STITCH: Simultaneous Thinking and Talking with Chunked Reasoning for Spoken Language Models
Authors: Cheng-Han Chiang
Xiaofei Wang
Linjie Li
Chung-Ching Lin
Kevin Lin
Shujie LIU
Zhendong Wang
Zhengyuan Yang
Hung-yi Lee
Lijuan Wang
Venue: ICLR 2026 Poster (2026)
Keywords: spoken language model
reasoning
chain-of-thought
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Spoken Language Models (SLMs) are designed to take speech inputs and produce
spoken responses. However, current SLMs lack the ability to perform an internal,
unspoken thinking process before responding. In contrast, humans typically engage
in complex mental reasoning internally, enabling them to communicate ideas clearly
and concisely. Thus, integrating an unspoken thought process into SLMs is highly
desirable. While naively generating a complete chain-of-thought (CoT) reasoning
before starting to talk can enable thinking for SLMs, this induces additional latency
for the speech response, as the CoT reasoning can be arbitrarily long. To solve
this issue, we propose STITCH, a novel generation method that alternates between
the generation of unspoken reasoning chunks and spoken response chunks. Since
the audio duration of a chunk of spoken response is much longer than the time to
generate the tokens in a chunk of spoken response, we use the remaining free time
to generate the unspoken reasoning tokens. When a chunk of audio is played to the
user, the model continues to generate the next unspoken reasoning chunk, achieving
simultaneous thinking and talking. Remarkably, STITCH matches the latency
of baselines that cannot generate unspoken CoT by design while outperforming
those baselines by 15% on math reasoning datasets; STITCH also performs equally
well on non-reasoning datasets as those baseline models.
Some animations and demonstrations are on the project page: https://d223302.github.io/STITCH.

Published as a conference paper at ICLR 2026
STITCH: SIMULTANEOUS THINKING AND TALKING WITH CHUNKED REASONING FOR SPOKEN LANGUAGE MODELS
Cheng-Han Chiangl.2
Xiaofei Wanga1 Linjie Li Zhengyuan Yang"
Chung-Ching Lin² Hung-yi Lee'
Kevin Lin²
Shujie Liu
Zhendong Wang"
Lijuan Wang"
National Taiwan University Miicossoft
ABSTRACT
Spoken Language Models (SLMs) are designed to take speech inputs and produce spoken responses. However, current SLMs lack the ability perform an internal, umspoken thinking process before responding. In contrast, humans typically engage in complex mental reasoning internally, enabling them to communicate ideas clearly and concisely. Thus, integrating an unspoken thought process into SLMs is highly desirable. While naively generating a complete chain-of-thouu of-thought (CoT) reasoning before starting to talk can enable thinking for SLMs, this induces additional latency for the speech response, as the CoT reasoning can be arbitrarily long. To solve this issue, we propose STITCH, a novel generation method that alternates between the generation of unspoken reasoning chunks and spoken response chunks. Since the audio duration of chunk of spoken response much longer than the time generate the tokens in a chunk of spoken response, we use the remaining free time generate the unspoken reasoning tokens. When chunk of audio is played to the user, the model continues to generate the next unspoken reasoning chunk, achieving simultaneous thinking and talking. Remarkably, STITCH matches the latency of baselines that cannot generate unspoken CoT by design while outperforming those baselines by 15% on math reasoning datasets; STITCH also performs equally well on non reasoning datasets as those baseline models. Some animations and demonstrations are on the project page: htttps://222330) wtt.:/33....ggthbb...... github..o ioo
STTTCH
INTRODUCTION
Spoken language models (SLMs) can take speech inputs and generate speech outputs (OpenAI 2024b Défossez al. 2024 Arora et al. 2025). SLMs generate speech by emitting some speech
tokens, discrete tokens that encode the information in speech (Défossez et al. 2023 Zhang et al. 2024). which will be converted into an audio waveform by speech decoder. Most SLMs are trained
from backbone text-based LLM (Défossez et al. 2024 Ding et al. [202);; we can obtain an SLM by teaching the backbone LLM to understand speech input and generate the speech tokens as output (Xiezhifei 2024 Fang et al. 2025a).
Teaching the backbone LLM to directly generate speech tokens is challenging, as the LLM is originally trained to predict text tokens, the speech tokens are completely new for the LLM backbone. To make SLMs better predict the speech tokens, current mainstream methods first generate the text tokens before generating the speech tokens (Chen et al., 2024; Zeng et al.) [2024] Nguyen jet al. 2025). By design, those text tokens and the speech tokens are aligned, in the sense that the text tokens correspond to the semantic content of the speech tokens.
While the aligned text-speech generation pipeline makes easier to generate the speech response, these SLMs directly generate the output text-speech tokens without any inner thinking process, though explicitly modeling such reasoning steps has been shown helpful in text generation tasks (OpenAl
Work done during an internship at Microsoft GenAL. dam1 0 77144 0cm1071440maill.... Coorespondence: xifi..i. wang@mi crosoft. com

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8 ends playing
starts playing
playing
First paokage latoney
User received speach timeline
3
8
Legends
Reasoning
Text
Sparch tokena
Speech
output
Recetve user Inout
tokens
The timing diagram during generation for STITCH-R. The model first generates the first N Neasoo CoT reasoning tokens, NNext text tokens, and Napasch speech tokens. Once the first N Nepeeh speech tokens generated, the speech decoder synthesizes the output audio that lasts chhun seconds. When the speech waveform is synthesized and played to the user, the SLM uses this time to generate the next N reasoning tokens, Niex text tokens, and Napssch speech tokens and synthesize the speech output. The duration Lehwnk is much longer than the time for generating the text tokens and the remaining time generate the reasoning tokens.
Figure 1:
speech tokens corresponding S₁, and
2024c Guo et al. 2025 Humans can perform complex internal reasoning before we speak: we disambiguate questions, recall facts, manipulate variables, and then verbalize only the polished answer. Intuitively, this unspoken internal thinking helps improve the accuracy and coherence of our spoken content. However, verbalizing these internal thoughts can make communication confusing and unnecessarily lengthy. Thus, is desirable to enable SLMs similar ability to think longer and silently before speaking out.
A naive way to do so is to teach SLMs to generate unspoken chain-offthoon of-thought (CoT) reasoning (Wei eet al., 2022) in text, and then generate the spoken responses based on the CoT reasoning. In this way, can benefit from the COT reasoning without making the spoken response overly long. While this does improve the speech output quality, as validated by our experiments in Section [5] the speech responses need to wait until the full text CoT is generated, creating an uncontrollable latency for the speech output.
To reduce and remove this latency, we propose STITCH: Simultancous Thinking and Talking with Chunked Reasoning. STITCH alternately generates unspoken reasoning chunks and spoken response chunks, allowing the spoken responses to condition on some thinking process. Since the audio duration of chunk of spoken response is much longer than the time generate the tokens in chunk of spoken response, we use the remaining free time to generate the unspoken reasoning tokens. When chunk of audio is played to the user, STITCH keeps generating the next unspoken reasoning chunk, achieving simultaneous thinking and talking. STITCH-S, a variant STITCH, has the same latency as the models that cannot generate unspoken reasoning, while significantly outperforming those baselines on math reasoning datasets.
We summarize contributions follows:
We propose STITCH, an SLM generation pipeline that outputs unspoken reasoning chunks and spoken response chunks alternately. To the best of our knowledge, we the first introduce the unspoken thinking process to SLMs.
STITCH-S, while improving the generation with unspoken reasoning, has latency the same as an SLM that does not generate text reasoning by design. 3. On five math QA datasets, STITCH consistently outperforms the baseline models that cannot reason with text COT.
2 RELATED WORK: SPOKEN LANGUAGE MODELS
Our paper improves SLMs by generating text reasoning before generating the speech output. Current SLMs first generate some speech tokens, and the speech tokens will be synthesized into a waveform by the speech decoder. In this section, we recap how existing SLMs generate speech tokens. The mainstream SLMSs nowadays adopt two types of speech generation methods: (1) Interleaved text-
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speech tokens (Zeg al. 2024 Li et al. 2025b) and the (2) Thinker-Talker architecture (Xu et al. 2025
Fang et al. 2025b).
In the interleaved decoding paradigm. the SLM backbone model generates chunk of text tokens and a chunk of speech tokens alternately. The text tokens serve as guidance for future speech tokens
(Zeng et al., 2024) interleaves between generating Mext = 13 text tokens and N Nspeech = 26 speech tokens. After a chunk of speech tokens is generated, is immediately synthesized into audio by the speech decoder and streamed to the user, enabling low latency and real-time interaction. A figurative illustration of this output format shown in Figure aa). When concatenating the chunk of text tokens, they should correspond to the transcription of the speech tokens. The ratio of the text tokens and speech tokens is carefully selected such that the text tokens are always faster than the speech tokens to ensure that the content of the speech tokens has already appeared in previous text tokens. Once all the text tokens are generated, the model will continue to generate the remaining speech tokens.
by transcribing what the speech token will say. For example, GLM-4-
The thinker-talker SLM uses thinker model to take speech input and generate text tokens, and uses a talker model to convert the text tokens and/or their continuous representations into speech tokens (Xu et al., 2025 Fang et al. 2025b). The talker model functions like a Text-to-Speech (TTS) model that converts the textual output from the thinker into speech tokens, and the output by the thinker and talker will be highly aligned in their semantics, while there may be slight differences.
The text-speech interleaved SLMs and thinker-talker SLMs differ in their architecture, but they share similarity: they generate text and then the speech, and the text is highly aligned with the speech. In this paper, we focus on enabling SLMs to generate an unspoken thinking process while speaking.
While reasoning, also sometimes referred to thinking, is widely explored in text-only LLMs (Ope) nAI, 2024c Wu et al. 2024 Guo et al. 2025 Hong et al. (025), reasoning is only included in audio LLMs recently (Wen et al.
2025a Xie et al. (025). Audio LLMS multimodal LLMs that can take audio and speech as input, but they cannot generate speech outputs (Changli al. 2023 Chu et al. 2024) Xu al. 2025 Lu al.) 2025); this marks significant difference from
2025 Li et al.
SLMS studied in our paper. Currently, no prior work introduces unspoken reasoning ability to SLMs.
