Pith. sign in

REVIEW 3 major objections 4 minor 28 references

A lightweight filter can make agents say "I don't know" instead of guessing wrong function calls.

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 · grok-4.5

2026-07-11 22:09 UTC pith:WNPDQ2MC

load-bearing objection Solid engineering paper: a practical multi-sample filter plus IDKS that raises reliability scores on open SLMs, with the consistency-proxy limits already flagged by the authors. the 3 major comments →

arxiv 2607.04034 v1 pith:WNPDQ2MC submitted 2026-07-04 cs.SE cs.AI

The "I Don't Know" Filter: Enhancing Agentic Reliability in Function Calling

Stefan Broecker , Mason del Rosario , Boris Selitser , Thomas Strohmer This is my paper
classification cs.SE cs.AI
keywords function callingagent reliabilityuncertainty quantificationhallucination detectionabstentionIDK Scoresynthetic training datasmall language models
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.

Language-model agents that call tools often guess when uncertain, because ordinary accuracy metrics reward any answer over abstention. That habit produces hallucinations that can be costly in production. This paper introduces a reliability score that treats incorrect function calls as harmful and abstentions as neutral, then shows that a small classifier can detect uncertain calls by looking at how much the model’s answers vary when the same query is sampled several times. The classifier uses features drawn from internal states, token probabilities, and output text, and replaces high-uncertainty groups with an explicit “I don’t know.” The method needs no change to the underlying model and can be trained on synthetic questions generated from existing function-calling data. Across open small models and standard tool-use benchmarks, the filter consistently raises the reliability score, offering a practical route to agents that know when not to act.

Core claim

A lightweight classifier trained on multi-sample white-, gray-, and black-box features can detect uncertain function calls and replace them with abstentions, raising the proposed IDK Score across open small language models and function-calling benchmarks without modifying the underlying model.

What carries the argument

The IDK Score, defined as (correct calls − incorrect calls) / total calls, with abstentions counted as neutral, together with a random-forest filter that treats output inconsistency across repeated generations as a proxy for uncertainty and replaces high-uncertainty groups with “I don’t know.”

Load-bearing premise

The method assumes that inconsistency across repeated samples is a reliable enough signal of uncertainty that high-variance groups can safely be turned into abstentions, even though consistently wrong answers produce no variance and therefore slip through.

What would settle it

Measure whether the same filter still raises IDK Score on held-out tasks where the model is systematically and confidently wrong on every sample, or where superficial formatting differences alone cause high measured variance; if the score collapses or correct answers are over-filtered, the consistency proxy fails.

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

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 / 4 minor

Summary. The paper argues that standard function-calling accuracy metrics incentivize hallucinations by equating incorrect answers with abstentions. It introduces the IDK Score (IDKS = (C - I)/N), which scores correct calls +1, incorrect calls -1, and abstentions as neutral. To raise IDKS, it proposes a lightweight random-forest filter that extracts multi-sample white-box (final-state), gray-box (logprobs), and black-box (embedding-based semantic volume, variation ratio, pairwise distances) features from repeated generations of an SLM, then replaces high-uncertainty groups with an explicit “I don’t know.” The filter is trained on synthetic user questions generated by constrained prompting from BFCL templates and is evaluated without modifying the underlying model (Phi-4-mini, Qwen2.5-3B, Llama-3.2-3B) on held-out BFCL variants and a cleaned APIGen subset. Three experimental axes—number of repeats, feature invasiveness, and cross-LM transfer—show consistent IDKS gains, with more invasive features and more samples generally helping, and partial portability of the classifier across model families.

