{"id":"134e06aa-e6c5-4491-b97c-c0f87947461b","arxiv_id":"2506.08427","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Know-MRI combines eleven existing LLM interpretation methods into one extensible toolkit with automatic input-to-method matching and dual UI and code interfaces.","lead":"Know-MRI is an open-source toolkit that wraps eleven existing interpretability methods for large language models, automatically matching inputs such as prompts or knowledge triples to the methods that can handle them. It offers both a web interface and a code API, and the paper backs it with case studies and a small human evaluation.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The central claim that Know-MRI automatically matches inputs to methods and consolidates outputs is plausible, but the weakest load-bearing step is the undeclared dependence of the matching logic on the completeness and correctness of user-supplied support_template_keys and requires_input_keys; a…","rationale":"The reader identified the inherited validity of interpretation methods as the weakest assumption. I agree that this is a real limitation, but it is somewhat outside the toolkit's control: any system integrating existing methods inherits their assumptions. The more load-bearing and more testable concern is the toolkit's own novel matching and consolidation logic. If support_template_keys or requires_input_keys are incomplete or semantically mismatched, the automatic matching silently produces wrong method-data pairs, and the consolidation step has no stated semantics for combining heterogeneous outputs. The paper even provides evidence of disagreement between methods (Table 3) but interprets it as corroboration without a quantitative overlap measure. This is a concrete, checkable weakness in the central claim. The verdict remains CONDITIONAL: the toolkit may well be useful, but the automatic matching and consolidation claims need a stress test with malformed key sets and a quantitative inter-method agreement analysis. I partially agree with the reader because the reader's concern about inherited method validity is real but secondary; the primary fix is to validate Know-MRI's own matching/consolidation behavior, not to re-validate all eleven methods.","tokens_in":647,"tokens_out":1011,"duration_ms":26166,"concrete_test":"Run a controlled experiment on the Know-MRI matching module: for each of the 11 integrated methods, construct a dataset whose support_template_keys intentionally omits one key that the method's requires_input_keys demands (e.g., give a KN dataset only [\"prompt\", \"ground_truth\"] instead of [\"prompts\", \"ground_truth\"]). Record whether Know-MRI raises an error, falls back to another method, or silently produces output. Then, for a fixed Llama2-7B input, compute the full ranked neuron list from KN and FINE (not just top-4) and report Spearman rank correlation and overlap at ranks 10, 50, 100. If the toolkit silently runs with wrong keys or the full-rank overlap is near zero, the 'mutual corroboration' claim and the automatic-matching guarantee are not supported.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The paper's core technical claim (Section 3.1.2 and 3.1.3) is that Know-MRI automatically matches each dataset to interpretation methods via support_template_keys and requires_input_keys, then consolidates outputs. For this to work, the user-supplied key lists must be both complete and semantically compatible across datasets and methods. However, the paper provides no formal specification of these keys, no validation layer detecting missing or incompatible keys, and no automated consistency check between the key taxonomy (Appendix C) and each method's actual implementation. For example, the KN method is said to require [\"prompts\", \"ground_truth\"], but the actual Dai et al. (2022) implementation typically also requires a relation or template to generate paraphrased prompts; if a dataset provides ground_truth as an answer string but not the relation field, the method may run on wrongly constructed prompts, and the toolkit will still report interpretability results. Similarly, the consolidation step in the abstract is only described as 'consolidate the interpreting outputs' without any stated normalization or conflict-resolution rule. If two methods produce contradictory localizations (as KN and FINE actually do in Table 3, with FINE finding L18.U327 and KN finding L1.U6972), the toolkit has no stated way to reconcile or flag the discrepancy; it simply presents them side by side. Thus the user is left with the impression that the toolkit validates or integrates these signals, when in fact it only juxtaposes them. This is not a fatal flaw for a systems paper, but it means the central usability and correctness claims are under-specified. The case study in Section 4.1 explicitly uses this juxtaposition to claim 'mutual corroboration', but the selected top-4 neurons overlap only at one neuron (L29.U3216); the other three FINE neurons differ from the other three KN neurons by layer and index.