Reasoning Gets Harder for LLMs Inside A Dialogue
| Authors | Ivan Kartáč et al. |
| Year | 2026 |
| Field | NLP |
| arXiv | 2603.20133 |
| Download | |
| Categories | cs.CL |
Abstract
Large Language Models (LLMs) achieve strong performance on many reasoning benchmarks, yet these evaluations typically focus on isolated tasks that differ from real-world usage in task-oriented dialogue (TOD). In this setting, LLMs must perform reasoning inherently while generating text and adhering to instructions on role, format, and style. This mismatch raises concerns about whether benchmark performance accurately reflects models' reasoning robustness in TOD setting. We investigate how framing reasoning tasks within TOD affects LLM performance by introducing BOULDER, a new dynamic benchmark covering eight travel-related tasks that require arithmetic, spatial, and temporal reasoning with both commonsense and formal aspects. Each problem is presented in both isolated and dialogue-based variants, enabling controlled comparison while mitigating data contamination. Experiments on eight LLMs reveal a substantial and consistent performance gap between isolated and dialogue settings. Through ablations and qualitative analysis, we show that this gap is largely driven by the multi-turn nature of dialogue, with additional effects from role conditioning and tool-use requirements. Our results highlight the need to evaluate LLM reasoning in realistic interactive scenarios.
Engineering Breakdown
Plain English
This paper identifies a critical gap between how LLMs are evaluated on reasoning benchmarks versus how they perform when reasoning must happen inside real dialogue interactions. The authors created BOULDER, a dynamic benchmark with eight travel-related tasks requiring arithmetic, spatial, and temporal reasoning that can be presented both as isolated problems and embedded in task-oriented dialogue (TOD) conversations. The key finding is that the same reasoning problems become significantly harder for LLMs when framed within dialogue context, where models must simultaneously generate natural responses, follow role and format constraints, and solve the reasoning problem. This mismatch suggests that standard reasoning benchmarks may substantially overestimate how robust LLM reasoning actually is in production dialogue systems.
Core Technical Contribution
The core technical novelty is the BOULDER benchmark design—a structured dataset that systematically varies task framing (isolated vs. dialogue-embedded) while keeping the underlying reasoning problem identical. This allows direct measurement of the performance delta caused purely by the dialogue context rather than task difficulty. The authors decompose reasoning into distinct types (arithmetic, spatial, temporal) and reasoning styles (commonsense vs. formal), creating a more granular understanding of where dialogue-embedded reasoning breaks down. This is fundamentally different from prior work that either evaluates reasoning in isolation or evaluates dialogue separately; BOULDER enables controlled contrastive analysis of the same reasoning problem under different presentation contexts.
How It Works
BOULDER operates by taking eight base reasoning tasks and creating two presentation variants: an isolated version where the task is presented as a standalone problem, and a dialogue-embedded version where the same task is embedded in a multi-turn task-oriented dialogue conversation. For each task variant, an LLM must either solve it directly (isolated case) or solve it while generating appropriate dialogue responses that maintain character consistency, follow format constraints, and naturally integrate the reasoning into conversation flow. The benchmark measures performance differences between these two conditions, isolating the cognitive load penalty introduced by dialogue context. Tasks span three reasoning domains (arithmetic: calculation-based, spatial: navigation/location-based, temporal: scheduling-based) and two reasoning styles (commonsense: intuitive reasoning about real-world scenarios, formal: rule-based or mathematical reasoning), creating an 8-task matrix that captures different failure modes and difficulty profiles.
Production Impact
For engineers building conversational AI and task-oriented dialogue systems, this research reveals that reasoning capability claims based on benchmark scores need significant discounting when those systems will operate in interactive dialogue. If you're deploying an LLM that requires reliable arithmetic, route planning, or scheduling reasoning within a chat interface, you should expect performance degradation of unknown magnitude compared to the model's isolated reasoning benchmarks. This means you need to either test your specific dialogue domain extensively, build in verification/correction mechanisms for reasoning outputs, or use ensemble approaches with specialized reasoning modules rather than relying on the LLM's end-to-end reasoning. The practical implication is that dialogue-aware fine-tuning or chain-of-thought prompting specifically designed for dialogue contexts becomes necessary rather than optional—generic reasoning optimization won't suffice.
Limitations and When Not to Use This
The paper focuses specifically on travel-related reasoning tasks, so findings may not generalize to other domains where dialogue context affects reasoning differently (e.g., medical diagnosis, code generation, or creative tasks). BOULDER appears to be limited to eight tasks, which may not capture the full spectrum of reasoning failure modes across the space of possible dialogue-reasoning combinations. The paper doesn't explore whether the performance gap can be closed through specific fine-tuning approaches, prompt engineering strategies, or architectural modifications—it documents the problem but doesn't provide validated solutions that are production-ready. Additionally, the work doesn't characterize how different model scales, architectures, or training approaches (RLHF-tuned vs. base models) interact with dialogue-embedded reasoning, leaving open the question of which model characteristics might inherently handle this coupling better.
Research Context
This work bridges two previously separate research streams: reasoning evaluation (which typically uses isolated benchmarks like MATH, ARC, DROP) and dialogue evaluation (which focuses on coherence, appropriateness, and task completion but not underlying reasoning correctness). It extends the tradition of diagnostic benchmarks (like BIG-Bench) by introducing controlled task variation as a mechanism to isolate specific performance factors. The paper contributes to a growing recognition in the field that benchmark performance doesn't predict real-world robustness, similar to findings about prompt sensitivity and distributional shift in other domains. It opens a research direction toward dialogue-aware reasoning evaluation and potentially toward dialogue-aware reasoning training methods.
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