Pith. sign in

REVIEW 3 major objections 5 minor 16 references

An LLM agent's behavior can be steered at runtime by swapping scenario definitions alone, without fine-tuning, as demonstrated in the ARDena embodied agent.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · deepseek-v4-flash

2026-08-02 09:51 UTC pith:SLLG6GGT

load-bearing objection A competent systems paper whose central control-effectiveness claim rests on the authors' own qualitative ratings; the latency data are solid, but the evaluation needs real transcripts, adherence metrics, and released artifacts before the main claim is convincing. the 3 major comments →

arxiv 2607.22651 v1 pith:SLLG6GGT submitted 2026-06-26 cs.AI

ARdena: Scenario-driven control of real-time LLM agents

Luka Borozan , Domagoj Matijevi\'c This is my paper
classification cs.AI
keywords scenario-driven controlruntime promptingLLM agentsembodied conversational agentreal-time interactionprompt engineeringagent behavior controlmultimodal interaction
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper sets out to show that a real-time LLM-based agent can be controlled by changing its 'scenario' — a block of task-specific rules injected into the prompt — while leaving the underlying model untouched and the conversation running. The authors claim that scenario definitions alone can produce substantially different, consistently followed behaviors: the same model can tutor math, teach a language, co-write a story, or chat freely. They support this with ARDena, a working system that runs on a laptop, replies in about three seconds on average, and stayed stable for 90 minutes of continuous use. The point matters because fine-tuning is slow and expensive, while scenario swapping is immediate, inspectable, and reversible — a practical control dial for agents deployed in dynamic settings.

Core claim

The central claim is that the scenario layer acts as a true runtime control unit: the agent's behavior is shaped by composing persistent static context, scenario-specific constraints, and current interaction state into the full prompt at every turn. Because the prompt construction engine rebuilds this composition each iteration, replacing the scenario changes the conditioning of the next response immediately and predictably, without parameter changes. Evidence comes from an evaluation in which four deliberately different scenarios all produced the behavior the scenario specified, with no observed deviations, while the same model was used throughout. The paper's own framing is that control is

What carries the argument

The load-bearing mechanism is the layered prompt architecture: a static layer (user identity, preferences, stable behavioral constraints, tools), a scenario layer (runtime-injected instruction block holding the immediate goal and dialogue rules), and an interaction loop that reassembles the full prompt on every turn. The scenario layer is the swappable control unit — modular, reusable, and replaceable mid-session — and everything else in the system (event orchestration, voice activity detection, avatar rendering, tool calls) exists to keep that swap fast and imperceptible.

Load-bearing premise

The entire control claim rests on the LLM faithfully following the injected scenario instructions; because the framework offers no formal guarantee and the paper reports no stress test with adversarial switches or repeated trials, the claim collapses if the model ignores, partially follows, or hallucinates the scenario.

What would settle it

A controlled test where a scenario instructs the agent to reject topic changes, and a user attempts to change the topic in varied phrasings, asked repeatedly; if any appreciable fraction of attempts succeeds, or if removing the scenario block produces no behavioral difference, the reliability of scenario-driven control would be refuted.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Behavior can be changed mid-session without interrupting the interaction and without retraining or re-deploying the model.
  • Scenarios are modular and reusable: the same scenario can be applied across users, and the same model can serve different domains by varying only the scenario definition.
  • The full-prompt rebuild on each turn means scenario and context changes propagate to the agent's next response immediately.
  • Stable real-time operation is achievable on consumer hardware: mean response latency of 2.86 s, median 2.73 s, and no crashes over roughly 90 minutes of continuous use.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • My inference: a stress test with adversarial or repeated topic-change attempts, run across several base LLMs, would likely show that instruction-following varies by model and by phrasing, so the framework's control strength is really a property of the underlying model, not of the prompt assembly.
  • My inference: expressing scenario constraints in a machine-checkable policy language would let the framework move from prompt-based suggestion to enforceable control; the paper hints at this in its future-work direction.
  • My inference: if scenario control holds up, it shifts the economics of conversational agents toward maintaining a prompt repository rather than training task-specific models — but that shift depends on API model suppliers keeping instruction-following reliable over time.
  • My inference: a falsifiable test worth running is the 'scenario removal' condition — if deleting the scenario block produces no measurable behavioral difference, then the static layer or the model's prior is doing the work, not the scenario.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes "scenario-driven LLM control," a framework in which agent behavior is shaped at runtime by a scenario layer of prompt-injected instructions combined with a static context layer and an interaction loop. The framework is implemented in ARdena, a Unity-based real-time multimodal agent using OpenAI Realtime API for speech-to-speech interaction, with vision-based user identification, tool use, and avatar output. The evaluation reports end-to-end latency (N=305, mean 2.86 s, median 2.73 s), stability over ~90 minutes of continuous operation with no crashes, and control effectiveness assessed through four qualitatively described scenarios. The central claim is that changing only scenario definitions produces substantially different interaction behaviors while maintaining stable real-time operation.

