{"id":"aec0c394-5422-4854-8852-dda686ec5fe5","arxiv_id":"2608.10875","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":1,"one_line_summary":"A 200-task living-world benchmark for life agents shows seven frontier LLM assistants score only 21 to 32.5 out of 100.","lead":"VibeLifeBench adds 200 simulated multi-week life-assistance tasks where the world changes silently and an agent must act without being prompted. Seven frontier assistants all score below 33 out of 100, showing how far current systems are from trustworthy long-term life help.","discovery_kind":"new_method","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The headline gap rests on an unvalidated scoring apparatus: no oracle run, no random or human baseline, and no audit of the 12,261 checks; unless a gold trajectory scores high, uniformly low model scores are uninterpretable.","rationale":"The paper makes a genuine contribution: a stateful living-world simulator with silent mutations is a real step toward measuring proactive, persistent assistance, and the uniformly low model scores are internally coherent with the design. My stress-test therefore targets not the design but the inference from scores to capability, which is the load-bearing assertion: 'low scores reflect a real capability gap' (Section 4.1). That inference holds only if the scoring predicates (i) are satisfiable by a competent agent and (ii) do not systematically penalize legitimate divergent behavior. Neither condition is demonstrated anywhere in the manuscript. I looked for internal evidence that would weaken this concern. In the authors' favor, Section 2.3 states that key entities are made discoverable through tool calls rather than hard-coded into the criteria, and Section 2.2.5 says checks read observable artifacts and weight safety failures heavily. These are sound design intentions, but they are assertions about the pipeline, not evidence about the resulting 12,261 predicates. The paper presents no check-level precision or recall analysis, no ablation, no baseline, and no released artifact to audit; the repeated phrase 'will open-source' (Abstract, Sections 1 and 7) confirms the benchmark is not currently inspectable. The mechanism I add to the Reader's concern is Table 3's 'Tool call' dimension combined with Appendix A's single canonical answer per event. If a check requires the right tool with the right arguments at the right stage, then two agents that produce identical world outcomes through different means receive different scores. This is the cheapest explanation of the uniform 21–33 band that does not invoke a capability gap, and the paper does nothing to rule it out. The conceded fragility of the capability-axis analysis (Section 5.1: 'keyword matching over check names... indicative rather than exact') further weakens the attribution of failure specifically to proactivity and persistence, though the headline result does not require the axes to be exact. The proposed oracle test settles the question directly: a gold trajectory that follows the authors' own intended behaviors should score near the top of the scale, and a random-action control should sit near the bottom. If gold also fails, the benchmark needs repair before any capability conclusion is drawn; if gold passes, the low model scores become interpretable as measuring something real, conditional on alternative-solution false negatives being checked during the same exercise. Because the Reader's CONDITIONAL verdict already conditions acceptance on the release of artifacts and a demonstration of scoring validity, my concern reinforces that condition rather than moving the verdict. I therefore report UNCHANGED and agree with the Reader's weakest_assumption.","tokens_in":18044,"tokens_out":10544,"duration_ms":96812,"concrete_test":"Hand-author a gold trajectory for a stratified sample of at least 20 tasks (preferably all 200) following the intended 'competent assistant' behaviors in Appendix A, execute it in the Terrarium harness, and require avg@3 of about 90 or above while a random tool-calling baseline scores near zero on the same tasks. If the gold run falls well short of 90 or reproduces the models' failure modes, the checkers contain false negatives and the low model scores cannot be read as a capability gap.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central empirical claim — that frontier agents are 'far from able' because they score at most 32.5 avg@3 — requires the 12,261 author-written checks (Sections 2.2.5, 2.3) to be both satisfiable by a competent agent and free of systematic false negatives. The paper runs no oracle, no random baseline, no human baseline, and offers no independent audit; the tasks and checks are not yet released, so the requirement is unverified. The risk is concrete. Table 3 lists 'Tool call: whether the agent called the right tool with the right arguments' as an evidence dimension, so checks credit a canonical action sequence rather than pure outcome: an agent that reaches the same world state through a different legitimate service, tool order, or timing can be scored as failing. Appendix A reinforces this by prescribing one 'what a competent assistant should do' answer per event. Section 2.3 asserts the pipeline 'avoids false negatives' but supplies no measurement, and Section 5.1 concedes the capability-axis attribution is 'keyword matching... indicative rather than exact.' If the checkers reject