Cache-to-Cache: Direct Semantic Communication Between Large Language Models
Authors: Tianyu Fu
Zihan Min
Hanling Zhang
Jichao Yan
Guohao Dai
Wanli Ouyang
Yu Wang
Venue: ICLR 2026 Poster (2026)
Keywords: LLM
multi-LLM
multi-agent
communication
Primary area: foundation or frontier models, including LLMs
TL;DR: We enable LLMs to communicate directly through their internal KV-Cache representations, rather than generating text

Abstract: Multi-LLM systems harness the complementary strengths of diverse Large Language Models, achieving performance and efficiency gains that are not attainable by a single model. In existing designs, LLMs communicate through text, forcing internal representations to be transformed into output token sequences. This process both loses rich semantic information and incurs token-by-token generation latency. Motivated by these limitations, we ask: Can LLMs communicate beyond text? Oracle experiments show that enriching the KV-Cache semantics can improve response quality without increasing cache size, supporting KV-Cache as an effective medium for inter-model communication. Thus, we propose Cache-to-Cache (C2C), a new paradigm for direct semantic communication between LLMs. C2C uses a neural network to project and fuse the source model's KV-cache with that of the target model to enable direct semantic transfer. A learnable gating mechanism selects the target layers that benefit from cache communication. Compared with text communication, C2C utilizes the deep, specialized semantics from both models, while avoiding explicit intermediate text generation. Experiments show that C2C achieves 6.4-14.2% higher average accuracy than individual models. It further outperforms the text communication paradigm by approximately 3.1-5.4%, while delivering an average 2.5x speedup in latency. Our code is available at https://github.com/thu-nics/C2C.

Published as a conference paper at ICLR 2026
CACHE-TO-CACHE DIRECT SEMANTIC COMMUNICA- TION BETWEEN LARGE LANGUAGE MODELS
Tianyu Fu" 1,2 Zihan Min Hanling Zhang Jichao Yan',
Guohao Dai Wanli Ouyang 3,5,6 Yu Wang
!Tsinghua University PInfinigence AI "Shanghai Jiao Tong University SSLAI
Thhe Chinese University of Hong Kong
Shhnghai Al Laboratory
ABSTRACT
Multi-LLM systems harness the complementary strengths of diverse Large Lan- guage Models, achieving performance and efficiency gains that are not attainable by single model. In existing designs, LLMs communicate through text, forcing internal representations to be transformed into output token sequences. This pro- cess both loses rich semantic information and incurs loken-by-token generation latency. Motivated by these limitations, we ask: Can LLMs communicate beyond text? Oracle experiments show that enriching the KV-Cache semanties can im- prove response quality without increasing cache size, supporting KV-Cache as effective medium for inter-model communication. Thus, we propose Cache-to- Cache (C2C), a new paradigm for direct semantic communication between LLMs. C2C uses a neural network to project and fuse the source model's s KV-cache with that of the target model to enable direct semantic transfer. A learnable gating mechanism selects the target layers that benefit from cache communication, Com- pared with text communication. C2C utilizes the deep, specialized semantics from both models, while avoiding explicit intermediate text generation. Experiments show that C2C achieves 6.4- 14.2% higher average accuracy than individual mod- els. further outperforms the text communication paradigm by approximately 3.1-5.4%, while delivering average 2.5x speedup in latency. Our code is avail- able at bttpttts////mm pettps:/giithue com thu nics/ /C2/
INTRODUCTION
context
e-prompt
contes!
context
context
query
Sharer LLM: S-Cache
3
Receiver LLM: R-Cache
S-Cache
Cache Fuser
Fused Cacha
R-Cache
comvunicaation text:
response tost:
R-Cache
rasporse teat:
Figure 1: (a) Previous Text-to-Text (T2T) communication passes information through explicit text generation. (b) Our Mache-to-Cache (C2C) communication directly projects and merges KV-Cache with rich semantics from different LLMs.
With the rapid progress of Large Language Models (LLMs) (Guo al. 2025 Yang al. 2025a OpenAi 2025). they are now applied across increasingly diverse domains and tasks. To meet ver- satile demands, LLMs matics (Yang et al., 2024a), visual understanding (Bai et al., 2025), edge computing (Zhang et al., 2024b), and so on. Meanwhile, general-purpose LLMs can also simulate specialized capabilities through prompt engineering, enabling flexible role adaptation across downstream applications.
trained with distinct focuses, such coding (Hui et al. [024), mathe-
Leveraging the diversity of LLMs, many multi-LLM systems are proposed to further enhance over-
all performance and efficiency (Guo et al. 2024 Tran et al. 2025). In collaborative multi-LLM
Equual contribution. "orresponning author: Yu Wang (yw.wang@tsinggua.cdu.mm)

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Published as a conference paper at ICLR 2026
User Input
Coder LLM
[8] Text-to-eext (T2T)
Writer LLM
@ do not know what sp> means
"cidoctypa ntmtt-cscctin -itt==-sttiis--p-- spppp------p-ststttooo
</dostype htmb: start doc
Coder
commun cation
<pt: don't this word
"Sorry, don't know the specilic location insert. Whiting plain
Keectiono: start of seciion <idllo: stort of Nile
ssection> wrapper
wragpor: some
Insert my self-otroddutioon lo the right place"
<BitGP: and of iitle spo: contont
[b] Cacho-to-Cache (C2C)
Writerr LLM
@ ep> Coder
direct KV Cacha projection
<po: the stort of contont
"Suro, moattying codo -ntle=-ntroduccioo <p>/mm Tom.. <p>></accton>"
Taxt Input
Text Output
place: after spp
KV-Cache: embedding
piace place
place: after sp>
Figure 2: Conceptual comparison of T2T and C2C communication in Coder-Write collaboration example. In T2T, the Coder's ambiguous text instruction fails to convey the structural semantics of <p> as a paragraph separator, causing the Writer to misplace the content. C2C directly projects the Coder's KV-Cache into the Writer, transferring both the semantic understanding and precise insertion location without intermediate text generation.
systems (Li et al. 2023; Wu et al. 2023). LLMs are assigned distinct roles and proactively exchange
text messages. Mirroring human collaboration, these systems accumulate partial understandings or sub-solutions from different agents via verbal communication. They harness the collective capabili- ties of multiple LLMs to solve complex problems that single model cannot. By contrast, routing- based multi-LLM inference systems rely on passive context inheritance rather than active message exchange. These systems coordinate models of varying parameter sizes reasoning depths for more dynamic and efficient responses (Li et al. 2024 Fu et al. 2025a Ong et al. 2024; OpenAl, 2025]. Downstream models inherit the context from preceding models in multi-rount conversa- tions, then generate follow-up responses to the new questions based on their own understanding of the conversation history.
However, current text-to-text (T2T) interfaces restrict information exchange among LLMs, particu- larly when conveying rich or diverse semantic interpretations of a shared context. As illustrated in Figure 2 these limitations arise from several inherent constraints of T2T communication. First, as a low- www-bandidthh medium, text introduces an information bottleneck. The nigh-dimensiooal inter- nal representations must be repeatedly compressed into linear strings and then decompressed by the receiver LLM. When models differ in knowledge or assigned roles, some signals may be irrecover- able (e.g., interpreting <p> section marker). Second, natural language is inherently ambiguous, with idioms, underspecified references, and vague expressions. Although recent agent protocols aim standardize text messages (Anthropic 2024) (Surapaneni al. 20255), rigid templates remain insufficient for flexible, open-domain collaboration. Third, T2T communication incurs noticeable latency. Every exchange requires exhaustive, loken-by-token decoding of contextual explanations in sequence. These limitations motivate key question:
Can I.LMs communicate beyond text?
