Pith. sign in

REVIEW 3 major objections 5 minor 17 references

LLM estimates of object-finding odds, fed into a cost-aware planner with map travel costs, beat pure LLM action selection for household object search, and the same abstraction speeds prompt and model selection at deployment.

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

2026-07-13 19:20 UTC pith:QUKDG6C3

load-bearing objection Solid empirical robotics paper: LLM likelihoods as planner parameters beat pure-LLM action selection, and the same abstraction speeds prompt selection via offline replay. the 3 major comments →

arxiv 2603.23800 v2 pith:QUKDG6C3 submitted 2026-03-25 cs.RO cs.AIcs.LG

Object Search in Partially-Known Environments via LLM-informed Model-based Planning and Prompt Selection

Abhishek Paudel , Abhish Khanal , Raihan I. Arnob , Shahriar Hossain , Gregory J. Stein This is my paper
classification cs.RO cs.AIcs.LG
keywords object searchlarge language modelsmodel-based planningprompt selectionpartially-known environmentsoffline replayrobot navigation
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 argues that robots searching for objects in partially mapped homes perform better when a large language model is asked only for likelihoods of finding the target in each container, and those numbers are combined with known travel costs inside a classical high-level planner, rather than letting the language model itself choose the next place to look. On simulated household maps the hybrid approach lowers average navigation cost by as much as 11.8 percent versus a pure language-model selector and by nearly 40 percent versus an uninformed optimistic baseline; real-apartment trials show the same pattern. Because the planner works with abstract “search this container” actions, past trajectories can be replayed offline, letting the robot select the best prompt and language model far faster than ordinary multi-armed bandit methods. A reader who wants reliable object search cares because pure language-model planners often act myopically or mishandle quantitative trade-offs, while this method keeps their commonsense knowledge yet restores long-horizon cost-aware reasoning.

Core claim

An LLM can supply scalar discovery likelihoods for a target object inside named containers; when those likelihoods are combined with map-derived travel costs and inserted into a high-level expected-cost search model, the resulting planner outperforms both a pure LLM that directly chooses the next container and an optimistic greedy baseline. The same high-level action abstraction further permits offline replay of past experience, enabling faster deployment-time selection among candidate prompts and LLMs than standard UCB bandit selection.

What carries the argument

LLM-informed model-based planning (LLM+MODEL): the LLM is queried solely for per-container discovery probabilities; those values plus travel costs instantiate a model that a planner optimizes over high-level “search container” actions. The identical abstraction enables offline-replay selection of the best policy-prompt-LLM triple.

Load-bearing premise

The language model’s numerical guesses about how likely an object is to sit inside each named container are accurate enough, or at least ranked well enough, to serve as useful parameters inside an expected-cost planner.

What would settle it

On a held-out suite of household maps and objects, insert the same LLM probability estimates into the model-based planner and measure average navigation cost; if that cost is no lower (or is higher) than the cost obtained by simply letting the identical LLM directly pick the next container, the claim that model-based integration improves search is falsified.

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

If this is right

  • Robots achieve lower average navigation cost for object search by letting LLMs parameterize a planner rather than replace it.
  • Prompt and LLM choice can be optimized at deployment without expensive on-robot trial-and-error, using offline replay of the high-level abstraction.
  • Performance gaps between prompts and LLMs are large enough that automatic selection matters more than fixing one prompt in advance.
  • The same hybrid framework transfers from simulation to a physical apartment robot with comparable relative gains.
  • Open-source LLMs also benefit, sometimes by larger relative margins than proprietary models.

Where Pith is reading between the lines

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

  • The same probability-plus-cost model can be extended to multi-object or multi-robot search without redesigning the selection machinery.
  • If LLM probabilities remain poorly calibrated on rare objects, a lightweight online update from failed searches would further cut cost.
  • Any high-level abstraction that separates perception from long-horizon cost can turn foundation models into selectable modules rather than fixed policies.
  • Automated prompt generation, listed as future work, could close the remaining gap between hand-written prompts and an adaptive system that invents better ones.

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 an LLM-informed model-based planner for object search in partially-known household environments: an LLM supplies per-container discovery likelihoods that, together with map-derived travel costs, instantiate a high-level expected-cost search model (LLM+MODEL). The same action abstraction is then used for deployment-time prompt/LLM selection via offline replay, compared against UCB. On 150 ProcTHOR maps the approach reports up to 11.8% lower average navigation cost than a fully LLM-driven selector (LLM-DIRECT) and up to 39.2% lower than optimistic greedy search; open-source LLM ablations and a five-trial apartment deployment show the same direction. Replay selection is reported to cut average cost by 6.5% and cumulative regret by 33.8% relative to UCB over 100-trial selection runs.