3 METHOD: SIMULTANBOUS THINKING AND TALKING WITH CHUNKED REASONING
While current SL.Ss also include some textual inner monologue (Dérossez et al. 2024 or thinking (Xu et al., 2025) (as in thinker-talker) before generating the speech tokens, these text tokens simply transcribe what the model will say next. In other words, they do not generate unspoken reasoning before speaking. In this paper, we aim improve the SLMs by generating some unspoken text reasoning before generating the speech responses (including the text and speech tokens).
Our proposed concept can be applied to both the interleaved and thinker-talker SLMs, but we adopt the interleaved SLMs in this paper for the following three reasons: (1) The interleaved SLM only contains a single backhone model to generate the text and speech tokens, simplifying the model architecture. (2) Wu et al. (2025) show that the thinker-talker SL.M requires careful tuning to make it properly generate well rell-aligned text and speech output, making thinker-talke harder to fine-tune. (3) Qwen-2.5-Omni, the most widely used thinker-talker SLM, does not publicly release the speech tokenizer, adding difficulties to fine-tune the talker model (Liu 2025
Shin 2025).
To unify the wording, we will use reasoning tokens to refer to the unspoken reasoning (in text), text tokens to refer to the original text tokens of the interleaved SLMs, and speech tokens to refer to the speech tokens. Text tokens and speech tokens are the output of interleaved SLMs from prior works (Zeng et al. 2024), and the reasoning tokens are introduced in this paper. Both text tokens and reasoning tokens are texts, while they differ in their function: reasoning tokens are for the unspoken thinking process, and text tokens are the transcriptions of the spoken output.
3.1 A TRIVIAL METHOD: THINK BEFORE SPEAKING IN SPEECH (TBS)
Before introducing STITCH, we start from a trivial baseline method that introduces the thinking process to SLMs: Thinking in text Before Speaking in speech (TBS). Introducing TBS can help us
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(al GLM-4-Voice
Speech
Speech
Speech
(b) TBS
Ressiniin
Tood
Speech
Texi
Speach
Text
Spaech
STITCH-R
Reasoning
Spaech
Roasoring
Reasoning
Speech
(d) STITCHS
Speech
Reasoning
Specch
Reasoning
Speech
Figure 2: Different generation method explored in this paper. The arrow represents the timeline for the SLM to generate the tokens; this timeline should not be confused with the timeline that the end user receives the audio, i.e., the upper timeline in Figure We plot tokens of the same type in a chunk using the same color. (a) GLM-4-Voice: Interleaving between text and speech token chunks (Section] 2 This is the design of the original interleaved SLMs. (b) TBS: Generating complete reasoning span and then interleaving between text and speech token chunks (Section (c) STITCH-R: Alternating between reasoning token chunks, text token chunks, and speech token chunks (Section 3.2). (d) STITCH-S: Alternating between text token chunks, speech token chunks, and reasoning token chunks (Section 3.3
understand the motivation and design of STITCH later. In TBS, given a user query in speech, the model first generates complete trace of reasoning span. A complete reasoning span is enclosed by two special tokens [SOR] and [EOR]. denoting the start and end of the reasoning span. The [EOR] token lets the model know that the reasoning has finished and it needs to start generating the tokens for the spoken response, including the text tokens and the speech tokens. The text tokens and the speech tokens are generated in an interleaved manner, following the original design of interleaved SLMs, with Nix and Nspeech alternating with each other.
To teach SI.Ms to operate TBS, we construct the training data DrBS where each training instance has the form (x, 2, y), x is the speech token sequence of the user input, is the reasoning token sequence, and y = [t o $ o t2 o S2, .. is the token sequence for the speech output that interleaves between Neer text tokens (t,) and Nopessh tokens (s,) (The last text token chunk may be less than Nua while the last speech token span can have more than Nppecch tokens); o denotes concatenating two token sequences. We defer how construct Dras from existing datasets until Section 4.1 A figurative illustration of the target output for TBS is in Figure bb).
During training, given an input x, train the SLM to predict the reasoning tokens and then the interleaved text-speech tokens y using the standard cross-entropy loss.
3.2 STITCH-R: R: SIMULTANEOUS THINKING AND TALKING WITH REASONING FIRST
While inserting text reasoning before the speech response in TBS improves the quality of the speech response, the speech response only be generated after the full reasoning has been generated. As later shown in Table 2 the reasoning by TBS can be long, creating significant latency before generating the speech response. To resolve the latency issue, draw inspiration from the fact that humans can think while speaking and propose STITCH-R.
STITCH-R realizes this "thinking when speaking" by alternating fixed-length (Nirssoo) partial reason- ing spans, fixed-length (Niex) text token spans, and fixed-length (Napesch) speech token spans. The partial reasoning spans are for inner thinking, while the text and speech token spans are for the spoken response. Stitching the partial reasoning spans together will form complete CoT reasoning. A figurative illustration of the output of STITCH-R is shown in Figure Ccc. and some samples generated by STITCH-R are shown in Table 5 in the Appendix. The "R" in STITCH-R stands for "reasoning first" since it generates a partial reasoning chunk before speaking: this used to distinguish the "speaking first" STITCH-S that will be introduced in Section 3.3
We explain how simultaneous thinking and speaking is achieved by STITCH-R, which can be most easily understood with Figure I After the SLM receives the speech input, it first generates Nrenson reasoning tokens, Niss text tokens, and Neseech speech tokens for the first audio output chunk St. Those speech tokens will be passed through the speech decoder to synthesize the audio with duration fehunk seconds and played to the user. When synthesizing the speech waveform and playing the audio for S1, the SLM continues to generate the next Nreason reasoning tokens; this is where simultaneous
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thinking and speaking happens. After generating the second reasoning chunk, the model continues to generate Nix text tokens, and Napeehh speech tokens for the next audio chunk S2. As long as the time to generate Neesson + Niex + NNspecch tokens, denoted as token is less than chhunk. the next audio chunk S2 can be played seamlessly after the previous audio chunk Si-
Let's understand with some concrete numbers. On an A A100-80G GPU, our model generate around 80 tokens per second using vLLM (Kwon et al., (023), , and the duration of the audio for speech = 26 speech tokens is around fchunk seconds. The number of text tokens and speech tokens sums to Niet + N -NSpeech = 13 + 26 39, so
seconds, so we can generate around 160 tokens in bchunk
we have time to generate at most 80 x 2 - 39 = 121 reasoning tokens]] [
Each partial reasoning chunk is enclosed in two special tokens: [SOPR] (start of partial reasoning) and [EOPR] (end of partial reasoning). If the reasoning chunk is fully generated, an additional [EOR] is used to denote the end of the full reasoning CO
To construct the training data for STITCH-R, R, we simply split the full reasoning CoT z in DiBS into chunks with Nerasso tokens {z1, - }. where each z, except the last chunk has Neeason tokens. Next, we interleave those chunks with the interleaved text-speech token sequence to create the interleaved data of the form [z] øt 081 oZ2 o ttg 082 o...). ). as shown in Figure
[t1 0s o tt S2. ...]
If the number of the reasoning span is more than the number of text spans, this indicates that the reasoning token spans think slower than the text token spans, so we remove the sample from the training data The model is fine-tuned to auto-regressively predict the interleaved reasoning-rext-speee token spans using standard language modeling cross- ennsopy loss.
3.3 STITCH-S: SIMULTANEOUS THINKING AND TALKING WITH SPEAKING FIRST
STITCH-R can have a latency shorter than that of TBS, since STITCH-R only needs to wait at most Nreason tokens before generating the text and speech tokens. However, compared with the original text-speech interleaved SLMs that directly generate the text and speech tokens for the spoken response, STITCH-R still needs to wait for Nieason reasoning tokens.
To fully remove the latency of waiting for the first partial reasoning span, we propose an alternative generative pipeline that directly starts to generate the text and speech token chunks and then generates the first reasoning chunk; the model continues to interleave this generation pattern. We call this STITCH-S since it generates a speech response first, and an illustrative figure is shown in Figure dd.
To construct the training data for STITCH-S, we similarly split the full reasoning z in DrBs into chunks with Nressn tokens, and insert a text token chunk of length NEs and a speech token chunk of length Napeoch before each reasoning span. Following previous notation, the target output for STITCH-S looks like [t| o Sj 1 o t2 o S2 o Z2 o t3 o $3, .. a training instance's 's number of reasoning chunks is more than the number of text chunks, the above data construction pipeline will result an instance that has a reasoning span after the text chunks are finished, which does not make sense since the thinking process hasn't 't ended while the content to be spoken is finished. We remove this kind of sample from the training dataset.
4 EXPERIMENT SETUP
4.1 TRAINING
We fine-tune a separate model for TBS, STITCH-R, and STITCH-S from GLMM---Vice-999 (Zeng
al. 2024). 9. During fine-tuning, we fully fine-tune the backbone transformer model (Vaswani et al.
2017)p00 of GLM-4- and freeze the speech encoder (Zeng et al. 2025)) and speech decoder (Du
ct al. 2024 Kong et al. 2020). The detailed hyperparameters for fine-tuning are listed in Appendix
We include three types of tasks in the training data: general dialogue dataset (Voice tant400K (Xiezhifei] 2024)), mathematical reasoning (Tulu-3-Persona Math, Mulu----Prrona-Mare
Since the taken generation and audio synthesis can be in parallel, we do not need to consider fyns the time to synthesize the audio, as long as byn < febunk-
2The number of text spans is always less than equal to the number of speech spans by design. This can be done by properly selecting the ratio hetween Nest and N Nepech-
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Grade, and answering (Natural Question Kwiatkowski et al., 2019) and TriviaQA (Joshi et al. 2017))
(Lambert al., 2024)), and chowledge-intensive question
We introduce how to construct Drss. the training data for TBS, while the training data for STITCH-R
and STITCH-S can be constructed from DTBS following the procedures in Section 3.2 and 3.3
To train TBS, each sample in the dataset needs have the spoken query x, text reasoning z, and the spoken responses y. For VoistAssista which is already a speech-to seeech-to-specch dialogue dataset, we generate the text reasoning using GPT-4o. For mathematical reasoning datasets, which only contain a question and its long answer in text, we synthesize the question audio x using GPT- 4o-mini-TTS (OpenAT 2024a), use the ground truth long answer the text CoT z, and rewrite the long answer and synthesize its audio to form the spoken response y. For mhowledge-intensiig QA datasets, which are also in text form, we synthesize the question audio x using GPT-4o-mini-TTS generate the text CoT with GPT-4o, and rewrite the answer and synthesize its audio to form the spoken response y. All the prompts used for constructing the datasets are in Appendix B.I The full training dataset contains around 400K instances.
