Beyond Continuity: Challenges of Context Switching in Multi-Turn Dialogue with LLMs

Aditya Sinha; Harald Steck; Matteo Rinaldi; Vito Ostuni

arxiv: 2605.09268 · v1 · submitted 2026-05-10 · 💻 cs.CL · cs.AI

Beyond Continuity: Challenges of Context Switching in Multi-Turn Dialogue with LLMs

Aditya Sinha , Harald Steck , Vito Ostuni , Matteo Rinaldi This is my paper

Pith reviewed 2026-05-12 04:44 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords multi-turn dialoguecontext switchingtopic pivot detectionLLM evaluationposition biassynthetic benchmarksconversation contextrefinement vs pivot

0 comments

The pith

LLMs often miss when users change topics in conversations and keep using irrelevant prior context.

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

Users commonly refine requests or switch subjects across turns with language models, yet the models frequently overlook these changes and produce responses based on outdated information. The work constructs synthetic benchmarks drawn from real datasets across domains to test detection of pivots versus refinements and selection of relevant past context at different difficulty levels. Evaluation of ten models shows that only some reasoning models with strong instructions accurately identify shifts, while open-weight models retain stale context even when cues are explicit. Every model tested also exhibits bias toward context based on its position in the history rather than its relevance. The results point to concrete gaps in current multi-turn capabilities.

Core claim

The paper establishes that large language models face substantial difficulties with context switching in multi-turn dialogues. Specifically, only certain reasoning and strongly instructed models perform reliably at detecting whether the current user turn introduces a pivot to a new topic or merely refines the prior one, and at shortlisting relevant prior turns. Open-weight models commonly fail at both sub-tasks and continue to draw on irrelevant earlier context despite clear signals. All models display a position bias that favors turns based on their placement in the sequence rather than semantic fit.

What carries the argument

Synthetic benchmarks that simulate context shifts of graded difficulty, used to assess the two sub-tasks of pivot detection and relevant-context shortlisting from conversation history.

If this is right

Targeted training or prompting strategies are needed to improve detection of topic shifts.
Architectural or decoding changes are required to reduce position bias in context selection.
Open-weight models need stronger mechanisms to ignore stale context when explicit cues appear.
Development of long-term multi-turn robustness should prioritize the two sub-tasks identified.

Where Pith is reading between the lines

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

Chat systems may benefit from an external lightweight pivot detector rather than relying on the base model alone.
Training data that explicitly includes many subtle topic shifts could close the observed performance gap.
Position bias might be tested by swapping turn order in otherwise identical histories to measure robustness.
Real-user studies could reveal whether the synthetic difficulty levels match the distribution of natural shifts.

Load-bearing premise

The synthetic benchmarks built from real-world datasets accurately capture the range and difficulty of context shifts that arise in actual user interactions.

What would settle it

Running the same ten models on a set of real multi-turn user logs with human-annotated pivot points and relevant-context labels, then comparing accuracy and position-bias patterns against the synthetic results.

Figures

Figures reproduced from arXiv: 2605.09268 by Aditya Sinha, Harald Steck, Matteo Rinaldi, Vito Ostuni.

**Figure 1.** Figure 1: TOPIOCQA: F1PIVOT ( ↑ ) and OC (↓) vs. Pivot Position (P). Majority of the models show a degradation in F1PIVOT as the Pivot Position increases, indicating challenging detection. 5 CONCLUSIONS AND FUTURE WORK In this work, we stress-test the multi-turn capabilities of LLMs and expose the challenges in pivotdetection and context resetting for practical scenarios. Our main findings are that a subset of the … view at source ↗