Significance. If the results hold, the work supplies a practical, model-agnostic reliability layer for production agents that does not require fine-tuning or white-box access to frontier models. The combination of an explicitly risk-sensitive metric (IDKS), a reproducible synthetic-data pipeline, a publicly linked code base, and controlled multi-axis experiments on public benchmarks constitutes a concrete step toward agents that can abstain under uncertainty—an acknowledged deployment bottleneck. The honesty about the two failure modes (confident incorrectness and superficial formatting variance) further strengthens the contribution by delimiting the scope of the consistency proxy.

major comments (3)
  1. [III-C.4, Table I, Figs. 4–5, V.B] Section III-C.4 defines group labels via a pass^k-style rule (valid iff every response in the multi-sample group is externally validated as correct) while the feature set is dominated by variance measures (semantic volume, variation ratio, pairwise distances, entropy). Consequently the classifier largely learns to detect multi-sample inconsistency rather than independent correctness. Table I and Figures 4–5 report only aggregate IDKS lifts; they never decompose those lifts into (a) true suppression of incorrect groups versus (b) conversion of already-inconsistent groups, nor do they supply precision/recall or a confusion matrix for the filter itself. Without that decomposition it remains unclear how much of the reported reliability gain is genuine error reduction versus re-labeling of easy, high-variance mistakes. Section V.B acknowledges the confident-incorrectness failure mode but does
  2. [IV, Table I] The experimental baselines are only “no filter.” There is no comparison against simpler uncertainty heuristics that use the same multi-sample features without a trained classifier (e.g., threshold on semantic volume or variation ratio alone, majority-vote consistency, or gray-box entropy). Such ablations are necessary to establish that the random-forest stage, rather than the multi-sample signal itself, is responsible for the IDKS gains.
  3. [IV, Fig. 3] All IDKS numbers are reported as point estimates with no standard errors, confidence intervals, or statistical tests across the multiple random seeds or data folds that would be expected for a reliability claim. Given that decoding is stochastic and the decision threshold is itself optimized on the training set (Fig. 3), the absence of uncertainty quantification on the metric undermines assessment of whether the observed lifts are robust.
minor comments (4)
  1. [Fig. 1] Figure 1 caption and the surrounding text use “FC” for both “function calls” and the classifier’s input; a single consistent expansion would improve readability.
  2. [III-C.1] The decoding parameters forced on Phi (Llama’s temperature/top-p) are stated but never ablated; a short note on sensitivity would clarify whether the reported gains for Phi are partly an artifact of non-default sampling.
  3. [References] Reference [4] is cited for production failures but appears only as an AAAI/ACM proceedings entry without a full title or DOI in the bibliography; completing the citation would aid verification.
  4. [III-A] In Section III-A the authors note that the –1 penalty is “a simplification that practitioners can adjust,” yet no sensitivity analysis over alternative penalties appears; even a one-paragraph discussion would strengthen the metric’s claimed generality.

Circularity Check

0 steps flagged

No significant circularity: IDKS is an independent evaluation metric, labels come from external BFCL validators, features are standard variance measures from prior literature, and reported gains are empirical on held-out data.

full rationale

The paper's chain is empirical rather than a claimed first-principles derivation. IDKS is defined directly as (C-I)/N with abstentions neutral (Section III-A); it does not depend on the classifier. Labels for training groups are produced by the external BFCL function-call validators of Patil et al. [25] under a pass^k-style rule (all members of the group must be individually valid). Features (semantic volume, variation ratio, pairwise distances, entropy, etc.) are extracted from the same multi-sample generations but are taken from independent prior work (Li et al., Huang et al., Wang et al.). A random-forest classifier is then supervised on those external labels; the decision threshold is chosen by ordinary maximization of training-set IDKS (Figure 3). Performance is measured on held-out BFCL variants and APIGen. Nothing reduces a claimed prediction to a fitted constant or to a self-definition by construction. The acknowledged failure mode of confident incorrectness (Section V-B) further shows that low-variance wrong groups are not automatically labeled reliable. Self-citations are absent; all load-bearing methodological citations are external. The mild threshold-tuning step is standard hyper-parameter selection, not circularity under the listed patterns. Hence score 0 with empty steps.

