{"id":"c0cab9cd-ac42-462e-ae22-af15e9d29a2f","arxiv_id":"2607.17701","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"ProEvent is a benchmark showing LLM agents keep a user's event timetable from chats poorly, with the best fully-correct score at 27.2%.","lead":"This paper introduces ProEvent, a benchmark that tests whether AI agents can maintain a user's calendar by reading instant-message chats without explicit instructions. Generalists should care because the results suggest even the strongest current models succeed only about a quarter of the time at a task central to proactive assistants.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Evaluation prompt says DELETE only on explicit cancellation, but ground truth includes implicit cancellations; reported TSR may be a prompt artifact.","rationale":"I read the paper in good faith and acknowledge its strengths: a well-specified operation-based scoring scheme, careful noise injection, and a serious attempt at human quality control. However, the single most load-bearing concern is an internal inconsistency between the instructions given to evaluated models and the ground-truth labels used for scoring. The prompt in Appendix I tells models to DELETE only when an event is 'explicitly cancelled,' yet the benchmark deliberately includes implicit cancellations as ground-truth Deletes (Section 5.1, Figure 6(a)). This means models are penalized for following the literal task definition. The paper interprets this as a 'first-person perspective' deficit and a 'fundamental limitation,' but it may simply reflect prompt compliance. This confound directly affects the central empirical claims: the low TSR, the Delete-recall gap, and the qualitative error analysis. The reader's weakest_assumption focuses on synthetic-data realism, which is a valid external-validity concern, but the prompt-label mismatch is a more immediate internal-validity threat. I also note that the human performance baseline in Table 1 lacks any methodological description, making the '90.5% vs 27.2%' comparison unverifiable. For these reasons, the paper's current experimental support for its central claim is not reliable; the benchmark may still be valuable after revision, but the reported results should not be accepted as-is. A focused re-evaluation with an aligned prompt would determine whether the LLM failure rates are real or an artifact of the instruction mismatch.","tokens_in":15356,"tokens_out":10551,"duration_ms":114448,"concrete_test":"Run a controlled re-evaluation of GPT-5.1 and DeepSeek-R1 on the ProEvent test set with a revised system prompt that aligns the DELETE rule with the ground-truth policy: remove the word 'explicitly' and add a few-shot example of an implicit cancellation (e.g., 'Maybe I can't join you this time!' -> DELETE). Keep all other prompt text, operation formats, and evaluation code unchanged. Compare TSR and Delete recall against the paper's reported numbers. If either metric rises by more than 5 points, the original scores are substantially a prompt artifact. Also, to quantify label ambiguity, have two independent annotators classify each ground-truth Delete case as 'explicit' or 'implicit' under the original prompt's definition; if a large fraction are implicit, the mismatch is systemic.","verdict_should_be":"REJECT","load_bearing_attack":"The evaluation protocol has an internal contradiction. Appendix I's prompt given to all models defines the DELETE rule as: 'DELETE an event when it is explicitly cancelled.' However, Section 5.1 explicitly states that cancellations are 'often expressed implicitly rather than through explicit user instructions,' and the ground-truth labels include such implicit cancellations. Concretely, Figure 6(a) presents a case where the user says 'Maybe I can't join you this time!' — a hedged, non-explicit withdrawal — and the ground truth is Delete(7604). The model instead outputs Update(7604, participant, ...), which is a reasonable reading under the stated rule: the event is not explicitly cancelled, so a cautious agent removes the user from participants rather than deleting the event. The paper labels this 'perspective misalignment' and a 'fundamental limitation.' This is not a model deficiency; it is the model following the written instructions. The benchmark's 'objective' scoring is therefore misaligned with its own task definition. If the prompt were aligned with the ground-truth policy (e.g., by removing 'explicitly' and providing examples of implicit cancellations), the reported TSR/Delete recall would likely improve substantially. The headline result — even GPT-5.1 only 26.7% TSR — is confounded by this instruction mismatch, so the central claim that current LLMs lack proactive event-tracking ability is not supported by the current experiments. Additionally, the human performance baseline in Table 1 is reported without any methodology (no participant count, instructions, or sample), so the 90.5% human TSR cannot be independently assessed.