read the original abstract

Users interacting with Large Language Models (LLMs) in a multi-turn conversation routinely refine their requests or pivot to new topics. LLMs, however, often miss these topic shifts and carry over irrelevant context from previous turns, leading to inaccurate responses. In this paper, we stress-test the multi-turn understanding of LLMs and study the following two sub-tasks: (1) detecting whether the user pivots or refines in the current turn, and (2) shortlisting relevant context from previous turns. To this end, we construct synthetic benchmarks based on real-world datasets from varied domains, as to simulate context shifts of different levels of difficulty. We then evaluate the zero-shot performance of ten LLMs (open-weight, closed-source and reasoning), and demonstrate that only some reasoning and strongly instructed LLMs are accurate in detecting pivots; open-weight LLMs struggle with the task and frequently carry stale context even with explicit cues; and all models suffer from a position bias. Based on the results, we discuss key takeaways for improving long-term robustness in multi-turn capabilities for LLMs.

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 shows LLMs miss user topic pivots and drag stale context in synthetic tests, with position bias hitting all models, but the benchmarks' match to real shifts is the main open issue.

read the letter

The main things to know are that only certain reasoning models with heavy instructions catch topic pivots reliably, open-weight models often keep irrelevant prior turns even with cues, and every model tested shows a clear position bias favoring recent context. These patterns come from zero-shot runs on new synthetic benchmarks for pivot detection and context shortlisting. The work breaks the multi-turn problem into those two sub-tasks and tests ten models across open, closed, and reasoning categories on data drawn from varied real-world sources to control difficulty. That focused construction and the position-bias analysis are the clearest additions beyond earlier general context-length papers. The comparative results give concrete failure modes that line up with what people see when using these systems for longer chats. The synthetic benchmark step is the soft spot. The authors base the shifts on real datasets and try to vary subtlety, yet the paper does not include external human validation that the generated pivots and refinements reproduce the distribution or ambiguity of actual user behavior. Without that check, the performance gaps could partly reflect how the test cases were built rather than intrinsic model limits. Minor details like exact prompt wording, scoring rules, and any statistical tests on the differences would also help readers assess how robust the numbers are. This is aimed at groups working on dialogue systems or long-context training. Anyone evaluating or fixing multi-turn robustness will get practical signals from the model-type breakdowns and bias observations. The empirical framing is straightforward and the claims stay tied to the observed outputs, so the paper deserves a serious referee even if the benchmark validation needs tightening.

Referee Report

2 major / 1 minor

Summary. The paper claims that LLMs often fail to handle context switches in multi-turn dialogues by missing pivots and carrying stale context. It introduces two sub-tasks: pivot/refinement detection and relevant context shortlisting. Synthetic benchmarks are created from real-world datasets to test different difficulty levels. Zero-shot evaluations of ten LLMs reveal that only some reasoning and strongly instructed models perform well on pivot detection, open-weight models struggle with stale context even with cues, and all models show position bias. The paper discusses implications for improving multi-turn LLM capabilities.

Significance. If the findings are robust, this study is significant for highlighting practical limitations in LLM dialogue systems, particularly for applications requiring adaptive context management. The evaluation across diverse model types provides a broad view of current capabilities. Credit is given for the empirical approach using synthetic data derived from real datasets and for identifying specific issues like position bias that can inform future model training or prompting strategies.

major comments (2)

[Benchmark Construction] The validity of the synthetic benchmarks in simulating real context shifts is not externally validated against human-annotated conversations. Since the central claims about LLM performance rely on these benchmarks accurately representing genuine user interactions, this lack of validation is a load-bearing concern that could affect the interpretation of model failures as intrinsic rather than benchmark-specific.
[Results and Evaluation] The paper lacks details on the exact metrics used for accuracy, any statistical tests performed to support the performance differences, and the full prompt templates employed. This makes it challenging to reproduce the experiments and assess the strength of the conclusions regarding which models succeed or fail at the sub-tasks.

minor comments (1)

[Abstract] The abstract mentions 'varied domains' but does not specify which real-world datasets were used, which would help readers understand the scope.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which helps us strengthen the manuscript. We appreciate the recognition of the work's significance for LLM dialogue systems. Below we respond point-by-point to the major comments.

read point-by-point responses

Referee: [Benchmark Construction] The validity of the synthetic benchmarks in simulating real context shifts is not externally validated against human-annotated conversations. Since the central claims about LLM performance rely on these benchmarks accurately representing genuine user interactions, this lack of validation is a load-bearing concern that could affect the interpretation of model failures as intrinsic rather than benchmark-specific.