In this work, we explore using KV-Cache as the medium for LLM communication. KV-Cache is naturally richer representation than text. It also enables fully parallel communication through direct projection, avoiding the slow sequential decoding in text exchanges. Our oracle experiments show that (1) enriching KV-Cache under the same context length increases accuracy, (2) V-CCchee is con- vertible between LLMs, (3) different LLMs encode distinct semantic understandings and contextual knowledge of the same input, reflecting their complementary strengths.
Encouraged by these findings, we propose Cache-to-Cache (C2C), faster multi-LLMM communication. As shown in Figure [b), C2C projects the KV-Cache from source model into the space of target model and merges them through neural cache fuser. Experiments show that C2C achieves 6.4-14.2% higher average accuracy than individual models. It further outperforms the T2T paradigm by approximately 3.1.5.4% while delivering an average 2.5x speedup in latency.
paradigm for richer and
2 RELATED WORK
2.1 KV-CACHE SHARING AND REUSE
Based on the similarity of KV-Cache between layers, intra-model cache sharing methods (Yang ct al. 2024b; Wu Tu 2024, Sun et al. 2024 Brandon et al.) 2024,) Wu et al. (025) have been proposed reuse shallow layers' KV-Cache for deeper layers accelerate single L.M inference.
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Another research focus is reuse portion of KV-Cache (e.g.. common prefix. reference docu- ments) for the same model in multiple user queries [Bang 2023 Ye et al. 2024 Yao et al. 2024 Qin et al. 2024 Yang et al., 2025b Ye et al. 2025 Eyuboglu al. 2025).
DroidSpeak (Lu al.
2024a) extends cache reuse to models fine-tuned from the same hase model. Unlike existing works that focus computational efficiency through cache reuse, approach leverages the KV-Cache as medium for semantic transfer between LLMs. Furthermore, unlike existing cache sharing meth- ods that are restricted to only a single model or models with identical structure and size, our method supports sharing across different model families and varying model sizes.
2.2 MULTI-LLM SYSTEMS
Collaborative multi-LLM systems. Collaborative systems treat multiple LLMs peers that ex- change information to improve collective performance. Chain-of-Agene (Zhang et al. 2024c) and MetaGPT (Hong al. 2023) create sequential message flows where agents directly communicate using natural language interfaces. Mixture-of-Agents (Wang al., (024). DyLAN (Liu et al.) 2024b), and Owl (Hu et al., 2025) introduce layered communication architectures. Target LLMs aggregate messages from multiple models using voting summarization mechanisms. Multi-agent debate methods (Estornell & Liu 2024, Liang et al. 2024| Du et al., 2023) involve iterative commu- nication rounds, letting LLM agents discuss and refine responses. Recent works such MCP An- thropic (2024) and A2A SSurapaneni et al. 2025) establish formal text protocols beyond natural lan- guage, standardizing agent interaction and tool usage in collaborative multi-LLM systems. These approaches rely on text-level interfaces, where communication requires one model to generate text token-by-token and another to ingest input. Our work explores deeper and more efficient col- laboration (Pidkuiko & Starkov representations.
2025 Zheng et al. 2025b) by directly sharing internal KV-Cache
Routing-bass based multi-LLM inference systems. To accelerate LLM inference, several systems leverage multiple models with different capabilities and costs. Dynamic model selection meth- ods (OpenAl| 2025) Ong et al.)
20244] Feng et al.) [2024] Ning et al.] (024) route queries to different models with varying sizes and configurations to balance efficiency and performance. Token-level routing methods (Zhang et al.)
2024a] Shen et al. 2024 Zheng et al. 2025a; Fu et al.) 2025a) enable finer-grained selection, utilizing smaller models for simple token generation within the rea- soning process of complex tasks. While these systems achieve efficiency through strategic model switching, they either completely drop context from other models, or simply rely on their own un- derstandings of the context. Without understanding sharing, smaller models cannot benefit from the richer representations already computed by larger models.
METHOD
3.1 PRELIMINARIES
LLM inference. Autoregressive LLM inference involves two stages: prefill and decode. Prefill encodes the full input to produce the first output token; decode then generates subsequent tokens iteratively using the last token and the cached key-value (KV) states. Formally, let [jo:n] [zo..... -n-1l be the input token sequence. After prefill, LLM produces a per-token KV-Cache C(X Co- E Raxd For notation brevity, denotes the KV dimensionality that flattened from all layers into single vector per token. The range subscripts are omitted when clear. During decoding, with the current token yı and caches from the input and the generated prefix, the next token is predicted as
(I) where t denotes sequence-wee wise concatenation. The cache updates as C(Yos+)) = C(Y p.4 (x()))
yi+1 = P(n C(X) @ C(Ypx:))
LLM communication. In LLM communication scenarios, we define the LLM that provides con- textual understanding knowledge as Sharer, and the one that utilizes it Receiver.
3.2 ORACLES FOR CACHE-TO-CACHI COMMUNICATION
We aim to explore whether LLMs can have direct semantic communication through KV-Cache. Specifically, we design two oracle experiments answer the following questions: (1) Benefit: can
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Target
Transformed
Top-K
20
10
Accuracy
55
0
50
45
-20
2
3
5
8
10
40
-30
-20
10 10
20
Layers
Figure 3: T-SNE visualization of KV-Cache representations from the source (Qwen3-4B), target (Qwen3-0.6B), and the transformed cache. After transformation, the source cache falls within the target's 's representation space.
Figure 4: Effect of selectively enriching differ- ent numbers layers' KV-Cache on accuracy. Enriching more best-performing layers yields increased accuracy, while enriching the worst- performing ones declines accuracy.
Average Effective Rank
Method Cache Len. Cache Enrich. Acc. (%)
Type
Sharer
Receiver
C2C
Direct
X
No
58.42
Few-shot
+
X
Yes
63.39
Cache
539
388
395
Oracle
X
Yes
62.34
V Cache
689
532
560
Table I: Cache enrichment experiment. Oracle prefills on exemplars (E) and question (X), then drops the exemplar cache before decoding, iso- lating semantic enrichment from cache length.
Table Average effective rank of KV-Cache from Sharer, Receiver, and the C2C-fused one. The increase after fusion indicates that C2C en- riches the Receiver KV-Cache semantics.
model's capabilities be improved through KV-Cache semantic enrichment without extending se- quence length? (2) Convertibility: can the KV-Cache of one model be effectively utilized by another model?
3.2.1 CACHE ENRICHMENT ORACLE
To validate the benefit of cache enrichment, we first explore whether the semantic quality of KV- Cache can be improved without increasing its size. Few-shot prompting suggests this might work: providing exemplars E before the question X often improves accuracy. But does this arise from attending to more context tokens, or from E enriching how X is embedded in KV-Cache?
We evaluate this via three setups: (1) Direct: prefill on X only and decode with C(X); (2) Few-shor: prefill on E & X and decode with C(E X)) (longer cache); (3) Oracle: prefill on E X but discard the exemplar segment and keep only the question-alignee slice
(x) CLEHEIHIII (E + X),
so that decoding uses question-length cache with no extra tokens. Here, |- denotes sequence length. In Equation] [ this corresponds to substituting C(X) with C* before decoding.