Significance. If the empirical ranking holds under proper statistical reporting, the work is a useful, practical contribution to embodied LLM planning: it shows that treating LLM outputs as model parameters inside a classical high-level planner can outperform both pure LLM action selection and uninformed search, and that the same abstraction enables fast offline-replay prompt/LLM selection without on-robot trial-and-error. Strengths include multi-LLM evaluation (GPT-5 Mini, Gemini 2.5 Flash, GPT-OSS, Llama3.2), consistent directional gains, a real LoCoBot apartment suite, and an explicit link to prior offline-replay model selection. The free parameters (hand-designed prompts, selection horizon) are disclosed. The result is incremental rather than foundational, but it is actionable for household object search and for deployment-time model selection in LLM-robot systems.

major comments (3)
  1. Table 1 and Sec. 6.2 report only mean navigation costs over 150 maps, with no standard errors, confidence intervals, or hypothesis tests. The headline claims (up to 11.8% vs LLM-DIRECT, 39.2% vs OPTIMISTIC+GREEDY) are therefore hard to assess for statistical reliability; the same holds for the open-source results in Table 3. With N=150 already collected, means should be accompanied by SE/CI (or bootstrap) and pairwise significance so the percentage improvements can be treated as load-bearing evidence rather than point estimates.
  2. Sec. 6.3 and the selection results (including the real-apartment Figure 7) likewise give average cost and cumulative regret without variance or trial-level trajectories with error bands. The claimed 6.5% cost and 33.8% regret gains of Replay Selection over UCB rest on these aggregates; reporting per-trial regret curves with uncertainty (and the UCB exploration constant used in Eq. (2)) is needed to support the selection contribution.
  3. The central modeling premise (Secs. 3–4 / LLM+MODEL) is that LLM scalar probabilities are useful parameters of an expected-cost search model. The paper never analyzes calibration or even rank correlation between those probabilities and empirical discovery rates on held-out maps. A short diagnostic (e.g., reliability diagram or Spearman correlation of LLM p vs. observed hit rates per container type) would show whether the planner is using well-ordered likelihoods or merely a soft ranking, and would strengthen the claim that the LLM is truly 'informing' the model rather than acting as a black-box prior.
minor comments (5)
  1. Real-apartment evaluation (Table 2, five target objects) is useful as a sanity check but is too small to support strong claims of 'similar improvements'; frame it explicitly as qualitative validation and avoid percentage comparisons that mirror the large-N sim results.
  2. Prompt templates (P-CONTEXT-A/B, P-MINIMAL, P-DIRECT) are described in Sec. 6.1 and Appendix A, but the main text should state more clearly which information (room–container lists, distances) is available to the LLM at query time versus what is known only to the planner, to avoid conflating LLM-DIRECT with a true planner.
  3. Figure 4 trajectories are illustrative; adding a short caption note on map identity and target object would make the qualitative comparison reproducible.
  4. Token/cost Table 4 is helpful; consider also reporting approximate wall-clock LLM latency per query, since that affects real-time deployment of LLM+MODEL versus LLM-DIRECT.
  5. Minor typography: spacing in titles ('OBJECTSEARCH', 'VIALLM-INFORMED') and occasional missing spaces around LLM acronyms should be cleaned for camera-ready.

Circularity Check

1 steps flagged

No derivation-by-construction circularity; empirical navigation costs and selection regrets are measured on held-out maps and a real apartment, with only minor non-load-bearing self-citation of the authors' prior offline-replay method.

specific steps
  1. self citation load bearing [Abstract; Sec. 5 / Sec. 6.3; Conclusion]
    "the abstraction upon which our approach relies is amenable to deployment-time model selection via the recent offline replay approach, an insight we leverage to enable fast prompt and LLM selection during deployment. ... the recent offline replay approach developed for model selection for learning-informed point-goal navigation (Paudel & Stein, 2023)"

    The offline-replay selection method that underpins the prompt/LLM bandit results is taken from the authors' own prior paper. This is ordinary self-citation of a reusable technique rather than a load-bearing uniqueness claim or identity that forces the measured cost/regret improvements; those improvements are obtained by replaying actual trajectories against held-out maps and are therefore independent of the citation.

full rationale

The paper is an empirical systems contribution. LLM scalar likelihoods are used as model parameters for expected-cost planning; the resulting policies are evaluated by actual measured navigation cost on 150 distinct ProcTHOR maps (Table 1), open-source LLMs (Table 3), and five real-apartment trials (Table 2). Offline-replay selection is likewise scored against incurred costs and cumulative regret versus UCB (Figure 7), not against a quantity defined from the selection rule itself. The sole self-citation (Paudel & Stein 2023) supplies the offline-replay technique that the high-level action abstraction makes applicable; it is not a uniqueness theorem, does not force the reported rankings, and is independently stress-tested by the new experiments. No equation reduces a claimed prediction to a fitted input, no ansatz is smuggled, and no known empirical pattern is merely renamed. Score 1 reflects only the presence of that non-load-bearing self-citation.