Authors: We acknowledge the referee's concern regarding external validation. Our benchmarks are derived directly from real-world dialogue datasets across multiple domains, with context shifts introduced through controlled modifications that preserve natural language patterns from the source data. While we did not conduct a separate human annotation study to validate the synthetic examples against genuine user interactions, we designed the generation process to reflect observed pivot and refinement behaviors. We agree this represents a limitation in fully establishing generalizability. In the revision we will expand the benchmark construction section with additional details on the adaptation methodology and add an explicit limitations paragraph discussing the absence of human validation, allowing readers to better contextualize the results. revision: partial
Referee: [Results and Evaluation] The paper lacks details on the exact metrics used for accuracy, any statistical tests performed to support the performance differences, and the full prompt templates employed. This makes it challenging to reproduce the experiments and assess the strength of the conclusions regarding which models succeed or fail at the sub-tasks.

Authors: We agree that greater transparency is needed for reproducibility. The revised manuscript will include precise definitions of all metrics (e.g., accuracy, precision, and recall for pivot detection and context shortlisting), a description of any statistical comparisons performed across models, and the complete prompt templates for all zero-shot evaluations in a new appendix. These additions will enable full reproduction and clearer assessment of the reported performance differences. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical evaluation with no derivations or self-referential reductions

full rationale

The paper conducts an empirical stress-test of LLMs on two sub-tasks (pivot detection and context shortlisting) using synthetic benchmarks built from real-world datasets. No mathematical derivations, equations, fitted parameters, or predictions appear in the abstract or described methodology. Results are obtained directly from zero-shot model outputs on the constructed test cases, with no self-citation load-bearing steps, ansatz smuggling, or renaming of known results. The central claims rest on observed performance differences across model types rather than any reduction to inputs by construction. This is a standard empirical study whose validity hinges on benchmark fidelity (an external concern), not internal circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the assumption that the synthetic benchmarks faithfully represent real multi-turn context shifts and that zero-shot prompting elicits the intended model behavior without hidden prompt engineering.

axioms (1)

domain assumption Synthetic benchmarks based on real-world datasets simulate context shifts of varying difficulty levels accurately enough to stress-test LLM multi-turn understanding.
Invoked in the benchmark construction section to justify the evaluation of pivot detection and context shortlisting.

pith-pipeline@v0.9.0 · 5494 in / 1116 out tokens · 35381 ms · 2026-05-12T04:44:11.057767+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

16 extracted references · 16 canonical work pages

[1]

6 I Can’t Believe It’s Not Better Workshop @ ICLR 2026 Microsoft

Model card. 6 I Can’t Believe It’s Not Better Workshop @ ICLR 2026 Microsoft. Phi-4.https://huggingface.co/microsoft/phi-4, 2024. Model card. Mistral AI. Mistral 7b v0.3. https://huggingface.co/mistralai/Mistral-7B-v0. 3, 2024. Model card. Chetan Naik, Arpit Gupta, Hancheng Ge, Lambert Mathias, and Ruhi Sarikaya. Contextual slot carryover for disparate sc...

work page arXiv 2026
[2]

Section Adiscusses additional related works in the domain of topic-switching, modeling and multi-turn interactions using LLMs not included in the main paper due to lack of space

work page
[3]

Section BandSection Cprovide details about the construction and use of datasets, experi- mental details about the evaluation, metrics, and setup

work page
[4]

Section Dprovides an additional discussion on the results, identifying the role of the dataset domain, identifying model-specific failures and offering recommendations for improving the capabilities of models for multi-turn interactions

work page
[5]