Comparing Direct and Oracle isolates the effect of cache enrichment: any gain arises from the richer question embeddings induced by F, not from attending to additional token caches as in Few-shot. As shown in Table | the Oracle setup improves response quality at the same cache length.
Additionally, we analyze how cache enrichment affects different transformer layers. Our findings show substantial variation across layers: while some layers benefit from cache enrichment, others experience performance degradation (details in Appendix I)..1). Furthermore, these layer-wise wise effects accumulate as more layers are augmented. As shown in Figure selectively applying cache enrichment to the perpeffoonnng layers (c.g., top-5) yields slightly higher accuracy than enriching all layers, while targeting the worst perfsrrerfoning layers leads to accuracy decline. This finding guides the gating mechanism of our cache fuser (Section 3.3.2 3.3.2
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Sharer cache
Fuser
dynamic weighting
Gate
Receiver
projection
fused cache reply
Receiver
feature fusion
cache
Figure 5: Cache fuser architecture and training scheme. Fuser projects and combines KV-Caches, then adds the result Receiver's 's cache through learnable gate. LLMs are frozen during training.
3.2.2 CACHE TRANSFORMATION ORACLE
To verify that one model's KV-Cache can be utilized by another, we conducted cross-model transfor- mation experiments. We train 3-layer MLP map the KV-Cache from source LLM (Qwen3-4B) to target LLM (owen3-0.6B) with more setups detailed in Appendix A...2
T-SNE visualizations in Figure reveal that the raw KV-Caches of the two LLMs are far apart in representation space. After transformation, the mapped KV-Cache lies inside the target model's representation space. These results demonstrate that KV-Caches from different models generally convertible, as the transformed cache is covered by the target model's representation space.
One thing note is that the transformed cache occupies only smaller subset of the target's space. indicates that the source model's semantic information cannot fully cover the target's s, despite the source being larger. This reflects inherent differences in how each model encodes context. Another observation also supports this interpretation: the correct-answer sets of different models exhibit lim- ited overlap (Figure 7.. despite the comparable aggregated accuracy respective models. These findings suggest that if specialized contextual understanding from different models can be success- fully projected and fused, it may harness the complementary strengths of the respective models.
3.3
C2C DESIGN
3.3.1
OVERVIEW
Building on the oracle experiments, we propose the C2C scheme. Its core objective is to extract useful contextual understanding or knowledge from one model (the Sharer) and fuse it into another model (the Receiver).
In general, the C2C paradigm contains set of key/value cache fusers F and a layer mapping strategy G. During the prefill stage, fuser F, takes the nth layer cache of the Receiver Model C, (X) and the corresponding ( th layer cache of the Sharer Model ()))) (X) and generates the corresponding fused cache with residual connection:
e = (cxx(())+Fn
CC (x))
(3)
During decoding, with the current token yi and caches from the input and the generated prefix, the next token is predicted as:
M++1 = PP11P(y c((o):))
(4)
3.3.2 FUSER STRUCTURE
To enhance the Receiver's KV-Cache without destructive overwriting its information, the fuser is designed under residual integration principle. As shown in Figure it contains three key modules:
(1) Projection module concatenates the Receiver's KV-Cache with the Sharer's KV-Cache, then processes the concatenated features through projection layer followed by feature fusion layer.
(2) Dynamic weighting module applies an input-aware head modulation layer to dynamically reweight the projected information.
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(3) Learnable gate introduces trainable per-layer gate value that decides whether to inject the Sharer's ss context. The gate applies a siimbl--sgmoid with temperature annealing to smoothly tran- sition from differentiable during training to binary at inference.
We also explore a more complex yet potentially more powerful fuser variant in Appendix A.1.3
3.3.3 MODEL ALIGNMENT
Fusing KV-Caches across model families and sizes requires alignment at two levels: tokens and lay- For token aligument, different tokenizers may produce slightly varied token sequences for the same input. We align them by decoding each Receiver token into its string form and re-encoding using the Sharer's ' tokenizer. When one-to-many mappings occasionally occur, select the Sharer token with maximal string coverage to preserve information. For layer alignment, we adopt a ter- minal alignment strategy: the final layers of both models aligned first, then the penultimate layers, and so on in reverse order until reaching the shallower model's 's first layer. Details in Appen- dices I.I.I and A.1.2
3.3.4 TRAINING SCHEME
During training, we freeze both the Sharer and Receiver models, training only the C2C module for KV-Cache fusion. We employ standard next-token prediction loss on the Receiver's response predictions, similar supervised fine-tuning (SFT). The key difference is that the Receiver predicts responses conditioned on fused KV-Cache rather than its own.
The training procedure consists of three stages: (1) Forward: both models encode the input context to produce their respective KV-Caches. (2) Fusion: the C2C module fuses both KV-Caches and replaces the Receiver's cache. (3) Supervision: the Receiver prefills the response using the fused cache, and gradients backpropagate through C2C to minimize prediction loss.
EXPERIMENT
4.1 SETUP
We brief the experiment setups here, with more details in Appendix A.3
Models. We evaluate C2C across various model families, including Qwen2.5 (Yang et al. 2024a Hui et al. 2024). Qwen3 (Yang et al. 2025a). Llama3.2 (Dubey et al. 2024). and Gemma3 (Tam al. 2025). To test generalizability. we select different configurations for the Sharer-Receive model combinations, including models of different generations (Qwen3 and Qwen2.5), different families (Qwen, Llama, and Gemma), different sizes (0.6B to 14B), different specializations (gen- eral, code, and math model), and different training stages (pretrained and instruction fine-tuned models). For ablative and diagnostic analyses (scaling behavior, ablation study, behavior analy- sis), we fix the Receiver and Sharer to Qwen3 models unless otherwise specified. This consistency climinates confounders from model alignment and isolates the core impact of C2C.
Baselines. We compare C2C with two LLM collaboration methods to contextualize performance: (1) Text-to-Text (T2T) communication: models collaborate through an analyze-then--repood hand- off for each query. The Sharer generates an analytical text of key information to solve the input question. This text is concatenated with the original question and fed the Receiver to mirror standard collaborative pipelines. Corresponding prompts are in Appendix A.3.6 (2) Query-level routing (Ong et al. 2024): models collaborate by selecting the appropriate I.L.M for different queries. We also include individual model performance (Sharer Receiver alonc) to establish a lower bound for collaborative gains.
Benchmarks. We evaluate on four widely used benchmarks spanning reasoning, knowledge, and language domains to ensure comprehensive coverage. OpenBookQA (Mihaylov et al., 2018) for fact-based reasoning. MMLU-Redux (Gema et al.] 2025) for knowledge in the general do- main, ARC-Challenge (ARC-C) (Clark et al.] 2018) for scientific and logistic reasoning, and C- Eval (Huang al. 2023) for comprehensive knowledge in the Chinese domain.

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Table Average token count and inference time breakdown on MMLU-Redux (Sharer: Qwen2.5- Receiver: Qwen3-0.6B). "Includes 90ms KV-Cache fusion time.
