{"id":"57944e5d-b69b-4e0a-b2f0-34bdf366e09f","arxiv_id":"2606.31522","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"FinPersona-Bench shows that behavioral mandates in LLM financial agents decay over time in a model-dependent way, with periodic re-grounding producing mixed effects by agent profile and market regime.","lead":"This paper introduces FinPersona-Bench, a simulation benchmark to quantify Mandate Salience Decay in LLM-based financial agents over long horizons. A smart generalist might read it to gauge risks when deploying autonomous AI for trading without ongoing human oversight.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Synthetic decoupling of price from hidden value may not produce generalizable agent behaviors","rationale":"The load-bearing concern is identical to the reader's weakest_assumption. The full text does not appear to add external validation that would resolve it, so the low-confidence UNVERDICTED stance is unaffected.","tokens_in":1762,"tokens_out":306,"duration_ms":20638,"concrete_test":"Re-run the crash-scenario experiments with a modified market generator in which price is 40-60% correlated with the hidden fundamental value (instead of fully decoupled); if the 4.4x gap and model-dependent MSD effect sizes change by more than 30% or lose statistical significance, the headline claim does not generalize beyond the original decoupling assumption.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim (MSD compounds over time, is model-dependent, and produces a 4.4x behavioral gap in crashes between static and re-grounded agents) rests on results from a synthetic market that deliberately decouples observable price from an unobserved fundamental value. This design enables the three defined failure modes, but the claim that these results inform real deployment requires that the induced behaviors and failure modes are representative rather than artifacts of the artificial information asymmetry. No external validation (real-market traces, human trader comparison, or alternative market models) is described that would confirm the MSD dynamics survive when price and fundamentals are more entangled or when agents have access to additional signals.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces FinPersona-Bench, a simulation-based benchmark to quantify Mandate Salience Decay (MSD) in LLM agents initialized with behavioral mandates for financial decision-making. A synthetic market is constructed that decouples observable price from an unobserved fundamental value, enabling controlled tests of three failure modes (trading without signal in calm markets, panic-selling in crashes, ignoring fundamentals in bubbles). Across 18 frontier and open-source LLMs assigned to three mandate profiles (conservative to aggressive), the evaluation finds that MSD compounds over simulation time, is model-dependent, produces a 4.4x widening behavioral gap between static and periodically re-grounded agents in crash quarters, and that re-grounding effects are non-uniform (beneficial for conservative agents in low-signal settings but detrimental for aggressive ones).","tokens_in":1862,"tokens_out":568,"duration_ms":19853,"significance":"If the reported dynamics hold under the benchmark conditions, the work supplies a falsifiable, multi-model evaluation framework for long-horizon stability of autonomous agents, a topic of growing practical relevance. The explicit definition of failure modes and the observation that re-grounding is not uniformly helpful constitute concrete, actionable findings. The absence of machine-checked proofs or parameter-free derivations is offset by the reproducible simulation setup and the scale of the 18-model comparison.","major_comments":[{"comment":"Benchmark design (market model description): The central claim that MSD dynamics inform real deployment risks rests on the synthetic decoupling of price from hidden fundamental value. No comparison to real-market traces, human-trader baselines, or alternative market models (e.g., with entangled price-fundamental signals) is provided, leaving open whether the three failure modes and the 4.4x gap are artifacts of the artificial information asymmetry rather than representative of deployment conditions.","section":"Benchmark design"},{"comment":"Results on crash scenarios (the 4.4x behavioral-gap claim): The reported 4.4x growth in the gap between static and re-grounded agents from first to final quarter is load-bearing for the model-dependence conclusion, yet the manuscript supplies neither the precise definition of the behavioral-gap metric, run-to-run variance, nor statistical tests, preventing assessment of whether the multiplier is robust or sensitive to simulation stochasticity.","section":"Results"}],"minor_comments":[{"comment":"The abstract states that re-grounding 'consistently helps conservative agents in low-signal markets but actively worsens behavior for aggressive agents,' but the corresponding per-profile, per-regime tables or figures are not cross-referenced, making it difficult to trace the non-uniform effect.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their constructive comments. We address each major comment below and indicate the revisions we will make.","responses":[{"response":"The synthetic market was constructed to isolate MSD through explicit decoupling of price and fundamental value, enabling controlled evaluation of the three failure modes. This design prioritizes internal validity and reproducibility over direct ecological validity. We agree that the absence of real-market comparisons leaves the generalizability open to question. In the revision we will add a limitations subsection that discusses the synthetic setup's advantages for falsifiability, its relation to real deployment conditions, and the value of future validation against entangled-signal markets.","revision_made":"partial","referee_comment":"[Benchmark design] Benchmark design (market model description): The central claim that MSD dynamics inform real deployment risks rests on the synthetic decoupling of price from hidden fundamental value. No comparison to real-market traces, human-trader baselines, or alternative market models (e.g., with entangled price-fundamental signals) is provided, leaving open whether the three failure modes and the 4.4x gap are artifacts of the artificial information asymmetry rather than representative of deployment conditions."},{"response":"We will strengthen the presentation of the crash-scenario results. The revised manuscript will supply the formal definition of the behavioral-gap metric, report run-to-run variance across the simulation seeds, and include appropriate statistical tests to assess the robustness of the observed growth.","revision_made":"yes","referee_comment":"[Results] Results on crash scenarios (the 4.4x behavioral-gap claim): The reported 4.4x growth in the gap between static and re-grounded agents from first to final quarter is load-bearing for the model-dependence conclusion, yet the manuscript supplies neither the precise definition of the behavioral-gap metric, run-to-run variance, nor statistical tests, preventing assessment of whether the multiplier is robust or sensitive to simulation stochasticity."