arxiv: 2601.09365 · v2 · submitted 2026-01-14 · 💻 cs.CL · cs.AI

Recognition: 2 theorem links

· Lean Theorem

Frame of Reference: Addressing the Challenges of Common Ground Representation in Situational Dialogs

Biswesh Mohapatra , Th\'eo Charlot , Giovanni Duca , Mayank Palan , Laurent Romary , Justine Cassell

Authors on Pith no claims yet

Pith reviewed 2026-05-16 14:50 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords common groundsituated dialogsrelational referencesreinforcement learningdialog systemsembodied agentsspatial reasoningtemporal reasoning

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The pith

Models improve at maintaining common ground in situated dialogs by representing relational references to space and time, with reinforcement learning on synthetic data providing further gains.

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

This paper examines how conversational models can track shared understanding when dialogs unfold in changing physical spaces and refer back to earlier events or locations. It focuses on relational references that connect new statements to previously grounded elements, such as linking a destination to a park visited the day before. Multiple ways of encoding this common ground are tested, and reinforcement learning is applied to synthetic dialog examples to strengthen the representations. Readers would care because embodied agents and robots need this ability to hold coherent conversations over time without repeated clarifications. If the claim holds, dialog systems would resolve and reuse spatial-temporal references more reliably in ongoing shared interactions.

Core claim

The central claim is that models can establish common ground by utilizing relational references in the dynamic and shared environments of situated dialogs. Multiple methods for representing common ground are evaluated, and approaches using reinforcement learning on synthetically generated dialog data are proposed to improve performance in leveraging grounded information during complex spatial and temporal scenarios.

What carries the argument

Relational references that connect current utterances to previously grounded entities, events, and spatial-temporal relations within a shared dynamic environment.

If this is right

Models achieve stronger results when resolving references that depend on prior spatial and temporal context in ongoing dialogs.
Synthetic data generation enables scalable training for tasks that require dynamic common ground maintenance.
Reinforcement learning supplies an effective optimization step beyond fixed representation methods for common ground.
Embodied conversational agents gain capacity for longer coherent exchanges in shared physical spaces.

Where Pith is reading between the lines

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

The same representation and learning methods could extend to tracking non-spatial references built across long conversation histories.
Deployment in physical robots would benefit from pairing the approach with actual sensor data from the environment.
Direct comparisons against human performance in real-world common ground tasks would clarify remaining limitations.

Load-bearing premise

Synthetically generated dialog data sufficiently captures the real challenges of maintaining common ground with spatial and temporal relational references in actual situated interactions.

What would settle it

Testing the trained models on transcripts from real human-robot situated interactions and finding that they fail to correctly resolve or reuse relational references to prior spatial or temporal elements at rates comparable to human performance.

Figures

Figures reproduced from arXiv: 2601.09365 by Biswesh Mohapatra, Giovanni Duca, Justine Cassell, Laurent Romary, Mayank Palan, Th\'eo Charlot.

**Figure 2.** Figure 2: Pipeline for the synthetic data generation involving three main phases: (1) Grounded World Generation, [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

**Figure 3.** Figure 3: Test example for inferred grounding from [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

**Figure 6.** Figure 6: Test example for temporal grounding from [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗

**Figure 7.** Figure 7: A visualization of a full_scenario. Unique objects, with their IDs, are crafted to have similar characteristics to induce ambiguity that has to be resolved. The ambiguity can arise from a navigator’s experience alone (intrapair) or for their experience combined (interpair). Each chunks (Cm,j ) is delimited by the densely dotted lines. Listing 3: Example of LLM-generated dialog segment Michael: I just arriv… view at source ↗

**Figure 8.** Figure 8: Training reward over time while using the [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗

**Figure 9.** Figure 9: The system prompt for LLM as a judge. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗

**Figure 10.** Figure 10: The system prompt for the LLM when provided with the full dialog for Meetup test cases. It was also [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗

