{"id":"575b6ae8-8616-44aa-83d0-eed2e0dbd471","arxiv_id":"2608.07899","paper_version":1,"verdict":"ACCEPT","confidence":"MODERATE","novelty_score":7.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Standard agent telemetry can detect most failures while hiding the decision and provenance evidence needed to identify the fault's origin; five frontier LLMs drop to near-zero origin-step accuracy on restricted views.","lead":"A new benchmark tests whether AI agent execution traces can reveal where a failure began, not just that it failed. Results show standard telemetry views detect most failures while stripping the evidence needed to locate the fault's origin, a gap that matters for debugging autonomous systems.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The headline standards claim depends on the authors' conservative OpenTelemetry/OpenInference field projections; actual standard spans can carry LLM decision and retrieval provenance fields, so the purported 0.5% origin-accuracy ceiling may be an artifact of the renderer rather than of the schemas…","rationale":"The paper is methodologically careful: deterministic renderers, verified exact-equal payloads, frozen splits, bootstrap intervals, a second mechanism pilot, and public-trace stress tests all support the core detection-localization gap. The single most load-bearing vulnerability is not the synthetic mechanism per se, which the paper honestly scopes, but the conflation of the authors' field projection with the two named standards. The abstract's quantitative claim ties the 0.5% ceiling to OpenTelemetry/OpenInference compatibility, while RQ2 shows that decision and provenance content are necessary for localization. Because those standards can carry LLM messages, tool arguments, and retrieval context, excluding such fields from the 'compatible' renderers may manufacture the ceiling. The proposed test is decisive: build standard-compliant spans and re-run the same evaluation. If the ceiling persists, the claim is robust; if it does not, the paper's practical guidance should be about which trace fields must be captured rather than about entire standards. I therefore accept the benchmark internally but recommend conditioning the headline claim on validation against actual standard mappings.","tokens_in":14258,"tokens_out":5707,"duration_ms":71846,"concrete_test":"Render the 108 matched delayed-binding groups into actual OpenInference spans using standard semantic conventions: llm.input_messages/llm.output_messages at LLM-call events, retrieval.documents for registry and provenance reads, tool.name/tool.arguments/tool.results at tool calls, and status/exception fields at verifier events. Run the five models on these spans with the same output protocol and candidate sets. If origin-step Top-1 remains at or below 0.5%, the standards ceiling claim survives; if it rises substantially (e.g., above 10%), the RQ1 claim must be rescoped from 'OpenTelemetry/OpenInference-compatible views' to the paper's conservative projections.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"RQ1's strongest practical conclusion is that Metadata, OpenTelemetry-compatible, and OpenInference-compatible views preserve detection while capping origin-step accuracy at 0.5%. The last two views are produced by the paper's own 'conservative generic field set' (Section III-D): only identity, tool, observation, status, and relation fields with 'direct generic mappings' are retained, while factor D (selected symbolic reference, binding kind) and factor P (registry/provenance documents) are deliberately excluded. However, OpenInference defines semantic conventions for LLM input/output messages and retrieval documents, and OpenTelemetry tool spans can carry tool arguments and results. These are exactly the fields that RQ2's ablations identify as load-bearing for localization (Full-D and Full-P losses in Table III). The result therefore does not establish that OpenTelemetry/OpenInference-compatible telemetry as used in practice lacks origin evidence; it establishes that the authors' conservative projections do. The Conclusion's boundary statement covers the synthetic mechanism, but not this renderer-to-standard mismatch. This is load-bearing because the abstract and RQ1 answer generalize from a particular field mapping to the capabilities of widely used standards.