Text-to-Text
Cache-to-Cache
Metric
Receiver-only Sharer-only
Sharer
Receiver
Sharer
Receiver
Input Tokens
170
187
103
332
170
170
Output Tokens
19
80
0
12
Prefill Time (ms)
27
20
32
20 + 90'
27
Decode Time (ms)
281
326
1312
231
0
308
Total Time (ms)
308
346
1596
445
Training dataset. To ensure the generalizability of C2C, we utilize the first 500k samples of the OpenHermes2.5 Dataset (Teknium, 2023). general finetuning dataset, to train C2C fusers. To reduce training cost, we use MMLU as the training set for scaling behavior and behavior analysis experiments, unless otherwise specified.
Evaluation settings. We use average accuracy as the performance metric. We use text genera- tion and answer extraction as the evaluation mode for C2C and baselines, with the max generation length set to 64 for multi-choice benchmarks. All experiments are conducted the zero-shot setting with zero generation temperature to ensure reproducibility. We use average inference time as the efficiency metric, measured using a single NVIDIA A100 GPU with batch size one.
4.2
MAIN RESULTS
Performance. As shown in Table C2C consistently improves the Receiver model perfor- mance across different settings and benchmarks. Representative example output is provided in Appendix A.4.4 After applying C2C, accuracy increases by an average of 11.00%, 9.64%, and 11.88% across the three Sharers. Compared with text-to-text communication, C2C achieves erage accuracy increase of 5.36%, 4.15%, and 3.06%. Query-level routing prioritizes efficiency but limits accuracy to the better of the two original models. Notably, Qwen3-4B Base the Sharer often ignores instructions, resulting in poor standalone performance and excessively long T2T com- munication times. In contrast, C2C bypasses this issue, highlighting interesting case which a weaker instruction-tun tuned Receiver can leverage a stronger base model's ss knowledge via C2C, even when base model cannot follow instructions. We also explore strong-to- to-wkak communication (de- tails in Appendix A.2.2 using Qwen3-4B as the Sharer, showing that C2C effectively enables the Receiver to benefit from stronger Sharer.
Efficiency. As shown in Table C2C achieves significant speedups of 3.46x, 1.51x, and 14.41 x over T2T by eliminating intermediate text generation. As detailed Table 3 T2T requires the Sharer to decode 80 output tokens, incurring 1312ms of decoding overhead, whereas C2C replaces this sequential decoding with parallel cache fusion 90ms. Further analysis of L.lam3.2-11B's exceptionally fast inference is provided in Appendix A.4.3
4.3 SCALING BEHAVIOR
Scaling sequence lengths. We evaluate how C2C scales with respect to sequence length on long- context tasks from the LongBenchV benchmark. All C2C fusers are trained and tested on different sets of I LongBenchV1. As shown in Table 3, C2C consistently outperforms text-to-text communica- tion across all sequence-length intervals. This demonstrates C2C's advantages across input length ranges. More detailed setups and results are in Appendices A.2.2 and A.3.4
Scaling model sizes. We investigate how C2C scales with respect to the Sharer and Receiver model sizes. All C2C fusers are trained on MMLU's auxiliary train split and evaluated on MMLU-Redux. As shown in Figure 6, the I-axis denotes Sharer size (Qwen2.5-Instae accuracy gains of C2C over Receiver-only baselines (A Accuracy), and each curve represents Receiver from the Qwen3 series. We find that the accuracy improvements C2C generally increase faster than T2T. This trend shows that when the Sharer possesses richer knowledge, C2C is able to
series), the y-axis shows

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Published as a conference paper at ICLR 2026
Table 4: Accuracy (%) and inference time (seconds) of different communication methods across
four benchmarks. The Receiver is fixed as Qwen3-0.6B, paired with three different Sharers.
Sharer
Task
Metric
Receiver
Sharer
Routing
Text-to-Text
Cache-to-Cache
Acc
35.53
38.42
35.58
41.03
42.92
MMLU-Redux
Time
0.29
0.34
0.27
1.52
0.40
Acc
39.20
45.60
40.80
44.00
52.60
OpenBook
Time
0.27
0.35
0.29
0.81
0.30
Qwen2.5-0.58
Acc
41.04
42.09
40.70
49.48
54.52
ARC-C
Time
0.29
0.39
0.29
1.00
0.36
Acc
32.04
40.21
34.61
35.88
41.77
C-Fval
Time
0.26
0.31
0.26
1.51
0.34
Acc
35.53
32.30
33.38
43.32
44.42
MMLU-Redux
Time
0.29
0.06
0.18
0.75
0.50
Acc
39.20
32.60
36,40
41.20
47.80
OpenBook
Time
0.26
0.07
0.17
0.70
0.43
Llama3.2-1B
Acc
41.04
33.57
37.22
50.00
53.39
ARC-C
Time
0.28
0.07
0.18
0.70
0.47
Acc
32.04
31.31
31.92
35.27
40.77
C-Eval
Time
0.25
0.04
0.15
0.71
0.49
Acc
35.53
1.03
16.39
43.87
43.95
MMLU-Redux
Time
0.29
2.06
0.28
7.54
0.45
Acc
39.20
2.20
22.20
46.40
53,20
OpenBook
Time
0.26
1.98
0,27
5.08
0.34
Qwen3-4B-Bas
Acc
41.04
1.48
19.65
$3.91
55.39
ARC-C
Time
0.28
2.06
0,28
6.56
0.40
Acc Time
32.04
5,65
15.10
38.92
42.79
C-Eval
0.25
2.02
0.26
3.59
0.39
Reciever Model Size (Accuracy) 0.68 (35 53)
1.78 (58.26)
(71.47)
(75.53)
14B (79.49)
(a)
(b)
30
30
A Accuracy
20
10
0.58 (38 42)
1.58
3B
7B
14B
0.5B
1.5B
38
78
148
(58.79)
(72.25)
(38.42)
(58.79)
(63.32)
(72.25)
(77.98)
Sharer Model Size (Accuracy)
Sharer Model Size (Accuracy)
Figure 6: Accuracy improvements (AAccuracy) on the MMLU-Redux benchmark. (a) commu- nication. (b) T2T communication. The æ-axis denotes the Sharer model from the 5-Intrucct series, while the curves correspond Receiver models from the Qwen3 series. The accuracy im- provements of C2C generally increase faster than T2T.
more effectively transmit useful information to the Receiver. Note that the relative gains for larger Receivers are less pronounced due to their stronger baselines and higher overlap with the Sharer's knowledge.
Different model combinations. We test different Sharer-Receiver combinations, including different model families and different task-specific models. The result in Table shows that C2C outperforms text-to-text communication on all five combinations by an average 8.59% on MMLU-Redun This supports that by employing C2C, the Receiver model can effectively utilize contextual understanding from different models to enhance performance. Notably, when using Qwen2.5-Man as the Sharer, the communication text becomes substantially longer, analyzed Appendix A.4.3 To further
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Length
Receiver Sharer
T2T
C2C
Setting
#Param.
OpenBook
ARC-C
MMLU
C-Eval
0-4k
30.52
24.94
33.46
37.31
Single
596M
45.80
47.65
36.81
35.81
4-8k
26.03
23.18
29.70
34.01
Identical
529M
50.60
52.52
42.17
40.34
8k+
25.99
16.44
25.64
30.72
C2C
478M
52.60
54.52
42.92
41.77
Table 5: Performance scaling with se- quence length on LongBenchVI, using Qwen3-0.6B Receiver and Qwen2.5- 0.5B Sharer.
Table 6: Ablation of C2C improvement sources. Single: fine-tunes Receiver without any Sharer. Identical: C2C with the same LLM as both Sharer and Receiver. C2C: use different LLMs as Sharer and Receiver.
test the generalizability C2C, we swap the Sharer and Receiver models. The results show that C2C robustly brings 5.05% increase in accuracy while applying T2T results in 6.30% decrease performance.