Axiom & Free-Parameter Ledger

2 free parameters · 4 axioms · 2 invented entities

The central claims rest on standard planning and multi-armed-bandit machinery plus domain assumptions about partial maps and LLM usefulness; almost no free parameters are fitted to the reported performance numbers. The main invented constructs are the LLM+MODEL policy interface and the application of offline replay to prompt/LLM selection. No new physical entities are postulated.

free parameters (2)
  • hand-designed prompt templates (P-CONTEXT-A/B, P-MINIMAL, P-DIRECT)
    Prompt wording is chosen by the authors and fixed; performance is sensitive to which template is used with which LLM, so the templates function as free design choices that affect the central numbers.
  • UCB exploration constant / selection horizon (100 trials)
    Bandit comparison uses a fixed trial budget and standard UCB form; exact exploration coefficient is not critical but is an implementation choice that shapes regret curves.
axioms (4)
  • domain assumption A partial metric map of rooms and containers is available so that travel costs between high-level search actions can be computed.
    Stated in the problem setting (partially-known environments); without it the model cannot be instantiated from map costs.
  • domain assumption LLM scalar outputs can be interpreted as (approximate) probabilities of object presence in named containers and are useful for expected-cost planning.
    Core modeling step of LLM+MODEL; if the numbers are pure noise the planner gains nothing over uninformed baselines.
  • domain assumption High-level actions of the form “search container C” form a sufficient abstraction for both planning and offline replay of alternative policies.
    Enables both the model-based planner and the offline-replay selection method; inherited from the authors’ prior navigation work and assumed to transfer.
  • standard math Standard expected-cost / MDP planning and UCB-style bandit regret analysis apply to the resulting discrete search problem.
    Background decision-theory and bandit results used without re-derivation.
invented entities (2)
  • LLM+MODEL policy no independent evidence
    purpose: Interface that turns LLM likelihoods plus map costs into a model that a classical planner optimizes for object search.
    The paper’s primary algorithmic construct; evaluated empirically against LLM-DIRECT and OPTIMISTIC+GREEDY.
  • Replay Selection for prompts/LLMs no independent evidence
    purpose: Apply offline replay over the shared high-level action abstraction to choose among policy–prompt–LLM triples at deployment time.
    Adaptation of the authors’ earlier offline-replay idea to the new prompt/LLM selection setting; evidence is the reported cost/regret curves versus UCB.

pith-pipeline@v1.1.0-grok45 · 12718 in / 3230 out tokens · 50672 ms · 2026-07-13T19:20:59.919339+00:00 · methodology

0 comments
read the original abstract

We present a novel LLM-informed model-based planning framework, and a novel prompt selection method, for object search in partially-known environments. Our approach uses an LLM to estimate statistics about the likelihood of finding the target object when searching various locations throughout the scene that, combined with travel costs extracted from the environment map, are used to instantiate a model, thus using the LLM to inform planning and achieve effective search performance. Moreover, the abstraction upon which our approach relies is amenable to deployment-time model selection via the recent offline replay approach, an insight we leverage to enable fast prompt and LLM selection during deployment. Simulation experiments demonstrate that our LLM-informed model-based planning approach outperforms the baseline planning strategy that fully relies on LLM and optimistic strategy with as much as 11.8% and 39.2% improvements respectively, and our bandit-like selection approach enables quick selection of best prompts and LLMs resulting in 6.5% lower average cost and 33.8% lower average cumulative regret over baseline UCB bandit selection. Real-robot experiments in an apartment demonstrate similar improvements and so further validate our approach.

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

17 extracted references · 10 linked inside Pith

  1. [1]

    Cognitive planning for object goal navigation using generative AI models

    PS Arjun, Andrew Melnik, and Gora Chand Nandi. Cognitive planning for object goal navigation using generative AI models. InNeurIPS 2024 Workshop on Open-World Agents,

    2024

  2. [2]

    Have LLMs advanced enough? A challenging problem solv- ing benchmark for large language models

    11 Daman Arora, Himanshu Singh, et al. Have LLMs advanced enough? A challenging problem solv- ing benchmark for large language models. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing,

    2023

  3. [3]

    Large language models and mathematical reasoning failures.arXiv preprint arXiv:2502.11574,

    Johan Boye and Birger Moell. Large language models and mathematical reasoning failures.arXiv preprint arXiv:2502.11574,

    Pith/arXiv arXiv

  4. [4]

    cat-shaped mug

    Vishnu Sashank Dorbala, James F Mullen Jr, and Dinesh Manocha. Can an embodied agent find your “cat-shaped mug”? LLM-guided exploration for zero-shot object navigation.arXiv preprint arXiv:2303.03480,

