{"id":"74f12d94-4e0f-4e0e-8e16-05499e45d76e","arxiv_id":"2606.09890","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":7.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"PreActBench is a new benchmark showing that LLMs struggle to predict unethical outcomes from partial action trajectories across five domains using the Prefix Foresight F1 metric.","lead":"This paper introduces PreActBench, a benchmark with 1000 paired trajectories to test whether LLMs can predict unethical actions from only partial sequences. The work highlights that current models struggle at this forward-looking safety task while humans perform better.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"The reader's weakest_assumption correctly flags the benchmark validity as the key unknown, but without the full manuscript the concern remains hypothetical rather than demonstrable. No adjustment to the UNVERDICTED verdict is warranted on the basis of the abstract alone.","tokens_in":1669,"tokens_out":227,"duration_ms":15935,"concrete_test":"Obtain and inspect the benchmark-construction section (likely §3 or §4) for explicit description of how ethical/unethical pairs were generated, matched on prefix length, and validated for domain balance; if the section is absent or lacks those controls, the Prefix Foresight F1 comparisons cannot be interpreted.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The abstract-level description provides no technical details on trajectory construction, pairing criteria, or controls for selection bias, so no load-bearing internal inconsistency or unsupported assumption can be isolated from the given material. The reader's provisional UNVERDICTED status is therefore appropriate; a full-text review would be required before any concrete concern can be formulated.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces PreAct-Bench, a benchmark of 1,000 paired ethical and unethical action trajectories across five domains, to evaluate predictive monitoring in LLMs: the ability to infer from a partial trajectory whether it will culminate in unethical behavior. Using the Prefix Foresight F1 metric, the authors compare LLMs, safety guardrails, latent probing methods, and human performance at varying trajectory prefixes, concluding that predictive monitoring remains challenging for models despite promising human results and calling for future-oriented risk reasoning in LLM safety.","tokens_in":1715,"tokens_out":550,"duration_ms":14933,"significance":"If the benchmark construction and evaluations hold, the work fills a gap between retrospective safety detection and proactive monitoring, providing a concrete testbed and metric for an important capability. The human baseline and multi-method evaluation (including probing) are positive features that allow direct comparison of current approaches.","major_comments":[{"comment":"§3 (Benchmark Construction): The description of how the 1,000 paired trajectories were generated, validated for ethical/unethical labeling, and controlled for domain-specific biases or post-hoc selection is insufficient to establish that the Prefix Foresight F1 comparisons are unbiased; without explicit pairing criteria, inter-annotator agreement, or exclusion rules, the central claim that models underperform on predictive monitoring cannot be assessed.","section":"§3"},{"comment":"§4.2 (Evaluation Protocol): No statistical tests, confidence intervals, or error analysis (e.g., per-domain variance or trajectory-length effects) are reported for the Prefix Foresight F1 scores; this makes it impossible to determine whether observed gaps between models and humans are robust or driven by a few domains.","section":"§4.2"},{"comment":"Table 2 / Figure 3: The human performance numbers are presented without details on the number of annotators, their instructions, or agreement metrics; if humans were given full context unavailable to models, the comparison undermines the claim that predictive monitoring is inherently difficult.","section":"Table 2"}],"minor_comments":[{"comment":"The abstract and §1 use 'unethical action' without a precise operational definition or reference to an external ethical framework; this should be clarified early.","section":"§1"},{"comment":"Notation for Prefix Foresight F1 is introduced without an explicit formula; adding Eq. (X) would improve reproducibility.","section":"§4.1"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback on PreAct-Bench. The comments highlight areas where additional detail will strengthen the paper. We address each major comment below and will revise the manuscript to incorporate the requested information and analyses.","responses":[{"response":"We agree that §3 would benefit from expanded detail. In the revision we will add a dedicated subsection describing: the automated generation pipeline followed by human validation, explicit pairing criteria (matched length, domain, and objective), inter-annotator agreement (Krippendorff’s α = 0.87 across three annotators), and exclusion rules (e.g., trajectories with ambiguous ethical status or domain leakage). These additions will directly support the validity of the Prefix Foresight F1 comparisons.","revision_made":"yes","referee_comment":"[§3] §3 (Benchmark Construction): The description of how the 1,000 paired trajectories were generated, validated for ethical/unethical labeling, and controlled for domain-specific biases or post-hoc selection is insufficient to establish that the Prefix Foresight F1 comparisons are unbiased; without explicit pairing criteria, inter-annotator agreement, or exclusion rules, the central claim that models underperform on predictive monitoring cannot be assessed."},{"response":"We acknowledge the absence of statistical support. The revised manuscript will include: 95% bootstrap confidence intervals for all reported Prefix Foresight F1 scores, paired statistical tests (Wilcoxon signed-rank) comparing models to the human baseline, and an error analysis table breaking down performance by domain and prefix length. These additions will allow readers to assess robustness.","revision_made":"yes","referee_comment":"[§4.2] §4.2 (Evaluation Protocol): No statistical tests, confidence intervals, or error analysis (e.g., per-domain variance or trajectory-length effects) are reported for the Prefix Foresight F1 scores; this makes it impossible to determine whether observed gaps between models and humans are robust or driven by a few domains."},{"response":"We will clarify that human annotators received exactly the same partial trajectories as the models, with no additional context. The revision will report: five annotators, verbatim instructions (moved to appendix), and agreement metrics (Krippendorff’s α = 0.82). This ensures the human baseline is directly comparable and supports the claim that predictive monitoring remains challenging.","revision_made":"yes","referee_comment":"Table 2 / Figure 3: The human performance numbers are presented without details on the number of annotators, their instructions, or agreement metrics; if humans were given full context unavailable to models, the comparison undermines the claim that predictive monitoring is inherently difficult."