Together, these experiments support the scalability of C2C as effective and efficient new LLM communication paradigm.
Pair Type
Receiver
Sharer
Metric Receiver Sharer
T2T
C2C
Acc 35.53
31.75
41.35
45.90
Qwen3-0.6B
Gemma3-1B
Time
0.29
0.54
1.04
0.30
Heterogeneous
Acc 35.53
39.86
43.71
46.13
Qwen3-0.6B
Qwen2.5-Math-1 1.5B
Time
0.29
8.71
6.60
0.27
Acc 35.53
25.09
39.74
46.89
Qwen3-0.6B
Qwen2.5-Coder-0.55
Time
0.29
0.26
1.59
0.27
Acc Time
38.42
35.53
32.12
43.47
Qwen2.5-0.5B
Qwen3-0.6B
0.34
0.29
0.98
0.21
Swap
Acc
35.53
38.42
41.03
46.50
Qwen3-0.6B
Qwen2.5-0.5B
Time
0.29
0.34
1.52
0.26
Table 7: Comparison of Receiver-only, Sharer-only, T2T, and C2C across accuracy and time. The pairs grouped into heterogeneous settings (where the Receiver paired with Sharers of different capabilities) and swap settings (where Receiver and Sharer roles are exchanged).
4.4 ABLATION STUDY
Sources of improvement. In Table 6 we ablate the source of C2C performance gain by fixing the Receiver (Qwen3-0.6B) and varying the Sharer. Single denotes standard full fine-tuning of the Re- ceiver without Sharer. Identical denotes C2C where both Sharer and Receiver are Qwen3-0.6B Our default C2C uses Qwen2.5-0.5B as the Sharer. Under the same training configuration, C2C consis- tently attains higher accuracy than both Single and Identical. This confirms that C2C improvements do not purely come from added trainable capacity or overfitting to the training set. Instead, it points to complementary contextual understanding contributed by the heterogeneous Sharer. Identical still outperforms Single, indicating that cache-level self-communication can provide useful auxiliary un- derstanding, echoing effects observed in latent reasoning and looped transformers (Saunshi al., Fu et al. 2025bb.
2025
Fuser architecture. In Table g we show the effect of different components in the C2C design. Compared with pure projection that discards the Receiver's cache, fusing both models' KV-Caches and retaining the Receiver's via residual connection increases accuracy by 24.18%. Adding gate for fused layer selection further increases the average accuracy by 3.07%.
4.5 BEHAVIOR ANALYSTS
Effective rank analysis. We analyze the effective rank of K KV-Cache before and after cache-to-cass to-cache communication. Effective rank (Roy & Vetterli 2007) is common measure of the intrinsic dimen-

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Published as a conference paper at ICLR 2026
Method
MMLU
ARC-C
OpenBook
CEval
Average
Project
20.01
19.57
21.80
21.41
20.70
+Fuse
43.36
51.65
47.60
36.91
44.88
+Gate (=C2C)
42.92
54.52
52.60
41.77
47.95
Table 8: Ablation of C2C fuser components. Project: directly replacing the Receiver's KV-Cache with projected Sharer cache. + +Fuse: fusing both caches and adding the result back to Receiver's ' via residual. +Gate: adding per-layer learnable gating.
sionality of model weights or activation values; higher intrinsic dimension means richer semantic information, as formalized in Appendix [A.4T] As Table 2 shows, after mache-to-cache fusing, the effective ranks of K and V increased from 388 to 395 and from 532 to 560, respectively. This indi- cates that C2C enriches the semantic space by successfully transforming the Sharer's representations and injecting knowledge into the Receiver model.
Accuracy breakdown. Our analysis reveals that the source of C2C's accuracy gains depends on the relative capacity of the communicating models and varies across task subcategories. Details can be found in Appendix A.2.3] We also find that in some specific cases, C2C may fail as the contextual understanding from the Sharer model is not always accurate and can mislead the Receiver into generating the wrong answer. Representative example is provided in Appendix [A.466]
Progressive behavior. We analyze the progressive behavior of C2C by gradually increasing the percentage of context KV-Cache being updated by C2C (details in Appendix [A.2.4). When the percentage is above 50%, increasing the percentage continuously yields better performance.
Gate behavior. We analyze the behavior of C2C 's learnable gates under different training regimes in Appendix AA.4.2] We can draw the conclusion that general-purpose training favors broad gate activation with fine-grained modulation via weights, whereas task-specific training favors sparse gate activation with stronger reliance on the selected layers.
DISCUSSION
Future work. C2C opens several directions for future research. (1) Cache communication multi- agent systems: C2C may
better communication primitive for real-world agentic tasks with complex multi-round reasoning, coding, and tool use. A preliminary case study on mathematical problem solving is provided in Appendix A.5.3 (2) Cross-modal collaboration: beyond text-only models, fusing caches among vision-language models (VLMs) and lasion-language-actt action (VLA) models may enable richer multi-modal collaboration. (3) Inference acceleration: C2C can enhance speculative decoding and enable token-level routing across heterogeneous models for lower latency and cost. (4) Privacy-aware collaboration: LLMs transmit KV-Cache segments without explicit text, limiting content exposure and improving privacy.
Limitations. (1) Multi-LLM systems experience performance degradation when a much weaker Sharer provides noisy information to a stronger Receiver. This limitation is shared by both T2T and C2C, as Sharer semantic quality directly impacts Receiver performance.
(2) While pairwise KV-Cache communication is feasible and advantageous, scaling up the number of communicating LLMs with O(N) training cost remains an open problem. Preliminary efforts towards this goal are in Appendix A.5. and A.5.2
6 CONCLUSION
We demonstrate that LLMs can communicate beyond text. We introduce Cache-to-Cacha (C2C), a general paradigm that transforms and fuses key-value (KV) caches across models to enable di- rect semantic communication. Across diverse tasks and model configurations, C2C consistently achieves higher task performance and better efficiency than text-to-text communication. These re- sults establish cache-to-cachi its promise for scalable, low-latency multi-LLM systems.
practical alternative tasen-based communication and highlight
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ACKNOWLEDGMENTS
This work was supported by National Natural Science Foundation of China (No. 62506197, 62325405, 62104128, U19B2019, U21B2031, 61832007, 62204164, 92364201). Tsinghua EE Xil- inx Al Research Fund, and Beijing National Research Center for Information Science and Technol- ogy (BNRist). We thank Xuefei Ning for her valuable discussions and suggestions.
ETHICS STATEMENT
This study raises no ethical issues. No human subjects or sensitive personal data were involved in the experiments.
REPRODUCIBILITY STATEMENT
We provide sufficient information to allow the results reported in this paper to be reproduced. All experiments were conducted using publicly available datasets along with open-source models and code. Implementation details, including data selection, model architectures, hyperparameters, and training procedures, are provided in Appendix The code, configuration files, and model check- points are released at https:: /ttp://ggtttbbbt com/ wwwoomtttuuuaaee nicss/2C
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A APPENDIX
A.1 DESIGN CHOICE EXPLORATION
We detail the design of C2C and discuss alternative design choices.