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper presents Know-MRI, an open-source toolkit for interpreting knowledge mechanisms in large language models. The toolkit integrates eleven interpretation methods across eight method types, supports thirteen datasets with different input formats, and is designed to automatically match datasets to interpretation methods through user-supplied support_template_keys and requires_input_keys. It offers both UI-based and code-based usage, and the authors evaluate it with a single case study comparing methods on a factual-knowledge example, a capability-localization experiment reproducing prior work, and a small human evaluation. The central claim is that Know-MRI enables users to comprehensively diagnose internal knowledge mechanisms from multiple perspectives in a single system.","tokens_in":13347,"tokens_out":3743,"duration_ms":43418,"significance":"If the central claims hold, Know-MRI would be a useful engineering contribution: it integrates a broad set of interpretability methods and model architectures, provides a clean interface for extending methods and datasets, and ships open-source code and a demonstration video. The extensibility design, covering nine model families and eleven methods, is a genuine strength. However, the validation is thin, and the two claims that distinguish the toolkit from existing systems — automatic matching of inputs to methods and consolidation of interpreting outputs — are underspecified and not rigorously tested. The case study is qualitative, the quantitative experiment reproduces a prior result rather than validating the toolkit's novel machinery, and the human evaluation is too small and underreported to support strong usability claims.","major_comments":[{"comment":"The automatic matching mechanism rests entirely on user-supplied support_template_keys and requires_input_keys, but the paper gives no formal specification of the key taxonomy, no validation layer for missing or incompatible keys, and no consistency check between the Appendix C taxonomy and the actual implementations. For example, the KN method is stated to require [\"prompts\", \"ground_truth\"], but the original Dai et al. (2022) pipeline commonly also needs a relation or template field to construct semantically similar paraphrase prompts; if a dataset provides ground_truth as an answer string but lacks that field, the method may run on incorrectly constructed prompts while the toolkit still reports interpretability results. This is a load-bearing gap for the paper's central claim of automatic matching.","section":"§3.1.2, §3.1.3, Appendix C"},{"comment":"The abstract claims that Know-MRI can \"consolidate the interpreting outputs,\" but the paper does not describe any normalization, conflict-resolution, or discrepancy-flagging rule for the consolidated outputs. Section 4.1, Table 3, shows FINE and KN reporting conflicting top neurons (L18.U327 vs. L1.U6972) for the same input example, and the text presents these results side by side without explaining how the toolkit reconciles or flags such disagreements. Without a stated consolidation mechanism, the claim of consolidation is misleading; the toolkit appears to aggregate results rather than consolidate them.","section":"Abstract, §3.1.3, §4.1"},{"comment":"The quantitative validation is too thin to support the paper's effectiveness claims. In §4.2, Figure 5 reports overlap and IoU ratios without error bars, standard deviations, or multiple replications, and the claim that the ratio \"will gradually converge\" is not backed by any convergence criterion or statistical analysis. The text explicitly states that the result is \"the same as Huang et al. (2025)\", which indicates this experiment is a reproduction of prior work rather than a validation of Know-MRI's automatic matching or consolidation functionality. Similarly, §4.3 uses ten participants and reports only mean scores in Figure 6, with no variance, significance testing, or details on questionnaire construction or participant selection; this does not substantiate the statement that \"Know-MRI is highly evaluated in terms of user experience.\"","section":"§4.2, §4.3, Figure 5"}],"minor_comments":[{"comment":"The dataset name is inconsistently written as \"Known-1000\" in the text and \"Known 1000\" in Figure 4, while the rest of the paper uses \"Know-1000\"; please unify the spelling.","section":"§3.1.2, Figure 4"},{"comment":"The paper alternates between claiming \"8 classic interpretation methods\" and \"eleven interpretation techniques\" without clearly distinguishing method types from individual techniques; please state the count consistently.","section":"Abstract, §1, §3.1.3"},{"comment":"Figure 5 lacks clear axis labels for the x-axis and does not explain what \"per 100 data\" means; please add a caption that defines the sampling procedure and the reported metric.","section":"§4.2, Figure 5"},{"comment":"The code example is described as requiring \"8 lines\", but the figure is too small to verify this claim; consider including the full code snippet in the appendix.","section":"§3.2.2, Figure 4"},{"comment":"The questionnaire is said to be available at a Google Forms link, but the link is not provided in the paper; please include the exact questionnaire or a link to it for reproducibility.","section":"§4.3"}],"recommendation":"major_revision","confidential_remarks":"The §4.2 experiment closely follows Huang et al. (2025), which includes overlapping authors, and the paper explicitly describes the result as the same as that prior work. This is not a correctness issue, but the manuscript would benefit from a clearer separation between novel toolkit functionality and replicated results, so that reviewers and readers can assess what is genuinely new. The toolkit itself appears to be a substantial engineering effort, and the open-source release is a positive feature; the main risk is that the automatic matching and consolidation claims are not yet backed by enough specification or evaluation."