4.2 EVALUATION
We include reasoning and non-reasoning datasets for evaluation. We use the former to evalu- whether reasoning before responding in speech can improve the spoken answer quality for reasoning tasks. We include five math reasoning datasets: AddSub (Hosseini et al. 2014). Sin-
glEq (Koncel-Kedziorski et al.) 2013), MultiArith (Roy & Roth, 2015). SVAMP (Patel et al. 202T). and GSM8K (Cobbe et al. 2021)) which are commonly used to evaluate the math reasoning ability of LLMs (Wei et al. 2022; Kojima et al. 2022 Wang al., 2023). The questions and answers in these datasets are not too difficult and can be described clearly in speech.Since these datasets are originally in textual form, we synthesize the question audio using -PTT--o-mii-TTS.
For non-reasoning datasets, tions (Nachmani et al.] 2024). TriviaQA (Joshi et al., 2017). and WebQuestions (Berant et al. 2013)) and AlpacaEval (Li al. 2023), dialogue dataset, from OpenAudioBench (Li et al.) (025b). We use these datasets to understand whether generating unspoken reasoning before responding in speech has any impact on non-reasoning datasets. The dataset statisties are in Appendix Table
include three
intensiiee QA datasets (Llama Ques-
For all reasoning datasets and knowledge-intensive QA datasets, we evaluate the SLM's performance using the accuracy. To calculate the accuracy, we GPT-4o judge whether the SL.'ss prediction matches the ground truth answer. For Avaa, we follow the setting in OpenAudioBent to use LLM-as-a-judge (Chiang Lee 2023 Liu 2023 Zheng et 2023) to rate the response from 1 to 10, and report the GPT-4o-score scaled to 10 and 100, where higher is better.
When evaluating the responses from SLMs, use the text tokens for evaluation. Alternatively, we can synthesize the audio from the speech tokens, transcribe the audio, and evaluate those transcriptions. However, since the SLM's text tokens are designed to align with the speech output, we only report the performance of the text tokens here. The general trend observed in the following experiment results holds when we evaluate the transcription, while the latter has a slight performance drop of around 3% to 6%, as shown in Table 7 in the Appendix.
4.3 COMPARED MODELS
We compare six models, which are based on the same base model: GLM-4- an interleaved SLM. GLM-4- 4-iee has an Nies 13 and Napwech 26. All the models we use follow the Nie and Napech in GL--4-
(1) GLM-4-Voice: The model takes speech input and generates interleaved text-speech tokens.
This model cannot generate unspoken reasoning by design. (2) No reasoning: We fine-tune
GLM-4-Voice on a dataset without reasoning, Dno-reason- Each instance in Dno-renson has the form
(x, y), which is constructed from DTBS by removing the text reasoning
The model is trained to predict interleaved text-speech tokens y based on the input x. (3) TBS: see Section 3.I (4)
Mix reasoning: We mix half of Dao-reason and half of Dres fine-tune GLM-4-Voice. We use a system prompt to control whether the model should generate reasoning before generating the interleaved text-speech tokens. This allows us control the model's behavior during inference.

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(a) The accuracy math QA.
Use Reasoning
Datasets SinglEq
Config
Latency
Avg.
Train
Infer.
AddSub
MultiArith
SVAMP
GSMSK
Baselines
GLM-4-Voice
Nist + Nipnech
x
59.42
62.00
71.00
44.00
29.00
53.08
No reasoning
Nissi + Nageech
x 66.06
70.69
77.98
64.43
35.73
62.98
Mix reasoning
Nicar + Nopooch
V
x
69.72
79.31
87.16
63.55
37.33
67.41
Full Reasoning before Speaking
TBS Mix reasoning
NEn + Niaa + Nopeech
79.82
85.62 63.63
89.91
75.29
64.94
79.12
Nua + Nosa + Nopoech
76.15
89.08
88.99
70.90
62.23
77.47
Proposed Methods: Simultaneous Thinking Speaking
STITCH-R
Neason
Nes
Nepooch
78.90
88.51
93.58
73.83
58.70
78.70
STITCH-S
Nica + Nopooch
81.65
87.93
91.74
72.15
56.72
78.04
(b) The performance on non-reasoning tasks. For factual QA, including Llama Question, TriviaQA, and WebQuestion, we report the accuracy. For AlpacaEval, we report the GPT-4o score.
Use Reasoning
Datasets
ld.
Config
Avg.
Train
Infer.
Llama Question
TriviaQA
WebQuestion
AlpacaEval
Inference without Reasoning
1
GLM-4-Voice
X
74.25
47.10
50.95
48.59
55,22
2
No reasoning
x
X
73.33
46.00
47.80
53.62
55.19
3
Mix reasoning
V+X
X
73.33
47.80
48.00
54.77
55.98
Full Reasoning before Speaking
TBS Mix reasoning
74.33
$1.50
$2.15
56.33
58.58
V+X
69.67
48.80
50.75
56.03
56.31
Proposed Methods: Simultaneous Thinking and Speaking
6 STITCH-R
70.00
49.60
50.25
$4.02
55.97
7
STITCH-S
73.33
50.00
50.15
54.62
57.03
Table 1: Performance reasoning dataset (1)) and non-reasoning datasets 1b). The latency column reports how many tokens the SLM should generate until the first chunk of speech can be synthesized, where Num denotes the uncontrollable length of the full reasoning. The "use reasoning" columns denote whether text reasoning z is in the training data or is generated during inference.
By comparing the performance of this model when inference with and without reasoning, understand unspoken reasoning really helps SLM's speech output. (5) STITCH-R: see Section
3.2
(6) STITCH-S: see Section 3.3
During training, we set Nreason 100 for STITCH-R and STITCH-S. In Section 3.2 we have derived that the length of the reasoning token can be at most 121 tokens when running so we select Niesson = 100. We do not compare with other SLMs as they use different training data and have distinct model architectures, making the comparison with our models less meaningful.
100-80G GPU,
MAIN RESULTS
We present the main results in Table We summarize several key observations as follows:
TBS significantly outperforms the baselines without reasoning on math QA. In row 4, TBS achieves an average accuracy of 79. 12%, outperforning GLM-4-Voice (row 1) by 26.04% and the "no reasoning" model (row 2) by 16. 14%. Most significantly, the accuracy of TBS almost doubles compared to the two baselines without reasoning on GSM8K. Using McNemar's 's test, we found that the TBS outperforms Voice and the no-reasoning baselines in statistically significant way, with p-vuuue less than 0.05. This shows that reasoning before speaking can help the SLM respond better to questions that require reasoning.
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Token Count
Quality Assesment
Datasels Metrics
MultiArith Reason
GSM8K
TriviaQA
MultiArith
GSM8K
MultiArith GSM8K
Trans
Reason
Trans
Reason
Trans
UTMOSv2
GPT-4o-score
GI Voice
66.17
92.38
51.67
3.10
3.05
4.60
4.28
No reasoning
45.67
60.22
28.64
3.15
3.13
4.91
4.49
TBS
178.95
45.09
360.04
70.49
68.03
27.55
3.14
3.16
4.90
4.78
STITCH-R
157.32
49.77
322.40
74.36
67.56
27.65
3.10
3.12
4.94
4.74
STITCH-S
203.28
80.87
334.71
61.85
69.45
28.21
3.17
3.11
4.88
4.66
Table Left: The average number of total reasoning tokens (summed over partial reasoning spans for STITCH-R and STITCII-S) and the text tokens. Right: The quality scores of the speech responses.
Reasoning before speaking is better than reasoning before speaking, even if the training data is the same. The strong result of TBS, compared with the "no reasoning" baseline, may raise a question on whether TBS is winning because the training data includes additional reasoning z, which are not included in the training data for the "no reasoning" baseline. This question can be answered by comparing the results the "mix reasoning" model with and without text reasoning before generating the speech response (row 3 vs. row 5). Since the two different inference modes use the same mix reasoning model, the performance difference can be attributed to the reasoning before the spoken response during inference. On average, reasoning before speaking (row 5) obtains an accuracy of 77.47%, outperforming not using reasoning by more than 10% (row 3). For the most difficult GSM8K, the improvement of using reasoning is 24.9%. The results again confirm the importance of generating reasoning before the speech response for reasoning tasks.
STITCH-R (row 6) shows almost performance drop average. STITCH-R alternates between partial text reasoning and text-speech tokens. While the reasoning is split by text-speech tokens, the average performance only drops by 0.42% compared TBS, In Table we report the average number of reasoning tokens on three testing datasets. Both TBS and STITCH-R generate 150 to 300 reasoning tokens for math reasoning datasets on average, but STITCH-R only needs to wait for the first Ntoken = 100 reasoning tokens before starting to generate the first chunk of the speech output, yielding a smaller latency for STITCH-R. Importantly, this speedup comes with almost no loss in accuracy. Still, on more difficult dataset (GSM8K), the performance drop is larger, but it still significantly outperforms the non-reasoning baselines (row and row 2). Again, using McNemar's test, find that the STITCH-R outperforms the no-reasoning baselines in a statistically significant way. We discuss two samples from STITCH-R R in Section 6
STITCH-S shows almost no performance drop on average without adding any inference latency. Remarkably, STITCH-S shows an average accuracy of 78.04% (row 7), slightly lower than TBS by 1.08% (row 4), but is still much better than the "no reasoning" baseline by 15.06% (row 2). We see a larger performance drop in GSM8K around 8% compared with TBS, but again, still significantly outperforms no reasoning baseline by 19.99%, again in statistically significant way based McNemar's 's test. Recall that the first packet latency of STITCH-S is exactly the same as the baselines that do not generate text reasoning; both of them only need wait for Nixt + NNscech tokens before synthesizing the speech waveform. This exciting result indicates that we can improve the answer quality without sacrificing any inference latency. By scrutinizing the responses from STITCH-S, we find that the first text token chunk is often a rephrase of the question, which can be generated without any reasoning. This explains why STITCH-S does not show a severe accuracy drop compared with S STITCH-R.