legitimate alternatives, all improvised LLM agents would be depressed roughly uniformly, which is exactly the observed 21–33 band across seven models. An oracle run is the minimal experiment that separates 'agents lack proactivity/persistence' from 'the checks only accept the canonical solution path.'","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper introduces VibeLifeBench, a benchmark of 200 long-horizon, multi-week tasks across ten everyday-life domains, simulated in a deterministic world of 22 mock services exposing 288 tool interfaces. Tasks are driven by scripted timelines of user messages, world observations, notifications, and silent mutations, and are graded by 12,261 weighted deterministic checks over observable artifacts. The authors evaluate seven frontier LLM agents under a uniform harness and report uniformly low scores (avg@3 from 21.1 to 32.5), concluding that current agents lack proactivity, persistence, and long-horizon coherence. The benchmark and evaluation framework are promised for open release.","tokens_in":18297,"tokens_out":7101,"duration_ms":68302,"significance":"If the instrument is valid, VibeLifeBench would be a useful addition: it operationalizes proactivity and silent mutation in a reproducible, offline, deterministic environment, with stage-aware, cross-stage, and final scoring tiers and weighted safety checks. The design has clear strengths: scoring reads observable artifacts rather than hidden reasoning, the event model cleanly separates environment-driven from user-prompted turns, and per-task variance and token costs are reported. The main weakness is that the paper does not validate the scoring apparatus. With no oracle run, no random or human baseline, and no independent audit of the check predicates, the low model scores cannot yet be attributed to a capability gap. The benchmark contribution is promising, but its central empirical claim is currently uncalibrated.","major_comments":[{"comment":"The headline conclusion in Section 4.1 ('contemporary agents are still far from able...') requires that the 12,261 weighted checks are satisfiable by a competent agent and free of systematic false negatives. The paper provides no oracle run, no random baseline, no human baseline, and no independent audit of the check predicates. Table 3 includes 'Tool call' as a scored dimension, and Appendix A prescribes a single 'what a competent assistant should do' per event, so an agent that reaches the same end state through a different legitimate service, tool order, or timing can be scored as failing. Section 2.3 states that the pipeline 'avoids false negatives' but supplies no measurement. As a result, the observed 21-33 avg@3 band is equally consistent with a genuine capability gap and with checkers that only accept the canonical solution path. A minimal fix is to add a hand-authored gold-trajectory (oracle) run that should score very high, a random or trivial baseline that should score very low, and a sample-based false-negative audit; report the resulting scores and any check revisions.","section":"Sections 2.2.5, 2.3, 4.1"},{"comment":"The capability-axis attribution used in Section 5.2 is assigned by keyword matching over check names, which the paper itself concedes is 'indicative rather than exact.' The subsequent conclusion that proactivity and persistence are the largest weaknesses leans on this mapping, yet 41.8% of failures fall outside the named categories and no validation of the axis assignment is reported. As written, this part of the failure-mode analysis is not load-bearing; please either validate the mapping (for example, by human annotation with inter-annotator agreement, or by defining the axes explicitly in the check schema) or present the axis results as exploratory rather than as evidence for the mechanism.","section":"Section 5.1"},{"comment":"There is a discrepancy in the scoring protocol: the formal task definition in Section 2.2.1 says scoring criteria are evaluated when the run ends, while Section 3.1 says 'once a stage's events are processed, the scoring criteria run once against the current world state.' These are materially different protocols for per-stage checks and timeliness credit. Please state explicitly when each tier is evaluated, whether a check failed at its stage can be recovered later, and how partial credit is awarded across stages.","section":"Sections 2.2.1 and 3.1"}],"minor_comments":[{"comment":"The abstract and Section 1 state 'No current benchmark measures this,' but Section 6 cites UniClawBench, which is described as a 'universal benchmark for proactive agents,' and ClawMark, a 'living-world benchmark' for multi-day agents. Please qualify the novelty claim by specifying the combination of properties (life domain plus proactivity plus living world plus long horizon) that is absent from prior work.","section":"Section 1"},{"comment":"Aggregate scores are reported as averages across 200 tasks with no confidence interval or standard error; given that each task is run only three times, please report task-level uncertainty (for example, bootstrap confidence intervals) or at least the distribution of per-task scores.","section":"Section 3.2"},{"comment":"The Spearman correlations with number of events, horizon, and number of stages do not state the unit of analysis (tasks? model-averaged scores?) or significance levels; as written they do not support the sentence that difficulty