Pith. sign in

REVIEW 4 major objections 5 minor 3 cited by

SAUP: Situation Awareness Uncertainty Propagation on LLM Agent

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read SAUP propagates per-step uncertainty weighted by the agent's inferred situation, and reports up to 20 percent higher AUROC than existing uncertainty methods for LLM agents.

desk verdict Propagating per-step uncertainty in LLM agents is a real and useful idea, but the paper's headline gains rest on an undescribed annotation protocol that could be circular. read the letter →

arxiv 2412.01033 v1 pith:6NUXOKEA submitted 2024-12-02 cs.CL cs.LG

Qiwei Zhao , Xujiang Zhao , Yanchi Liu , Wei Cheng , Yiyou Sun , Mika Oishi , Takao Osaki , Katsushi Matsuda
show 2 more authors
Huaxiu Yao Haifeng Chen
This is my paper · ORCID
classification cs.CLcs.LG
keywords uncertaintyestimationLLMagentssituationawarenesspropagationhiddenMarkovmodelReActAUROCmulti-stepreasoning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that uncertainty in LLM-based agents should be measured by propagating per-step uncertainty through the whole reasoning process, not by looking only at the final answer. The proposed SAUP framework assigns each step a situational weight, learned from a continuous hidden Markov model, and aggregates the weighted step uncertainties into one overall score. On HotpotQA, MMLU, and StrategyQA, across three backbone LLMs, SAUP is reported to outperform existing single-step and self-assessment uncertainty methods by up to 20 percent AUROC. If correct, this gives a practical way to flag unreliable agent outputs in multi-step decision-making and to know when to abstain or escalate.

What carries the argument

The central object is the weighted propagation formula $$U_{\text{agent}} = \sqrt{\frac{1}{N}\sum_{i=1}^N (W_i U_i)^2}$$ where $U_i$ is the per-step uncertainty and $W_i$ is the situational weight. The weights come from a continuous hidden Markov model (CHMM) with three hidden states described as correct, moderately deviated, and highly deviated trajectories; its observations are two RoBERTa-based distances, one between the question and the step's content and one between the action and its observation. The CHMM is trained with the Baum-Welch algorithm on annotated examples, turning raw distances into a surrogate for the agent's unobservable situation, while each $U_i$ is a length-normalized token entropy.

What would settle it

Collect a held-out set where humans label each reasoning step's situation directly from the trace, train the CHMM distance surrogate on those labels alone, and compare SAUP's AUROC with the paper's reported version; if the gap over plain RMS propagation disappears, the learned situational weights are encoding the final answer's correctness rather than the agent's situation.

Watch

Extended reading notes

Core claim

The paper's central claim is that the overall uncertainty of an LLM agent is a weighted aggregation of per-step uncertainties, with weights reflecting whether the agent is on a correct, moderately deviated, or highly deviated trajectory. Concretely, SAUP computes a normalized-entropy uncertainty for each thinking/action step, estimates a situational weight from two distances derived from a fine-tuned RoBERTa model, and aggregates everything through a weighted root-mean-square formula. The learned CHMM surrogate is reported to beat simple propagation, equal-weight aggregation, and all single-step baselines, with the largest gains on the most reasoning-heavy datasets.

Load-bearing premise

The load-bearing premise is that an agent's hidden situation can be recovered from two text-similarity distances through three hidden states, and that the labels used to train that recovery do not already encode whether the final answer is correct.

Editorial extensions

If this is right

  • SAUP wraps any one-step uncertainty estimator, so its ranking gains should stack on top of future single-step uncertainty methods.
  • High-stakes deployments can use the propagated score as an abstention or escalation trigger when the agent's overall uncertainty is high.
  • Evaluation of multi-step agents can move beyond final-answer correctness to include the quality of intermediate reasoning steps.
  • Even equal-weight RMS propagation beats one-step baselines, so the benefit does not depend entirely on the learned situational weights.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If situational labels are collected independently of final-answer correctness, the learned CHMM would become a reusable check on reasoning quality that could transfer across different agent frameworks.
  • A cheaper variant could replace RoBERTa fine-tuned distances with embedding cosine similarities, trading some accuracy for much lower surrogate cost.
  • Applying SAUP to tools other than search, such as code execution or database queries, would test whether the situation surrogate generalizes beyond text retrieval.
  • The weighted RMS aggregation resembles risk aggregation, so a natural extension is to turn SAUP's ranking score into a calibrated probability of error rather than just an AUROC signal.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes SAUP (Situation Awareness Uncertainty Propagation), a framework for estimating uncertainty in multi-step LLM-based agents. SAUP computes per-step uncertainty using a one-step estimator (normalized entropy) and aggregates these step uncertainties with situational weights. The situational weights are obtained either from simple surrogates (position, distance, hybrid) or from a learned surrogate based on a Continuous Hidden Markov Model (CHMM) whose hidden states represent trajectory quality. The method is evaluated on HotpotQA, MMLU, and StrategyQA using LLAMA3 8B/70B and GPT-4o as backbones, reporting AUROC improvements over single-step baselines of up to 20%.

