well across different model architectures.\"","section":"Experiment, Table 2"},{"comment":"The backtest uses 500 proprietary meta-features, and no code or data are released. Combined with only four runs and no statistical tests, the central empirical result is not independently verifiable. The paper should release code and data, or use a public dataset (e.g., Qlib's public data) and report the random seeds, so that the reported improvements can be reproduced and checked.","section":"Appendix: Dataset and Reproducibility"}],"minor_comments":[{"comment":"The stage index is inconsistent: the text defines h_N as the final output, but Eq. (4) sums to K−1 and uses h_K; the relationship among L, K, and N should be standardized.","section":"Formulation of Guided Learning, Eq. (4)"},{"comment":"There are typos: \"wonorized\" should be \"winsorized,\" and \"Addtional Results\" should be \"Additional Results.\"","section":"Appendix: Additional Experimental Details"},{"comment":"The sign convention for the \"Max. Drawdown\" improvement is not stated; since drawdown values are negative, a smaller absolute value is an improvement, but the \"Improve\" row mixes signs without explanation.","section":"Experiment, Table 2"},{"comment":"The claim that deep supervision \"regularizes each sample independently\" in contrast to guided learning is too categorical; deep supervision can also use varied or sample-level objectives, so the distinction should be qualified.","section":"Discussion: Compared with Other Learning Paradigms"},{"comment":"The related-work section mentions E2EAI (Wei et al., 2023) as an end-to-end investing framework but does not compare against it in the experiments; adding such a comparison or explaining its absence would strengthen the empirical evaluation.","section":"Related Work"}],"recommendation":"major_revision","confidential_remarks":"The manuscript is an early preprint with a promising but unproven idea. The main risk is that the headline result is the product of post hoc selection over a single backtest with no statistical testing. If the authors can provide a validation-based selection protocol, paired significance tests, and code/data release, the paper could become publishable. As it stands, I would not recommend acceptance. The editor may also want to consider whether proprietary data without release meets the journal's reproducibility standards."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague—\n\nQuick take: the paper repackages deep supervision with flexible intermediate losses and calls it Guided Learning. That is not a new mechanism, and the paper admits as much. What is new-ish is the application to quantitative investment with IC guides on intermediate embeddings, and the experiments do suggest a plausible benefit from adding staged losses to an end-to-end portfolio model. The paper is well-written, cites relevant work, and is honest about the ad-hoc nature of guide design in the conclusions.\n\nThe soft spots are real and load-bearing. The headline claim that guided learning 'significantly outperforms' stage-wise and end-to-end baselines is not statistically established. Table 1 reports Sharpe 1.05 vs 0.94 for the guide-free baseline, but with four runs the standard deviation is 0.09, so the gap is about one standard deviation. No significance tests, no confidence intervals, no paired comparison across seeds. The best guide configuration is selected after comparing many positions and types (Tables 3 and 4) and grid-searching coefficients (Figure 5), with no multiple-testing adjustment. The paper's own Figure 5 shows non-monotonic, bumpy sensitivity to both coefficients, and Table 4 shows the Rank guide collapses to zero variance across all metrics. So the improvement is configuration-dependent, not a generic property of the framework. The authors themselves write that guide design relies on 'ad-hoc manual design with domain expertise' and that analyzing guide effectiveness is future work. That is honest but it undercuts the strength of the empirical claim.\n\nThere is no code and no data release, and the dataset is a proprietary-feature Chinese A-share backtest. That makes the central quantitative result unverifiable. The generalization experiment across LSTM/TCN/PatchTST is nice, but the PatchTST gains are negligible and within noise.\n\nWhat the paper does well: it gives a clean formalization of intermediate guides, situates the idea relative to deep supervision, multi-task learning, and RL, and demonstrates a plausible use case. If a reader wants a survey-style entry point into intermediate supervision for end-to-end decision pipelines, this is a reasonable reference.