A.1.1 LAYER ALIGNMENT
Terminal alignment. In this strategy, the layers of the two models are aligned starting from the output side. Specifically, the final layer of the smaller model is paired with the final layer of the larger model, the penultimate layer with the penultimate layer, and so on. This scheme prioritizes alignment between the deeper layers across models, which typically capture higher-level semantic representations.
Depth- mopthliorml alignment. In this strategy, both models' layer indices are normalized to Jo, 1] by dividing by (L-1), 1), where is the total number of layers in the model. Let the model with fewer layers (Lmin) serve as the anchor. For each anchor layer (with normalized index i/ (Lmin 1)), we select the layer j in the other model (mmax) whose normalized index j/ [Lmax - 1) is closest:
j arg min
(5)
Limin
Lmmax
This method produces alignment that distributes correspondences approximately uniformly across the model depth.
C2C Choice. In our design, we adopt terminal alignment, as it provides simpler and more direct layer mapping strategy that empirically performs slightly better in our experiments.
A.1.2 TOKENIZATION ALIGNMENT
For dialogue inputs, we first apply the chat template each tokenizer, which produces a sequence consisting of alternating sections of (1) template tokens and (2) message tokens. These two types of sections are handled differently during alignment.
Template sections. Template tokens structural markers (e.g., role delimiters, formatting tokens) that differ across tokenizers and carry no semantic content. To preserve sequence consistency with- introducing unnecessary distortions, these sections are aligned by simple length padding: the shorter side is padded with <pad> tokens until both tokenizers' sequences are of equal length.
Message sections. Message tokens correspond to the actual textual content of user or assistant di- alogs. Each target model token in message section is decoded into its string form and re-encoded using the source model tokenizer. Special tokens (e.g., <pad>, <eos>) are mapped directly if pos- sible; otherwise, the source model's 's unknown token is used. For regular tokens, if the re-encoding produces single source model token, direct one-to-one mapping is established. If multiple source model tokens are produced (a one-to-many case), one of the two selection strategies is applied: (1) first-ocurreecce selection: choose the first source model token from the candidate set, yielding deterministic and computationally efficient mapping. (2) candidate token, compute its string length, and select the longest; this heuristic aims to preserve maximal surface correspondence with the original target model token.
selection: decode each
C2C choice We observed that the two selection strategies generally produce very similar results, with more than 80% of sequences yielding identical alignments across strategies. Based on this observation, we empirically adopt Maximal-coverage selection as the default strategy to reduce the risk of losing information in one-to-many tokenization cases.
Through this design, template sections are aligned structurally via padding, while message sections aligned semantically at the token level, ensuring robust correspondences between target model and source model representations in chat-formatted inputs.
A.1.3 FUSER ARCHITECTURE
Beyond the C2C fuser, we also examined a more complex yet potentially more powerful variant, which we denote as C2C-C (Complex). The main complexity comes from the introduction of an
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Table Comparison of the default C2C fuser and the complex C2C-C variant (Sharer: Qwen3-4B,
Receiver: Qwen3-0.6B) PGR (Performance Gap Recovered) measures the fraction of the accuracy gap between the Receiver and Sharer that recovered.
C-Eval
ARC-C
MMLU-RReuux
OpenBook
Method
Ace
PGR
Time
Acc
PGR
Time
Acc
PGR
Time
Acc
PGR
Time
Qwen3-4B
68.09
100%
0.24
87.48
100%
0.24
71.38
100%
0.24
79.40
100%
0.25
Qwen3-0.6B
32.04
0%
0.18
41.04
0%
0.19
35.53
0%
0.18
39.20
0%
0.21
T2T
36.96
14%
0.92
52.00
24%
0.80
42.95
21%
0.99
46.40
18%
1.70
C2C
444.00
34%
0.27
60.17
41%
0.27
45.92
29%
0.27
55.20
40%
0.28
C2C-C
60.63
79%
0.21
80.96
86%
0.23
62.78
76%
0.15
70.40
78%
0.26
additional projection stage: instead of directly concatenating Sharer and Receiver caches as C2C, Sharer cache is first projected into the receiver's dimensionality through 3-layer MLP. The con- catenated representation is then processed along two familiar routes routes-feature fusion and dynamic weighting to yield the final S&R cache.
The main experiment results are presented in Table Note that we fix the maximum response length to 8 tokens and the maximum communication length to 256 tokens in this experiment to reduce eval- uation cost. C2C-C attains stronger performance than the default C2C, suggesting that increasing the architectural sophistication of fuser can further amplify the benefits of C2C communication. In this table, we also report Performance Gap Recovered (PGR) (Ong et al. 2024) metric, which quantifies how much of the performance gap between weak and strong model recovered. Nevertheless, the focus of this work is on introducing the C2C paradigm itself. For this purpose, we adopt simple yet effective fuser design, leaving systematic investigation of more elaborate architectures future work.
A.2
ADDITIONAL EXPERIMENTAL RESULTS
A.2.1 CACHE ENRICHMENT DETAIL
In Table TO we show the effect of single-layer cache enrichment. Layer 4 and 16 benefit from the cache enrichment approach by replacing the KV-Cache with the few-shot one, while cache enrich- ment other layers shows performance degradation.
Table 10: Accuracy (%) when enriching only a single transformer layer's KV-Cache via the cache enrichment oracle. Baseline without enrichment: 58.42%. Bold indicates layers that benefit from enrichment.
Layer
Acc.
Layer
Acc.
Layer
Acc.
Layer
Acc.
56.36
7
56.82
14
54.24
21
57.74
56.36
8
55.01
15
58.06
57.23
2
57.14
9
56.78
16
58.45
23
55.22
57.53
10
55.29
17
57.88
24
55.75
58.52
11
57.05
18
57.21
25
56.16
56.45
12
55.04
19
56.71
55,79
54.56
13
54.83
20
55.93
27
55.01
A.2.2 STRONG-TO- WEAK COMMUNICATION
Table reports the results on LongBenchVV when pairing the weak receiver Qwen3-0.6B with a much stronger sharer, Qwen3-4B, under different input lengths. Across all length regimes. C2C consistently outperforms both the receiver alone and the T2T baseline. On average, C2C achieves a 51.01 % PGR over the weak-to-strong gap. These results demonstrate that in strong-to-weak settings,
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Table 11: VongBenchVI scores comparing Receiver-only, Sharer-only, T2T, and C2C the strong- to- lo-w setting (Sharer: Qwen3-4B, Receiver: Qwen3-0.6B) across different input lengths.
Length
Receiver
Sharer
T2T
C2C
0-4k
30.52
50.48
38.52
41.46
4-8k
26.03
48.28
35.79
37.57
8k+
25.99
44.36
33.79
34.23
Average
27.63
47.90
36.18
37.97
C2C
Receiver
C2C
157
Recelver
564
188
751
405
206
1330
498
174
235
910
Sharer
Sharer
(a) Sharer: Qwen2.5-Math-1.35 Receiver: Qwen3-0.6B
(b) Sharer: Qwen3-4B, Receiver: Qwen3-0.6B
Figure 7: Venn diagrams of correctly answered questions MMLU-Redux, comparing Receiver- only, Sharer-only, and C2C (C2C-C variant) answer sets.
C2C can effectively transfer the stronger model's contextual understanding, yielding notable gains for the weaker receiver.