**Figure 11.** Figure 11: The system prompt for in-context learning with full dialog history for Meetup test cases. [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗

**Figure 12.** Figure 12: Failure case from Qwen-QWQ where it produces a long answer despite reasoning in it’s <reasoning> [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗

read the original abstract

Common ground plays a critical role in situated spoken dialogs, where interlocutors must establish and maintain shared references to entities, events, and relations to sustain coherent interaction in a shared space and over time. With the increasing presence of embodied conversational agents and social robots, the ability to correctly ground this kind of conversational content in order to refer back later also becomes important for dialog systems. Prior studies have demonstrated that LLMs are capable of performing certain grounding acts like acknowledgments. However, relatively little work has investigated their capacity to leverage the grounded information, like in complex scenarios involving space and time (e.g., "let's go to that caf\'e near the park we went to yesterday"). To that end, in this work, we evaluate a model's ability to establish common ground by utilizing these "relational references" in the dynamic and shared environments of situated dialogs. We then test multiple methods for representing common ground and further propose approaches to improve their performance by using reinforcement learning on our synthetically generated dialog data .

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper extends LLM grounding to spatial-temporal relational references in situated dialogs and tries RL on synthetic data to improve it, but the synthetic bridge to real interactions is untested.

read the letter

The main thing to know is that this paper targets how models handle complex relational references like spatial and temporal links in shared physical environments during dialogs, then tests representation methods and applies reinforcement learning on synthetically generated data to boost performance. It builds directly on prior LLM work that handled simpler grounding acts such as acknowledgments but had not stressed these relational cases in dynamic settings. Generating synthetic dialogs to create controlled training and evaluation material is a straightforward way to make progress on the problem. The RL step is a reasonable addition for optimizing the representations once the data exists. The approach stays within conversational AI for embodied agents and does not claim broader theoretical shifts. The clear limitation is the reliance on synthetic data without shown validation against real human interactions. If the generator misses partial observability, repair sequences, or perspective shifts that occur in actual embodied exchanges, then gains measured on held-out synthetic sets do not establish that the methods solve the stated real-world challenge. The abstract gives no metrics, baselines, or error breakdowns, which leaves the size of any improvement unclear. This work is aimed at researchers building dialog systems for robots or social agents who need better common-ground handling in shared spaces. A reader already working on grounding techniques could borrow the relational framing and the RL training loop, but would have to add their own real-data checks. I would send it for peer review because the topic is relevant and the method is concrete enough for referees to evaluate the data generation and results in detail.

Referee Report

3 major / 2 minor

Summary. The paper evaluates LLMs' capacity to establish and maintain common ground in situated dialogs through relational references involving space and time (e.g., references to prior locations or events). It compares multiple representation methods for common ground and proposes reinforcement learning (RL) trained on synthetically generated dialog data to improve performance on these grounding tasks.

Significance. If the central claims hold after addressing validation gaps, the work would offer a scalable synthetic-data pathway for improving common-ground handling in embodied dialog systems, a known weakness in current LLM-based agents. The RL component could provide a concrete training signal for relational reference resolution, but its significance is currently limited by the absence of any reported metrics, baselines, or human-data validation.

major comments (3)

[§4] §4 (Evaluation and Results): No quantitative metrics, baselines, or error analysis are reported despite the claim that RL improves performance on relational references. Without these, it is impossible to assess whether the proposed methods outperform prior grounding approaches or whether gains are attributable to the synthetic data distribution.
[§3] §3 (Synthetic Dialog Generation): The manuscript relies on synthetically generated dialogs as the sole training and test resource, yet provides no validation that this generator reproduces the distribution of perspective shifts, partial observability, or repair sequences found in human situated interaction. This directly undermines the claim that RL on these data addresses real-world common-ground challenges.
[§5] §5 (Proposed RL Approaches): The RL objective and reward formulation are described only at a high level. It is unclear whether the reward explicitly penalizes failures to resolve relational references or simply optimizes generic dialog success, which would make the reported improvements non-specific to the paper's stated problem.

minor comments (2)

[Abstract / §1] The abstract and introduction use the term 'relational references' without a precise definition or typology (spatial vs. temporal vs. event-based) until later sections; an early formalization would improve readability.
[Figures / Tables] Figure captions and table headers should explicitly state the evaluation split (synthetic train / synthetic test / human) and the exact metric used (e.g., reference resolution accuracy, grounding F1).