On non-reasoning datasets, reasoning models perform comparably well or slightly outperform non-reasoning baselines. Table Ib shows the performance on non-reasoning datasets. We find that TBS (row 4). STITCH-R R (row 6), and STITCH-S (row 7) all have performance comparable to or better than the GLM-4- Voice (row 1) and the reasoning baseline (row 2). This shows that fine-tuning SLMs to think in text before responding does not harm the performance on non-reat non-reasoning datasets.
Generating text reasoning before generating the speech responses does not harm the perceptual quality and the fluency of the speech response. In the rightmost block in Table we report some quality assessment of the responses generated by the SLMs. We report (1) the perceptual quality
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of the speech response evaluated by UTMOSv2 (Baba al. 2024), an automatic MOS evaluation model, and (2) the fluency score of the text tokens judged by GPT-4o (Chiang & Lee 2023). 3. The fluency score focuses whether the text tokens form fluent narrative. Both metrics range from to 5, with 5 being the best. We observe that all fine-tuned models have a similar UTMOSv2 score over 3, similar the original GL.M---Voice indicating that our fine-tuning does not harm the perceptual quality of the speech output. We also observe that the fluency scores for STITCH-R and STITCH-S are high, indicating that alternating between the text reasoning token spans and the text-speech token spans does not harm the fluency of the text tokens.
Human evaluation shows that STITCH-S is more responsive than STITCH-R, followed by TBS. We conduct human evaluation on the responsiveness of different models. We present the human evaluator with a question from the user and two audio responses from different models, asking them to compare the responsiveness of the two models on seven-poim scale, where +3 indicates that model A is more responsive than model B and -3 indicates that model B is more responsive than model A. To simulate the first packet latency that the user may experience, each audio includes silence in the beginning, accounting for the prefilling and decoding time of the SLM and the speech synthesis latency. We compare the following pairs: TBS/ / STITCH-R, TBS / STITCH-S, STITCH-R STITCH-S, and STITCH no-reasoning. For each pair, we use the same 100 questions from GSM8K, and we recruit 3 human evaluators judge the responsiveness of a pair of models. The results presented in Table 6 in the Appendix, and we summarize the results as follows: 1. STICH-R and STITCH-S are both more responsive than TBS. 2. STITCH-S is more responsive than STITCH-R. 3. STITCH-S and no-reasoning are equally responsive.
The reasoning segments finish much earlier than the text segments. Based on the statistics in Table 2 we can see that the average number of reasoning chunks are less than the number of text chunks. For example, on GSM8K, STITCH-R has on average 322.40/100 while the average number of text chunks is 74.36/15 113 = 5.72. During inference time, we do not find the interleaved text-speech chunks to end before the reasoning chunks finish, i.e., before the FOR is emitted. This is mainly because when we construct the training data for STTTCH-R and STTTCH, we remove the samples whose reasoning chunks are more than the text chunks, i.c., those samples that have reasoning slower than the response. As result, after training, the model will learn that it must finish the reasoning before concluding its spoken response.
3.22 reasoning chunks,
6 QUALITATIVE RESULTS
In this section, we provide two outputs from our STITCH-R in Table a sample for STITCH-S in Table 8 in the Appendix. The top sample in Table 5 shows a typical pattern of the output from STITCH-R, where all the calculation results from the text token chunks are already calculated in previous reasoning chunks. When generating the text tokens, the model simply needs to rewrite the lengthy reasoning into more concise sentences that are easier for spoken communication.
The bottom sample in Table shows somewhat rare pattern, where the second text token chunk includes a new calculation result (the number 120) that has not been explicitly calculated in previous reasoning chunks. This shows that, besides thinking during the reasoning chunks, the model some- times also incorporates the text tokens for thinking. However, this pattern is rare: We use GPT-4o to parse the 1310 responses to GSM8K and find that only 5% of the samples illustrate this pattern. The same pattern is also rare on STITCH-S.
Last, find that the first text token chunk tends to rephrase the information of the question without adding new calculation results, which can be seen in the two samples in Table This shows that the first text token chunk can be generated without relying on additional reasoning. This can explain why STITCH-S S can have a performance similar to TBS and STITCH-R. R: since the first text token chunk can be generated without thinking, we can directly generate those texts without conditioning on any reasoning.
ADJUSTING THE LENGTH OF THE REASONING CHUNK DURING INFERENCE
During training, we fix Niskens the number of tokens in each partial reasoning chunk, to 100 tokens. In this section, ask whether can adjust N Nooenn at test time without retraining the model.

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One might want to adjust Moken at test time if the hardware is slower and the time to generate Ntoken + -Nex + Napesch tokens may exceed the duration fchunk of the audio for Nspeech tokens. In this case, the model deployer may want to reduce Nooeen during inference time to ensure that generating the Loken + Nee + Nopeech does not exceed the duration lehunk- Thus, it would be interesting to see if we can train single model with fixed Nooken and vary N token during inference.
Recall that each reasoning chunk ends with [EOPR]. To adjust the length of the reasoning chunk to Nioken S Ntoken. during the inference time, we can append [EOPR] after Nokenn reasoning tokens are generated. Once the model sees [EOPR], it will cease generating the reasoning tokens and switch to generating text-speech interleaved tokens. We intervene in the model's generation by the above process whenever the model generates reasoning chunk until the [EOR] token is generated.
We vary the number of N Noken from 60 100 with a 10 increment on the math reasoning datasets for STITCH-R R and STITCH-S. The results are shown in Appendix Figure Ba and 3b, we summarize the main findings below. We see that the accuracy on all tasks mostly rises when increasing N token from 60 to 100. While the performance of Moken < 100 is mostly worse than the performance of Ntoken = 100, using N No < 100 is still better than the "no reasoning" baseline when N Nisken 2 70. We also observe that when Ny. token 2 80 tokens, the accuracy recovers to 90% of the accuracy of Ntoken = 100. The results indicate that the model deployer can control the number of N token in a reasonable range during inference without retraining the model with some accuracy trade-off.
8 USING REASONING FROM OTHER MODELS
Previously, we showed that STITCH-R R and STITCH-S can generate reasoning before responding in speech. Here, we discuss whether we can improve the speech response by using the reasoning from other models. The motivation is that sometimes the reasoning generated by the SLM itself is not good enough or takes too long to generate, and we may use other stronger or smaller models to generate the reasoning. We call the model used to generate the reasoning the "reasoning augmentation model".
We discuss how to improve STITCH-R by using a reasoning augmentation model. Given a user input speech, we first use an ASR model to transcribe the audio and feed the transcription into the reasoning augmentation model to generate a response, which will be used as the reasoning for STITCH- R. Next, crop the reasoning into chunks of N token enclosed by [SCPR] and [ECPR], and then feed the speech input and the first reasoning chunk to STITCH-R and generate the first text-speech interleaved token span. After the first text-speech interleaved tokens generated, append the second reasoning chunk to the input and generate the second text-speech interlcaved token spans. We continue the above process to generate the full response.
In the following experiment, we replace the text reasoning of STITCHIR using four models; GLM- 4-9B-Chat (GLM et al. 2024). Llama-3.1-8) 1-BB, Llama-3.2-3B, and Llama-3.2-1B (Grtttatiori et al.] 2024). The results are shown in Figure Sc the Appendix, and we summarize the main findings below. In general, stronger reasoning augmentation models (GLM-4-9B-Chi and larger Llama models) can improve the accuracy of the spoken response, while weaker reasoning augmentation model like Llama-3.2-IB worsens the performance of STITCH-R. This shows that STITCH-R can incorporate the reasoning ability of stronger models to improve the spoken answer. The experiment also shows that STITCH-R really considers the contents in the reasoning to form the spoken response, instead of independently generating the reasoning and not using that reasoning in the spoken responses.
CONCLUSION
In this paper, we introduce STITCH, a generation framework for SLM that enables simultancous thinking and speaking. We achieve simultaneous thinking and speaking by using the long duration of an audio chunk to generate additional reasoning tokens before generating the speech tokens for the next audio chunk. By design, STITCHIS induces no additional latency compared to the models that cannot generate reasoning before they speak. On five math reasoning QA datasets, we show that STITCH-R and STITCH-S significantly improve the accuracy over reasoning baselines by over 15%. On non-reasoning datasets, STITCH-R R and STITCH-S perform comparably well with non-reasoning baselines. Overall, the low latency and high performance of STITCH show a promising future direction for spoken language models.
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10 ETHICS STATEMENT
Our paper fully follows the ICLR Code of Ethics. We do not see any potential harm in the paper.
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Lianmin Zheng, Wei-Lin Chiang. Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang. Zi Lin, Zhuohan Li, Dacheng Li. Eric Xing, et al. Judging Ilm- llm-as-a-juee as-a-judge with mt- at-bench and chatbot arema. Advances in neural information processing systems. 36:46595 46623, 2023.
Yaowei Zheng, Richong Zhang, Junhao Zhang, Yanhan Ye, and Zheyan Luo. LlamaFactory: Unified efficient fine-tuning of 100+ language models. In Yixin Cao, Yang Feng, and Deyi Xiong (eds.). Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pp. 400-410, Bangkok, Thailand, August 2024. Association for Computational Linguistics. doi: 10.18653/v 1/201//acl-eemeemeeee https://eeLanttooooyy.oo///////2
URL
A CHANGE LOG
The following change log is included to help readers understand the modifications made throughout the author-reviewer discussion period.
Sep. 24th The initial submission version.