We additionally evaluated the strong-to-weak setting (Qwen3-0.6B as receiver and Qwen3-4B as sharer) other benchmarks beyond LongBenchVi. The detailed results are provided in Sec- tion 1.1
Table
A.2.3 ACCURACY BREAKDOWN
We analyze where the accuracy gains of C2C come from by using Venn diagrams the MMLU- Redux benchmark, as illustrated in Figure / For this analysis, we use the C2C-C variant introduced in Section A.I.3] it has the potential to achieve stronger performance and provides clearer breakdown of where C2C accuracy originates.
Models with comparable capacity. When the Receiver (Qwen3-0.6BB) and the Sharer (Qwen2.5- Math-1.5B-Instruce denoted as Qwen2.5-Math-11. 1.5th have comparable overall capacity but com- plementary strengths, C2C not only inherits part of the Sharer's ability but also solves additional questions by integrating understanding from both models.
Models with disparate capacity. When the Sharer (Qwen3-4B) is substantially stronger than the Receiver (Qwen3-0.6B) C2C tends to integrate more of the stronger model's understanding. Quan- titatively, in the disparate-capant capacity case (Figure 7b). among the questions that the Sharer can answer correctly, C2C also answers 72.1 % correctly. In contrast, in the comparable-capacity case (Fig- ure 7ap. C2C succeeds on only 50.97%.
Subcategory breakdown. We analyze the accuracy gains on different subcategories using the radar plot and histogram plot shown in Figure S Figure [ and Figure 10 The result shows that for the Qwen2.5-0.55 and Qwen3-0.6B model pair, C2C outperforms T2T on 12 out of 17 total cate- gories. Using C2C history. law, and chemistry can bring an additional 7, 7.6, and 7.5 accuracy improvement compared to T2T. For the Qwen3-4B and Owen3-0.6B model pair, C2C outperforms T2T on all categories. Using T2T engineering results in a 1% performance drop, showing the case where text communication between models fails to help the receiver models achieve better
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Omio3 0.68
0Me13 0:68 C2C
pycreegg
ptloozyyi
pelrcc
greening
belgo
chembrry
deeebry
(a) Sharer: Qwen2.5-0.5B Receiver: Qwen3-0.6B
(b) Sharer: Qwen3-4B, Receiver: Qwen3-0.6B
Figure 8: Per-subcategory accuracy on MMLUURedux, comparing Receiver, Sharer, T2T, and C2C.
C2C
12%
10%
Accuracy
8%%
6%
2%
math
computer science
physics
biology
chemistry
engineering
cutture
psy psychelogy
politccs
econamics
gengraphy
philosophy
history
health
other
business
Figure Per-suhcategory accuracy gain of T2T and C2C over the Receiver baseline on MMLU-Redux. (Sharer: Qwen2.5-0.5B, Receiver: Qwen3-0.6B)
performance, At the same time, C2C better utilizes the 4B model's contextual understanding of the problems and achieves a 24% increase in accuracy.
A.2.4 PROGRESSIVE BEHAVIOR
To investigate the impact of fused KV-Cache proportion on the accuracy of the receiver model, we gradually added the proportion of fused KV-Cache derived from the sharer to the receiver model be- fore generating outputs. Specifically, former and latter refer to progressively replacing the receiver's KV-Cache with the fused KV-Cache from front to back and back to front, respectively. We observe that the overall accuracy first decreases and then increases as the replacement ratio grows. The per- formance reduction may stem from the gap between training and testing, where only the full receiver KV-Cache used during training. When the fused proportion goes up to over 50%, the performance of C2C continues to increase with respect to the proportion, reflecting the progressive benefits of C2C. Note that projecting using the latter cache generally has a larger impact than projecting the former, since it is closer the final response.
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35%
T2T
30%
Accuracy Gain
25%
20%
15%
10%
5%
0%
math
computer science
physics
biology
chemistry
engineering
culture
psychology
politics
economics
gengraphy
philosophy
histary
law
health
other
business
Figure 10: Per-subcategory accuracy gain of T2T and C2C over the Receiver baseline MMLU-Redux. (Sharer: Qwen3-4B, Receiver: Qwen3-0.6B)
Accurary
50
30
Latter
Sharer Midel
2.5%
50%
75%
100%
Proportion of C2C Pused KV-Cache
Figure II: MMLU-Redux accuracy as the proportion of the Receiver's 's KV-Cache replaced by C2C-fused cache increases. "Former" replaces tokens front-to-back; "Latter" replaces back-to-front. (Sharer: Qwen3-4B, Receiver: Qwen3-0.6B)
A.3 ADDITIONAL EXPERIMENT SETUP
A.3.1 CACHE ENRICHMENT
We conducted Oracle experiments with Qwen3-0.6B and Qwen3-4B examine how KV-Cache enrichment influences model performance. The evaluation was performed on MML.URRedx. The few-shot examples are selected from MMLU while excluding overlaps with MMLU-Redux to ensure fairness. To probe different ways of applying cache enrichment, we compared four cache enrichment strategies: All-layer Cache Enrichment (apply cache enrichment on all layers), Single-Layer Cache Enrichment (apply cache enrichment only on single layers), Selective Cache Enrichment Best (se- lect layers that have the highest accuracy according Single-Layer Cache Enrichment), Selective Cache Enrichment Worst (select layers that have the lowest accuracy according to Single-L Cache Enrichment). All methods utilized Few-Shot-optimized KV-Caches while maintaining the same cache length as Zero-Shof enabling controlled evaluation of cache enrichment and its layer- specific effects.
A.3.2 CACHE TRANSFORMATION
We employed the MMLU-Redux dataset to train a 3-layer MLP that maps the last layer KV-Cache in the source LLM (Qwen3-4B) to the last layer of the target LLM (Qwen3-0.66 In this oracle experiment, we adopt the MSE loss of the projected KV-Cache and the target KV-Cache instead of the next-token prediction loss, as we aim
whether the representation space can be transformed
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using a neural network. The Key Cache and Value Cache first concatenated the hidden space dimension, then put for MSE calculation. A detailed calculation of loss is shown in the following code:
source_prefille source_model
carget__rrefill = (orre__moddlfforaddddpppppp (pt.r/arddreffllliiinpp......p.p
fowward
_id)
source_k, source_v source sooree_peeflllee.passeeeeeeeveee [-1]
target_k, target_v =
targe__reeflleedpsst_eevvvvllll -1]
project_k = (apofe
project v pnojjcctorsouuree
mseloss (torch. ([project_] k, (eo)) torch.dstack [[target_,, target_vl))
For visualization, 300 samples were randomly selected from the dataset. The source, target, and transformed KV-Cache were all projected into two-dimensional space using t-SNE, allowing to examine the alignment representations between the two models. For t-SNE generation, we set perplexity 50 and max iterations to 1000.