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"TelemetrySuffBench is a controlled benchmark that separates failure detection, fault-origin localization, and safe abstention for LLM agent systems. The authors generate deterministic synthetic traces with delayed-binding reference faults, render them through six coarse telemetry views and a seven-factor ablation panel, and evaluate five frontier models under a fixed protocol with explicit candidate sets, invalid-output accounting, cluster-bootstrap intervals, and a frozen blind holdout. The headline findings are that restricted telemetry views preserve near-perfect detection while origin-step accuracy drops to at most 0.5%, removing decision content reduces origin-step accuracy to zero for every model, provenance and identity are large positive contributors, and exact-payload-equivalent ambiguous pairs trigger abstention only for some models and prompts.","tokens_in":14452,"tokens_out":9934,"duration_ms":117702,"significance":"The paper has genuine methodological strengths: the renderer is deterministic and never reads gold labels, the matched-pair Full-D proposition is verified by canonical serialization for all 108 groups, the splits are frozen, the holdout is blind, and invalid outputs are scored as errors. These design choices make the internal comparisons reproducible and give the detection-localization gap a clean operational meaning. The main caution is that the benchmark is built around one synthetic delayed-binding mechanism family plus a small one-model second-mechanism pilot, and the OpenTelemetry/OpenInference-compatible views are deliberately conservative field projections. The contribution is therefore solid as a controlled demonstration, but the abstract and conclusions reach somewhat beyond the evidence when they state that standard-compatible telemetry cannot support origin localization.","major_comments":[{"comment":"The OpenTelemetry-compatible and OpenInference-compatible views are defined as retaining only canonical identity, tool, observation, status, and relation fields with 'direct generic mappings,' which excludes factor D (selected symbolic reference and binding kind) and factor P (registry and provenance documents). The abstract and RQ1 answer then conclude that these views 'limit origin-step accuracy to at most 0.5%.' However, OpenInference defines semantic conventions for LLM input/output messages and retrieval documents, and OpenTelemetry tool spans can carry tool arguments and results; these are exactly the fields that RQ2 identifies as load-bearing for localization through the Full-D and Full-P ablations in Table III. The measured ceiling is therefore a property of the paper's conservative projections, not of the representational capacity of the standards themselves. The paper should either add a standard-expressible renderer that includes decision and provenance content, or rephrase all standard-level conclusions as conclusions about the specific conservative field set used here.","section":"Section III-D, Figure 2, Section V-A"},{"comment":"The conclusion that decision content is indispensable for exact origin localization rests on the delayed-binding generator family, whose gold origin is, by construction, the event that selected the incorrect symbolic reference. The second-mechanism evidence-gate pilot contains only 25 fault traces and uses one fixed evaluator, so cross-mechanism generality is thin. The Conclusion's boundary statement is honest, but the abstract and the RQ2 answer state the indispensability claim without the same qualifier. The authors should add an explicit mechanism-relative scope to these claims, e.g., 'within this controlled delayed-binding mechanism family and the matched evidence-gate pilot,' so that readers do not infer that no agent telemetry without explicit decision-to-provenance links can support localization in general.","section":"Section III-B, Section V-C, Section VI"}],"minor_comments":[{"comment":"Because the OpenTelemetry-compatible and OpenInference-compatible views use the same conservative generic field set, the corresponding columns in Figure 2 are duplicates under different names; this should be stated explicitly in the caption or the two columns should be merged in the display.","section":"Section III-D, Figure 2"},{"comment":"Confidence intervals are reported for the Rich-Ambiguous FAR contrasts but not for the Rich-Answerable UAR columns; since the paper emphasizes the safety-utility trade-off, adding intervals or a brief explanation of their omission would improve comparability.","section":"Table IV"},{"comment":"The causal-stage assignment rule says that when stage labels coincide, assignment follows the listed order; this ordering should be justified because it can affect the reported stage distributions when activation and first visible deviation coincide, which the paper notes happens in this corpus.","section":"Section III-G"},{"comment":"The evidence-gate pilot reports 92.0% and 0% localization on 25 fault traces without uncertainty estimates; giving exact counts or bootstrap intervals would make the strength of this second-mechanism replication easier to assess.","section":"Section V-C"}],"recommendation":"major_revision","confidential_remarks":"To the editor: I see the core experimental design as careful and the benchmark as a useful contribution; my recommendation is driven by the gap between the title/abstract claims about OpenTelemetry and OpenInference and the conservative projections actually evaluated. This is fixable by re-scoping the language and, ideally, by adding one non-conservative standard-expressible renderer. The authors should also keep the mechanism-relative qualifier in the abstract. The anonymous repository, frozen splits, and verification details are strengths, and I saw no concerns about novelty or disclosure relative to Who&When, TraceElephant, and AgentRx."