Nov. 19th First revision based on the initial reviews from the reviewer. The modifications are highlighted in blue. The main differences are summarized as follows:
1. Explain the difficulty of fine-tuning 2.5mmm.5- In Section add sentence to explain why we do not fine-tune Qwen-2.5-Onee
2. Statistical tests in Section 5 We run McNemar's test to see the performance between no-reasoning baselines and other models is statistically significant and report the results in Section
3. Explaining that reasoning always finishes before text-speech segments end: In Section 5 we include paragraph explaining that the reasoning segments generated by STITCH will always be less than the text/speech segments.
4. Human evaluation in Section 5) We add a paragraph Section 5 where we run a human evaluation comparing the responsiveness of different pairs of models.
5. Explain when to use STITCH-R/STITCH.S: Based on all the previous experiment results,
in the last paragraph in Section we explain when to use STITCH-R and STITCH-S.
6. Qualitative results moved to Section 6 We move the qualitative results into the main content, where we illustrate how the reasoning of STITCH-R and STITCH-S precedes the text responses most of the time.
7. Add Figure 5
We add Figure 5 to show that different tayn will not break the thinking while speaking pattern of STITCH as long as taya tchunk.
Feb. 8th Camera-ready version. Minor edits for typos and add links for datasets and demos. We remove the summary in Section 5 due to space constraints. The exact contents follows: When choosing between STITCH-R and STITCH-S, if latency is the main consideration, choose STITCH-R R: if a small latency is acceptable while the task accuracy is more important, choose SITTCH-R.
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g gm 2b-chatt
Usmm intturt
Ad:5.bb
Ad:5.a
Nu lie th
Singlea
SingEe
SWAMP
SHAMP
GSN5K
GSN5X
(a)
(b)
(c)
3b show the accuracy when varying Noken for STITCH-R R and STITCH-S. S, respectively, and the dots in the figure are the performance of the "no reasoning" baseline (Section 7 Figure shows the performance when using a reasoning augmentation model to generate the text reasoning for STITCH-R (Section & the accuracy is averaged over five math reasoning datasets.
Figure 3: Figure 3a and
B DATASET
B. TRAINING DATASET
We use three types of training data: dialogue datasets, math reasoning datasets, and knowledge- intensive QA datasets. The sizes of the datasets are in Table We explain how construct training datasets from those existing datasets as follows.
Dialogue Datasets VoiceAAssistan4000K (Xiezhfeii 2024) is spoken dialogue dataset built from text dialogue dataset, including OpenAssistant (Köpf et al. 2023) and HH-RLHF (Bai et al. 2022). We subsample the dataset by filtering out (1) the instances whose 'round attribute is None or O, (2) the instances from the subset identity and (3) the instances whose response audio is less than seconds. Given user query and the response answer from the dataset, generate a text reasoning given some in-context samples. GPT-4o is instructed to generate CoT's that understand the user's intention, recall relevant information, and provide the final answer. The prompt we use is shown in Table [3
GPT-4o to
Math Reasoning Datasets We include three math reasoning datasets for fine-tuning, Tulu-3- and Sitt-3-Persoon-AAgebra The datasets include the question and the CoTT answer in text. We directly the ground truth CoT answer the text reasoning, To obtain the spoken answer, we prompt GPT-4o to rewrite the answer such that the answer suitable for speech communication and then synthesize it with GPT-4O-mini-TTS. The voice is sampled from the following list; alloy, ash, ballad, coral, echo. The prompt used to rewrite the spoken answer is shown in Table I0] The question audio also synthesized with GPT-44-mini-TTe while the list of voices we sampled from is the same as the previous. The voice of the spoken question and response is selected randomly and independently. The total size of the math reasoning datasets used for fine-tuning is 220K.
Persona Math, nutu---Peroon--aath--nnaaa
Knowledge-Intensie Datasets We include two knowledge-intensive QA datasets, Natural Ques- tion (NQ) (K (kwiatkosski et al.) 2019) and TriviaQA (TQA) (Joshi et al.) 2017). to preserve the SLM's ability to respond knowledge-seekee seeking questions. These two datasets are text-based datasets. To obtain the dataset for fine-tuning, we rewrite the ground truth answer from the dataset to make it more suitable for spoken communication, and then synthesize the question and answer audio using GPT-4O-mini-TTS. We sample from the same subset of voices randomly. The text reasoning is also generated by GPT-4o by prompting it with the question and ground truth answer, and asking it to generate inner monologue that understands the user's intention, recalls relevant information, and provides the final answer. The prompts used for rewriting the answer and generating the reasoning shown in Table 12 and Table respectively. When subsampling from NQ and TQA, simply
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take the first 50K instances from NQ and the first 20K instances from TQA without shuffling the dataset before subsampling. The size of knowledge-intensing datasets is around 70K instances.
Dataset
Size in Training Data
Download Link
VoiceAssistant
177K
Tulu3 Math
150K
Tulu3 Algebra
20K
Tulu3 GSM
50K
allorav/tutrr-3-sstttrroooss maatssoonn--matggaaaata
Natural Question
50K
Soogll-feecarrbb--haasss//annuu questions
TriviaQA
20K
mandarjoshi/trivia.ccu qa
Table 3: Training data sizes for each dataset. The download link in the above table is the link where we download the original datasets and then apply our filtering and preprocessing. Directly downloading from those links will not get the training dataset use. Please follow the steps detailed in Appendix to construct the training data.
B.2 TESTING DATASET
The testing dataset statistics are shown in Table 4 We release the audio of the math datasets we use ht/ttss: http:///hggggggggeeeeees
wttp////aaeeeeeeeeooeeeeeee....a aaetsstttssseeeeeee..s. /tcm//ccl111/ sspeech_mattee We use Kimi-
Audio-Evalkit Ding et al. 2025) to download OpenAudioBench. All our evaluation is run using Evni-audio-Evaliit, When evaluating the accuracy for math reasoning datasets and knowledge- intensive QA datasets, we use GPT-4o to judge if the model prediction matches the ground truth; this supported by Kimi-Audio-Fvallke The numbers our result for GI.M-4-Viice slightly differ from the numbers reported in DDng al. (2025) since we use GPT-4o as the judge, while Ding et al. (2025) use GPT-4o-mini as the judge. In the original paper that proposes OpenAudioBench, [i et al. (2025b) also use GPT-4o the judge, and our numbers for GLM-4 Voice are very close to those reported in Li et al. 2025b).
Math Reasoning Datasets
AddSub 109
MultiArith
SinglEq
SVAMP
GSM8K 1310
174
109
299
(a)
OpenAudioBench
I. Ilama Questions
TriviaQA 1000
Web Questions
AlpacaEval
300
1000
199
Table 4: Counts for Math Reasoning benchmarks (top) and OpenAudioBench (bottom).
C TRAINING DETAILS
We use LlamaFactory (Zheng al. 2024 to fine-tune the model. Fine-tuning a model using Lla- maFactory (Zheng et al. | (022)) takes 17 hours on 32 A100-80GB GPUs. To train with LlamaFactory, we use the speech tokenizer encode the audio of the question and answer into speech tokens. Since LlamaFactory does not support fine-tuning GLM-4- Voice, the reader will need to slightly modify some source code in Llamalactory to allow it to support 4LM-4-Voice. We provide the exact yaml configuration file for fine-tuning below, including the detailed list of hyperparameters we use.
### model
model_nen hae___nme______ satee_or___aaee pa_hhth: SRUD//gL--4-vicc-- trust_ monst_remete_code true
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Question: Josh decides to try fipping a house. He buys a house for $60,000 and then puts in $50,000 in repairs. This increased the value of the house by 150%. How much profit did he make?
Response:
[SOPR) tt' Inner Monologue This math question. I need think step step. solve this prohlem, need determine the final value of the house after the repairs and then calculate the profit Josh made. buys the house for $80,000. 2. **Cost of **; He spends S50,000 repaits.
I.iniil Purchase Cost
3. **Tota Investment** The total investment the sum the purchase cost the repair cost [EO2 RJ
spent $80,000 on house $50, caudio Lokens> (..))
[S02R] Total Investment $80,000 + $50,000 $130,000
4. **Increuse Value**; The value of the house increuses 150% after the repairs. To find the value of the house, calculate
150% of the initial purchase cost and add it to the initial purchase cost. Incrcase in Value = 1.5 $80, [BOP3]
000 on repairs, totaling 130,000, After repairs.
<audio.i oxens> (...)
:S02R] 000 = $120,000. New Value of the House = $80,000 $120,000 $200,000 profit
**Prolit Calculation
new house
investment. Profit
$130,000 = $70,000
Thus, Josh made prolit of [ЕCPк]
the house" 's value incrensed by 1509, making $200
<audio saaditt.tkeess. (...)
[SOPR] $70,000. #### $70,000 (EOPR] [EOR,
.000 So. his profit $200,000 minus
sundiottokens: (...) $130,000, which equals $70,000. 'Th <audic,t ckens>> (...)
answer is 70,000. caudic.tokenc> (...)
Question: Hannah's city having big display fireworks
July. They's going
lireworks
Hannah's ' house is at the right angle see 40% of the city's fireworks. Hamnah will also set off boxes of lireworks each in her backyard. How many fireworks will Hannah total?
Response:
Momologue This mumber of fireworks Hannah will see from both the city's display and her own backyard display.
[SOPR]
question.
step by step.
problem, we calculate
**Step Calculate the total number of fireworks
city's display.**
The city's display consists of 15 boxes of 20 fireworks cach. To find the total number fireworks from the city's display, multiply the
number of [ЕOPK]
Hannah's 's house will see 40% the city's <audi autokens>> (...)
SSOPR boxes by the number of fireworks in each box: 15 boxes x 20 fireworks box 300 fireworks *Stoop 2: Calculate the total number of fireworks from Hannah's backyard display. Hanmah off 3 boxes of fireworks each her backyard. To lind the total number lireworks from her backyard display. multiply the number of hoves by the number of fireworks in ench box: (SOPR)
fireworks, which is 120. Plus, she has
<audi o. tokenaz (...)
[S02R] 3 boxes x fireworks per box = 15 ftreworks
*SStep 3: Calculate total number of
Hannah will see 40% of the city't firewarks. To find out how many fireworks this is, calculate 40% of the total fireworks from the fireworks [EOPR]
city't display: 40%
boxes of her own fireworks, adding 15 more. So in caudio.takens> (..)