Section Ediscusses the current scope of this study and limitations that we recognize in the analysis

work page
[6]

virtual context

Section Fmentions the Ethics Statement, Reproducibility Statement, and Statement on LLM Usage for our research. A ADDITIONALRELATEDWORK Topic Shift Detection and Topic Segmentation.Hearst (1997) proposes a lexical cohesion method for detecting topic boundaries by establishing foundational segmentation signals, while Galley et al. (2003) extends segmentati...

work page 1997
[8]

Classify the CURRENT message as PIVOT (switching to a new topic) or REFINE (continuing on the same thread)

work page
[9]

PIVOT" or

Select which PRIOR message ids (ints) are relevant context to answer the CURRENT message. STRICT OUTPUT FORMAT RULES: - Return ONE JSON object only (no prose, no markdown, no code fences). - Use EXACTLY these keys: answer, predicted_label, relevant_context. - Valid predicted_label values must be one of these: "PIVOT" or "REFINE". - relevant_context must b...

work page
[10]

Respond to the user with an ANSWER (concise, <= 40 words)

work page
[11]

Classify whether the CURRENT user message persona is PIVOT (switching to a new topic and persona) or REFINE (continuing on the same thread and persona)

work page
[12]

PIVOT" or

Select which PRIOR message ids (ints) are relevant context and consistent persona with the CURRENT message. STRICT OUTPUT FORMAT RULES: - Return ONE JSON object only (no prose, no markdown, no code fences). - Use EXACTLY these keys: answer, predicted_label, relevant_context. - Valid predicted_label values must be one of these: "PIVOT" or "REFINE". - relev...

work page 2026
[13]

On PIVOT,do not carryprior turns

Instruction Tuning for boundary awareness.Extend SFT with samples that (i) lack lexical cues but require PIVOT and (ii) require empty carry on PIVOT. Short, explicit rubrics (“On PIVOT,do not carryprior turns”) help close the label–shortlist gap

work page
[14]

This schedule would help in improving robustness to late pivots

Late–pivot curriculum.To reduce position bias, oversample sessions with large refine–prefix upto P (heavy–tail over P ) and include near–pivothard negatives. This schedule would help in improving robustness to late pivots

work page
[15]

At the same time, keep a small held–out cue set to check over–fitting to specific phrases

Cue diversity and ablations.Mix cue/no–cue data (and paraphrastic cues) so the model learns pivots as a semantic property rather than a token separator. At the same time, keep a small held–out cue set to check over–fitting to specific phrases. Inference Time System design

work page
[16]

Similarly, including challenging examples of late-pivot detections can help reduce the degradation in performance, as context grows

Prompt Engineering with Boundary awareness.Chain-of-thought style instructions in the prompt along with a large number of few-shot in-context examples for challenging boundary cases can improve the inference time performance of the out-of-the-box models for both detection and context resetting tasks. Similarly, including challenging examples of late-pivot...

work page
[17]

boundary rationale

Two Stage Controller.We can modify the system to decompose the prediction problem into multiple steps. (a) Stage–1 pivot gate (binary).Train a compact classifier pθ(yt ∈ {PIVOT,REFINE} | ht) on top of hidden summaries ht (e.g., final token CLS for the current turn plus a pooled history embedding) to trade off pivot recall vs. false positives. (b) Stage–2 ...

work page 2026

[1] [1]

6 I Can’t Believe It’s Not Better Workshop @ ICLR 2026 Microsoft

Model card. 6 I Can’t Believe It’s Not Better Workshop @ ICLR 2026 Microsoft. Phi-4.https://huggingface.co/microsoft/phi-4, 2024. Model card. Mistral AI. Mistral 7b v0.3. https://huggingface.co/mistralai/Mistral-7B-v0. 3, 2024. Model card. Chetan Naik, Arpit Gupta, Hancheng Ge, Lambert Mathias, and Ruhi Sarikaya. Contextual slot carryover for disparate sc...