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces ProEvent, a synthetic event-centric benchmark for evaluating whether LLM-based agents can proactively maintain a user's timetable by monitoring instant-messaging chats. Chat scenarios are generated from hand-designed scheduling trajectories, annotated with Insert/Update/Delete timetable operations, and scored with timing, single-step, and multi-step metrics (FDR/MNR, precision/recall, ESR/TSR). Experiments on eight LLMs and pipelines report uniformly low performance, e.g., GPT-5.1 reaches only 26.7% Timetable Success Rate versus 90.5% for humans, and qualitative analysis identifies categories such as implicit-event misses and first-person perspective misalignment. The paper concludes that current LLMs are far from dependable proactive event trackers.","tokens_in":15656,"tokens_out":4913,"duration_ms":56753,"significance":"The task formulation is timely and the evaluation design is more objective than prior semantic-similarity or LLM-judge-only protocols: operation-level ground truth permits exact scoring of most fields, and the authors include a human performance baseline. The paper also contributes a useful diagnosis of over-triggering (FDR) and a demonstration that a deliberative 'ProCoT' prompting strategy reduces it. If the benchmark is valid and the numbers are trustworthy, the main claim—that strong LLMs still cannot reliably maintain user timetables from chat—is significant for the proactive-agent community. However, validity is currently undercut by an instruction/ground-truth mismatch in the cancellation definition and by the absence of a released dataset, so the strength of the empirical conclusion is not yet established.","major_comments":[{"comment":"The evaluation prompt states: 'DELETE an event when it is explicitly cancelled.' Section 5.1, however, says cancellations are often expressed implicitly, and Figure 6(a) shows ground truth Delete for a hedged 'Maybe I can't join you this time!' while GPT-5.1 outputs Update. The model is following the written instruction; labeling this as 'perspective misalignment' or a 'fundamental limitation' is not justified. This mismatch directly deflates Delete recall, single-step metrics, and TSR, and confounds the headline 26.7% result. Please align the prompt with the ground-truth policy (define cancellation to include implicit expressions and give examples) or restrict ground truth to explicit cancellations, then rerun all models and reclassify the qualitative error cases.","section":"Appendix I, §5.1, Fig. 6(a)"},{"comment":"The realism/validity evidence is weak and partly self-undermining. The pairwise realism check relies on GPT-5.4 as a judge, with no human realism validation. The rewrite control uses only 50 cases and reports 6/50 output changes, but repeated runs on unchanged synthetic data already yield 8/50 inconsistencies; 6/50 is therefore within run-to-run noise and cannot support the claim that synthetic style has negligible impact. The Limitations section concedes the dialogues 'tend to be structured and repetitive' and all negotiation for an event occurs in a single chat. A larger human-based realism or consistency study, or inclusion of human chat data, is needed before model failures can be attributed to genuine proactive-assistant limitations rather than generator artifacts.","section":"§4.3, Appendix C, Limitations"},{"comment":"No URL, repository, or release plan is provided for the ProEvent dataset, prompts, or evaluation harness. Since the paper's contribution is a benchmark, the central artifact must be accessible for reproduction and adoption. Please provide a public link and, ideally, a data card with usage terms.","section":"General (data availability)"}],"minor_comments":[{"comment":"Typo: 'Inert(time=...)' should be 'INSERT(time=...)'.","section":"Fig. 11"},{"comment":"The model is referred to as both 'GPT-5.1' and 'GPT-o5.1'; please standardize.","section":"Table 2, Fig. 6"},{"comment":"The highlight criterion is stated as 'FDR and MNR >90%, all other metrics <10%', but several highlighted entries (e.g., 96.5% FDR) are not paired with >90% MNR. Clarify the exact highlighting rule.","section":"Table 1 caption"},{"comment":"Section 3.2 says ESR/TSR resolve ambiguities such as Update versus Delete+Insert, but §5 says no such ambiguity was observed. Reconcile the two statements.","section":"§3.2, §5"},{"comment":"The related work cites 'ProAgent (Yang et al., 2025b)', but the reference list entry is titled 'Contextagent'. Please align the citation and reference.","section":"§2, References"}],"recommendation":"major_revision","confidential_remarks":"The prompt/ground-truth mismatch in cancellation is the most serious issue because it is load-bearing for the main empirical claim; fixing it may change reported TSR and the qualitative error taxonomy. The benchmark is promising, but I would not accept it in its current form. Data release is also necessary for a benchmark contribution. I recommend the authors rerun the evaluation with an aligned prompt and add a larger human validation of dialogue realism."