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The genuinely new thing here is the benchmark construction: paired coarse views, seven-factor semantic masks, and exact-equal ambiguous origin pairs. That lets the paper separate detection from localization and measure abstention under constructive non-identifiability, which prior attribution benchmarks do not do. The internal methodology is strong: deterministic renderers, matched two-origin groups, verified identical payloads via serialization, frozen splits, a blind holdout, bootstrap intervals, and invalid-output accounting. The TraceElephant public-trace stress test is a nice external check, even if it only covers the content-redaction contrast and not the factor masks.\n\nThe central empirical result holds up within the stated scope: on this synthetic delayed-binding family, full telemetry supports localization for four of five models, while the restricted views keep detection high but origin-step accuracy near zero. The Full-D ablation collapsing to zero across every operator, domain, and component is striking and well supported. The second-mechanism pilot is small but does what it claims.\n\nWhere the paper overreaches is RQ1's generalization to real telemetry standards. The OpenTelemetry- and OpenInference-compatible views are the authors' own conservative generic field set, deliberately excluding decision content and provenance. But OpenInference defines semantic conventions for LLM input/output and retrieval documents, and OpenTelemetry tool spans can carry arguments and results—exactly the fields that RQ2 identifies as load-bearing. So the 0.5% origin-accuracy ceiling is a property of this renderer's projection, not a demonstrated property of the standards as used in practice. The conclusion scopes the synthetic mechanism but not this renderer-to-standard mismatch, and the abstract inherits the overstatement.\n\nOther soft spots are minor. The Full-D zero would be easier to interpret with an explicit chance baseline over candidate events. The RQ3 answerable and ambiguous conditions are not matched on all payload-level cues, and the paper acknowledges that. The main external-validity caveat—one synthetic mechanism family plus one small pilot—is honestly disclosed.\n\nBottom line: this deserves a serious referee and, after the standard-telemetry claim is reworded, likely publication. The benchmark itself is reusable, the methodology is careful, and the detection-localization gap is a useful caution for AgentOps. I would cite it and would bring it to a reading group.","headline":"A carefully built synthetic benchmark that cleanly separates failure detection from origin localization, but its headline claim about OpenTelemetry/OpenInference standards overreaches because the tested projections exclude exactly the fields its own ablations show are load-bearing.","tokens_in":14989,"tokens_out":1293,"would_cite":true,"duration_ms":18545,"reading_group":"yes","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 shows that telemetry which lets an agent system detect a failure does not, by itself, reveal where that failure originated, and that exact localization depends on explicit decision-to-provenance links rather than downstream…","keywords":["agent telemetry","failure-origin localization","detection-localization gap","delayed-binding faults","semantic factor ablation","safe abstention","LLM agents","benchmark"],"falsifier":"Take real agent execution logs whose true failure origins are known from replay or fault injection, render each trace with and without the decision fields, and compare origin-step accuracy. If redacting those fields leaves substantial localization intact, the claim that decision content is indispensable is an artifact of the synthetic generator; if standard restricted views still yield near-zero origin accuracy on real logs, the gap generalizes beyond the benchmark family.","tokens_in":14029,"feed_emoji":"🐞","tokens_out":8492,"duration_ms":84727,"temperature":0.7,"pith_summary":"TelemetrySuffBench separates three questions that agent-observability work often collapses: did the execution fail, where did the failure originate, and does the visible evidence support one unique origin. The paper's central result is that detection and localization require different evidence. Five frontier language models detect failures almost perfectly from restricted telemetry views (99.5–100% detection F1), yet the same views limit exact origin-step accuracy to at most 0.5%, exposing a detection–localization gap. Removing the decision content from a full trace drives origin-step accuracy to