Significance. If the central claim holds, SAUP is a practical and timely contribution to uncertainty estimation for LLM agents: it is compatible with any one-step uncertainty estimator, it moves beyond final-step-only scores, and the paper's experiments span three datasets and three backbone LLMs, with consistent AUROC gains for simple propagation baselines over single-step methods. The ablation of position, distance, and learned surrogates is a useful first step toward understanding what situational weighting contributes. However, the evaluation currently lacks uncertainty estimates (e.g., confidence intervals, significance tests), and the learned surrogate depends on an annotation protocol that is not described, leaving a circularity risk that must be resolved before the headline gains can be taken at face value.

major comments (4)
  1. [§3.3, §4.3 Q4, §6] The CHMM hidden states are named 'correct trajectory, moderately deviated trajectory, and highly deviated trajectory,' but the manuscript never specifies how the training annotations are obtained. Section 3.3 states only that 'Using training examples, we calculate (Da, Do) and annotate the hidden states,' and Section 4.3 Q4 says that learned surrogates 'rely on manually annotated data.' If the annotations are derived from whether the agent's final answer is correct, then the situational weights Wi are supervised by the very outcome that the AUROC evaluation measures, and the reported gains for SAUP-Learned would reflect label leakage rather than situation-aware propagation. The authors should describe the annotation protocol in full (who annotated, what instructions they received, whether they had access to the final answer, and agreement statistics) and, ideally, report a control where hidden-state labels are deliberately derived from final-answer correctness to show the comparison is not circular.
  2. [§3.2, Eq. (3)] Equation (3) is mathematically incorrect as written: it equates (1/(N+M)) times a product of token probabilities with (1/(N+M)) times a sum of log probabilities, and it omits the negative sign required for normalized entropy. The correct normalized entropy is Un = -1/(N+M) * Σ log p(ti | ...). Because this quantity is the per-step uncertainty used throughout the pipeline, the error should be corrected and the resulting values checked for sign consistency.
  3. [§3.1, §3.3, Algorithm 1] The exact aggregation used in the experiments is incompletely specified. Section 3.1 mentions an 'extra logical term' used 'for numerical stability' but never defines it; Section 4.3 Q2 mentions a 'factor' in SAUP-PD without giving its value or estimation procedure; and Algorithm 1 feeds the scalar (Dan+Don) into the HMM, while Section 3.3 says the observable state is the two-feature vector (Da, Do). The authors should state the full formula for Uagent, including the extra logical term, and clarify whether the HMM observations are one-dimensional or two-dimensional.
  4. [§4.2, §4.3, Tables 1–3] The claim that SAUP-Learned consistently outperforms other methods is not supported by the tables in all cells. On HotpotQA with GPT-4O, Table 2 shows SAUP-P achieves 0.797 and Table 3 shows Geometric Mean achieves 0.785, both higher than SAUP-Learned's 0.778; the text's statement that 'the HMMD-based (learned) surrogate outperforms others by a clear margin' is therefore contradicted. Additionally, no confidence intervals, standard deviations, or significance tests are reported for any AUROC value, so the 'significantly outperforms' claim in the abstract is not statistically substantiated. Please add uncertainty estimates (bootstrap or multiple runs) and reconcile the discrepancy.
minor comments (5)
  1. [§3.2, Eq. (3)] In Eq. (3), the summation index 'i≤N+M' should be written as i=1,...,N+M for clarity.
  2. [Figure 2] Figure 2 labels the model as 'HMM' while the text refers to CHMM; please harmonize the terminology.
  3. [§3.2 and Algorithm 1] The indices in Eq. (2) and Algorithm 1 are inconsistent: Eq. (2) uses Zn-1 as conditioning context, while Algorithm 1 passes Zn to FU; please clarify the step indexing.
  4. [Table 1] The paper alternates between 'GPT4O' and 'GPT4-O' in Table 1; please standardize the notation.
  5. [§6] The Limitations section appropriately notes that single-step uncertainty errors can hurt the framework, but it does not discuss the potential circularity of the annotated hidden states; this should be acknowledged.