work page arXiv 2026

[2] [2]

Section Adiscusses additional related works in the domain of topic-switching, modeling and multi-turn interactions using LLMs not included in the main paper due to lack of space

work page

[3] [3]

Section BandSection Cprovide details about the construction and use of datasets, experi- mental details about the evaluation, metrics, and setup

work page

[4] [4]

Section Dprovides an additional discussion on the results, identifying the role of the dataset domain, identifying model-specific failures and offering recommendations for improving the capabilities of models for multi-turn interactions

work page

[5] [5]

Section Ediscusses the current scope of this study and limitations that we recognize in the analysis

work page

[6] [6]

virtual context

Section Fmentions the Ethics Statement, Reproducibility Statement, and Statement on LLM Usage for our research. A ADDITIONALRELATEDWORK Topic Shift Detection and Topic Segmentation.Hearst (1997) proposes a lexical cohesion method for detecting topic boundaries by establishing foundational segmentation signals, while Galley et al. (2003) extends segmentati...

work page 1997

[7] [8]

Classify the CURRENT message as PIVOT (switching to a new topic) or REFINE (continuing on the same thread)

work page

[8] [9]

PIVOT" or

Select which PRIOR message ids (ints) are relevant context to answer the CURRENT message. STRICT OUTPUT FORMAT RULES: - Return ONE JSON object only (no prose, no markdown, no code fences). - Use EXACTLY these keys: answer, predicted_label, relevant_context. - Valid predicted_label values must be one of these: "PIVOT" or "REFINE". - relevant_context must b...

work page

[9] [10]

Respond to the user with an ANSWER (concise, <= 40 words)

work page

[10] [11]

Classify whether the CURRENT user message persona is PIVOT (switching to a new topic and persona) or REFINE (continuing on the same thread and persona)

work page

[11] [12]

PIVOT" or

Select which PRIOR message ids (ints) are relevant context and consistent persona with the CURRENT message. STRICT OUTPUT FORMAT RULES: - Return ONE JSON object only (no prose, no markdown, no code fences). - Use EXACTLY these keys: answer, predicted_label, relevant_context. - Valid predicted_label values must be one of these: "PIVOT" or "REFINE". - relev...

work page 2026

[12] [13]

On PIVOT,do not carryprior turns

Instruction Tuning for boundary awareness.Extend SFT with samples that (i) lack lexical cues but require PIVOT and (ii) require empty carry on PIVOT. Short, explicit rubrics (“On PIVOT,do not carryprior turns”) help close the label–shortlist gap

work page

[13] [14]

This schedule would help in improving robustness to late pivots

Late–pivot curriculum.To reduce position bias, oversample sessions with large refine–prefix upto P (heavy–tail over P ) and include near–pivothard negatives. This schedule would help in improving robustness to late pivots

work page

[14] [15]

At the same time, keep a small held–out cue set to check over–fitting to specific phrases

Cue diversity and ablations.Mix cue/no–cue data (and paraphrastic cues) so the model learns pivots as a semantic property rather than a token separator. At the same time, keep a small held–out cue set to check over–fitting to specific phrases. Inference Time System design

work page

[15] [16]

Similarly, including challenging examples of late-pivot detections can help reduce the degradation in performance, as context grows

Prompt Engineering with Boundary awareness.Chain-of-thought style instructions in the prompt along with a large number of few-shot in-context examples for challenging boundary cases can improve the inference time performance of the out-of-the-box models for both detection and context resetting tasks. Similarly, including challenging examples of late-pivot...

work page

[16] [17]

boundary rationale

Two Stage Controller.We can modify the system to decompose the prediction problem into multiple steps. (a) Stage–1 pivot gate (binary).Train a compact classifier pθ(yt ∈ {PIVOT,REFINE} | ht) on top of hidden summaries ht (e.g., final token CLS for the current turn plus a pooled history embedding) to trade off pivot recall vs. false positives. (b) Stage–2 ...

work page 2026