Axiom & Free-Parameter Ledger

3 free parameters · 4 axioms · 2 invented entities

The central claim rests on a small set of modeling choices (inconsistency ≈ uncertainty, fixed –1 penalty in IDKS, random-forest decision threshold) and on standard ML and LM assumptions. No new physical entities are postulated; the invented objects are the metric and the filter. Free parameters are ordinary hyper-parameters selected on training data.

free parameters (3)
  • classifier decision threshold = selected per training run to max IDKS
    Chosen by maximizing average IDKS on the training set (Fig. 3); directly controls the precision-recall trade-off of the filter.
  • number of repeated generations = 2–5
    Varied from 2 to 5; both baseline degradation and filter gain depend on this choice.
  • decoding temperature / top-p / top-k = Llama 0.9/0.6; Qwen 0.7/0.8/20
    Set to model defaults or Llama defaults for Phi; controls the amount of output variance that the features measure.
axioms (4)
  • domain assumption Disagreement among repeated samples of the same prompt is a usable proxy for the probability that a function call is incorrect.
    Stated in III-D and used for both labeling (pass^k variant) and feature construction; failure modes in V.B show the proxy is incomplete.
  • ad hoc to paper An incorrect function call should be scored –1 relative to a correct call of +1, with abstentions neutral.
    Definition of IDKS in III-A; authors note practitioners may re-weight the penalty.
  • domain assumption Constrained prompting (functions + expected calls → new user questions) yields valid, label-consistent training examples.
    Section III-C.2 and Fig. 2; open-ended prompting is rejected because it produced misaligned triples.
  • standard math Standard mutual-information and recursive-feature-elimination steps remove irrelevant features without destroying the uncertainty signal.
    III-C.4; uses scikit-learn utilities.
invented entities (2)
  • IDKS (I Don’t Know Score) no independent evidence
    purpose: Evaluation metric that penalizes incorrect function calls while treating abstentions as neutral.
    Defined in III-A; not present in the cited benchmarks.
  • IDK filter (random-forest classifier on multi-sample features) no independent evidence
    purpose: Runtime gate that replaces high-uncertainty function-call groups with an explicit abstention.
    Core technical contribution of III-B; trained and evaluated in IV.

pith-pipeline@v1.1.0-grok45 · 14136 in / 2909 out tokens · 33403 ms · 2026-07-11T22:09:07.473884+00:00 · methodology

0 comments
read the original abstract

The language models that underpin agents have seen a rapid rise in performance on function calling benchmarks. However, the metrics used in the training and evaluation of these models often encourage models to make positive claims even when the answer is uncertain, leading to hallucinations. Such hallucinations can be disastrous when language models are trusted to use function calls to make decisions in high stakes applications. To that end, we propose an agent evaluation metric that takes into account the negative outcomes associated with incorrect function calls. Further, to catch hallucinations before they can cause harm, we propose a lightweight trainable filter that can quantify a language model's uncertainty and remove potentially harmful function calls. By training that filter to detect and suppress uncertain function calls without modifying the underlying model, we demonstrate a practical path toward agents that know when to say "I don't know," a property we argue is essential to production reliability.

Figures

Figures reproduced from arXiv: 2607.04034 by Boris Selitser, Mason del Rosario, Stefan Broecker, Thomas Strohmer.

Figure 1
Figure 1. Figure 1: A classifier being used as a filter for an LM agent. Given the same [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Average performance of a Llama agent with a classifier filter applied [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: IDK Score for Llama, Phi, and Qwen agents averaged across all [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] 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

28 extracted references · 17 linked inside Pith

  1. [1]

    A survey on large language model based autonomous agents.Frontiers Of Computer Science.18(2024,3), http://dx.doi.org/10.1007/s11704-024-40231-1

    Wang, L., Ma, C., Feng, X., Zhang, Z.,et al.. A survey on large language model based autonomous agents.Frontiers Of Computer Science.18(2024,3), http://dx.doi.org/10.1007/s11704-024-40231-1

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

  2. [2]

    & Chen, Y

    Hatami, M., Pham, V ., Lakadawala, H. & Chen, Y . Secur- ing AI Agents in Cyber-Physical Systems: A Survey of Envi- ronmental Interactions, Deepfake Threats, and Defenses. (2026), https://arxiv.org/abs/2601.20184

    arXiv 2026

  3. [3]