SSPP] 0.40 x 300 120 fireworks
**Stop Add firewerks from city's display Hannah's backyard display.** Finally, add the fireworks city's
display and Hannah's backyard display find the total mumber of fireworks Hannah will see: 120 fireworks from city display fireworks from backyard display = 135 fireworks #*## [BOPR] [BOR]
total, she'll she'll see 135 fireworks.
<audto_ (..)
Table 5: Example outputs from STITCH-R. The text reasoning tokens are highlighted in green the text tokens are highlighted in orange: the audio tokens, which are omitted for simplicity, are highlighted in red. We remove and insert some newlines for formatting.
MFF method
stage: sft
do_train: true finetuning_typp: full
10 ### dataset
template: glm4voice
12 cutoff_len: 8192
overwrite_cache: true
14 preprocessing_num_eee 16
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dataloacer_ workersss
workers:
16
cache_dir:
17
18
FFF output logging_steps: 10
20 save_steps: 500
21
plot_loss: true
22 overwrite_cuttut__.hhe true
save_only_model:
report_to: false
26
MFF train
27 [er__eeloo_ttriin_bttco.lll 2
8
learning_rate: 1.0e-5
30
num num_trrin__pochss 2.0
31
1t_schcaulor_typer cosine
32
warmup_ratic: 0.1
bf16: true
34
ddp_t :ip_timeuut: 180000000
35
snable_liger_kerne true
36
deepspeed: exaap
D HUMAN EVALUATION
This section elaborates on how we conduct human evaluations to evaluate the responsiveness of different model pairs. We conduct pairwise evaluation of the responsiveness of the following model pairs: (1) STITCH-R vs TBS, (2) STITCH-S vs TBS, (3) STITCH-R STITCH-S, and (4) STTTCH- S no-reasoning. We select the above model pairs because believe they can form partial order of the responsiveness of the four models.
We generate the responses of each model from the same 100 questions from GSM8K; the reason for selecting GSM8K is that the number of reasoning tokens required for this dataset is longer, as shown in Table 2. which may make the difference of responsiveness among the models more observable. For each audio, we insert a silence to represent the first packet latency. The first packet latency considers the prefilling and decoding time for the SLM,, and the speech synthesis latency of the speech decoder. The prefilling and decoding latency is profiled using an A A100 GPU with vLLM, while the speech synthesis latency is caleulated based the reported latency from Cosyvoice.
The human evaluators are given a question in GSM8K and two audios from pair of models, and they are asked judge the responsiveness of the two models on 7-point comparison category rating. following the P.808 (Rec 2018), using the following rubrics:
+3 A is more responsive than B
+2 A is somewhat responsive than
+1 A is slightly responsive than B
0 A and B are equally responsive
-1 B is slightly responsive than A
-2 B is somewhat responsive than
-3 B is more responsive than A
We use Amazon MTurk (AMT) to recruit the human evaluators, and each pair of comparisons is rated by three annotators. We require the annotators to have a HIT approval rate 2 98 and to hold master qualification Zealand in this study. The interface for the human evaluation is shown in Figure 4 Human evaluators paid $1.70 for rating 8 pairs of responses, and estimate that the hourly rate is roughly $12.75.
AMT. We also only include participants from the US, UK, Australia, and New
The results are summarized Table STITCH-S reaches a score of 1.687 against TBS, while STITCH-R obtains 1.164 compared to TBS, indicating that while both STITCH models more
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Model STITCH-R
STITCH-S
STITCH-R
STITCH-S vs no-reasoning
Model
TBS
TBS
STITCH-S
Mean score
1.164
1.687
-0.850
0.290
Table 6: The mean responsiveness scores of the four pairs of models. A positive score indicates that the upper model (model A) is more responsive than the lower one (model B), while a negative number means the lower model more responsive. The larger the absolute value of the score is, the more obvious the responsiveness of the two models is for human evaluators.
responsive than TBS, the responsiveness of STITCH-S is slightly better than STITCH-R. The pairwise comparison between STITCH-R and STITCH-S also shows that STITCH-S perceived as more responsive than STITCH-R. Comparing STITCH-S and the no-reasoning baseline, the average score 0.290, while the 95% confidence interval is [0.131, 0.449], meaning that the difference in the responsiveness is not very significant.
E DETAILED LATENCY CALCULATION
In the main content, assume that the duration of each audio chunk fchunk synthesized from N Napsec tokens is the same, and we use fchunk = 2 seconds in the paper. This is a simplification to make the discussion easier. fact, for the first chunk of 26 speech tokens, the corresponding audio is about 1.6 seconds, while the later audio chunks all have duration of about 2 seconds. As a result, safer N Nressn on an 110-880 GPU, with token per second around 86, should be x 1.6 (13 + 26) = 98.6, which is still around 100, the number we use in our paper. It is also possible to make only the first reasoning chunk shorter while all the later reasoning chunks have Nessson 100. If the model is deployed H100-80G GPU, the token per second can be as high as 123, the maximum number for Nuken can be 123 x 1.6 - (13 + 26) = 157.8. Using speculative decoding (Leviathan et al. 2023) to speed up and increase the number of reasoning tokens for each chunk is also valid option.
UNSUCCESSFUL ATTEMPTS
We detail the unsuccessful attempts and negative findings observed our experiments. Our aim is to guide future research by highlighting these pitfalls so that others can avoid them.
LoRA Fine-tuning Initially, we tried to use LoRA (Hu et al. 2022 for fine-tuning, However, when fine-tuning TBS using LoRA, we found that the resulting model cannot generate useful reasoning. After fine-tuning, the performance accuracy of GI.M-- Voice. By observing the outputs from the fine-tuned model, we observed that the model can generate outputs that satisfy the output format in the training data, i... reasoning and then interleaved text-speech tokens. However, the reasoning is seldom correct and can make very trivial mistakes. We suspect that this is because teaching GLM-4-Voice mathematical reasoning requires significant model parameter updates, making LoRA fine-tuning a bad choice.
GSM8K is still around 35%, which is almost the same as the
Fine-tuning only on Math Datasets We have tried to fine-tune only on the math datasets teach the model reasoning. However, we found that the resulting model will treat every input query math question and list some equations to solve the question, even if the question simply asks "Which city is located at the intersection of the Tigris and Euphrates rivers?" This is possibly because the model overfits to the pattern in the training data and forgets how to solve other non non-math questions. However, this be easily solved by including diverse types of tasks in the training data, which is what we do in the end.
Customized Attention Mask When training the STITCH-R R model, we tried with a variant with customized attention masks. Precisely, make the reasoning tokens unable to attend to the text tokens and speech tokens, while the text and speech tokens can attend to all types of tokens. The attention mask still remains causal. The rationale for this customized attention mask is make the
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You will be given user's question and two audio responses from voice assistants A and B. Your task is to assess which assistant is more responsive.
The user asks a question, and then the voice assistant responds. Each audio clip from the voice assisiants begins at the moment the user finishes speaking question. Please play both clips full and rate responsiveness 7-point scale. Responsiveness meens how quickly and smoothly assistant assistant "thinking". please walt patiently.
speaking
(minimal pauses).
may because
+3: is more resporsive than
+2: is somewhel responsive than B
+1. slightly respensive than B equelly responsive
-1: B is slighty responsive than
-2: B is somewhat responsive than
33 B is more responsive than A
Important: Audio May Start with Silence
Some clips intentionally begin with silence while the assistant "thinking:" Do not increase volume during this silence. When speech sudden loud burst could your
Set your volume using the calibration clip below before you play the main experiments.
Use the following audio to calibrate your volume (this clip has no latency):
0:18
Thank you listening safety,
Please listen to FULL audio before you make your judgement. we found evidence that you listen to the audio, we reject your answer. We will reject the answers from those who are obviously spamming the task.
Question 1
treasure hunter found a buried treasure the number of emeralds than the rubies. How many of the gams were there in the chest?
gems. There were 175 diamonds, fewer rubies diamonds, twice
Voice Assistant A
0:00 0:17
40 i
Voice
Which assistant is more responsive? (A B)
+3: responsive than B
+2: A is somewhat responsive than
+1: is slightly responsive than B
and equally responsive
-1: slightly responsive than somewhat responsive than
C -3: more responsive than A
Figure The interface of the human evaluation.
reasoning tokens feel like there is gap between two chunks of reasoning tokens, since they cannot see the text and speech tokens that separate two chunks reasoning tokens. We use the customized attention mask during training and inference. However, found that the results the math reasoning datasets are similar to the results of not using customized attention mask. Considering that the customized attention mask requires some customization during training, complicates the inference procedure, and does not improve the performance, we do not recommend this configuration.
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Use Reasoning Train
Datasets SinglEq
Id
Config
Avg.
Infer.
AddSub
MultiArith
SVAMP
GSM8K
Baselines
GLM-4-Voice
X
49.54
55.75
66.06
40.13
25.80
47.46
No reasoning
62.39
65.52
71.56
57.53
33.13
58.03
Mix reasoning
<*
x 62.39
77.59
85.32
60.87
36.49
64.53
Full Reasoning before Speaking
TBS
70.64
79.31
84.40 83.49
71.57
60.69
73.32
Mix reasoning
+x
67.89
86.21
69.57
59.92
73.42
Proposed Methods: Simultaneous Thinking and Speaking
STITCH-R STITCH-S
67.89
83.33
88.07
69.13
53.05
72.29
73.39
84.48
81.65
66.78
52.18
71.70
Table 7: The performance math reasoning datasets when evaluating the transcription of the speech output. We use Whisper-large-33 Radford et al. (2023) to transcribe the audio. The "use reasoning" columns denote whether text reasoning in the training data or is generated during inference.
Question:
Claire makes a egg omelet every morning for breakfast, How many dozens of eggs will she eat weeks?
Response:
Claire makes a 3-cgg omelette every morning
Caudio.Lokesi (..)
question. think step by step. solve problem, we need determine many dozens of eggs Claire will cat in woeks if she makes 3-cgg omeletic every morning.
.SOPR Tnner Monologue This
1. **Caluulate the number of days in weeks:
There are
in week.