\n\nVerdict: this deserves a serious referee, because the problem matters and the framework is coherent. But the empirical claim needs much more work: code/data release, pre-registered protocol or significance tests, and ideally a repeated bootstrap over multiple time periods. As it stands, I would not accept it based on Table 1. Recommend: send to peer review with a clear request for statistical rigor.","headline":"Useful survey-style framing of intermediate supervision, but the claimed empirical win is not statistically established and depends on post hoc guide selection.","tokens_in":12316,"tokens_out":2356,"would_cite":false,"duration_ms":24216,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper claims that adding intermediate \"guides\" to an end-to-end neural network stabilizes multi-stage decision-making training and improves the final objective, and shows on a portfolio task that an information-coefficient guide…","keywords":["guided learning","end-to-end learning","multi-stage decision-making","intermediate supervision","deep supervision","portfolio optimization","Sharpe ratio","quantitative investment"],"falsifier":"A reader could run the same IC guide on a new asset universe or a new temporal-encoder architecture and check whether the Sharpe-ratio gain over guide-free end-to-end persists; if it does not, or if a guide with randomly generated phased goals produces the same improvement, then the central claim that guide content matters would be refuted.","tokens_in":11345,"feed_emoji":"🧭","tokens_out":7350,"duration_ms":66552,"temperature":0.7,"pith_summary":"Guided Learning is a framework for training multi-stage decision-making models end to end by attaching small auxiliary objectives, called guides, to intermediate layers. The paper's central claim is that these guides steer gradients away from suboptimal collapse and improve the final decision utility, especially when the final objective is not a standard supervised label. The experiments on cross-sectional portfolio construction with Chinese A-share data support this: an information-coefficient guide on the temporal embedding raises the Sharpe ratio from 0.94 (guide-free) to 1.05 and the Calmar ratio from 2.58 to 3.27, outperforming stage-wise optimization (Sharpe 0.73) and multi-task variants (0.62). The paper also positions guides as a flexible middle ground between deep supervision and multi-task learning, and argues the framework carries over to other multi-stage systems such as autonomous driving.","feed_headline":"An IC guide lifts end-to-end portfolio Sharpe to 1.05","feed_subtitle":"Adding a small intermediate objective on temporal embeddings beats stage-wise pipelines and plain end-to-end training.","key_machinery":"The central object is the guided composite loss $\\mathcal{L} = \\sum_{i=1}^{K-1} \\lambda_i \\mathcal{L}^c_i(c_i, y^c_i) + U(h_K)$, where $h_K$ is the final output, $U$ is the final utility (e.g., Sharpe ratio), $\\mathcal{L}^c_i$ is the guided loss at stage $i$, $c_i$ is the phased output produced by an optional guided head $g_{\\phi_i}(h_i)$, and $y^c_i$ is the phased goal. The guide is the function that induces an intermediate layer to move toward a phased goal, thereby redirecting gradients away from suboptimal regions of the final loss landscape. It is the mechanism that carries the argument: by adding these intermediate pulls, the paper claims the end-to-end model avoids collapse and reaches better optima than guide-free training.","core_discovery":"The paper's discovery is that an end-to-end model for multi-stage decision-making can be trained more reliably when chosen intermediate representations are guided by small auxiliary losses. A guide consists of an optional head that maps a layer's output to a phased output, a phased goal, and a guided loss; the total training objective is the final utility plus a weighted sum of guided losses at each conceptual stage. This differs from multi-task learning because the auxiliary objectives are placed at distinct stages rather than only at the output, and from deep supervision because the goals can be sample-specific, dimension-specific, and heterogeneous across stages. In the quantitative investment study, the guide that works best is an information-coefficient loss on the temporal embedding; the wrong choice can be harmful, as the ranking-based guide collapses training, yielding a Sharpe ratio of only 0.33 with near-zero variance across runs.","pith_inferences":["A principled selection rule for guides is the missing piece implied by the paper's own evidence; automated search over guide placements, losses, and coefficients would likely be the next step, and the paper's non-monotonic sensitivity plots suggest this is not a trivial extension.","The collapse of the ranking guide suggests that guides can act as a strong regularizer that may dominate the final objective; in practice, users should validate guide choices carefully and consider annealing the guide weights during training.","The guided-learning recipe may be applicable to other multi-stage systems with natural intermediate semantic goals, such as perception-prediction-planning stacks in autonomous driving, where each stage already has a standard loss; a testable extension is to compare guided end-to-end training against stage-wise baselines on such tasks.","The comparison to reinforcement learning suggests that guides could serve as a credit-assignment mechanism for sparse-reward problems, but the