    State of AI in the Enterprise: 2026 Report.Deloitte Insights

    Deloitte. State of AI in the Enterprise: 2026 Report.Deloitte Insights. (2026) Available at:https://www.deloitte.com/ content/dam/assets-zone3/us/en/docs/services/ consulting/2026/state-of-ai-2026.pdf

    2026

  4. [4]

    Riedl and Deven R

    Mark O. Riedl and Deven R. Desai. InProceedings of the AAAI/ACM Conference on AI, Ethics, and Society, volume 8, pages 2189–2198, 2025

    2025

  5. [5]

    Measuring Agents in Production

    Pan, M., Arabzadeh, N., Cogo, R., Zhu, Y .,et al.. Measuring Agents in Production. (2026), https://arxiv.org/abs/2512.04123

    Pith/arXiv arXiv 2026

  6. [6]

    & Zhang, E

    Kalai, A., Nachum, O., Vempala, S. & Zhang, E. Why Language Models Hallucinate. (2025), https://arxiv.org/abs/2509.04664

    Pith/arXiv arXiv 2025

  7. [7]

    & Hansen, A

    Bastounis, A., Campodonico, P., Schaar, M., Adcock, B. & Hansen, A. On the consistent reasoning paradox of intelligence and optimal trust in AI: The power of ’I don’t know’. (2024), https://arxiv.org/abs/2408.02357

    Pith/arXiv arXiv 2024

  8. [8]

    & Wei, H

    Liu, X., Chen, T., Da, L., Chen, C., Lin, Z. & Wei, H. Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey.Proceedings Of The 31st ACM SIGKDD Conference On Knowledge Discovery And Data Mining V .2. pp. 6107-6117 (2025), https://doi.org/10.1145/3711896.3736569

    doi:10.1145/3711896.3736569 2025

  9. [9]

    & Majumdar, A

    Shorinwa, O., Mei, Z., Lidard, J., Ren, A. & Majumdar, A. A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions.ACM Comput. Surv..58(2025,9), https://doi.org/10.1145/3744238

    doi:10.1145/3744238 2025

  10. [10]

    & Sun, J

    Lin, Z., Trivedi, S. & Sun, J. Generating with Confidence: Uncer- tainty Quantification for Black-box Large Language Models. (2024), https://arxiv.org/abs/2305.19187

    Pith/arXiv arXiv 2024

  11. [11]

    & Beniwal, A

    Li, X., Yu, Z., Zhang, Z., Zhuang, Y ., Shah, S., Sadagopan, N. & Beniwal, A. Semantic V olume: Quantifying and Detect- ing both External and Internal Uncertainty in LLMs. (2025), https://arxiv.org/abs/2502.21239

    arXiv 2025

  12. [12]

    Huang, Y ., Song, J., Wang, Z., Zhao, S., Chen, H., Juefei-Xu, F. & Ma, L. Look before you leap: An exploratory study of uncertainty measure- ment for large language models.ArXiv Preprint ArXiv:2307.10236. (2023)

    Pith/arXiv arXiv 2023

  13. [13]

    & Munakata, K

    Wang, Z., Huang, Y ., Ma, L., Yokoyama, H., Tokumoto, S. & Munakata, K. An Exploratory Study of AI System Risk Assess- ment from the Lens of Data Distribution and Uncertainty. (2022), https://arxiv.org/abs/2212.06828

    Pith/arXiv arXiv 2022

  14. [14]

    & Tegmark, M

    Marks, S. & Tegmark, M. The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets. (2024), https://arxiv.org/abs/2310.06824

    Pith/arXiv arXiv 2024

  15. [15]

    LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations

    Orgad, H., Toker, M., Gekhman, Z., Reichart, R.,et al.. LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations. (2025), https://arxiv.org/abs/2410.02707

    Pith/arXiv arXiv 2025

  16. [16]

