Conversational Orientation Reasoning (COR)

• Conversational Orientation Reasoning (COR) is a spatial navigation task converting egocentric landmark descriptions into absolute cardinal directions within a structured grid.
• It employs a multimodal chain-of-thought framework to integrate speech-derived text and structured coordinates for explicit reasoning over spatial relations.
• Empirical benchmarks demonstrate near-perfect accuracy even with noisy ASR transcripts, highlighting the method's robustness in GPS-denied environments.

Conversational Orientation Reasoning (COR) is a spatial reasoning task in conversational navigation in which a system infers a user’s allocentric facing direction from egocentric spoken descriptions of nearby landmarks together with a structured representation of the environment. In its explicit formulation, COR maps utterances such as “in front of me,” “behind me,” “on my left,” or “on my right” to one of the four cardinal directions $\{\text{north, east, south, west}\}$, and it is motivated by indoor, transportation-hub, and other GPS-denied settings where users rely on dialogue with conversational agents rather than on continuous localization infrastructure (Huang, 20 Sep 2025). Related work uses adjacent notions of “orientation” for interpersonal stance, evolving retrieval intent, preference polarity, persistent task framing, and duplex speech behavior, but the term COR is defined directly in the literature as this egocentric-to-allocentric conversational navigation problem (Huang, 20 Sep 2025).

1. Definition and formal problem structure

The defining operation in COR is an egocentric-to-allocentric conversion. The user provides a spoken description of landmarks from the user’s own perspective, while the system must recover the user’s absolute orientation in a world-centered frame. In the benchmark formulation, the environment is represented as a discrete $10\times 10$ grid $\mathcal{G}$, with user position $u=(x_u,y_u)\in\mathcal{G}$ and landmarks $\mathcal{L}=\{\ell_i\}$ with coordinates $p(\ell_i)\in\mathcal{G}$ (Huang, 20 Sep 2025).

For a reference landmark $\ell_r$, the landmark-user displacement is

$\Delta = p(\ell_r)-u=(\Delta_x,\Delta_y).$

The landmark’s absolute direction from the user is then determined by the sign and dominant axis of $\Delta$, yielding one of $\text{N},\text{E},\text{S},\text{W}$. The user’s orientation $10\times 10$0 is recovered by rotating that allocentric landmark direction according to the egocentric relation $10\times 10$1. The benchmark also provides the relative-to-absolute mapping table, for example: if the user is facing North, then Front $10\times 10$2 N, Back $10\times 10$3 S, Right $10\times 10$4 E, Left $10\times 10$5 W; if the user is facing East, then Front $10\times 10$6 E, Back $10\times 10$7 W, Right $10\times 10$8 S, Left $10\times 10$9 N (Huang, 20 Sep 2025).

This formulation matters because many navigation systems assume heading is already known, or operate on action spaces such as left/right/up/down rather than reasoning from natural egocentric utterances to absolute heading. COR instead treats conversational orientation itself as the prediction target. A plausible implication is that COR is best viewed as a grounded dialogue-state inference problem in which language, spatial structure, and timing constraints jointly determine orientation.

2. Benchmark design and data regime

The benchmark introduced for COR is built from the Gongguan MRT area in Taiwan, projected into a structured grid, and contains 4,600 instances. Each instance comprises a Traditional Chinese egocentric utterance, structured landmark coordinates, an allocentric orientation label, and a step-by-step reasoning trace. The benchmark is split into 3,216 training, 688 validation, and 696 test examples (Huang, 20 Sep 2025).

The language setting is central rather than incidental. Training and evaluation are conducted in Traditional Chinese, while the benchmark also includes multilingual artifacts characteristic of Taiwan: English landmark names appear in $\mathcal{G}$0 of the main test set and $\mathcal{G}$1 of the cross-domain set, and ASR occasionally produces simplified-Chinese variants. To simulate deployment through speech, the authors synthesize speech from clean descriptions and transcribe it with ASR, yielding both clean text $\mathcal{G}$2 and noisy transcript $\mathcal{G}$3 (Huang, 20 Sep 2025).

The evaluation regime extends beyond the main Gongguan test set. It includes a cross-domain set of 540 examples from an unseen Taipei Station area, a 200-case referential-ambiguity set with ambiguous references, disfluency, incomplete utterances, and semantically underspecified mentions, and a 400-example subset with controlled linguistic variations such as synonym substitutions and word-order changes (Huang, 20 Sep 2025). This design makes COR a benchmark not only for coordinate reasoning but also for robustness under ASR degradation, multilingual contamination, domain shift, and referential ambiguity.

