Delay-of-Gratification as a Multi-Agent Survival Micro-benchmark for Long-Horizon LLMs: Social Exposure, Personas, and Tool Use Budgets

Olga Manakina; Igor Bogdanov; Chung-Horng Lung

cs.AIAug 26, 2026

Delay-of-Gratification as a Multi-Agent Survival Micro-benchmark for Long-Horizon LLMs: Social Exposure, Personas, and Tool Use Budgets

Authors: Olga Manakina, Igor Bogdanov, Chung-Horng Lung

Organizations: Department of Cognitive Science Carleton University, Ottawa, ON K1S 5B6 · Systems & Computer Engineering Carleton University, Ottawa, ON K1S 5B6

Abstract

Large language models (LLMs) are increasingly deployed as multi-turn agents that must sustain goals, use tools, and adapt to other agents over extended interactions. However, existing research lacks auditable, multi-turn, multi-factorial experiments that quantify LLM behavior under explicit constraints, with time-resolved statistics that reveal how behavior unfolds over long horizons. To address this gap, we develop a multi-agent micro-benchmark inspired by the Stanford marshmallow experiment: ReAct agents operate minute-by-minute with a "raise a question" tool under a per-step budget, while we factorially manipulate social context (broadcast vs. isolated), personas (age, hedonic drive), and metacognitive policy (mandatory vs. optional tool use). We analyze outcomes with Kaplan-Meier (KM) survival curves and discrete-time hazard models over a long risk horizon across 19,200 agent trajectories in 64 cells. Behavior shows a sharp early "eat" impulse, and only 75.9% of agents persist to the end. In a discrete-time hazard model, isolation reduces per-minute risk relative to broadcast, whereas a must-use self-questioning policy increases risk. On average, agents ask $\approx 7.12$ questions and hit the per-step budget in $\approx 6\%$ of minutes. Questioning declines faster under broadcast than isolation. Ablation experiments demonstrated that removing hedonic drive and/or persona age increases survival and completion, narrows the broadcast/isolated gap, but leaves the must vs. may ordering intact. The combined ablation (no hedonic + no persona age) yields the highest completion (approaching $1.0$ ). These results establish delay-of-gratification as a compact, multi-turn interaction benchmark that captures social contagion and tool-use dynamics in LLM agents, providing a reproducible testbed and statistics for analyzing long-horizon, multi-agent behavior.

Figures & tables

Panel of Figure 1 — Figure 1: Interaction loops for (a) the partially observable Markov decision process (POMDP, broadcast condition) and (b) the fully observable Markov decision process (MDP, isolated condition). In (a), the agent observes time and peer responses, introducing partial observability. In (b), the agent observes only time and its own history, rendering the environment fully observable. At each step, the agent internally uses a capped raise_a_question tool, then chooses an action $a_{t}\in\{\text{wait},\text{eat}\}$ . The environment provides a reward, updates the state, and advances to the next observation.

Data Summary		Survival Metrics (Overall)		Data Quality
Agents per Cell	6	Initial Eat Rate	0.125	Data Quality Rate	99.9%
Total Agent Trajectories	19,200	Total Eat Rate	0.241	Invalid Outcome Rate	0.1%
Total Cells	64	Winners Rate	0.759	Valid Trajectories	19,185
Total Trials	3,200	Median TTE	14.8 steps	Invalid Trajectories	15
Risk Horizon	19	RMST (steps)	14.8
		Total Winners	14,566

Table 1: Dataset overview: counts, survival metrics, and quality.

Panel of Figure 2 — Figure 2: (a) Overall Event Distribution. The plot shows the probability of an agent eating the marshmallow for the first time at each step, aggregated across all conditions; (b) Forest Plot of OR from the discrete-time hazard model. Values less than 1 indicate a reduction in the hazard of eating, while values greater than 1 indicate an increase.

Panel of Figure 3 — Figure 3: KM survival curves by agent characteristics, shown with social context. The y-axis represents the proportion of agents still waiting.

Figure 4: Social Influence on Tool Usage (Broadcast vs. Isolated). The plot shows the mean number of questions asked per step.

Panel of Figure 5 — Figure 5: Ablation Completion Rates: Broadcast vs. Isolated and Survival Curves for all Ablation Conditions

AI Agent Benchmarks

AI agent benchmarks measure how reliably autonomous systems plan, use tools, interact, and complete tasks across controlled and real-world settings.