Significance. If the control-effectiveness claim were adequately supported, the framework would be a practical contribution: runtime-switchable behavior without model fine-tuning is relevant for embodied conversational agents. The latency and stability data are concrete and useful: N=305 with a clear histogram, median 2.73 s, no crashes over 90 minutes, and operation on a mid-range consumer laptop. The architecture description is detailed and maps well to the proposed layered control model. However, the central contribution is the control mechanism, and the only evidence for that mechanism is the author-rated qualitative table in §4.2. The lack of reproducible evidence for the core claim is a substantive gap, not merely a missing robustness check.

major comments (3)
  1. [§4.2, Table 1] The central claim that scenario definitions alone produce substantially different behavior is supported solely by Table 1, where each row contains an 'Expected behavior' and an 'Observed behavior' written by the authors, e.g., 'The agent consistently followed the prescribed workflow.' No raw interaction transcripts are provided, no turn-level outcome data are reported despite 305 recorded turns, no quantitative adherence metric is defined, no repeated trials are described, and no independent raters or inter-rater reliability are used. The exact scenario prompt texts are not released, so a reader cannot verify that the observed differences were caused by the scenario layer rather than by undocumented differences in static context, tool configuration, or conversation order. Because this is the paper's main contribution, the manuscript as it stands does not establish the control-effectivene
  2. [§5, Discussion and Limitations] The paper itself concedes that 'control is implemented through prompting rather than formal guarantees, meaning that adherence to scenario instructions depends on the language model.' This is an honest limitation, but the preceding assertion that 'no deviations were observed' is not backed by any defined deviation criterion or measurement. Without an operationalization of what counts as a deviation — e.g., changing topic when instructed not to, providing a solution when hints are required, violating the one-sentence constraint in storytelling — the claim that the agent 'successfully adhered to the intended interaction logic' (end of §4.2) is not falsifiable. The evaluation needs a pre-specified adherence rubric, systematic scoring of turns, and ideally repeated runs of each scenario to quantify compliance.
  3. [§4.1, latency/stability vs. central claim] The latency and stability results are presented as part of the same evaluation, but they do not bear on behavioral control. The 305 latency measurements span all four scenarios, yet no breakdown by scenario is given, and it is not stated whether the same static context, user identity, and tool configuration were used across the four control-effectiveness sessions. To make the 'scenario alone' claim credible, the authors should describe the experimental protocol in enough detail to show that the only intended difference between conditions was the scenario definition, and ideally provide controlled comparisons where the same user inputs are run under different scenarios.
minor comments (5)
  1. [Title/abstract] The title and abstract use 'ARdena' while the body text consistently uses 'ARDena.' Please standardize.
  2. [§4.1] The latency text says 'Note that all of the latency outliers occurred during wireless connections drops' but no data or criterion for outlier or connection-drop identification is provided; consider reporting network conditions separately.
  3. [§4.1] The histogram in Figure 3 is clear, but the text reports mean/median/IQR without any confidence intervals or statistical tests; given the small number of high-latency cases, a box plot or percentile table would add useful detail.
  4. [§2.2] The formal definition in §2.3 states the scenario 'can be defined as a function that maps interaction state to constrained model behavior,' but no mathematical notation is actually introduced. Either remove the word 'formally' or provide an actual function signature.
  5. [References] Some references are incomplete or inconsistently formatted (e.g., the LLAMA paper lists authors without abstract page numbers, and the Frontiers of Computer Science reference has a 2026 issue). A final proofread of the bibliography is needed.

Circularity Check

0 steps flagged

No circularity: scenario control is empirically evaluated, and no claim reduces to its inputs by construction.

full rationale

The paper does not derive its central claim from the definition of a scenario; it defines a scenario as a runtime-injected prompt block (Section 2.3: 'At runtime, the scenario is combined with the static layer and the current interaction state to form the complete prompt presented to the language model') and then tests whether the LLM actually follows such prompts across four different scenarios (§4.2). The Table 1 entries are qualitative author observations, which raises reproducibility and evidence-quality concerns, but that is not circularity: the observed behavior is not a formal consequence of the scenario text, and no parameter was fitted and then renamed a prediction. The framework contains no equations whose outputs equal inputs, no fitted quantities, and no load-bearing self-citations (references are external, e.g., BlazeFace, ReAct, LoRA). The limitation in §5 that 'adherence to scenario instructions depends on the language model' correctly identifies the assumption underlying the control claim rather than assuming it away. Thus no circular step can be exhibited under the required standard.