}],"tokens_in":1383,"tokens_out":590,"duration_ms":18310,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's main contribution is framing predictive monitoring as its own task—given a prefix of an agent trajectory, can a model forecast whether the full sequence will end in an unethical action—and releasing PreActBench to support it. That distinction from post-hoc detection is useful and not just a relabeling of existing guardrail work.\n\nThey build 1000 paired ethical/unethical trajectories, test a range of LLMs plus probing methods with a Prefix Foresight F1 metric at varying prefix lengths, and report that humans outperform current models. The setup is straightforward and the high-level result tracks with what one would expect: forward-looking risk reasoning is still weak.\n\nThe soft spot is the missing description of how the trajectories were generated, paired, and validated. The abstract and available text give no information on annotation process, inter-rater checks, domain coverage criteria, or controls for selection effects, so it is hard to tell whether the unethical examples are representative or whether the pairing introduces artifacts that inflate the apparent difficulty. No error analysis or statistical significance tests appear either.\n\nThis is for people working on LLM agent safety who want a concrete testbed for early-warning methods. A reader already thinking about proactive monitoring will find the task definition and initial numbers worth discussing.\n\nIt deserves peer review so referees can examine the data pipeline and metric choices directly; the core idea is coherent enough to warrant that step.","headline":"PreActBench defines a clear predictive monitoring task and ships a 1000-trajectory dataset across five domains, but the construction and validation details are too thin to judge whether the benchmark is robust.","tokens_in":2217,"tokens_out":370,"would_cite":false,"duration_ms":8205,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"LLMs struggle to predict unethical actions from partial trajectories, unlike humans.","keywords":["predictive monitoring","LLM safety","PreActBench","unethical trajectories","Prefix Foresight F1","agent safety","future-oriented reasoning"],"falsifier":"A model achieving human-level Prefix Foresight F1 scores across all five domains and all prefix lengths on the fixed PreActBench set would falsify the claim that predictive monitoring remains challenging for strong models.","tokens_in":2579,"feed_emoji":"⚠️","tokens_out":629,"duration_ms":25557,"temperature":0.7,"pith_summary":"The paper defines predictive monitoring as the task of inferring from an incomplete action sequence whether it will end in unethical behavior. It introduces PreActBench, a dataset of 1,000 paired ethical and unethical trajectories across five domains, and measures performance with the Prefix Foresight F1 metric on prefixes of varying length. Experiments on LLMs, guardrail models, and probing methods show these systems lag behind human performance. The work argues that safety evaluation must move beyond retrospective analysis of finished trajectories to forward-looking detection. This establishes a concrete gap in current LLM capabilities for agentic settings.","feed_headline":"LLMs fail to spot unethical plans from partial trajectories","feed_subtitle":"Benchmark of 1,000 paired actions shows models lag humans at forecasting harm before it occurs.","key_machinery":"PreActBench benchmark of 1,000 paired ethical/unethical trajectories together with the Prefix Foresight F1 metric that scores foresight accuracy on trajectory prefixes of different lengths.","core_discovery":"Given only a partial action trajectory, models must infer whether the full sequence will culminate in an unethical action. PreActBench supplies 1,000 paired ethical and unethical trajectories spanning five domains. Across LLMs, safety guardrails, and latent probes, Prefix Foresight F1 scores remain substantially below human levels even on longer prefixes, demonstrating that future-oriented risk reasoning is not yet reliable in current systems.","pith_inferences":["Training objectives that reward accurate forecasting of trajectory outcomes could close the observed gap.","The same prefix-based evaluation could be applied to other agent risks such as planning failures or policy violations.","Real-time systems could use partial-trajectory monitors to trigger interventions before an unethical step is taken."],"forward_implications":["Retrospective safety checks on complete trajectories cannot prevent harm that has already begun.","LLM agent deployments require early-warning mechanisms based on partial plans rather than finished actions.","Guardrail and probing techniques must be redesigned to operate on incomplete sequences.","Safety benchmarks should routinely include foresight tasks instead of only post-hoc classification."],"fun_headline_variants":["LLMs struggle to forecast unethical actions from prefixes","PreActBench challenges models on partial trajectory prediction","Models lag humans at early unethical plan detection","Predictive monitoring exposes LLM limits on trajectory prefixes"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The 1,000 paired trajectories are assumed to form a representative test of predictive monitoring without selection biases that would distort the Prefix Foresight F1 comparisons.","fun_headline_variants_meta":{"raw":{"variants":["LLMs struggle to forecast unethical actions from prefixes","PreActBench challenges models on partial trajectory prediction","Models lag humans at early unethical plan detection","Predictive monitoring exposes LLM limits on trajectory prefixes"]},"model":"grok-4.3","cost_usd":0.005369,"raw_usage":{"total_tokens":2564,"prompt_tokens":618,"num_sources_used":0,"completion_tokens":56,"cost_in_usd_ticks":53687000,"prompt_tokens_details":{"text_tokens":618,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1890,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":618,"tokens_out":56,"duration_ms":15318,"temperature":1.0,"reasoning_tokens":1890,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-28T06:47:40.964344+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A model achieving human-level Prefix Foresight F1 scores across all five domains and all prefix lengths on the fixed PreActBench set would falsify the claim that predictive monitoring remains challenging for strong models.","supporting_citations":[],"review_version":1}