}],"tokens_in":1512,"tokens_out":418,"duration_ms":34217,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's main contribution is FinPersona-Bench, a simulation setup that measures Mandate Salience Decay across 18 LLMs in three defined failure modes. It reports that decay compounds over quarters, varies by model, and produces a 4.4x behavioral gap in crashes between static agents and those getting periodic re-grounding. Re-grounding helps conservative profiles in low-signal conditions but can worsen aggressive ones.\n\nThe evaluation is straightforward and covers a useful range of frontier and open models with explicit behavioral profiles. The three failure modes are cleanly operationalized, and the finding that re-grounding effects are not uniformly positive is a useful caution.\n\nThe central limitation is the market design itself. By construction the observable price is decoupled from hidden fundamentals, which lets the failure modes appear but also means the induced behaviors may be artifacts of that information asymmetry rather than something that would appear when price and value are more entangled or when agents receive additional real signals. No comparison to historical market traces or human trader data is described, so the claim that the results inform deployment standards rests on an untested generalization.\n\nThis is worth a referee for groups building or auditing autonomous financial agents. The benchmark is new and the multi-model results are concrete enough to discuss, even if the external validity needs more evidence. I would send it to review rather than desk reject.","headline":"FinPersona-Bench gives a concrete way to track mandate drift in simulated financial agents, but the synthetic price-fundamental split makes it unclear how far the results travel.","tokens_in":2359,"tokens_out":350,"would_cite":false,"duration_ms":11583,"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":"Large language models serving as financial agents lose the influence of their initial behavioral mandates as market context accumulates over time.","keywords":["LLM agents","financial simulation","mandate salience decay","behavioral stability","autonomous agents","market simulation","long-horizon deployment"],"falsifier":"Running the same agents on historical real-market data and checking whether the rate of panic-selling or signal-ignoring increases over successive quarters at a rate matching the simulated 4.4x gap growth would confirm or refute the decay pattern.","tokens_in":2659,"feed_emoji":"📉","tokens_out":705,"duration_ms":16780,"temperature":0.7,"pith_summary":"The paper establishes a benchmark to quantify how explicit behavioral mandates in LLM-based financial agents gradually lose their effect, a process termed Mandate Salience Decay. It uses a synthetic market that separates observable prices from hidden fundamental values to create testable failure modes including trading without signals, panic selling in crashes, and ignoring value in bubbles. Across 18 models and three mandate profiles, decay compounds with time and differs by model, with the performance gap between static agents and periodically refreshed ones expanding 4.4 times in crash scenarios by the final quarter. Re-grounding helps conservative agents in calm markets but harms aggressive ones in the same conditions, indicating that uniform refresh strategies are insufficient.","feed_headline":"Financial LLM agents lose mandate influence over time","feed_subtitle":"Benchmark shows initial instructions fade as context builds, with re-grounding helping some profiles but harming others in identical markets","key_machinery":"Mandate Salience Decay (MSD), the gradual loss of behavioral influence from explicit initial mandates as market context accumulates, quantified via falsifiable failure modes in a price-fundamental decoupled simulation.","core_discovery":"Mandate Salience Decay occurs when initial behavioral mandates lose influence over long deployment horizons in accumulating market context. FinPersona-Bench measures this through a synthetic market that decouples price from fundamental value, enabling evaluation on three failure modes. Tests on 18 LLMs show the decay is model-dependent and compounds, with the behavioral gap between static and re-grounded agents in crashes growing 4.4 times from first to last quarter; re-grounding effects are profile- and regime-specific rather than uniformly beneficial.","pith_inferences":["The benchmark approach of tracking mandate influence through synthetic decoupling could be adapted to measure stability in non-financial LLM agents such as those handling legal or medical decisions.","If decay proves general, agent systems may need built-in self-monitoring mechanisms that detect salience loss without external intervention.","The profile-specific effects of re-grounding suggest that hybrid human-AI oversight protocols should vary by risk tolerance of the mandate."],"forward_implications":["Long-horizon financial agent deployment requires selective rather than uniform mandate re-grounding.","Re-grounding frequency and necessity depend on both the agent's behavioral profile and the prevailing market regime.","Model selection for deployment must account for differing rates of Mandate Salience Decay across frontier and open-source LLMs.","Static mandate initialization alone is insufficient for stable behavior beyond short time horizons."],"fun_headline_variants":["LLM financial agents lose mandate influence as context builds","FinPersona-Bench tracks mandate salience decay in autonomous agents","Initial mandates fade differently across LLM profiles in simulations","Re-grounding aids conservatives but harms aggressive financial LLMs","Behavioral gap in crashes widens 4.4 times by simulation end"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"Behaviors observed in the synthetic market with decoupled price and fundamental value will match those of agents operating in real financial markets.","fun_headline_variants_meta":{"raw":{"variants":["LLM financial agents lose mandate influence as context builds","FinPersona-Bench tracks mandate salience decay in autonomous agents","Initial mandates fade differently across LLM profiles in simulations","Re-grounding aids conservatives but harms aggressive financial LLMs","Behavioral gap in crashes widens 4.4 times by simulation end"]},"model":"grok-4.3","cost_usd":0.004411,"raw_usage":{"total_tokens":2230,"prompt_tokens":716,"num_sources_used":0,"completion_tokens":79,"cost_in_usd_ticks":44112000,"prompt_tokens_details":{"text_tokens":716,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1435,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":716,"tokens_out":79,"duration_ms":11004,"temperature":1.0,"reasoning_tokens":1435,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-02T19:48:39.253264+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Running the same agents on historical real-market data and checking whether the rate of panic-selling or signal-ignoring increases over successive quarters at a rate matching the simulated 4.4x gap growth would confirm or refute the decay pattern.","supporting_citations":[],"review_version":2}