paper does not test this; a concrete next experiment would be to apply guided learning to a control task with a sparse terminal reward."],"forward_implications":["If the guided-learning claim holds, end-to-end training of multi-stage systems becomes viable in settings where the final objective is not a standard supervised loss, because guides can inject phased domain knowledge without abandoning end-to-end differentiability.","Guide placement and type become a new design axis: the paper's tables show temporal embedding guidance beats embedding-level or cross-sectional guidance, and MSE beats ranking-based guidance, so practitioners can treat stage selection and loss choice as hyperparameters.","The utility-function component extends guided learning to decision-making problems without ground-truth labels, such as portfolio optimization, where the paper directly optimizes risk-adjusted return.","The framework implies that deep-supervision-style losses can be made stage-specific and sample-specific, giving a richer interface for injecting prior knowledge into deep networks.","Because the paper's results are demonstrated on one dataset and architecture family with repeated runs, a direct consequence is the expectation that similar guide recipes can be transferred to other architectures and modalities, as the paper's own architectural generalization experiments suggest."],"supporting_citations":[{"why":"Defines the portfolio optimization step that the stage-wise baseline uses to convert alpha predictions into positions.","marker":"(Markowitz 1952)"},{"why":"Provides the Sharpe ratio, the primary risk-adjusted utility that guided learning optimizes end to end.","marker":"(Sharpe 1966)"},{"why":"Introduces an end-to-end deep learning framework for active investing that combines final portfolio metrics with intermediate losses, the closest existing approach the paper compares against.","marker":"(Wei, Dai, and Lin 2023)"},{"why":"Foundational deep-supervision method that guides training with intermediate losses, which guided learning extends by making goals stage-specific and flexible.","marker":"(Lee et al. 2015)"},{"why":"Surveys multi-task learning, the paradigm that guided learning is explicitly contrasted with in terms of loss placement and parameter sharing.","marker":"(Zhang and Yang 2021)"}],"fun_headline_variants":["IC guide lifts end-to-end Sharpe to 1.05","Guided learning steers multi-stage decisions safely","End-to-end fails without a guide in investing","Wrong guide collapses training, right one yields 1.05","Small IC loss on embeddings guides end-to-end training"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing assumption is that the chosen guide configuration—which layer to guide, which loss to use, and how much weight to give it—is aligned with the final decision objective; the paper offers no principled rule for selecting it, and its own experiments show a poor choice (the rank guide) can collapse training.","fun_headline_variants_meta":{"raw":{"variants":["IC guide lifts end-to-end Sharpe to 1.05","Guided learning steers multi-stage decisions safely","End-to-end fails without a guide in investing","Wrong guide collapses training, right one yields 1.05","Small IC loss on embeddings guides end-to-end training"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001235,"raw_usage":{"total_tokens":5078,"prompt_tokens":961,"completion_tokens":4117,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":577,"completion_tokens_details":{"reasoning_tokens":4039}},"tokens_in":577,"tokens_out":4117,"duration_ms":31024,"temperature":1.0,"reasoning_tokens":4039,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-12T20:04:26.863459+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A reader could run the same IC guide on a new asset universe or a new temporal-encoder architecture and check whether the Sharpe-ratio gain over guide-free end-to-end persists; if it does not, or if a guide with randomly generated phased goals produces the same improvement, then the central claim that guide content matters would be refuted.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Defines the portfolio optimization step that the stage-wise baseline uses to convert alpha predictions into positions."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the Sharpe ratio, the primary risk-adjusted utility that guided learning optimizes end to end."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Introduces an end-to-end deep learning framework for active investing that combines final portfolio metrics with intermediate losses, the closest existing approach the paper compares against."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Foundational deep-supervision method that guides training with intermediate losses, which guided learning extends by making goals stage-specific and flexible."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Surveys multi-task learning, the paradigm that guided learning is explicitly contrasted with in terms of loss placement and parameter sharing."}],"review_version":1}