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Know-MRI is a useful integration toolkit, not a scientific breakthrough. The real contribution is the automatic dataset-to-method matching via support_template_keys and requires_input_keys, plus a unified API and UI. That is genuinely new, and the code is public. The paper is honest that the integrated methods are prior work.\n\nWhat the paper does well: it consolidates a broad set of interpretability methods (KN, FINE, Causal Tracing, etc.) and diverse datasets into one system, with an extensible design. The matching idea is practical, and the UI lowers the barrier for beginners. The case study and extended application are reasonable demonstrations, not strong validations.\n\nThe soft spots are in the evaluation and specification. The matching logic is under-specified: there is no formal definition of the key taxonomy, no validation that a dataset's support_template_keys actually satisfy a method's requires_input_keys. If the user provides incomplete keys, the toolkit will happily run a method on wrongly constructed inputs. The stress-test note is right: the paper doesn't describe any consistency check or error reporting for mismatched keys. Also, the 'consolidation' step is just side-by-side display, not reconciliation; when KN and FINE localize different neurons, the user is left to interpret the contradiction. The paper claims 'mutual corroboration' in the case study, but only one of the top-4 neurons overlaps, so that's a stretch.\n\nThe human evaluation (n=10, no error bars) is thin, and the extended application is essentially a reproduction of Huang et al. (2025). None of this is fatal for a systems paper, but it means the 'comprehensive' and 'effectiveness' claims are overreaching.\n\nFor a reader: this is for practitioners who want a ready-made tool to run several interpretability methods without writing glue code. It deserves a serious referee; with a clearer specification of the key matching and output consolidation semantics, plus a small robustness check on mismatched keys, it could be a solid system demonstration paper.","headline":"A genuinely useful integration toolkit whose automatic matching claim is under-specified and whose validation is thin; still worth a serious referee.","tokens_in":13902,"tokens_out":3365,"would_cite":true,"duration_ms":34582,"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":"Know-MRI is an open-source toolkit that automatically matches any input data format to the right LLM interpretation method and consolidates the results.","keywords":["Know-MRI toolkit","LLM interpretability","knowledge mechanisms","neuron localization","interpretation methods integration","input-method matching","model explainability","open-source toolkit"],"falsifier":"Run Know-MRI on a randomized version of Know-1000 in which subject-object associations are broken (shuffled ground truths). If Knowledge Neurons, FINE, and Causal Tracing still localize the same subject neurons with comparable strength as on the intact dataset, then the consolidated diagnosis cannot distinguish genuine knowledge mechanisms from artifacts, and the toolkit's central promise fails.","tokens_in":12876,"feed_emoji":"🧠","tokens_out":4660,"duration_ms":50080,"temperature":0.7,"pith_summary":"Know-MRI is a unified, open-source toolkit for diagnosing how large language models store and use knowledge. The paper's central claim is that a single system can accept many input formats, automatically route each input to the interpretation methods that can consume it, and merge the outputs into one multi-perspective diagnosis. If that works, researchers can compare methods like Knowledge Neurons and Causal Tracing on the same example without writing glue code, and can add new datasets, models, or methods by declaring their input requirements rather than re-engineering the pipeline.","feed_headline":"Open-source tool auto-matches LLM inputs to explanation methods","feed_subtitle":"Know-MRI unifies 11 interpretability methods, 13 datasets, and 9 model architectures under one interface.","key_machinery":"The load-bearing mechanism is key-based input-method matching: datasets carry support_template_keys and methods carry requires_input_keys, and Know-MRI routes an input to every method whose required keys are present and then consolidates the outputs. This abstraction converts method integration into declaring field lists and wrapping a method as a diagnose function, which is what makes the toolkit extensible to new datasets, models, and interpretation techniques.","core_discovery":"The paper introduces Know-MRI as a framework organized around three components—models, datasets, and interpretation methods—connected by a matching mechanism. Each dataset declares a support_template_keys list describing the input fields it contains (for example prompt, ground_truth, triple_subject), and each interpretation method declares a requires_input_keys list describing the fields it needs; Know-MRI then automatically selects applicable methods for a given input and consolidates their outputs. The toolkit integrates eleven interpretation techniques spanning external and internal perspectives, supports thirteen datasets with different formats on nine model architectures, and offers both a UI and an eight-line code API. Case