Axiom & Free-Parameter Ledger

0 free parameters · 3 axioms · 0 invented entities

No mathematical parameters or invented physical entities. The hand-authored scenario prompts are experimental conditions rather than fitted parameters, but their exact text is not provided, which hinders independent evaluation. The axioms are domain assumptions about LLM reliability, network/inference latency externalities, and representativeness of the single evaluation session.

axioms (3)
  • domain assumption The underlying LLM (OpenAI Realtime API / GPT-5.2) is sufficiently instruction-following for scenario instructions to reliably constrain behavior.
    Explicitly assumed throughout; the framework provides no enforcement mechanism beyond prompt text. Acknowledged in §5: 'adherence to scenario instructions depends on the language model.'
  • domain assumption End-to-end latency is dominated by model/network inference, and the framework's software overhead (prompt assembly, event loop, Unity rendering) is negligible or stable.
    The evaluation measures total latency on one laptop/network without isolating framework overhead. §4.1.
  • domain assumption The evaluation session (one user, 305 turns, four scenarios) is representative of real-world usage.
    No diversity of user demographics, task types, or environmental noise is tested. §4.

pith-pipeline@v1.3.0-alltime-deepseek · 9239 in / 10049 out tokens · 96226 ms · 2026-08-02T09:51:11.229148+00:00 · methodology

0 comments
read the original abstract

Large language models (LLMs) have enabled increasingly capable conversational agents, but reliably controlling their behavior in real-time interactive environments remains a significant challenge. Existing approaches often rely on model fine-tuning or alignment procedures that are difficult to adapt to changing interaction requirements. This paper introduces layered scenario-driven LLM control, a framework that enables runtime behavior control through structured prompting. By combining persistent context with scenario-specific constraints, the approach allows agent behavior to be modified during interaction without changing the underlying model. The framework is implemented in ARDena, a real-time multimodal embodied agent that integrates speech interaction, visual perception, tool use, and avatar-based response generation. The proposed approach is evaluated with respect to control effectiveness, response latency, and operational stability. The results demonstrate that scenario definitions alone can produce substantially different interaction behaviors while maintaining stable real-time operation, highlighting the effectiveness of scenario-driven prompting for controlling LLM agents.

Figures

Figures reproduced from arXiv: 2607.22651 by Domagoj Matijevi\'c, Luka Borozan.

Figure 1
Figure 1. Figure 1: The proposed scenario-driven control framework. Static context, scenario-specific constraints, and interaction history are combined to guide the language model and generate real-time embodied responses. describe the control layer structure used to meet the proposed requirements. Finally, we discuss the most important aspect of the framework: the concept of scenario-driven interaction control. 2.1. Operatio… view at source ↗
Figure 2
Figure 2. Figure 2: Architecture of the ARDena system implementing the proposed scenario-driven control framework. The diagram illustrates the flow from mul￾timodal user input through prompt construction and LLM processing to em￾bodied output, together with the mapping between conceptual framework com￾ponents and system modules. 3.1. Runtime Orchestration. Runtime orchestration in ARDena is implemented as an event￾driven infr… view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of end-to-end response latency measured over 305 interaction instances. The dashed and dotted vertical lines denote the mean (2.86 s) and median (2.73 s) latency, respectively. observed, and no manual intervention or system restart was required. Resource utilization remained stable, with consistent memory consumption, CPU/GPU usage, while the Unity ap￾plication maintained a stable frame rate. … view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

16 extracted references · 12 linked inside Pith

  1. [1]

    Language models are few-shot learners,

    T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amod...

    Pith/arXiv arXiv 2020

  2. [2]

    Llama: Open and efficient foundation language models,

    H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozi` ere, N. Goyal, E. Hambro, F. Azhar, A. Rodriguez, A. Joulin, E. Grave, and G. Lample, “Llama: Open and efficient foundation language models,” 2023. [Online]. Available: https://arxiv.org/abs/2302.13971

    Pith/arXiv arXiv 2023

  3. [3]

    Palm: Scaling language modeling with pathways,

    A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, P. Schuh, K. Shi, S. Tsvyashchenko, J. Maynez, A. Rao, P. Barnes, Y. Tay, N. Shazeer, V. Prabhakaran, E. Reif, N. Du, B. Hutchinson, R. Pope, J. Bradbury, J. Austin, M. Isard, G. Gur-Ari, P. Yin, T. Duke, A. Levskaya, S. Ghemawat, S. Dev, H...