is driven mainly by sustaining staged constraints rather than by tasks being longer.","section":"Section 5.2"},{"comment":"In Table 8, the stage/date label 'D1, 4/18' appears twice for two different events; please correct the duplicate date or clarify the intended timeline.","section":"Appendix A, Table 8"},{"comment":"The caption's symbol legend is not rendered legibly in the provided text; please ensure the legend appears correctly in the published version.","section":"Table 1"},{"comment":"The paper says it 'will open-source all tasks, environments, and the evaluation framework,' but no release URL or artifact is provided; for reproducibility, please include a link or state an embargo date if applicable.","section":"Section 2.4"}],"recommendation":"major_revision","confidential_remarks":"The paper's contribution is a resource benchmark, and the missing validation is fixable by adding oracle, random, and where feasible human baselines plus a check audit; that should be the gate for acceptance. I also note that the novelty claim should be softened relative to the cited proactive and living-world benchmarks, and the promised open-source release is not yet available, which the editor may want to confirm."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Xiaohongshu's VibeLifeBench is a real step forward in benchmark design: 200 multi-week tasks across ten life domains, with a stateful simulated world, silent mutations that don't trigger turns, and a tiered set of weighted checks (12,261 predicates, 58 median per task) that score observable artifacts rather than hidden reasoning. The mechanism that separates a mutation from a turn is genuinely useful for measuring proactivity, and the paper is transparent about what it does and does not measure: Section 5.1 admits the capability-axis attribution is keyword matching and 'indicative rather than exact.' The per-domain and per-stage analyses are coherent, and the uniformly low scores across seven frontier models are consistent with the design.\n\nThe soft spots are the ones the reader flagged. The headline claim that 'contemporary agents are far from able to manage life affairs' rests on an unvalidated scoring apparatus. There is no oracle run, no random baseline, no human baseline, and no independent audit of the 12,261 checks. Section 2.3 asserts the pipeline 'avoids false negatives' but supplies no measurement. And the checks are not purely outcome-based: Table 3 includes 'Tool call: whether the agent called the right tool with the right arguments,' and Appendix A prescribes one 'competent assistant' response per event. If the checkers only accept a canonical solution path, all improvised LLM agents would be depressed roughly uniformly — exactly what is observed. An oracle run is the minimal experiment that separates 'agents lack proactivity' from 'the checks only reward the canonical path.'\n\nNone of this is fatal. The benchmark itself is plausible and large, and the authors say they will release everything. But the empirical conclusion should be labeled conditional until the artifacts are out and scoring validity is demonstrated. The literature comparison in Table 1 is also a bit generous to prior work that already contains living-world properties, though the combination here is new.\n\nBottom line: this is a solid benchmark contribution for agent-evaluation researchers. It deserves peer review, but a referee should require the release of the tasks and checks plus at least an oracle or human baseline before accepting the gap claim.","headline":"Solid new benchmark for proactive living-world agents, but the claim that frontier models are 'far from able' needs an oracle or human baseline to be trusted.","tokens_in":18794,"tokens_out":2826,"would_cite":true,"duration_ms":27418,"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 introduces VibeLifeBench, a 200-task benchmark that places agents in multi-week simulated life worlds with silent changes, and reports that seven frontier models all score low, with the best at an average of 32.5 out of 100.","keywords":["LLM agents","life assistance","proactive agents","long-horizon tasks","living-world simulation","agent evaluation","silent mutations","benchmark"],"falsifier":"Re-run the benchmark with an agent that is told every silent change the moment it happens; if that agent also scores far below 100, the checks may be impossible or false-negative, and the measured gap would not be purely a capability gap.","tokens_in":17816,"feed_emoji":"🏠","tokens_out":8898,"duration_ms":79624,"temperature":0.7,"pith_summary":"VibeLifeBench is built around a simple reframing: a life-assistance task is not a prompt but a world with a clock. Each of its 200 tasks runs a multi-week simulated life scenario across 22 mock services, and about a fifth of the 7,453 scripted events are silent background mutations that fire with no notification, so an agent that never re-checks the world cannot see them. The benchmark scores agents with 12,261 weighted checks that read only the observable artifacts the agent leaves behind, rewarding timely action, correct silence, and cross-stage constraint-holding. Seven frontier models were run on every task; the best scored only 32.5 on average (max@3 41.2), and all models' check pass rates decayed 10-15 points from the first to the last third of a task. The paper's central claim is that contemporary agents are far from being trustworthy