studies on Llama2-7B show that different methods agree on the same conclusion—that the subject token dominates factual-knowledge processing—and the extended capability-localization experiments reproduce the finding that localized neurons, once enhanced, outperform random neurons.","pith_inferences":["The real new value is workflow integration: the paper does not introduce a new theory of knowledge mechanisms, so the toolkit's scientific output is only as trustworthy as the methods it packages.","A natural extension is to treat the key-matching scheme as a general registry for interpretability beyond text—vision or multimodal methods that declare their input schemas could be plugged into the same router.","The convergence of location consistency with dataset size, reproduced from prior work, would be a stronger result if paired with confidence intervals or significance tests across random data splits.","Testable extension: hold out a set of inputs, have experts manually select the best interpretation method, and measure how often the automatic key-based match agrees with the expert choice."],"forward_implications":["A user can run one workflow that spans factual, commonsense, counterfactual, math, sentiment, and translation datasets and get comparisons across all compatible interpretation methods.","Adding a new interpretation method requires only a diagnose function and a requires_input_keys list; adding a dataset requires only support_template_keys.","Cross-method comparison becomes routine: the paper shows KN and FINE agreeing on subject-relevant neurons, and Causal Tracing and Integrated Gradients both highlighting the subject token.","The capability-localization experiment shows that neuron sets located with 100-700 samples converge to 98%/96% overlap/IoU on GSM8K and 94%/90% on Emotion, and enhancing located neurons beats random or excluded neurons.","Know-MRI's UI-based matching also handles imprecise user input by rewriting it with GPT-4o and retrieving nearest dataset entries via BGE, making the toolkit usable by non-experts."],"supporting_citations":[{"why":"Supplies Causal Tracing, the Know-1000 dataset, and the architectural encapsulation pattern that Know-MRI's ModelAndTokenizer class builds on.","marker":"Meng et al., 2022"},{"why":"Contributes the Knowledge Neuron method whose required inputs (prompts and ground truth) define the matching example.","marker":"Dai et al., 2022"},{"why":"Provides Integrated Gradients, one of the integrated attribution methods used in the case study.","marker":"Sundararajan et al., 2017"},{"why":"Contributes PatchScopes, used to show earlier-layer hidden-state interpretation than Logit Lens.","marker":"Ghandeharioun et al., 2024"},{"why":"Provides Logit Lens used as the baseline hidden-state method in the comparison.","marker":"nostalgebraist, 2020"},{"why":"Contributes the FINE neuron-localization method compared against Knowledge Neurons.","marker":"Pan et al., 2025"},{"why":"Supplies the Knowledge Circuit method integrated into the circuit perspective.","marker":"Yao et al., 2024"},{"why":"Defines the capability-localization scoring setup and the baseline results that Know-MRI's extended application reproduces.","marker":"Huang et al., 2025"}],"fun_headline_variants":["Know-MRI auto-picks interpretation methods for LLM inputs","Open-source Know-MRI unifies 11 methods to probe LLM knowledge","Auto-match LLM inputs to explanation methods with Know-MRI","One toolkit, 11 methods: Know-MRI reveals LLM knowledge mechanisms","Know-MRI: the LLM knowledge interpreter that selects its own tools"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The toolkit's consolidated diagnosis inherits the validity of the eleven integrated interpretation methods; Know-MRI itself does not establish that those methods reveal true internal knowledge mechanisms, so if any of them is unreliable the merged output is misleading.","fun_headline_variants_meta":{"raw":{"variants":["Know-MRI auto-picks interpretation methods for LLM inputs","Open-source Know-MRI unifies 11 methods to probe LLM knowledge","Auto-match LLM inputs to explanation methods with Know-MRI","One toolkit, 11 methods: Know-MRI reveals LLM knowledge mechanisms","Know-MRI: the LLM knowledge interpreter that selects its own tools"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000773,"raw_usage":{"total_tokens":3402,"prompt_tokens":903,"completion_tokens":2499,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":519,"completion_tokens_details":{"reasoning_tokens":2405}},"tokens_in":519,"tokens_out":2499,"duration_ms":19638,"temperature":1.0,"reasoning_tokens":2405,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T05:10:26.258032+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run Know-MRI on a randomized version of Know-1000 in which subject-object associations are broken (shuffled ground truths). If Knowledge Neurons, FINE, and Causal Tracing still localize the same subject neurons with comparable strength as on the intact dataset, then the consolidated diagnosis cannot distinguish genuine knowledge mechanisms from artifacts, and the toolkit's central promise fails.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Contributes the FINE neuron-localization method compared against Knowledge Neurons."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Defines the capability-localization scoring setup and the baseline results that Know-MRI's extended application reproduces."}],"review_version":1}