    Pith/arXiv arXiv

  5. [5]

    Commonsense scene graph-based target localization for object search.arXiv preprint arXiv:2404.00343,

    Wenqi Ge, Chao Tang, and Hong Zhang. Commonsense scene graph-based target localization for object search.arXiv preprint arXiv:2404.00343,

    Pith/arXiv arXiv

  6. [6]

    Abhish Khanal, Abhishek Paudel, Hung Pham, and Gregory J. Stein. Multi-robot learning-informed task planning under uncertainty.arXiv preprint arXiv:2603.20544,

    arXiv

  7. [7]

    Zero-label prompt selection.arXiv preprint arXiv:2211.04668,

    Chonghua Liao, Yanan Zheng, and Zhilin Yang. Zero-label prompt selection.arXiv preprint arXiv:2211.04668,

    Pith/arXiv arXiv

  8. [8]

    ELHPlan: Efficient long- horizon task planning for multi-agent collaboration.arXiv preprint arXiv:2509.24230,

    12 Shaobin Ling, Yun Wang, Chenyou Fan, Tin Lun Lam, and Junjie Hu. ELHPlan: Efficient long- horizon task planning for multi-agent collaboration.arXiv preprint arXiv:2509.24230,

    arXiv

  9. [9]

    LLM+P: Empowering large language models with optimal planning proficiency.arXiv preprint arXiv:2304.11477, 2023a

    Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone. LLM+P: Empowering large language models with optimal planning proficiency.arXiv preprint arXiv:2304.11477, 2023a. Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre- train, prompt, and predict: A systematic survey of prom...

    Pith/arXiv arXiv

  10. [10]

    Abhishek Paudel and Gregory J. Stein. Deployment-time selection of prompts for LLM-informed object search in partially-known environments. InICRA 2025 Workshop on Foundation Models and Neuro-Symbolic AI for Robotics,

    2025

  11. [11]

    A fragile number sense: Probing the elemental limits of numerical reasoning in LLMs.arXiv preprint arXiv:2509.06332,

    Roussel Rahman and Aashwin Ananda Mishra. A fragile number sense: Probing the elemental limits of numerical reasoning in LLMs.arXiv preprint arXiv:2509.06332,

    Pith/arXiv arXiv

  12. [12]

    A systematic survey of prompt engineering in large language models: Techniques and applica- tions.arXiv preprint arXiv:2402.07927,

    Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha. A systematic survey of prompt engineering in large language models: Techniques and applica- tions.arXiv preprint arXiv:2402.07927,

    Pith/arXiv arXiv

  13. [13]

    PDDL planning with pretrained large language models

    Tom Silver, Varun Hariprasad, Reece S Shuttleworth, Nishanth Kumar, Tom ´as Lozano-P´erez, and Leslie Pack Kaelbling. PDDL planning with pretrained large language models. InNeurIPS 2022 Foundation Models for Decision Making Workshop,

    2022

  14. [14]

    An information-theoretic approach to prompt engineering without ground truth labels.arXiv preprint arXiv:2203.11364,

    Taylor Sorensen, Joshua Robinson, Christopher Michael Rytting, Alexander Glenn Shaw, Kyle Jef- frey Rogers, Alexia Pauline Delorey, Mahmoud Khalil, Nancy Fulda, and David Wingate. An information-theoretic approach to prompt engineering without ground truth labels.arXiv preprint arXiv:2203.11364,

    Pith/arXiv arXiv

  15. [15]

    Large lan- guage models still can’t plan (A benchmark for LLMs on planning and reasoning about change)

    Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati. Large lan- guage models still can’t plan (A benchmark for LLMs on planning and reasoning about change). InNeurIPS 2022 Foundation Models for Decision Making Workshop,

    2022

  16. [16]

    SELP: Generating safe and efficient task plans for robot agents with large language models.arXiv preprint arXiv:2409.19471,

    13 Yi Wu, Zikang Xiong, Yiran Hu, Shreyash S Iyengar, Nan Jiang, Aniket Bera, Lin Tan, and Suresh Jagannathan. SELP: Generating safe and efficient task plans for robot agents with large language models.arXiv preprint arXiv:2409.19471,

    Pith/arXiv arXiv

  17. [17]

    Improv- ing probability-based prompt selection through unified evaluation and analysis.arXiv preprint arXiv:2305.14877,

    Sohee Yang, Jonghyeon Kim, Joel Jang, Seonghyeon Ye, Hyunji Lee, and Minjoon Seo. Improv- ing probability-based prompt selection through unified evaluation and analysis.arXiv preprint arXiv:2305.14877,

    Pith/arXiv arXiv