Therefore, weeks, there are (4 - [RCPR]
and there are 28 days in weeks. That
coudio,.okenst (..)
[SOPR]
days.
2. Determine the number of eggs Claire eats per duy:
Claire makes 3-egy omelette every momiin.
Therefore, she cats 3 eges per day.
Calculate
number cggs Claire
28 days:**
Since she cats eggs day, in 28 days, she will cal (3 28 84) eggs. the number [EOPR]
egus total. Since there egus
<audio.Lokens>
.S02R] eggs from whole eggs dozens:**
There are 12 eggs in dozen.
To how many dezens eggs divide
eggs
frac (84) (12) 7
Thus, Claire will cat 7 dozens of eggs in weeks. ###0 7 [ECPR] [BOR]
in a dozen, she'll 1I eat 7 dovens of eggs in <aud ookeess>>> (..)
4 weeks. The answer dozzns.
Table An Example output from STITCH-S. The text reasoning tokens are highlighted green the text tokens are highlighted in orange the audio tokens, which are omitted for simplicity, are highlighted in red We remove and insert some newlines for formatting.
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You are helpful evaltation assistant. You will giver. a question, its ground cruth answer, and preciction by an assistant. Your job evaluate fluency of the answer into single score, The score you can choose from is 1, 2, 3, 4, 5, Note that you 44do not need to jadge the accuracy of the AI assistant's prediction; you only need to consider
fluent sentences are.
# Evaluation Criteria:
#luency and Coherence (1(3): This rating measures the quality of individual sentences lare they well-written and grammatically correct? It also covers the overall organization and structure of the prediction.
if Output Format:
Please provide some reasoning about you evaluata prediction and explain what factors affect your judgment. Conclude your answer with 'Final verdic.: score', where 'score' is an integer from [2, 2,,2,,44 4,
QuesLion (questian)
if Ground Truth answer (answer)
#f Prediction by AI assistant (prediction)
# Evaluation Task How fluent and coherent is the prediction? (On a scale of 145, with being the lowest)
Table The prompt used to evaluate the fluency of the text tokens from the model with LLM-as-a- judge.
5,
52
First package latency
oll
Speach
Achami
Speach decoder synthesize
(die)
(idle)
(iie)
(Adle)
Fage
SUM token generation
fyolen
generated
(a) An illustration of timing diagram with smaller Leyn. the time for synthesizing Napooch tokens in the the corresponding audio.
First package Iatenay
Speech outpot user Spezch synthosize
First package ialengy
(dee)
Eye
(klee)
(idte)
taken ganeration
genenited Hivens
(b) An illustration of timing diagram with larger t'yn. the time for synthesizing Napoach tokens in the the corresponding audio.
The above two figures show the same STITCH-R model with different Lyyn- The upper one has smaller /syn. while the lower one has larger Layn- The figure is used to show that the speech decoder and the token generation STTTCH can be run in parallel. Consequently, as long as the tayn < tehunk, two audio chunks can be played seamlessly to the user. The difference in tayn only affects the first packet latency.
Figure 5:
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You will 11 be given a math quast on and its inner reasoning about the mach question that is used to derive the final answer. Your job is to summarize the reasoning that suitable to spoken in dialogue. You
into short and concise should avoid using excessive equations and math notations but only briefly mentior how the result is caleclated. You don't t need to restate each step and intermediate results the inner reason' ng; just provide brinf overall surmary.
If Example 1:
#+ Question 4+4
Natalis sold clips clips in May, How many clips did Natalia sell altogether in April and May?
friends in April, then suld
many
#++ Inner reasening A##
I need Lo calculate the number of clips Natalia seld in April and May. am given that
April, and need calculate how many she sold May. We know that she sold half many in Kay, so 48 / 24. She sold 24 in May. Combining the answer, she sold 48 + 24 72 in April and May.
sold
#+ Summarized, spoken anawer 444
April she sold 48 clips and May sold half that amount, which altogether she sold 72 clips.
Examble fft Question ++t
Wang earns $12 hour for babysitting. Yesterday, she iest did 50 minutes of babysitting. How much did she earn?
iff Inner reascning ng tff
need calculate how much she earned in minutes.
qiven that she
12 per hour. 50 minutes wazresponds La 50 60 - 5 6 heurs. As a result, she can earn 12 (5 6) 10. The answer is 10.
#ft Summarized. spaken anawer t#
She warked 50 minutes, which is five-sixths of haus at $12 per hour, she earned $13.
Example #++ Question ++
Betty is saving money for a new wallet which costa $100, Betty has only half of the money she needs. Eer parents decided to give her $15 for that purpose, and her parents. much money Betty need to
grandpareats twice buy the wallet?
#++ Inner reascning A##
need to calculate how much more money Betty needs. She saved $50, her parents gave $15, and grandparents gave $30, has $95 total and needs more. The answer is 5.
#FF Summarized apoken answer ### her own savings gifts, since walles cests $100,
She $95 she still needs $5.
f Now it is your turn FFf Question (quesLian)
tff Inner reascning ##F (reasening)
Summarized spaken answer ++#
Table 10:
The prompts used rewrite the answers the dataset into the spoken answer for the math reasoning dataset in the training data. The {question} is the placeholder of the question; the (reasoning) will be replaced by the reasoning, which is the original answer the training data.
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Published as a conference paper at ICLR 2026
You will 1. given factual question and its answer. is possible that there are the question, and Your job is to generate some irner thinking to derive the answer. Imagine that you are a human and you heard somebody ask you that question, and you should think question in spesch. Your rephrasing the quastior into better way for understandirg, and clarifying any possible ambiguity, relzieving the correct anawer and some relevant information. Your inner reasoning should include the short answer. You should not say "I know answer" without explicitly saying answer. infermation" and end your reasoning since this is nst helpful at all.
multiple
only providing for simplicity.
before responding
thinking can include
not say "I need
Example
QuesLion:
what color John Wilkes Booth's hair?
#t Answer
jet-black
Inner thinking
1 think the user is asking about John Kilkes Booth who assassinated united States President Lincoln on april 14, 1865. His hair color is jet-black Goz the answer ready to provide to the user.
If Example 2
#f Question:
Can you make receive calls airplane mode? Answer
No
ff Inner thinking
Airplane mode setting smartphones other portable devices that suspends all redio-rrequency tranamissions. So you can't t make receive calls while iL's
The enswer is no.
Example 3
# Question:
Who played "Mill Munson" on "As the World Turna"?
#f Answez
Soffer
# Inner thinking
The user likely means the CBS soap "As the World Turns." " The character Will Munsor.
was most recently portrayed by Jesse Lee soffer. Ready to speak the answer.
# Now it is your turn if Question:
[question]
if Answer
(answer)
# inner thinking
Table 11: Prompts used to generate reasoning for NQ and TriviaQA in the training data.
25

---

Published as a conference paper at ICLR 2026
We will also provide you It is possible that there are multiple answers to the question, and we are only providing one Your job is to rewrite the short anawer such that it is appropriate for speken form. When asked a Factual question, humans will think about the question in their mind and come ap with an änswer that summarizes their inner reasoning in concise and response that answers Your job is to form this spoken answer based on the question, the inner reasoning, and the short answer.
You will be given Iaclual quesLion and iLs short some inner reasening that leads to the answer.
simplicity.
queatien keepa only relevant information.
Examole 1
# Question:
what color was John wiles Booth's s hair? Inner thinking
is asking about John Wi Wilkes Rooth who assassinatt inted United States president linceln on April 14, 1865. His hair color is jet-black. Goz the answer and resdy to provide it to the user.
I think the
Shart aлsker
jet-black
if Spaken answer
You mean the one who killed lincoln? His hair color was jet-b' ack.
if Example 2
#t Question:
Can you make and receive calls in airplane mode?
Inner thinking
Airplane mode is setting available smarlphones and other portable devices. when activated, it suapends all radio-frequency transmissions, disabling voice and data services. So you can't make or receive calls while it's on. Short
## Spoken anawer
Since sirplane made disables all signal tramsmissions, you cannot make receive calls airplane mode.
#f Example 3
Question:
who played "will Munsor"
the world Torns"?
If Snner thinking
The user likely means the CBS soap opera "As the World Turns." The character Wi11 was most recenlly parlzayed by Jesse Lee Seffez.
if Shart Jasse lee Soffer
ef Spoken answer It's Jesse Sofferlke played Will
soap opera.
# Now 1L is your Luzn question:
(question]
# inner thinking (reascning)
Short ansker (anawer)
#f Spaken aпaкет
Table 12: Prompts used to rewrite the original answer into spoken forms for NQ and TriviaQA.
26

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Published as a conference paper at ICLR 2026
Task Overviex: Inner Monal ague Creation
A voice assislant seceives now want to come with brief Vinner manologue" that the assi stant might have before speaking.
query ructioniio and produces We
Oser GuesLionIInst:uttin Assistant Answer: (answer)
(question)
Task Introduction
Generate concise inner thinking that the assistant could had before speaking. Your inner monoloque should include understanding of the user's question instruction and then a draft answer. Make suze the innez monologue will lead to tae assistant's provided anawer.
Examples
Example 1
User Question/ Instiionnnnnttiiiiiin Assistant Answer: it's completely nornal to feel afraid of the dark, and there are steps you can take to feel more comfortable. Start by gradually exposing yourself to the dark small, manageable steps. You can usa nightlight as transitiona cool. Establish bedlime rouline chat makes you [sel relaxed, like reading book or listening to calming music. Breathing exercises anxiety, Rearrange your badroom to make it feel safer and more inviting. Remind yourself
How stop being afraid of the dark?
meditation can help reduce
the
about about your fears
knowing whal's there, salber Lhan alsa be really helpful.
dangers. Talking to
Inner Monologue: The caer wants to stop being afraid of the dazk. I will first acknowledge that iL's cormal to feel fear, Lhen suggest gradual: exposure, a exercises,
comforting bectime zoutine, anxiety-reducet
talking to
#f Example 2
User Deettion/Instretiinn Yes, can. Help you counting down from 100 each time. Here go: 100, 93, 36, 79, 72, 65, 58. Do you need more?
can you count down frem 190 by aubtracting each time?
