{"id":"42317781-e030-4256-b3fe-914ba68aae06","arxiv_id":"2607.12385","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"On PM-Bench, a Virtual Week-style prospective-memory test for LLM agents, the best of eight models under eight setups scores only 65.1% F1, and no single fix dominates.","lead":"PM-Bench is a text benchmark that tests whether LLM agents can keep and act on delayed intentions over a simulated week while other work continues. It matters because long-horizon agent reliability hinges on this kind of future-oriented memory, and current systems still fail it often.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.5","headline":"No significant objection identified beyond the abstract-only information limit already flagged by the reader.","rationale":"The Reader’s UNVERDICTED / LOW-confidence stance is the only defensible position given an abstract-only review. The strongest claim is an empirical performance ceiling and non-dominance result; both rest on a task design that the abstract asserts but does not detail. No equation, table, or method section is present to probe for internal inconsistency, hidden assumptions, or statistical fragility. The Reader already identified the key validity premise (Virtual Week isolation of intention maintenance / delayed execution / latent cue monitoring). My role is not to invent a deeper attack when the material does not support one. Hence agreement is full, the verdict stays UNVERDICTED, and the concrete test is simply the natural next verification once the full artifact appears.","tokens_in":1956,"tokens_out":435,"duration_ms":3912,"concrete_test":"Obtain the full paper (or released PM-Bench code/data) and re-run the eight-model, eight-configuration evaluation after ablating context length and instruction-following difficulty (e.g., shorter week, explicit cue reminders, or non-PM control tasks). If relative rankings and the 65.1% ceiling remain essentially unchanged, the isolation claim is supported; large shifts would confirm the confound the Reader flagged.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The Reader correctly treats the abstract as insufficient to audit task design, confounds, or statistics, and therefore leaves the paper UNVERDICTED. With only the abstract available, there is no further load-bearing technical soft spot that can be isolated and tested: the central empirical claim (best 65.1% F1; no strategy dominates) cannot be checked for validity of the Virtual Week-style isolation of prospective memory versus context-window or instruction-following confounds, nor for evaluation details. Manufacturing an additional concern would violate the good-faith rule. The Reader’s weakest_assumption already names the right premise that would need to hold; it simply cannot be examined further from the given material.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.5","summary":"The manuscript introduces PM-Bench, a text-based benchmark for prospective memory in LLM agents, inspired by the Virtual Week paradigm from cognitive science. Over a simulated seven-day week, agents must maintain ongoing activities while deciding whether deferred intentions are due, thereby testing intention maintenance, delayed execution, and latent cue monitoring. The authors report results for eight state-of-the-art LLMs under eight agent configurations; the best reported result is 65.1% F1 (a GPT-5.4 agent), and no single improvement strategy dominates across models. PM-Bench is released as a controlled testbed for diagnosing failures and developing training or inference-time interventions for reliable prospective behavior.","tokens_in":2115,"tokens_out":789,"duration_ms":12507,"significance":"If the task design validly isolates prospective memory rather than mainly measuring context length, instruction following, or prompt format, PM-Bench would fill a clear gap in agent evaluation: delayed intention execution under ongoing activity is central to reliable agentic systems and is under-tested by existing benchmarks. The multi-model, multi-configuration comparison and the claim that no strategy dominates would be useful diagnostic evidence for the community. Release of a controlled testbed is a concrete contribution. Significance is conditional on task validity, scoring transparency, and statistical rigor, none of which can be audited from the abstract alone.","major_comments":[{"comment":"Only the abstract is available for this review. The central empirical claims (best 65.1% F1; no strategy dominates across eight models and eight configurations) cannot be audited for task validity, scoring protocol, statistical error, confounds with context length or instruction-following, or data leakage. A full-text review is required before any accept/reject decision on the load-bearing results.","section":null},{"comment":"Abstract framing: the claim that PM-Bench measures intention maintenance, delayed execution, and latent cue monitoring via a Virtual Week-style seven-day simulation is the key validity premise. Without the full task specification, cue design, distractor schedule, and scoring rules, it is not possible to assess whether performance primarily reflects prospective memory or context-window limits and prompt format. This premise is load-bearing for the paper’s interpretation of the 65.1% F1 ceiling.","section":null},{"comment":"Abstract metric claim: F1 is presented as the primary success metric, but the abstract does not define the unit of scoring (per intention, per day, per cue type), positive/negative class construction, or how partial/late executions are treated. Without that protocol, the headline number and the cross-strategy comparison cannot be interpreted or compared to other agent benchmarks.","section":null}],"minor_comments":[{"comment":"Abstract: “GPT-5.4” is nonstandard naming relative to publicly known model identifiers; the full paper should map configuration names to exact model versions and API/checkpoint identifiers for reproducibility.","section":null},{"comment":"Abstract: “eight different agent configurations” and “no single strategy … dominates” would benefit from a one-line enumeration of the strategy families (e.g., external memory, reminder prompts, planning) so readers can situate the claim before the full methods section.","section":null}],"recommendation":"uncertain","confidential_remarks":"This is an abstract-only review (full text not available). Recommendation is uncertain by necessity: the contribution is plausible and the circularity burden is low for an external multi-model benchmark, but load-bearing validity and scoring details cannot be checked. Please supply the full manuscript for a standard review cycle; I would re-evaluate with major/minor comments grounded in sections, tables, and evaluation protocol once the full text is available."