Circularity Check

0 steps flagged · score 0.0 of 10

No demonstrated circularity: SAUP's weighted propagation is an external benchmark comparison, and the underspecified hidden-state annotation protocol is a leakage risk rather than a proven circular reduction.

full rationale

I walked the derivation chain from Eq. (1) backward. The step uncertainties U_i come from normalized entropy (Eq. 3), a pre-existing single-step method, and the aggregation is an RMS combination; neither is defined in terms of final-answer correctness. The learned situational weights W_i come from a CHMM over two RoBERTa-derived distances (Da, Do), with hidden states labeled 'correct trajectory, moderately deviated trajectory, and highly deviated trajectory.' The paper states 'Using training examples, we calculate (Da, Do) and annotate the hidden states' but never states the annotation protocol. It therefore does not exhibit the specific reduction needed for a circularity finding: nothing in the text equates the hidden-state labels with the final-answer correctness labels used in the AUROC evaluation. The Limitations section acknowledges that the learned surrogate 'relies on manually annotated datasets' and that 'manual labels are still prone to errors,' which is a reproducibility and possible leakage concern, not a demonstrated circular step. If the hidden-state annotations were later shown to be derived from final answers, the learned weights would encode the AUROC target; but that is not asserted by the paper, so under the hard-evidence rule I do not count it as circularity. The comparative evaluation is against external baselines (predictive and semantic entropy, normalized entropy, P(True)) and does not depend on a self-citation chain.

Assumptions & free parameters 4 free parameters · 6 assumptions · 2 invented entities

The central claim depends on a small set of modeling choices: the per-step uncertainty estimator, the RMS aggregation, and especially the HMM-based situational weight surrogate. The latter is the only genuinely new component, and it is also the least specified: the hidden state definition, annotation protocol, and distance features carry most of the unverified load.

free parameters (4)
  • Number of CHMM hidden states = 3
    Hidden states 'correct, moderately deviated, highly deviated' are fixed by design in Section 3.3; no selection procedure is given.
  • CHMM parameters (transition, observation, initial) = Not reported
    Trained with Baum-Welch on manually annotated distances; exact values and hyperparameters are not given.
  • SAUP-PD balance factor = Not reported
    The hybrid surrogate 'combines both approaches with a factor' in Section 4.3 Q2; the factor value and fitting procedure are absent.
  • Extra logical term coefficient in SAUP formula = Not specified
    Section 3.1 mentions an extra logical term for numerical stability but never defines it.
assumptions (6)
  • domain assumption Per-step uncertainty can be accurately captured by normalized token log-likelihood.
    Used in Eq. (3); the Limitations state that errors in single-step uncertainty compromise the whole framework.
  • ad hoc to paper Agent trajectory situation is representable by three discrete hidden states.
    Section 3.3 defines correct/moderately/highly deviated states without independent evidence for that partition.
  • ad hoc to paper Two distances (Da, Do) from a fine-tuned RoBERTa model are a sufficient observable signal for the hidden state.
    Section 3.3 and Algorithm 1 use inverse SQuAD v2 scores as distances; no validation that these distances capture situational quality.
  • domain assumption Uncertainty accumulates and RMS-weighted propagation is the right aggregation.
    Eq. (1) is asserted in Section 3.1 based on 'intuitive logic', not derived from a principled model.
  • domain assumption Manually annotated hidden states are reliable and generalize.
    The Limitations acknowledge this is time-consuming, costly, and prone to errors.
  • domain assumption The ReAct workflow is representative of multi-step LLM agents.
    Only ReAct agents are evaluated, and the paper claims the thinking-acting-observation workflow is general.
invented entities (2)
  • Hidden trajectory state (correct/moderately/highly deviated)
    purpose: Latent variable used to compute situational weights W_i in Eq. (1).
    No external falsifiable handle; state labels are manually assigned from training examples in Section 3.3.
  • Situational weight W_i
    purpose: Adjusts per-step uncertainty contribution based on the agent's situation.
    A constructed quantity, observable only through surrogates; no independent measurement is provided.

how reviews work

0 comments
Cite this review

Pith. "Pith review of SAUP: Situation Awareness Uncertainty Propagation on LLM Agent." pith.science (2026). https://pith.science/paper/6NUXOKEA

@misc{pith2026241201033,
  author       = {Pith},
  title        = {Pith review of: SAUP: Situation Awareness Uncertainty Propagation on LLM Agent},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6NUXOKEA}},
  note         = {Machine review of arXiv:2412.01033}
}
read the original abstract