The data model is notable because the benchmark provides reasoning supervision rather than only final labels. This suggests an interpretation of COR as a structured reasoning task rather than a flat four-way classification problem.

3. Multimodal Chain-of-Thought and curriculum learning

The main modeling contribution paired with the benchmark is a Multimodal Chain-of-Thought (MCoT) framework. Here “multimodal” does not denote image-text fusion; rather, it denotes the integration of speech-derived text and structured spatial coordinates in a unified reasoning process. MCoT decomposes COR into three explicit steps: (1) extracting spatial relations, (2) mapping coordinates to absolute directions, and (3) inferring user orientation (Huang, 20 Sep 2025).

In Step 1, the model identifies the egocentric relation(s) and corresponding reference landmark(s) from the transcript. In Step 2, it computes the direction vector from user coordinates to landmark coordinates and maps that vector to East, West, North, or South. In Step 3, it combines the absolute landmark direction with the egocentric relation to infer the user’s facing direction. The training traces serialize these steps in a fixed schema, including explicit vector arithmetic such as

$\mathcal{G}$4

This is not free-form prompting; it is reasoning supervision over a constrained symbolic scaffold (Huang, 20 Sep 2025).

Training is organized by curriculum learning. The stages are: $\mathcal{G}$5

$\mathcal{G}$6

$\mathcal{G}$7

followed by end-to-end integration with

$\mathcal{G}$8

where $\mathcal{G}$9 serializes the coordinate structure, and the final output sequence $u=(x_u,y_u)\in\mathcal{G}$0 contains the intermediate reasoning and the final orientation. The objective is standard autoregressive sequence loss,

$u=(x_u,y_u)\in\mathcal{G}$1

The base model is Taiwan-LLM-13B-v2.0-Chat, chosen as a Traditional Chinese mid-sized model representative of resource-constrained settings (Huang, 20 Sep 2025).

Implementation details are unusually explicit: LoRA with 4-bit quantization, rank $u=(x_u,y_u)\in\mathcal{G}$2, $u=(x_u,y_u)\in\mathcal{G}$3, dropout $u=(x_u,y_u)\in\mathcal{G}$4, AdamW with learning rate $u=(x_u,y_u)\in\mathcal{G}$5, batch size $u=(x_u,y_u)\in\mathcal{G}$6, gradient accumulation $u=(x_u,y_u)\in\mathcal{G}$7, $u=(x_u,y_u)\in\mathcal{G}$8 epochs, maximum sequence length $u=(x_u,y_u)\in\mathcal{G}$9, bfloat16 mixed precision, and Whisper-base (zh) for ASR on 16 kHz mono audio (Huang, 20 Sep 2025).

4. Empirical performance, robustness, and failure modes

The headline results are unusually strong. On the main test set, the full MCoT system achieves $\mathcal{L}=\{\ell_i\}$0 orientation accuracy on clean transcripts and $\mathcal{L}=\{\ell_i\}$1 on ASR transcripts, with $\mathcal{L}=\{\ell_i\}$2 format error in both cases and reasoning quality $\mathcal{L}=\{\ell_i\}$3. The baselines are far weaker: Zero-shot $\mathcal{L}=\{\ell_i\}$4, Few-shot (no CoT) $\mathcal{L}=\{\ell_i\}$5, Few-shot (with CoT) $\mathcal{L}=\{\ell_i\}$6 with $\mathcal{L}=\{\ell_i\}$7 format errors, and Fine-tuned (no CoT) $\mathcal{L}=\{\ell_i\}$8 with $\mathcal{L}=\{\ell_i\}$9 format errors (Huang, 20 Sep 2025).

The ablations show that coordinates help, but structured reasoning helps much more. On ASR text only without coordinates, accuracy is $p(\ell_i)\in\mathcal{G}$0 with $p(\ell_i)\in\mathcal{G}$1 format error. Adding coordinates without CoT raises accuracy to $p(\ell_i)\in\mathcal{G}$2 and reduces format error to $p(\ell_i)\in\mathcal{G}$3. Adding structured CoT on top of multimodal input yields $p(\ell_i)\in\mathcal{G}$4 and $p(\ell_i)\in\mathcal{G}$5 format error (Huang, 20 Sep 2025). The paper’s strongest empirical claim is therefore that the task is easy when decomposed correctly, but hard when treated as unconstrained end-to-end generation.