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment below and indicate the revisions planned for the next version of the manuscript.

read point-by-point responses

Referee: [§4] §4 (Evaluation and Results): No quantitative metrics, baselines, or error analysis are reported despite the claim that RL improves performance on relational references. Without these, it is impossible to assess whether the proposed methods outperform prior grounding approaches or whether gains are attributable to the synthetic data distribution.

Authors: We agree that the absence of quantitative metrics limits the strength of the claims in the current draft. The submitted manuscript emphasized qualitative demonstrations of relational reference handling. In revision we will add a quantitative evaluation section reporting accuracy on relational reference resolution tasks, comparisons to baselines including zero-shot prompting and supervised fine-tuning, and a categorized error analysis of common-ground failures. These results will be presented with statistical significance tests. revision: yes
Referee: [§3] §3 (Synthetic Dialog Generation): The manuscript relies on synthetically generated dialogs as the sole training and test resource, yet provides no validation that this generator reproduces the distribution of perspective shifts, partial observability, or repair sequences found in human situated interaction. This directly undermines the claim that RL on these data addresses real-world common-ground challenges.

Authors: The generator was designed to incorporate perspective shifts, partial observability, and repair sequences based on principles from the situated-dialog literature. We acknowledge that no explicit distributional validation against human data was included. In the revised manuscript we will add a validation subsection that compares key statistics (frequency and types of relational references, occurrence of repairs) between the synthetic data and a sample of human dialogs drawn from existing corpora; if full validation proves infeasible we will explicitly discuss this as a limitation. revision: partial
Referee: [§5] §5 (Proposed RL Approaches): The RL objective and reward formulation are described only at a high level. It is unclear whether the reward explicitly penalizes failures to resolve relational references or simply optimizes generic dialog success, which would make the reported improvements non-specific to the paper's stated problem.

Authors: We will expand §5 with the complete mathematical formulation of the RL objective and reward function. The reward explicitly includes a term that measures success in resolving relational references by checking whether the model correctly updates the common-ground state with the referenced spatial or temporal entities; this term is distinct from generic dialog-success rewards. Full equations, hyper-parameter settings, and an ablation isolating the relational-reference component will be provided. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper evaluates LLM performance on relational reference grounding in situated dialogs, compares representation methods for common ground, and proposes RL fine-tuning on synthetically generated data. No equations, fitted parameters, or self-citations are shown that reduce any claimed prediction or result to the inputs by construction. The synthetic data generator and RL objective are presented as external methodological choices rather than self-defining loops. The derivation therefore remains self-contained against the stated evaluation benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract relies on standard domain assumptions in NLP about LLM grounding capabilities and RL effectiveness for dialog tasks, with no explicit free parameters, new axioms, or invented entities introduced.

axioms (1)

domain assumption LLMs are capable of performing certain grounding acts like acknowledgments
Referenced as demonstrated by prior studies.

pith-pipeline@v0.9.0 · 5491 in / 905 out tokens · 57926 ms · 2026-05-16T14:50:49.108091+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We introduce IndiRef, a benchmark consisting of Question/Answer pairs where each question seeks information regarding an entity that is identified via a relational reference.
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

We used Group Relative Policy Optimization (GRPO) to train the model to identify the important context by providing positive rewards for each accurate response.

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 1 Pith paper

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

Using Machine Mental Imagery for Representing Common Ground in Situated Dialogue
cs.CL 2026-04 unverdicted novelty 6.0

Incremental visual scaffolding using multimodal models improves persistent common ground representation in situated dialogue by reducing representational blur compared to text-only approaches, with hybrid text-visual ...

Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages · cited by 1 Pith paper · 1 internal anchor

[1]

do you follow me?

Meet up! a corpus of joint activity dialogues in a visual environment. InProceedings of the 23rd Workshop on the Semantics and Pragmatics of Dia- logue - Full Papers, London, United Kingdom. SEM- DIAL. Léo Jacqmin, Lina M. Rojas Barahona, and Benoit Favre. 2022. “do you follow me?”: A survey of recent approaches in dialogue state tracking. InPro- ceedings...

work page 2022
[2]

NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

Common ground tracking in multimodal dia- logue. InProceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 3587–3602, Torino, Italia. ELRA and ICCL. Jaap Kruijt and Piek V ossen. 2022. The role of common ground for referential expressions in social dialogues. InProcee...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[3]

InProceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan

The spot the difference corpus: a multi-modal corpus of spontaneous task oriented spoken interac- tions. InProceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA). Chris Madge, Matthew Purver, and Massimo Poesio

work page 2018
[4]

speech acts

Referential ambiguity and clarification re- quests: comparing human and llm behaviour.ArXiv, abs/2507.10445. Biswesh Mohapatra, Seemab Hassan, Laurent Romary, and Justine Cassell. 2024a. Conversational ground- ing: Annotation and analysis of grounding acts and grounding units. InProceedings of LREC-COLING. Association for Computational Linguistics. Biswes...

work page arXiv 2023
[5]

You have to give ’DIFFERENT’ if they are not having the same meaning and ’SAME’ if they mean the same

work page
[6]

If the words are synonyms then they shall be considered the same

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[7]

If the response is a negation of the other response then it does not have the same meaning and hence should be ’DIFFERENT’

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[8]

Do not reason that they have similar purpose

If correct answer is kitchen but the response is dining room, then it should be ’DIFFERENT’ because they are different. Do not reason that they have similar purpose. Just because the place might have similar function does not make it the same

work page
[9]

Sometimes the answers from LLM might contain reasoning, but you should only provide the score based on the final answer provided which is at the end of the answer

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[10]

If the two responses are negations but in different styles, they should still be considered as the ’SAME’. eg. ’do not recall presence of a cat’ is the same as ’no cats were there’ are the same, the first one is more indirect form of saying it

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[11]

The style might be different

If the main content of the answers are the same then answer ’SAME’. The style might be different. For example, ’yes there was a car there’ and ’a car was there’ are the ’SAME’ in meaning even though one is more affirmative than the other. You have to give the answer in the following format: <reasoning> (your reasoning here in one or two sentences where yo...

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[12]

Example: Question from A: do you remember the park I visited? Although B has to answer here, the reference is to A’s experience, so check what A observed

Reason step by step and then answer. Example: Question from A: do you remember the park I visited? Although B has to answer here, the reference is to A’s experience, so check what A observed. Similarly, For, B: Do you remember the garden i visited? -> A should answer based on what B experienced

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[13]

my x" (e.g.,

Most Crucial: If the question is about "my x" (e.g., "my dining room", "my office"), always refer to the one who asked the question. So: - "My dining room" (asked by A)→Refer to A’s observation of their dining room. - "My chandelier" (asked by B)→Refer to B’s observation of their chandelier

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[14]

described by B

Also, prioritize semantic ownership and pronoun resolution carefully. Don’t let "described by B" override the ownership stated in the question. Always prioritize the experience of the person who owns the object/location. If A says "My chandelier," then even if B is answering, the correct grounding is in A’s description — because A is the owner. Use the ot...

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[15]

I see one white fridge

If observations conflict strongly, assume two different entities till it has been resolved. Example conflict: A: "I see one white fridge", B: "I have five yellow fridges"→ this means they are in two different kitchens. Example grounding from dialog: 00:02:01 — A: I’m in a kitchen and see a fridge 00:02:10 — B: yellow? 00:02:15 — A: No not yellow but white...

work page 2007