Large language models (LLMs) integrated into multistep agent systems enable complex decision-making processes across various applications. However, their outputs often lack reliability, making uncertainty estimation crucial. Existing uncertainty estimation methods primarily focus on final-step outputs, which fail to account for cumulative uncertainty over the multistep decision-making process and the dynamic interactions between agents and their environments. To address these limitations, we propose SAUP (Situation Awareness Uncertainty Propagation), a novel framework that propagates uncertainty through each step of an LLM-based agent's reasoning process. SAUP incorporates situational awareness by assigning situational weights to each step's uncertainty during the propagation. Our method, compatible with various one-step uncertainty estimation techniques, provides a comprehensive and accurate uncertainty measure. Extensive experiments on benchmark datasets demonstrate that SAUP significantly outperforms existing state-of-the-art methods, achieving up to 20% improvement in AUROC.

Figures

Figures reproduced from arXiv: 2412.01033 by the authors.

Figure 1
Figure 1. The overall uncertainty of an agent based [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Overview of our proposed SAUP, which is illustrated in three parts. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. The Performance Comparison of Learned￾based Surrogates with Various S2S Backbone Models insights into its applicability and performance un￾der different conditions. Q1: Is the uncertainty measurement of the in￾ternal steps beneficial for the overall uncertainty measurement of the agent? Yes, measuring uncertainty at each internal step significantly contributes to a more accurate overall uncertainty estimation. By co… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Visualization analysis of SAUP on the StrategyQA dataset. Detailed explanations of this figure are [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Towards Agents That Know When They Don't Know: Uncertainty as a Control Signal for Structured Reasoning

    cs.AI 2025-09 conditional novelty 6.0 of 10

    An LLM agent using retrieval and summary uncertainty as training rewards and inference filters produces more factual, useful multi-omics summaries and better downstream survival predictions.

  2. UProp: Investigating the Uncertainty Propagation of LLMs in Multi-Step Agentic Decision-Making

    cs.CL 2025-06 conditional novelty 6.0 of 10

    UProp estimates extrinsic uncertainty in multi-step LLM decision-making by approximating pointwise mutual information over trajectory samples, improving AUROC for identifying incorrect agent outputs by 2.3 to 11% over...

  3. A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions

    cs.CL 2024-12 conditional novelty 4.0 of 10

    A review that organizes LLM uncertainty quantification into token-level, self-verbalized, semantic-similarity, and mechanistic interpretability categories.

Reference graph

Works this paper leans on

42 extracted references · 6 canonical work pages · cited by 3 Pith papers

  1. [1]

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774

    arXiv 2023

  2. [2]

    Leonard E Baum and Ted Petrie. 1966. Statistical inference for probabilistic functions of finite state markov chains. The annals of mathematical statistics, 37(6):1554--1563

    work page 1966

  3. [3]

    Leonard E Baum, Ted Petrie, George Soules, and Norman Weiss. 1970. A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains. The annals of mathematical statistics, 41(1):164--171

    work page 1970

  4. [4]

    Andrew P Bradley. 1997. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern recognition, 30(7):1145--1159

    work page 1997

  5. [5]

    Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al. 2024. A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology, 15(3):1--45

    2024

  6. [6]

    Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374

    arXiv 2021

  7. [7]

    Pei Chen, Soumajyoti Sarkar, Leonard Lausen, Balasubramaniam Srinivasan, Sheng Zha, Ruihong Huang, and George Karypis. 2024. Hytrel: Hypergraph-enhanced tabular data representation learning. Advances in Neural Information Processing Systems, 36

    work page 2024

  8. [8]

    Maxime Darrin, Pablo Piantanida, and Pierre Colombo. 2022. Rainproof: An umbrella to shield text generators from out-of-distribution data. arXiv preprint arXiv:2212.09171

    work page Pith review arXiv 2022

Show all 42 references
  1. [9]

    Jinhao Duan, Hao Cheng, Shiqi Wang, Alex Zavalny, Chenan Wang, Renjing Xu, Bhavya Kailkhura, and Kaidi Xu. 2024. Shifting attention to relevance: Towards the predictive uncertainty quantification of free-form large language models. In Proceedings of the 62nd Annual Meeting of ...

    2024

  2. [10]

    Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783

    2024 arXiv

  3. [11]

    Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Fr \'e d \'e ric Blain, Francisco Guzm \'a n, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, and Lucia Specia. 2020. Unsupervised quality estimation for neural machine translation. Transactions of the Association for Computation...