See all 1,441 papers

Aug 26, 2026

Evaluation of Multi-Turn Consistency in LLM Agents: Survival Analysis and Failure-Rationale Taxonomy

Large language model (LLM) agents may perform well on isolated tasks yet drift into inconsistency over extended interaction. We evaluate temporal consistency in a controlled 20-step multi-agent setting inspired by delayed-gratification studies. At each step, an agent chooses between continuing to delay a reward or claiming it immediately (terminating the episode). Across a full-factorial manipulation of social visibility (private vs public), persona stressors, and deliberation policy, we run 84,540 trajectories spanning 8 model families. Treating the first reward-claim as a time-to-event outcome, we estimate Kaplan-Meier survival curves and fit discrete-time hazard regression to quantify how experimental factors shift failure risk over time. Then, to analyze rationales and language patterns associated with failure, we build a seven-category taxonomy from 13,780 deliberation traces from agents who choose to terminate the episode, using an LLM-assisted labeling paired with human audit (

κ=0.83

). Rationale profiles change systematically with time and context: early failures are more impulse-driven, later failures more fatigue- and cost-benefit-framed, while public settings increase norm-oriented justifications. We also find a deliberation-inconsistency association: among failures, longer deliberation correlates with higher rates of intra-rationale contradiction (simultaneous pro-delay and pro-claim statements), challenging the assumption that more reasoning text implies greater consistency. Together, the survival and rationale analyses reveal distinct temporal reliability regimes and model-specific "failure fingerprints", offering an evaluation lens for diagnosing inconsistency in multi-turn agent behavior.

Igor Bogdanov, Olga Manakina, Chung-Horng Lung

Systems and Computer Engineering Carleton University Ottawa, ON, Canada · Department of Cognitive Science Carleton University Ottawa, ON, Canada

Behavioral Consistency of LLM Agents LLM Agent Reliability Reasoning Consistency in Language Models Long-Horizon Agent Evaluation

Ask PDF

May 27, 2026

Ask Now, Use Later: Benchmarking the Proactivity Gap in Long-Lived LLM Agents

A long-lived LLM agent, such as OpenClaw, earns its value by acting on a user's preferences and constraints across sessions, not just the current request. Yet today's agents keep what a user volunteers but rarely ask for what stays unspoken, leaving a proactivity gap in long-lived LLM agents: an agent cannot act on a preference it never obtained. As users delegate more of their affairs to agents, the impact of this gap grows. We isolate one concrete, controllable slice of this gap as Ask-to-Remember (ATR): the agent decides whether to ask now for a reusable user preference that the current task does not need but a later session with the same user will. ATR is hard even to evaluate: the right question is underdetermined and its payoff deferred to tasks that may never arise. ATRBench, to the best of our knowledge the first ATR benchmark, makes it measurable by fixing each user's preferences as hidden ground truth, so success demands asking, not recall. Across eight frontier LLM agents, defaults fall at least 62 points below an oracle handed the relevant preference, and prompting closes little of it. Diagnostics identify acquisition as the bottleneck. ATRBench surfaces this proactivity gap in current agents and offers a diagnostic testbed for closing it.

Bin Wu, Guanyun Zou, Bingbing Wang +2

Beijing University of Posts and Telecommunications · Nanjing University of Aeronautics and Astronautics · Harbin Institute of Technology (Shenzhen) +1

Proactive Assistance LLM Agent Memory LLM Agent Evaluation Agent Benchmarks Long-Horizon LLM Agents Preference Elicitation+1

Ask PDF

Feb 23, 2026

Interaction Theater: A case of LLM Agents Interacting at Scale

As multi-agent architectures and agent-to-agent protocols proliferate, a fundamental question arises: what actually happens when autonomous LLM agents interact at scale? We study this question empirically using data from Moltbook, an AI-agent-only social platform, with 800K posts, 3.5M comments, and 78K agent profiles. We combine lexical metrics (Jaccard specificity), embedding-based semantic similarity, and LLM-as-judge validation to characterize agent interaction quality. Our findings reveal agents produce diverse, well-formed text that creates the surface appearance of active discussion, but the substance is largely absent. Specifically, while most agents (

67.5\%

) vary their output across contexts,

65\%

of comments share no distinguishing content vocabulary with the post they appear under, and information gain from additional comments decays rapidly. LLM judge based metrics classify the dominant comment types as spam (

28\%

) and off-topic content (

22\%

). Embedding-based semantic analysis confirms that lexically generic comments are also semantically generic. Agents rarely engage in threaded conversation (

5\%

of comments), defaulting instead to independent top-level responses. We discuss implications for multi-agent interaction design, arguing that coordination mechanisms must be explicitly designed; without them, even large populations of capable agents produce parallel output rather than productive exchange.

Sarath Shekkizhar, Adam Earle

Salesforce AI Research

Multi-Agent Systems Multi-Agent Communication Emergent Behavior in Multi-Agent Systems LLM Agent Evaluation

Ask PDF

Abstract

AI Agent Benchmarks

Explore similar work

Evaluation of Multi-Turn Consistency in LLM Agents: Survival Analysis and Failure-Rationale Taxonomy

Ask Now, Use Later: Benchmarking the Proactivity Gap in Long-Lived LLM Agents

Interaction Theater: A case of LLM Agents Interacting at Scale