zero for every model, while removing provenance causes large model-dependent losses. The paper concludes that terminal status supports detection, but causal attribution requires explicit decision-to-provenance links and abstention safeguards that remain effective across models.","feed_headline":"Agent telemetry catches failures but hides their origins","feed_subtitle":"Standard traces detect 99–100% of failures yet miss nearly every origin; decision links are the missing evidence.","key_machinery":"The load-bearing object is a deterministic renderer $g_m(E,G,X)$ that converts a canonical execution trace into a model-visible view while never reading the private labels $Y$, together with a seven-factor manifest that assigns every renderable field to one semantic group: identity and event semantics, decision and latent-reference content, provenance mapping, propagation relations, tool/state transitions, verifier evidence, and terminal status. The benchmark's fault construction injects a globally valid but task-inconsistent symbolic reference at a selected origin component and lets a later resolver bind it, so the origin, activation, first visible deviation, symptom, and terminal events are distinct causal stages. An 11-mask fixed panel removes one factor at a time while holding execution and labels fixed, making any accuracy change attributable to that factor. Exact-equal ambiguous pairs are generated only when two traces with different injected origins have byte-identical canonicalized model payloads, so any unique-origin answer on them is unsupported by construction.","core_discovery":"On the paper's own terms, the discovery is that telemetry sufficiency is task-relative: a trace can be sufficient to reveal a failure and insufficient to locate its cause. In the delayed-binding fault family, rendering the same executed trace through a metadata view or the two standard-schema-compatible views preserves 99.5–100% detection F1 while origin-step accuracy never exceeds 0.5%, and predictions systematically land on the downstream symptom rather than the injected origin. Under full telemetry, four of five models localize the origin step with 84.7–97.2% accuracy, so the gap is not a uniform model failure; it is the view stripping the decision-to-provenance link. Removing the decision factor alone zeroes origin-step accuracy for all five models at every operator, domain, and origin component, and the frozen holdout reproduces the pattern. For exact-equal ambiguous origin pairs, explicit contrastive-elimination prompting cuts unsupported unique-origin answers by 12.5–48.6 percentage points for three models, while two models answer every ambiguous case under every prompt.","pith_inferences":["A consequence the paper leaves implicit: the same detection-localization gap probably applies to human operators reading agent dashboards, so displaying a failed executor or terminal error could actively mislead root-cause work.","The benchmark's exact-equal ambiguity construction is a general audit method: any diagnostic system can be tested for unsupported attribution by building observational equivalence classes and measuring how often it still names a unique cause.","The factor-ablation zero is a testable design rule for telemetry schemas: record the selected symbolic reference and its binding as first-class fields; without them, no amount of downstream state or verifier detail makes the origin identifiable.","The model-dependence of abstention suggests that prompt-only safeguards are not enough; one could train or calibrate models on ambiguous telemetry pairs, or post-hoc gate outputs with a detector for observational equivalence."],"forward_implications":["Detecting a failure at 99–100% F1 does not imply the model knows its cause; diagnostics must report detection and exact-origin localization separately, or a high detection score will mask an absence of origin evidence.","Telemetry views that omit decision and provenance fields are sufficient for monitoring but not for automated root-cause analysis; applying them to attribution will collapse to downstream symptoms.","Because removing decision content zeroes origin accuracy for every model, any localizer that relies on downstream manifestation alone cannot recover the origin in this fault family.","Abstention on genuinely ambiguous telemetry is model-dependent; prompt-level evidence gating helps only some models, so operational safeguards must be model-specific.","Frozen holdout and repeatability results preserve the ordering and central contrasts, indicating the pattern is stable within the generator family."],"supporting_citations":[{"why":"Defines the responsible-agent and decisive-error-step attribution targets that TelemetrySuffBench refines into detection, localization, and abstention levels.","marker":"[2]"},{"why":"Supplies the public multi-agent trace corpus used as an external stress test showing content redaction zeroes localization.","marker":"[3]"},{"why":"Provides