proactive, persistent life assistants, and that this is now directly measurable.","feed_headline":"Best life-assistant AI scores 32.5 out of 100 on new benchmark","feed_subtitle":"Seven frontier models all miss silent changes and lose coherence across multi-week life tasks.","key_machinery":"The load-bearing mechanism is the mutation: a background change to the world state that produces no agent turn and no notification, so the only way to notice it is to re-query the world unprompted. Around this, the benchmark organizes each task as a scripted timeline of user messages, world observations, notifications, and mutations, and grades the agent with stage-aware weighted checks in three tiers (per-stage, cross-stage, and final) that read only observable artifacts: backend state, workspace files, sent email, and reply text. The weight distribution is deliberately uneven, so a single safety or budget violation outweighs many routine sub-tasks, and high-weight checks usually require a durable artifact rather than a transient chat reply.","core_discovery":"The central claim is that no existing benchmark measures what everyday life assistance actually requires, and VibeLifeBench is a working instrument for doing so. A competent life assistant must act when action is due, stay silent when it is not, notice changes that nobody announced, and keep a single plan coherent across weeks; VibeLifeBench encodes exactly those demands as weighted checks over the state of a simulated world. Evaluated on the full suite, seven frontier models all score low, with the strongest at an avg@3 of 32.5 and a best-of-three ceiling of 41.2, and the lowest capability axes are proactivity, persistence, and propagation of silent mutations. The same models show a consistent decline in per-stage pass rate as the timeline advances, which the paper reads as direct evidence that long-horizon coherence and self-consistency are precisely where current agents are weakest.","pith_inferences":["If the check suite is valid, the same living-world design could be ported to other high-stakes long-horizon domains such as health, legal, or finance, by swapping the mock services and re-authoring weighted checks.","The benchmark rewards calibrated initiative, not just activity: an agent that constantly re-queries and acts could pass tool-call checks but fail the staying-silent checks, so it implicitly measures judgment about when not to act.","A natural next experiment is to give each model a perfect summary of all prior world state at every stage; if scores stay near 32.5, the bottleneck is decision policy rather than memory, and if they jump, the gap is partly a context-management limitation.","The narrow spread between the best and worst model suggests that simply scaling models or improving single-turn tool use will not close the gap; a testable prediction is that training on long-horizon proactive tasks is required."],"forward_implications":["An agent that only answers the prompt in front of it will systematically miss silent mutations, so deployed life-assistant products need scheduled re-inspection loops rather than pure instruction following.","Cross-stage bookkeeping in durable artifacts is the largest identified source of failure, so persistent state management is a prerequisite for trustworthy long-horizon assistance.","Per-stage pass rates fall by 10-15 points from the first to the last third of a task for every evaluated model, meaning output quality decays with horizon regardless of task length.","The cross-stage and final tiers carry disproportionate weight and the lowest pass rates, so safety and budget-cap failures are not hidden behind routine task completion.","No model is competent across all ten life domains, and the relative difficulty of domains is consistent across models, making the suite usable as a training target and a progress instrument."],"supporting_citations":[{"why":"Coding benchmark used in the comparison table as a contrast on proactivity and living world.","marker":"[1]"},{"why":"Office and professional benchmark used as a contrast on domain and long-horizon assistance.","marker":"[3]"},{"why":"Ultra-long-horizon benchmark used as a comparison point in the table.","marker":"[8]"},{"why":"Dynamic-environment travel planning benchmark used as a comparison point.","marker":"[13]"},{"why":"Living-world multi-day benchmark that supplies the closest prior comparison on proactive agents.","marker":"[14]"},{"why":"Evolving-information-environment benchmark used to position the living-world property.","marker":"[15]"},{"why":"Evaluation infrastructure that provides the stage-wise execution and isolated sandbox for all runs.","marker":"[16]"}],"fun_headline_variants":["Life AI fails long-haul: best benchmark score is 32.5","Proactive AI? VibeLifeBench says best score 32.5","Agents score 32.5 on VibeLifeBench: proactivity is the bottleneck","New benchmark: best life-agent scores 32.5 on week-long tasks"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The empirical conclusion rests on the assumption that the benchmark authors' 12,261 weighted checks reward all and only the competent behaviors, with no false negatives, and that all 200 tasks are actually solvable through the provided tool interfaces.","fun_headline_variants_meta":{"raw":{"variants":["Life AI fails long-haul: best benchmark score is 32.5","Proactive AI? 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