    & Narayanan, A

    Rabanser, S., Kapoor, S., Kirgis, P., Liu, K., Utpala, S. & Narayanan, A. Towards a Science of AI Agent Reliability. (2026), https://arxiv.org/abs/2602.16666

    Pith/arXiv arXiv 2026

  17. [17]

    & Anaby Tavor, A

    Rabinovich, E. & Anaby Tavor, A. On the Robustness of Agentic Function Calling.Proceedings Of The 5th Workshop On Trustworthy NLP (TrustNLP 2025). pp. 298-304 (2025,5), https://aclanthology.org/2025.trustnlp-main.20/

    2025

  18. [18]

    & Peng, W

    Wang, W., Haddow, B., Birch, A. & Peng, W. Assessing the Reliability of Large Language Model Knowledge. (2023), https://arxiv.org/abs/2310.09820

    Pith/arXiv arXiv 2023

  19. [19]

    & Narasimhan, K.τ-bench: A Bench- mark for Tool-Agent-User Interaction in Real-World Domains

    Yao, S., Shinn, N., Razavi, P. & Narasimhan, K.τ-bench: A Bench- mark for Tool-Agent-User Interaction in Real-World Domains. (2024), https://arxiv.org/abs/2406.12045

    Pith/arXiv arXiv 2024

  20. [20]

    & Mehta, M

    Sharma, R. & Mehta, M. Small Language Models for Agentic Sys- tems: A Survey of Architectures, Capabilities, and Deployment Trade offs. (2025), https://arxiv.org/abs/2510.03847

    arXiv 2025

  21. [21]

    HuggingFace’s Transformers: State-of-the-art Natural Language Processing

    Wolf, T., Debut, L., Sanh, V ., Chaumond, J.,et al.. HuggingFace’s Transformers: State-of-the-art Natural Language Processing. (2020), https://arxiv.org/abs/1910.03771

    Pith/arXiv arXiv 2020

  22. [22]

    Phi-4-Mini Technical Report: Compact yet Power- ful Multimodal Language Models via Mixture-of-LoRAs

    Microsoft, :, Abouelenin, A., Ashfaq, A., Atkinson, A., Awadalla, H.,et al.. Phi-4-Mini Technical Report: Compact yet Power- ful Multimodal Language Models via Mixture-of-LoRAs. (2025), https://arxiv.org/abs/2503.01743

    Pith/arXiv arXiv 2025

  23. [23]

    Qwen2.5: A Party of Foundation Models

    Team, Q. Qwen2.5: A Party of Foundation Models. (2024,9), https://qwenlm.github.io/blog/qwen2.5/

    2024

  24. [24]

    The Llama 3 Herd of Models

    Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A.,et al.. The Llama 3 Herd of Models. (2024), https://arxiv.org/abs/2407.21783

    Pith/arXiv arXiv 2024

  25. [25]

    & Gonzalez, J

    Patil, S., Zhang, T., Wang, X. & Gonzalez, J. Gorilla: Large Language Model Connected with Massive APIs.ArXiv Preprint ArXiv:2305.15334. (2023)

    Pith/arXiv arXiv 2023

  26. [26]

    APIGen: Auto- mated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets

    Liu, Z., Hoang, T., Zhang, J., Zhu, M.,et al.. APIGen: Auto- mated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets. (2024), https://arxiv.org/abs/2406.18518

    Pith/arXiv arXiv 2024

  27. [27]

    & Yavuz, S

    Liu, Y ., Meng, R., Jot, S., Savarese, S., Xiong, C., Zhou, Y . & Yavuz, S. CodeXEmbed: A Generalist Embedding Model Fam- ily for Multiligual and Multi-task Code Retrieval.ArXiv Preprint ArXiv:2411.12644. (2024)

    Pith/arXiv arXiv 2024

  28. [28]

    Scikit- learn: Machine Learning in Python.Journal Of Machine Learning Research.12pp

    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V .,et al.. Scikit- learn: Machine Learning in Python.Journal Of Machine Learning Research.12pp. 2825-2830 (2011)

    2011