},"author_rebuttal":null,"desk_editor":{"model":"grok-4.5","letter":"This is a new controlled text benchmark for prospective memory in LLM agents—intention maintenance, delayed execution, and latent-cue monitoring during ongoing activity—built as a seven-day Virtual Week-style simulation. That is the punchline: a concrete diagnostic for a failure mode long-horizon agents actually hit, not a re-derivation of general agent evals.\n\nWhat is new and done well is the framing and the comparative design. Virtual Week is established in cognitive science; applying it as a multi-day text environment with deferred tasks under ongoing activity is a solid method contribution. They run eight models under eight agent configurations and report a clear ceiling (best 65.1% F1, GPT-5.4) plus the useful negative result that no single strategy dominates. Circularity is low: external models on a new suite, not a fitted identity. If the tasks and scoring hold up, this is the kind of tool people will use to diagnose and intervene on delayed intention.\n\nThe soft spot is information, not a demonstrated flaw. We only have the abstract. We cannot check whether the simulation isolates prospective memory versus context length, instruction following, or prompt format; we cannot see scoring rules, statistics, confounds, or release artifacts. The reader’s weakest assumption is exactly right and currently untestable. Stress-test agrees: no further load-bearing objection can be isolated without methods. So treat the headline numbers as provisional, not as settled evidence of hardness.\n\nWho it is for: people building or evaluating long-horizon agents who need a focused PM diagnostic rather than another general suite. Worth a serious referee if the full paper ships task specs, scoring, and preferably code/data. I would not desk-reject on the abstract; I would send it out and demand the validity checks. For reading group, maybe once the paper is complete. I would cite the suite later if it becomes usable; not yet from abstract alone.","headline":"Useful subfield benchmark for delayed intention in agents; abstract-only so the 65.1% F1 and “no strategy dominates” claims are not yet auditable.","tokens_in":2699,"tokens_out":490,"would_cite":false,"duration_ms":5362,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.5","headline":"PM-Bench shows LLM agents fail at prospective memory: best score is 65.1% F1 on a seven-day Virtual Week-style test.","keywords":["prospective memory","LLM agents","PM-Bench","Virtual Week","intention maintenance","delayed execution","agent evaluation","agentic AI"],"falsifier":"A model-plus-agent configuration that reaches near-ceiling F1 on PM-Bench while still failing ordinary short-horizon instruction-following or context-tracking tests would show the benchmark is not isolating prospective memory; conversely, interventions that raise PM-Bench F1 without merely lengthening context would support the claim.","tokens_in":2837,"feed_emoji":"🤖","tokens_out":597,"duration_ms":5105,"temperature":0.7,"pith_summary":"This paper argues that modern LLM agents still lack reliable prospective memory—the ability to hold an intention, wait for a future cue or state, and act on it while other work continues. The authors introduce PM-Bench, a text-based seven-day simulation inspired by the Virtual Week paradigm from cognitive science, in which agents must keep an ongoing activity going while deciding whether any deferred task is due. Across eight models and eight agent configurations, the best reported result is only 65.1% F1 (a GPT-5.4 agent), and no single strategy for improving prospective memory wins for every model. The claim is that this controlled setting isolates intention maintenance, delayed execution, and latent cue monitoring, and that the low scores show these capabilities remain unsolved. If the diagnosis is right, PM-Bench becomes a diagnostic testbed for training and inference interventions that make agents keep promises across time instead of only reacting to the current prompt.","feed_headline":"LLM agents top out at 65% on a week-long memory test","feed_subtitle":"PM-Bench finds no dominant fix for keeping and acting on deferred intentions","key_machinery":"PM-Bench, a text-based seven-day simulation adapted from the Virtual Week cognitive-science paradigm: it forces agents to interleave ongoing activity with deferred-task decisions, scoring intention maintenance, delayed execution, and latent cue monitoring under controlled cues and schedules.","core_discovery":"PM-Bench is a challenging controlled benchmark for prospective memory in LLM agents: over a simulated seven-day week, agents must maintain user intentions, execute delayed intentions, and monitor latent environment changes while continuing an ongoing activity. Across eight state-of-the-art LLMs and eight agent configurations, the best method reaches only 65.1% F1, and no single improvement strategy dominates across models.","pith_inferences":[],"forward_implications":[],"fun_headline_variants":["LLM agents peak at 65% F1 on PM-Bench week-long memory test","PM-Bench: no LLM tops 65% on deferred intentions over seven days","Best agents score 65.1% F1 on prospective memory while multitasking","No fix dominates as LLMs struggle with delayed cues in PM-Bench","Top LLMs manage only 65% on maintaining and acting on intentions"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"That a text-based seven-day Virtual Week simulation mainly measures prospective memory (holding intentions, delayed execution, and watching for latent cues) rather than context-window limits, instruction-following, or prompt format.","fun_headline_variants_meta":{"raw":{"variants":["LLM agents peak at 65% F1 on PM-Bench week-long memory test","PM-Bench: no LLM tops 65% on deferred intentions over seven days","Best agents score 65.1% F1 on prospective memory while multitasking","No fix dominates as LLMs struggle with delayed cues in PM-Bench","Top LLMs manage only 65% on maintaining and acting on intentions"]},"model":"grok-4.5","effort":"low","cost_usd":0.005278,"raw_usage":{"total_tokens":1399,"prompt_tokens":731,"num_sources_used":0,"completion_tokens":87,"cost_in_usd_ticks":52780000,"prompt_tokens_details":{"text_tokens":731,"audio_tokens":0,"image_tokens":0,"cached_tokens":128},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":581,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":731,"tokens_out":87,"duration_ms":6314,"temperature":1.0,"reasoning_tokens":581,"cache_read_input_tokens":128,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-15T06:30:43.936786+00:00","model_set":{"reader":"grok-4.5"},"falsifier":"A model-plus-agent configuration that reaches near-ceiling F1 on PM-Bench while still failing ordinary short-horizon instruction-following or context-tracking tests would show the benchmark is not isolating prospective memory; conversely, interventions that raise PM-Bench F1 without merely lengthening context would support the claim.","supporting_citations":[],"review_version":1}