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nI read ProEvent with interest. The authors have built the first event-centric benchmark for proactive agent timetable maintenance, and the evaluation suite is genuinely thoughtful: operation-based scoring that avoids fuzzy LLM-judge correctness, FDR/MNR for timing, and trajectory-level success rates that handle operation ambiguity. The construction pipeline, with explicit scheduling trajectories and human QA, is a real step up from prior synthetic chat benchmarks, and the diversity analysis is credible. The empirical finding that current LLMs overreact and miss implicit cancellations is plausibly important.\n\nBut there is a load-bearing flaw in the evaluation protocol that the stress-test note caught. Appendix I's prompt tells every model: 'DELETE an event when it is explicitly cancelled.' Section 5.1, however, says cancellations are 'often expressed implicitly,' and the ground-truth labels include implicit cancellations. Figure 6(a) is the exact case: the user says 'Maybe I can't join you this time!' and the ground truth is Delete, while GPT-5.1 outputs Update(participant removal). Under the written instructions, the model's output is reasonable—even conservative. Calling this 'perspective misalignment' and a 'fundamental limitation' is unfair; the model is following the prompt. If the prompt were aligned to the ground-truth policy (e.g., by removing 'explicitly' and adding examples of implicit cancellation), the TSR and Delete recall would very likely improve. That means the headline 'even GPT-5.1 only 26.7% TSR' is confounded by this instruction mismatch.\n\nOther soft spots are minor by comparison: the dataset and code are not released, the human baseline in Table 1 is presented without any methodology (no participant count, instructions, or sample size), and the realism check relies on GPT-5.4 as a judge without human validation. The authors' own Limitations section concedes the synthesized chats are structured and repetitive, which is honest but undercuts the claim that failures generalize.\n\nThe core idea is good and worth pursuing. For a benchmark, the evaluation prompt must be consistent with the target task definition. This paper needs that fix before the central empirical claim can be trusted. With releases and a revised evaluation prompt, it would be a useful resource; right now I would not take the 26.7% at face value.\n\nI'd send it back for revision, not desk reject. It deserves a serious referee, but the referee should be asked to verify the prompt-answer alignment and the human baseline.","headline":"Novel benchmark and careful evaluation, but the DELETE instruction contradicts the ground truth on implicit cancellations, so the headline performance gap is likely overstated.","tokens_in":16133,"tokens_out":3045,"would_cite":false,"duration_ms":31369,"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":"ProEvent benchmark shows LLM agents keep a user's timetable right only 26.7% of the time, versus 90.5% for humans.","keywords":["proactive agents","event tracking","timetable maintenance","benchmark","LLM evaluation","instant messaging","dialogue understanding","agent evaluation"],"falsifier":"If a study applied ProEvent to real-world chat logs (or to human-written rewrites of the same scheduling trajectories) and found that the same models scored near-human success rates, the benchmark's synthetic style would be the main driver of failure. Conversely, a real-chat test reproducing the overreaction and cancellation errors would validate the benchmark.","tokens_in":15221,"feed_emoji":"📅","tokens_out":3938,"duration_ms":38496,"temperature":0.7,"pith_summary":"This paper introduces ProEvent, a benchmark that tests whether proactive AI agents can maintain a user's calendar by reading ongoing instant-messaging chats. The authors formalize the task as updating a timetable through Insert, Update, and Delete operations, and evaluate both when actions are triggered and whether they are correct. Testing eight LLMs and pipelines, they find a systematic overreaction bias: agents act when they should stay quiet, and they frequently delete events without sufficient evidence. Even the strongest tested model only produces a fully correct timetable in 26.7% of multi-step scenarios, while humans succeed 90.5% of the time. The paper argues that current agents lack key abilities: detecting implicitly stated events, reasoning from the user's first-person perspective, and handling cancellations.","feed_headline":"AI agents get user timetables right just 26.7% of