Inner Monologue: The user wants countdown by sevens from 100. I'll compute 100-7-93, then subtract each step until have a sequence. I'm ready to present the numbers.
#f Example 3
my Assistent Answer: Scrambled eggs can be delight ful canvas for a variety of ingredients! You can try adding cheese like cheddar, feta, or Swiss for extra creaminess. Fresh
User Quest ion Innttionn:: scrarbled eqqs?
are some interesting thirgs
such as chives, parsley, flaver. Diced vagetebles like bell peppors, comatess, spinach, add texture and nutrition. For prozein, consider mixing in smoked salmon, ham, or crumbled bacen. If you enjoy bil of heat, a dash of hot sauce or chupped jalapeños can be great. Even bit sour cream cream cheese can extra creamy. Enjoy experimenting :o Iind your perfect blend!
can bring burst mushrooms
the eggs
Inner Monologue: The user is askiny fur mix-ins for scrambled eggs. I'll revall commen add-ina like cheeses, herbs, vegetables, proteins, and creams, then frame as suggestions.
Your Task
Generate the inner monoloque for this instruction/anl /nnwer pair. User Cuestion/Inst:seriinn Assi stant Answet: (answer)
(question)
Inner Konoloque: (provide the concise inner monoloque only)
Table 13: The prompt used to generate the reasoning for VoiceAssisttnn400K
27


Reviews:
[{"review_id": "7NP5y1ci6z", "reviewer": "Reviewer_DbXS", "summary": "This paper introduces STITCH, a new framework that allows Spoken Language Models (SLMs) to “think” while they are “talking.” Current SLMs can only generate speech responses directly, without any internal reasoning before speaking. In contrast, humans silently think before responding aloud. STITCH mimics this behavior by dividing the reasoning and response generation into small “chunks.”\nInstead of generating a long chain of reasoning first, STITCH alternates between reasoning chunks and speech chunks, so the model can keep thinking while speaking. The paper shows that STITCH improves reasoning performance by 15–20% especially on math tasks compared to baseline models, while keeping response latency nearly the same. It performs equally well on non-reasoning tasks, showing that “thinking” doesn’t harm general speech quality.", "questions": "- The paper compares only within GLM-4-Voice variants. Is it possible to see how STITCH performs when applied to other open-source SLMs like Qwen2.5-Omni or Baichuan-Audio to prove generalizability?\n- How does STITCH behave under actual streaming conditions where speech synthesis latency varies?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The idea is novel — enabling SLMs to think internally while speaking. The chunked reasoning design, STITCH-R and STITCH-S is creative and practical for real-time systems.\n- Overall the paper is very well-written", "weaknesses": "- The experiments mainly focus on math reasoning datasets like GSM8K and SVAMP. It would be valuable to test STITCH on more diverse reasoning domains such as commonsense, dialogue reasoning, or multi-hop factual reasoning.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "60oQH2lTZ9", "reviewer": "Reviewer_G3Jr", "summary": "The paper proposes STITCH, a generation method that alternates between unspoken reasoning chunks and spoken response chunks. It aims to significantly reduce the latency between reasoning generation and spoken response. Experimental results on both reasoning (e.g., math) and non-reasoning datasets, such as TriviaQA, show that STITCH performs on par with or better than baselines that either reason before speaking or do not perform reasoning at all.", "questions": "In STITCH-S, reasoning occurs after the response. In this case, the reasoning could be merely post-hoc justification. Why does this variant outperform baselines that reason before speaking or models without reasoning?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The authors introduce three ways of integrating reasoning into spoken language models: Thinking Before Speech (TBS), Simultaneous Thinking and Talking with Reasoning First (STITCH-R), and Simultaneous Thinking and Talking with Speaking First (STITCH-S). The methodology is clearly described, and Figure 2 effectively visualizes the differences between these methods.\n- The analysis is good. The authors report performance while varying the length of reasoning chunks during inference and analyze the number of tokens used in the reasoning process. They also conduct experiments using different reasoning models to study how reasoning quality affects spoken responses.\n- Experiments are conducted on both reasoning-oriented and non-reasoning datasets, making the experiments comprehensive.", "weaknesses": "- Based on the performance tables (Table 1-(a) and 1-(b)), there is no clear winner that consistently outperforms all other baselines. On math datasets, STITCH-R and STITCH-S show mixed performance across models, sometimes performing significantly worse than TBS (e.g., TBS 64.94, STITCH-R 58.70, STITCH-S 56.72). Similarly, on non-reasoning datasets, STITCH-R and STITCH-S perform inconsistently relative to other baselines. The paper does not clearly explain the reasons behind these trends.\n- Following the previous point, there are no clear guidelines on when to use STITCH-R versus STITCH-S.\n- The main motivation for reducing interaction latency is to improve user experience while maintaining response quality. However, the paper does not include any user-centric evaluation (e.g., human preference, perceived responsiveness, or satisfaction).\n- It is unclear how STITCH handles cases where reasoning takes longer than speech generation. Would the model introduce pauses until reasoning is completed? Such pauses might make the users feel more inconvenient to use it.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ckUmv1p4pW", "reviewer": "Reviewer_Yahr", "summary": "This study introduces STITCH, a method designed to incorporate reasoning into speech by interleaving it within the existing Speech-Language Model (SLM) framework—specifically building upon GLM-4-Voice, one of the text–speech interleaving architectures (e.g., GLM-4-Voice, VITA-Audio). The authors propose two variants, -R and -S, depending on whether reasoning appears at the very beginning or after the first text–speech chunk.\nThe proposed approach is optimized based on the A100 + vLLM configuration. Compared to the baseline method, in which GLM-4-Voice performs “thinking” before speaking, STITCH demonstrates comparable performance on mathematical reasoning tasks and achieves performance on general conversational tasks that is similar to the original GLM-4-Voice.", "questions": "See weaknesses", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "**[S1]** The paper is written clearly and is easy to follow. The operational mechanism of the proposed method is straightforward to understand.\n\n**[S2]** Notably, the model exhibits comparable results on mathematical tasks compared to approaches that explicitly perform reasoning with reduced latency.", "weaknesses": "While the study presents an interesting direction, the scope of its **novelty and generalizability appears somewhat limited**. The following points are offered as considerations rather than criticisms:\n\n**[W1]** The reported optimization is based on the **A100 + vLLM** setting, which may limit the applicability of the results. It remains uncertain whether the proposed approach would generalize well to limited hardwares, larger models, or alternative architectures, such as those that jointly optimize a separate decoder (or talker) during inference rather than interleaving, or models that predict codecs (e.g., *moshi*). In addition, the interleaving method applied in this study appears to extend the existing text–speech interleaving approach, already performed in models such as GLM-4-Voice and VITA-Audio, to the reasoning component, without introducing additional architectural or methodological considerations specific to reasoning itself.\n\n- **[Q1]** To what extent do the reported gains persist across different hardware budgets, model scales, and architectural variants (e.g., non-interleaving pipelines with a joint talker/decoder, or codec-predictive systems such as moshi)?\n\n**[W2]** The dataset used comprises general dialogue, mathematical, and knowledge-intensive questions generated through **GPT-4o**. It is possible that employing reasoning-specialized models, such as **Qwen3-235B** or **DeepSeek**, for path construction might have yielded broader improvements, particularly in the non-reasoning tasks presented in Table 1. The comparable performance to the baseline GLM-4-Voice on general tasks therefore leaves some ambiguity regarding how the reasoning path contributes in those contexts.\n\n- **[Q2]** Does the choice of path-generating model (e.g., GPT-4o vs. reasoning-oriented models) materially affect downstream performance, especially on non-reasoning tasks (Table 1)?\n\n**[W3]** The paper does not provide an analysis or guarantee as to whether the reasoning path consistently concludes earlier in the 100 / 13 / 26 configuration, nor whether reasoning reliably precedes the corresponding text segments. Given that human reasoning typically precedes linguistic expression, a more explicit discussion or examination of this alignment could further strengthen the study.\n\n- **[Q3-1]** Does reasoning precede and complete before corresponding text/speech segments, and does early termination of reasoning path occur in the 100 / 13 / 26 configuration?\n\n- **[Q3-2]** If the reasoning segment tends to finish much earlier than the corresponding text, could the authors examine (1) whether adjusting the reasoning chunk length during training so that it depletes at a pace similar to the text segment, and (2) independently, enforcing a structure where reasoning always precedes the textual response (to prevent the model from answering first and reasoning afterward), would lead to different experimental results?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "pn7AASFcd8", "reviewer": "Reviewer_TVGk", "summary": "This paper introduces STITCH, a Spoken Language Model (SLM) that performs internal reasoning alongside speech generation, theoretically without increasing interaction latency. STITCH generates speech chunk-by-chunk, with each chunk containing reasoning tokens, text tokens, and speech tokens. Experimental results demonstrate that this method improves reasoning performance compared to baseline approaches.", "questions": "N/A.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The idea of chunk-wise reasoning alongside speech generation is novel.\n- The investigation into optimal reasoning chunk length and the use of reasoning from other models adds further novelties.\n- Experimental results clearly show the effectiveness of the proposed method.", "weaknesses": "- The paper does not compare STITCH against thinker-talker models. The substantial drop in reasoning performance observed in interleaved models (relative to text-only LLMs) often stems from the interleaved LLM's need to generate both text and speech tokens. In contrast, the thinker component in thinker-talker models generates only text, addressing this limitation. Although the authors mention that thinker-talker models are harder to fine-tune—which may imply challenges in adapting STITCH to such architectures—it would be fairer to include thinker-talker models in the comparisons.\n- Evaluation is conducted solely on text outputs. While this is somewhat acceptable, it would be more robust to also evaluate directly on speech outputs, since the performance gap between text and speech evaluations can depend on the backbone LLM’s text-speech alignment capabilities.\n- There is an inconsistency in the introduction section. Lines 084-087 state that STITCH-S cannot generate reasoning by design, but it actually generates text reasoning—it simply does not generate text reasoning in the first chunk. Am I missing something here?\n- Statistical analysis should be performed to assess whether there are significant differences in responses among TBS, STITCH-R, STITCH-S, etc.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)