    2020

  4. [12]

    Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning, pages 1050--1059. PMLR

    2016

  5. [13]

    Yarin Gal, Riashat Islam, and Zoubin Ghahramani. 2017. Deep bayesian active learning with image data. In International conference on machine learning, pages 1183--1192. PMLR

    2017

  6. [14]

    Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. 2021. Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies. Transactions of the Association for Computational Linguistics, 9:346--361

    2021

  7. [15]

    Jiuzhou Han, Wray Buntine, and Ehsan Shareghi. 2024. Towards uncertainty-aware language agent. arXiv preprint arXiv:2401.14016

    2024 arXiv

  8. [16]

    Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300

    2020 arXiv

  9. [17]

    Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig. 2021. How can we know when language models know? on the calibration of language models for question answering. Transactions of the Association for Computational Linguistics, 9:962--977

    2021

  10. [18]

    Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al. 2022. Language models (mostly) know what they know. arXiv preprint arXiv:2207.05221

    2022 arXiv

  11. [19]

    Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. 2023. Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation. arXiv preprint arXiv:2302.09664

    2023 arXiv

  12. [20]

    Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. 2023. Camel: Communicative agents for" mind" exploration of large language model society. Advances in Neural Information Processing Systems, 36:51991--52008

    2023

  13. [21]

    Yinhan Liu. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692

    2019 arXiv

  14. [22]

    Andrey Malinin and Mark Gales. 2020. Uncertainty estimation in autoregressive structured prediction. arXiv preprint arXiv:2002.07650

    2020 arXiv

  15. [23]

    Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, and Jianfeng Gao. 2024. Large language models: A survey. arXiv preprint arXiv:2402.06196

    2024 arXiv

  16. [24]

    Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. 2021. Webgpt: Browser-assisted question-answering with human feedback. arXiv preprint arXiv:2112.09332

    2021 arXiv

  17. [25]

    Shuofei Qiao, Honghao Gui, Chengfei Lv, Qianghuai Jia, Huajun Chen, and Ningyu Zhang. 2023. Making language models better tool learners with execution feedback. arXiv preprint arXiv:2305.13068

    2023 arXiv

  18. [26]

    Mohaimenul Azam Khan Raiaan, Md Saddam Hossain Mukta, Kaniz Fatema, Nur Mohammad Fahad, Sadman Sakib, Most Marufatul Jannat Mim, Jubaer Ahmad, Mohammed Eunus Ali, and Sami Azam. 2024. A review on large language models: Architectures, applications, taxonomies, open issues and c...

    2024

  19. [27]

    Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018. Know what you don't know: Unanswerable questions for squad. arXiv preprint arXiv:1806.03822

    2018 arXiv

  20. [28]

    Matthew Renze and Erhan Guven. 2024. Self-reflection in llm agents: Effects on problem-solving performance. arXiv preprint arXiv:2405.06682

    2024 arXiv

  21. [29]

    SerpAPI . 2024. Real-time search api for google results. https://serpapi.com

    2024

  22. [30]

    Noah Shinn, Beck Labash, and Ashwin Gopinath. 2023. Reflexion: an autonomous agent with dynamic memory and self-reflection. arXiv preprint arXiv:2303.11366, 2(5):9

    2023 arXiv

  23. [31]

    Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, et al. 2024. A survey on large language model based autonomous agents. Frontiers of Computer Science, 18(6):186345

    2024

  24. [32]

    Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021. Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. arXiv preprint arXiv:2109.00859

    2021 arXiv

  25. [33]

    Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, and Chi Wang. 2023. https://arxiv.org/abs/2308.08155 Autogen: Enabling next-gen llm applications via mu...

    2023 arXiv

  26. [34]

    Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, et al. 2023. The rise and potential of large language model based agents: A survey. arXiv preprint arXiv:2309.07864

    2023 arXiv

  27. [35]

    Yijun Xiao and William Yang Wang. 2019. Quantifying uncertainties in natural language processing tasks. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 7322--7329

    2019

  28. [36]

    Yijun Xiao and William Yang Wang. 2021. On hallucination and predictive uncertainty in conditional language generation. arXiv preprint arXiv:2103.15025

    2021 arXiv

  29. [37]

    Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. arXiv preprint arXiv:1809.09600

    2018 arXiv

  30. [38]

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2022. React: Synergizing reasoning and acting in language models. arXiv preprint arXiv:2210.03629

    2022 arXiv

  31. [39]

    Zhangyue Yin, Qiushi Sun, Qipeng Guo, Jiawen Wu, Xipeng Qiu, and Xuanjing Huang. 2023. Do large language models know what they don't know? arXiv preprint arXiv:2305.18153

    2023 arXiv

  32. [40]

    Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023. A survey of large language models. arXiv preprint arXiv:2303.18223

    2023 arXiv

  33. [41]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  34. [42]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.