the time","feed_subtitle":"New event-centric benchmark shows LLMs overreact and miss cancellations; humans score 90.5%.","key_machinery":"The central object is the timetable-maintenance formalism: at each discrete time step the agent maps the previous timetable and newly received chats to a set of operations (Insert, Update, Delete). Ground truth comes from scheduling trajectories that drive chat synthesis, allowing objective correctness via Event Success Rate and Timetable Success Rate, alongside timing metrics (False Detection Rate, Missed Need Rate). The benchmark's construction pipeline—contact profiles, LLM-generated trajectories, chat skeletons, noise injection, and concurrent-thread merging—is what makes the task concrete and evaluable.","core_discovery":"The central claim is that proactive event tracking from chat is a measurable, unsolved capability. ProEvent provides 1,249 synthesized chats with ground-truth scheduling trajectories, requiring agents to output explicit timetable operations at each time window. Across eight models and pipelines, no system approaches human performance; the best multi-step success is 27.2% correct timetables. The authors identify two systematic deficiencies: models overreact (FDR far exceeds MNR) and struggle with cancellations and implicit instructions. Ablations show that making implicit instructions explicit dramatically improves recall, suggesting the bottleneck is perception of action requirements in natu","pith_inferences":["The operation-based formalism could transfer to other asynchronous contexts like email or calendar feeds, making this a general schedule-sync task beyond chat.","The overreaction finding suggests a calibration problem: agents might be improved by tuning a confidence threshold for triggering actions, not just by prompting.","Because the chats are synthetically generated and the paper notes they are 'structured and repetitive,' the absolute success rates may not transfer directly to messy human chats; the benchmark is best read as a lower-bound difficulty probe.","The first-person perspective failure hints that fine-tuning on egocentric narratives or adding explicit 'act as the user' grounding could be a promising intervention."],"forward_implications":["If ProEvent is valid, no current LLM agent can be trusted to maintain a user's timetable autonomously; human oversight is still required.","The systematic overreaction bias (high FDR) suggests proactive agents should incorporate explicit 'need assessment' steps; one necessity-reasoning pipeline cut a model's FDR by 70%.","Performance degrades with longer negotiation threads and more concurrent chats, so scaling to real-world multi-party coordination will be hard for current architectures.","Reasoning improvements help distinguish genuine cancellations from ambiguity: the best delete-operation precision reached 99.5%.","The benchmark's success-rate metrics resolve single-step ambiguities (e.g., Update vs. Delete+Insert) and provide a stable target for future agents."],"fun_headline_variants":["Proactive agents flunk event tracking: best AI scores 27.2%","New benchmark: AI agents track events correctly only 27% of time","AI agents overreact, miss cancellations in proactive event test","Event benchmark: humans 90.5%, best AI agent 27.2% success","Proactive agent benchmark: AI lags humans by 63% on event tracking"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The load-bearing premise is that the LLM-synthesized chat dialogues are realistic enough that model failures on ProEvent reflect genuine proactive-assistant limitations rather than artifacts of the generator's style.","fun_headline_variants_meta":{"raw":{"variants":["Proactive agents flunk event tracking: best AI scores 27.2%","New benchmark: AI agents track events correctly only 27% of time","AI agents overreact, miss cancellations in proactive event test","Event benchmark: humans 90.5%, best AI agent 27.2% success","Proactive agent benchmark: AI lags humans by 63% on event tracking"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000743,"raw_usage":{"total_tokens":3146,"prompt_tokens":736,"completion_tokens":2410,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":480,"completion_tokens_details":{"reasoning_tokens":2308}},"tokens_in":480,"tokens_out":2410,"duration_ms":17675,"temperature":1.0,"reasoning_tokens":2308,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-01T17:13:10.808378+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"If a study applied ProEvent to real-world chat logs (or to human-written rewrites of the same scheduling trajectories) and found that the same models scored near-human success rates, the benchmark's synthetic style would be the main driver of failure. Conversely, a real-chat test reproducing the overreaction and cancellation errors would validate the benchmark.","supporting_citations":[],"review_version":1}