    Pith/arXiv arXiv 2022

  4. [4]

    React: Synergizing reasoning and acting in language models,

    S. Yao, J. Zhao, D. Yu, N. Du, I. Shafran, K. Narasimhan, and Y. Cao, “React: Synergizing reasoning and acting in language models,” 2023. [Online]. Available: https://arxiv.org/abs/2210.03629

    Pith/arXiv arXiv 2023

  5. [5]

    Toolformer: Language models can teach themselves to use tools,

    T. Schick, J. Dwivedi-Yu, R. Dess ` ı, R. Raileanu, M. Lomeli, L. Zettlemoyer, N. Cancedda, and T. Scialom, “Toolformer: Language models can teach themselves to use tools,” 2023. [Online]. Available: https://arxiv.org/abs/2302.04761

    Pith/arXiv arXiv 2023

  6. [6]

    ”like having a really bad pa

    E. Luger and A. Sellen, “”like having a really bad pa”: The gulf between user expectation and experience of conversational agents,” inProceedings of the 2016 CHI Conference on Human Factors in Computing Systems, ser. CHI ’16. New York, NY, USA: Association for Computing Machinery, 2016, p. 5286–5297. [Online]. Available: https://doi.org/10.1145/2858036.2858288

    arXiv 2016

  7. [7]

    A survey on large language model based autonomous agents,

    L. Wang, C. Ma, X. Feng, Z. Zhang, H. Yang, J. Zhang, Z. Chen, J. Tang, X. Chen, Y. Lin, W. X. Zhao, Z. Wei, and J. Wen, “A survey on large language model based autonomous agents,”Frontiers of Computer Science, vol. 18, no. 6, Mar. 2024. [Online]. Available: http://dx.doi.org/10.1007/s11704-024-40231-1

    doi:10.1007/s11704-024-40231-1 2024

  8. [8]

    Adaptable logical control for large language models,

    H. Zhang, P.-N. Kung, M. Yoshida, G. V. den Broeck, and N. Peng, “Adaptable logical control for large language models,” 2024. [Online]. Available: https://arxiv.org/abs/2406.13892

    Pith/arXiv arXiv 2024

  9. [9]

    Lora: Low-rank adaptation of large language models,

    E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” 2021. [Online]. Available: https://arxiv.org/abs/2106.09685

    Pith/arXiv arXiv 2021

  10. [10]

    A survey of large language models,

    W. X. Zhao, K. Zhou, J. Li, T. Tang, Z. Dong, Y. Hou, B. Zhang, Y. Min, J. Zhang, P. Liu, X. Wang, Y. Du, C. Yang, Y. Chen, Z. Chen, J. Jiang, R. Ren, Y. Li, X. Tang, Z. Liu, Y. Hu, J.-Y. Nie, and J.-R. Wen, “A survey of large language models,”Frontiers of Computer Science, vol. 20, no. 12, p. 2012627,

  11. [11]

    Training language models to follow instructions with human feedback,

    L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, J. Schulman, J. Hilton, F. Kelton, L. Miller, M. Simens, A. Askell, P. Welinder, P. Christiano, J. Leike, and R. Lowe, “Training language models to follow instructions with human feedback,” 2022. [Online]. Available: https://arxiv.org/abs/2203.02155

    Pith/arXiv arXiv 2022

  12. [12]

    Constitutional ai: Harmlessness from ai feedback,

    Y. Bai, S. Kadavath, S. Kundu, A. Askell, J. Kernion, A. Jones, A. Chen, A. Goldie, A. Mirhoseini, C. McKinnon, C. Chen, C. Olsson, C. Olah, D. Hernandez, D. Drain, D. Ganguli, D. Li, E. Tran-Johnson, E. Perez, J. Kerr, J. Mueller, J. Ladish, J. Landau, K. Ndousse, K. Lukosuite, L. Lovitt, M. Sellitto, N. Elhage, N. Schiefer, N. Mercado, N. DasSarma, R. L...

    Pith/arXiv arXiv 2022

  13. [13]

    A prompt pattern catalog to enhance prompt engineering with chatgpt,

    J. White, Q. Fu, S. Hays, M. Sandborn, C. Olea, H. Gilbert, A. Elnashar, J. Spencer-Smith, and D. C. Schmidt, “A prompt pattern catalog to enhance prompt engineering with chatgpt,” 2023. [Online]. Available: https://arxiv.org/abs/2302.11382

    Pith/arXiv arXiv 2023

  14. [14]

    Generative agents: Interactive simulacra of human behavior,

    J. S. Park, J. C. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein, “Generative agents: Interactive simulacra of human behavior,” 2023. [Online]. Available: https://arxiv.org/abs/2304.03442

    Pith/arXiv arXiv 2023

  15. [15]

    Blazeface: Sub-millisecond neural face detection on mobile gpus,

    V. Bazarevsky, Y. Kartynnik, A. Vakunov, K. Raveendran, and M. Grundmann, “Blazeface: Sub-millisecond neural face detection on mobile gpus,” 2019. [Online]. Available: https://arxiv.org/abs/ 1907.05047 Email address:lborozan@mathos.hr

    Pith/arXiv arXiv 2019

  16. [2026]

    Available: https://doi.org/10.1007/s11704-026-60308-3

    [Online]. Available: https://doi.org/10.1007/s11704-026-60308-3

    doi:10.1007/s11704-026-60308-3