the constraint-based step-diagnosis line the benchmark contrasts with controlled evidence-pair ablations.","marker":"[4]"},{"why":"Defines the telemetry standard whose field projection is rendered as one restricted view.","marker":"[7]"},{"why":"Defines the AI-tracing conventions whose field projection is rendered as another restricted view.","marker":"[8]"},{"why":"Formalizes selective classification as a risk-coverage trade-off, the theoretical basis for RQ3 abstention metrics.","marker":"[9]"},{"why":"Introduces unanswerable questions and the requirement to refrain, extending the abstention construction to evidential non-identifiability.","marker":"[10]"},{"why":"Evaluates prompting strategies for abstention, the baseline for RQ3 prompt interventions.","marker":"[11]"},{"why":"Documents persistent abstention failures across frontier models, motivating the model-dependence analysis in RQ3.","marker":"[14]"}],"fun_headline_variants":["Telemetry finds failures but not their origins","Detection 99%+, origin <1%: telemetry gap","Failures detected, causes hidden: decision links missing","Trace detects failures, hides causes: benchmark proves gap"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing premise is that the synthetic delayed-binding fault family, together with the smaller second-mechanism pilot, is representative of real agent failures; if real failures corrupt traces with different causal structure, the measured detection-localization gap may not transfer.","fun_headline_variants_meta":{"raw":{"variants":["Telemetry finds failures but not their origins","Detection 99%+, origin <1%: telemetry gap","Failures detected, causes hidden: decision links missing","Trace detects failures, hides causes: benchmark proves gap"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000219,"raw_usage":{"total_tokens":1515,"prompt_tokens":1090,"completion_tokens":425,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":706,"completion_tokens_details":{"reasoning_tokens":373}},"tokens_in":706,"tokens_out":425,"duration_ms":4960,"temperature":1.0,"reasoning_tokens":373,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-12T00:42:38.556168+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Take real agent execution logs whose true failure origins are known from replay or fault injection, render each trace with and without the decision fields, and compare origin-step accuracy. If redacting those fields leaves substantial localization intact, the claim that decision content is indispensable is an artifact of the synthetic generator; if standard restricted views still yield near-zero origin accuracy on real logs, the gap generalizes beyond the benchmark family.","supporting_citations":[{"cited_title":"Which agent causes task failures and when? on automated failure attribution of LLM multi-agent systems,","cited_arxiv_id":null,"evidence_quote":"Defines the responsible-agent and decisive-error-step attribution targets that TelemetrySuffBench refines into detection, localization, and abstention levels."},{"cited_title":"Seeing the whole elephant: A benchmark for failure attribution in LLM-based multi-agent systems,","cited_arxiv_id":null,"evidence_quote":"Supplies the public multi-agent trace corpus used as an external stress test showing content redaction zeroes localization."},{"cited_title":"AgentRx: Diagnosing AI agent failures from execution trajectories,","cited_arxiv_id":null,"evidence_quote":"Provides the constraint-based step-diagnosis line the benchmark contrasts with controlled evidence-pair ablations."},{"cited_title":"What is OpenTelemetry?","cited_arxiv_id":null,"evidence_quote":"Defines the telemetry standard whose field projection is rendered as one restricted view."},{"cited_title":"OpenInference: OpenTelemetry instru- mentation for AI observability,","cited_arxiv_id":null,"evidence_quote":"Defines the AI-tracing conventions whose field projection is rendered as another restricted view."},{"cited_title":"Selective classification for deep neural networks,","cited_arxiv_id":null,"evidence_quote":"Formalizes selective classification as a risk-coverage trade-off, the theoretical basis for RQ3 abstention metrics."},{"cited_title":"Know what you don’t know: Unanswerable questions for SQuAD,","cited_arxiv_id":null,"evidence_quote":"Introduces unanswerable questions and the requirement to refrain, extending the abstention construction to evidential non-identifiability."},{"cited_title":"Do LLMs know when to NOT answer? investigating abstention abilities of large language models,","cited_arxiv_id":null,"evidence_quote":"Evaluates prompting strategies for abstention, the baseline for RQ3 prompt interventions."},{"cited_title":"Abstention- Bench: Reasoning LLMs fail on unanswerable questions,","cited_arxiv_id":null,"evidence_quote":"Documents persistent abstention failures across frontier models, motivating the model-dependence analysis in RQ3."}],"review_version":1}