Robustness results are likewise strong: $p(\ell_i)\in\mathcal{G}$6 accuracy under linguistic variation, $p(\ell_i)\in\mathcal{G}$7 $p(\ell_i)\in\mathcal{G}$8 on the unseen Taipei Station cross-domain set, and $p(\ell_i)\in\mathcal{G}$9 $\ell_r$0 on referential ambiguity. In the main ASR set, only $\ell_r$1 of samples are perfectly transcribed, with moderate errors the largest bucket at $\ell_r$2, yet accuracy remains $\ell_r$3 (Huang, 20 Sep 2025).

Residual errors are categorized as direction-understanding errors, relation extraction errors, and ASR misrecognition errors. On the main ASR test set there are 13 residual errors total, most of them direction-understanding errors (9), with relation extraction mistakes (2) and ASR misrecognition errors (3), with category overlap. On the cross-domain set there are 29 residual errors, again mostly direction-understanding failures (Huang, 20 Sep 2025). This suggests that the bottleneck is less the coordinate arithmetic than the mapping from extracted relation to final orientation rule.

5. Adjacent formulations and terminological landscape

The explicit COR benchmark is spatial, but adjacent research uses “orientation” in broader conversational senses. “Social Orientation: A New Feature for Dialogue Analysis” models utterance-level interpersonal stance through eight circumplex-based tags such as Warm-Agreeable and Arrogant-Calculating, and shows that social orientation tags improve dialogue outcome prediction, especially in low-resource settings (Morrill et al., 2024). “RECOR: Reasoning-focused Multi-turn Conversational Retrieval Benchmark” treats conversational orientation as evolving retrieval intent across turns; its central result is that combining conversation history with reasoning improves retrieval from $\ell_r$4 to $\ell_r$5 nDCG@10 (Ali et al., 9 Jan 2026). “Agentic Conversational Search with Contextualized Reasoning via Reinforcement Learning” likewise frames multi-turn search as orientation to context-dependent user intent, with mixed-initiative actions such as answer, clarify, and no-answer (Mo et al., 19 Jan 2026). “CORE: A Conceptual Reasoning Layer for LLMs” addresses persistent task orientation in multi-turn interaction through a Local Concept and cognitive operators rather than transcript replay (Hegde et al., 10 Dec 2025).

Other acronymically adjacent works are not COR in the strict sense. “Chain-of-Reasoning” defines CoR for multi-paradigm mathematical reasoning (Yu et al., 19 Jan 2025). “CORA” denotes Consistency-Oriented Reasoning Alignment in multimodal RLVR (Cao et al., 12 Jun 2026). “CORAL” denotes contrasting-preference conversational recommendation, where positive and negative user preferences are modeled separately in retrieval scoring (Kook et al., 27 Mar 2025). These works are conceptually related to conversational orientation, but they do not define COR as egocentric-to-allocentric navigation.

A plausible implication is that “conversational orientation reasoning” now names both a specific spatial benchmark and a broader design pattern: representing latent interaction state explicitly rather than reconstructing it anew from raw token history.

6. Limitations, extensions, and prospective directions

The direct COR benchmark has several explicit limitations. It uses a $\ell_r$6 grid and grid-based rules, synthesized speech followed by ASR rather than fully natural interaction, and depends on structured coordinates. The paper states that future work should extend to larger and continuous environments, use real-time multilingual speech recognition under varied noise conditions, and incorporate additional modalities such as vision or motion cues (Huang, 20 Sep 2025).

Related work suggests several plausible extensions. “Conversational Behavior Modeling Foundation Model With Multi-Level Perception” formalizes a duplex spoken-dialogue setting in which high-level communicative intents and low-level speech acts are predicted causally at 1-second granularity, and reasoning is organized through a Graph-of-Thoughts over streaming dialogue (Zhou et al., 11 Feb 2026). “Conversational Neuro-Symbolic Commonsense Reasoning” studies hidden presumptions in commands of the form “if-(state), then-(action), because-(goal),” which is highly relevant to COR if orientation is understood as recovery of latent constraints and goal-conditioned interpretation (Arabshahi et al., 2020). “Conversational Markers of Constructive Discussions” shows that idea flow, uptake, balance, and convergence can predict whether early discussion is productive or wasteful (Niculae et al., 2016).

Taken together, these strands suggest that COR may develop along two paths. One path extends the original spatial benchmark toward richer embodied navigation with speech, vision, and motion. The other path generalizes “orientation” toward persistent conversational state, including intent, stance, floor control, evidence demand, and action readiness. The existing benchmark establishes the first path directly, while adjacent literature supplies technical components for the second.