A.3.3 QUERY-LEVEL ROUTING
Query-level routing aims improve the cerffrrmanne-fffciiene trade-off by dynamically assigning harder queries to stronger L.M.. Following prior work, we adopt matrix factorization framework. Query embeddings are obtained from the OpenAI eext-rmbedding---smeee embeddings are taken from pretrained vectors of gpt-4-1 106-preview and mixtral-8x7b-insttud v0.1. These embeddings are used to compute a strong win rate score for each query, which reflects its relative difficulty. Queries the strong model as the one achieving higher standalone benchmark accuracy and the weak model the lower-performing one. Queries in the upper half of the ranking routed to the strong model, while those in the lower half are routed to the weak model.
encoder, while model
then ranked by this score. For each evaluated model pair, we define
A.3.4 EVALUATION METHOD
Main evaluation. We evaluate C2C four multiple-choice benchmarks: OpenBookQA, MMLU- Redux, ARC-Challenge, and C-Eval. For MMLU-Redux, we exclude questions annotated with the error type correct answer. For all evaluations, without sampling, using greedy decoding to ensure reproducibility. Specifically, we use Non-CoT prompts, following the unified format described in Section A.3.6 Model outputs are then matched to the correct option labels to compute accuracy. To control evaluation cost, we set the maximum response length 64 tokens unless otherwise specified, where the response refers the final answer generated by the Receiver, since the base models do not always follow instructions, and longer limits would substantially increase inference time. For the T2T setting, we additionally set the maximum communication length to 256 tokens, where the communication refers to the messages passed from the Sharer to the Receiver.
adopt deterministic generation configuration
LongBench evaluation. We evaluate C2C on the LongBench-E dataset, which comprises total of 13 individual datasets. The prompts and evaluation procedures use the official LongBench settings, with maximum output length of 2,048 tokens.
A.3.5 C2C TRAINING
Training data. (1) Performance experiment. The fuser was trained on the OpenHermes--255 Dataset with a maximum sequence length of 2.048 tokens. Training used 500,000 samples for one epoch with a macro batch size of 256, corresponding to 1,929 total training steps.
(2) Scaling sequence lengths experiment. The fuser was trained the LongBench-E benchmark with a maximum sequence length of 12,000 tokens. The data was randomly split into 3/4 for train- ing and 1/4 for evaluation by data index, ensuring independence between training and evaluation. Training used 1,896 samples for one epoch with a macro batch size of 16, corresponding to 118 total training steps.
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(3) Scaling model sizes and different model combinations experiment. The fuser trained the auxiliaryytrran train split of the MMLU dataset with a maximum sequence length of 1,024 tokens. Training used 15,000 samples for one epoch with macro batch size of 128, corresponding to 116 total training steps.
Training scheme. All experiments
conducted with fixed random seed of 42 ducibilityy Unless otherwise noted, the training configuration was as follows: optimization employed learning rate of 10-4 4 with linear scheduler and 10% warmup ratio, weight decay of 0.01, and maximum gradient norm of I. The temperature was linearly annealed from 1.0 to 0.001 across the total number of training steps. Layer alignment was configured with the last aligned scheme across all experiments. The tokenization alignment was applied only when the paired models em- ployed different tokenizers, in which case the longest strategy was used. For data preparation, each dataset was partitioned into training split (99%) and small held-out validation split (1 1%). The val- idation split was used for model updates but was monitored during training report evaluation loss.
repro-
A.3.6 EVALUATION PROMPTS
Text presents the prompts used for the main evaluation on multiple-choice datasets. Text the alternative non-CoT version used for response diagnosis in Appendix [A.4.4] Text provides the prompt for the Sharer model the T2T evaluation; the Receiver model uses the same prompt as in the C2C setting. Text 4 shows the prompt used in the cache enrichment experiment. For the zero-shot method, no shots are included in the prompt. The few-shot method uses exactly the same prompt as Text 4. For Oracle methods, associated with the shots after the forward pass. The prompt for LongBench evaluation follows its official configuration, which varies across the different sub-datasets.
adopt the few-shot prompt but remove the KV-Cache
Text 1. Prompt for Non-CoT Evaluation
Accurately answer the following question:
(QUESTION]
Choices:
(CHOICES}
Instructions:
Carefully read the question and all options.
Select the single most correct answer.
Respond ONLY in the following format: "The correct answer is A/B/C/D".
Do not include any explanations, additional text, or punctuation besides the answer.
The correct answer is
Text 2. Prompt for CoT Evaluation
Accurately answer the following question:
(QUESTION]
Choices:
(CHOICES]
Instructions:
Carefully read the question and all options.
Let's think step by step and explain your reasoning briefly.
Then give the final answer starting with The correct answer is.
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Text 3. Prompt for Sharer model in T2T Evaluation
In one clear sentence, describe the most essential background knowledge needed to answer the question: (QUSETION) Do NOT directly solve or give answer to the question.
Text 4. Prompt for Oracle Experiment
The following single choice questions (with answers) about (SUBJECT).
Shot 1:
Question: (QUSETION]
Options: (OPTIONS}
Answer: (ANSWER) (r D)
Shot N:
Question: {QUSETION)
Options: (OPTIONS)
Answer: (ANSWER) (A.B,Cor D)
Question: (QUSETION)
Options: {OPTIONS}
Answer:
A.4 ADDITIONAL ANALYSIS
A.4.1 EFFECTIVE RANK
We list the definition of effective rank that was proposed by Roy & Vetterli (2007) here refer- ence. For a matrix that has size M x N, the singular value decomposition of it can be expressed W UVV and the singular values (01, 02 .... Mnin(M,N)) are the non-negative diagonal entries of the matrix Σ. The singular value distribution is denoted as:
P:
|
(6)
Denote the Shannon Entropy as:
(M,
H (pi P2. ... Pmin(AS,N))
Σ
pt log ps
The effective rank is defined as:
(M N) pclogp:
erank(W) = - Ei-1
(8)
In Figure 12 we present the effective rank key and value caches across all the layers. The plot
shows continuous increase in the effective rank of value caches after applying C2C, especially
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Effective Rank (Key)
600
Effective Rank (Value)
700
400
600
300
Sharer Receiver
Sharer
500
200
Receiver
100
C2C
400
C2C
15
20
10
25
Layer
Layer
(a) Effective rank of the Key cache.
(b) Effective rank of the Value cache.
Figure 12: Per-layer effective rank of Key and Value caches for the Receiver (oven3-0.6B), Sharer (Qwen3-4B), and after C2C fusion. Higher effective rank indicates richer semantic information.
in the shallow layers. Key caches after applying C2C also have a comparable effective rank and increase at deep layers.
A.4.2 GATING BEHAVIOR
We analyze the behavior of the learnable gates by contrasting models trained on ceneral-purpontt versus task-specific data. This comparison reveals markedly different gating dynamics across the two regimes.
General-purpose training. When C2C is trained on the OpenHermes-22 2.5 dataset, the learned key and value gates remain almost fully open. Across the three model combinations reported in Ta- b the average gate activation ratio exceeds 98.21 %. Despite this near-complete activation, we observe that in certain layers the dynamic weights are concentrated at very small values- exam- ple, the average key weight in some layers falls below 0.1. This suggests that under general-purpose training. C2C leverages the dynamic weighting mechanism to modulate how much information is in- corporated from the sharer on a per-query basis, effectively treating dynamic weights as the primary control signal while leaving most gates open.
Task-specific training. In contrast, when C2C is trained on the MMLU auxiliary train split, the gates exhibit much sparser activation pattern. Across model combinations shown in Table 7 the average gate activation ratio drops to 32.67%. For the layers where gates do open, however, the dynamic weights are substantially larger, with most layers exhibiting average weights above 0.4. This indicates that under task task-specific training, the gating mechanism selects a smaller subset of layers that are consistently useful, while the dynamic weights primarily regulate the contribution strength of these selected layers.
Overall, these findings highlight the adaptive interplay between gates and weights: general-purpose training favors broad gate activation with fine mee-graindd modulation via weights, whereas task specifcc training favors sparse gate activation with stronger reliance on the selected layers.
A.4.3 OUTLIER CASES IN INFERENCE TIME
Llama3.2. We observe that the Llama3.2 2 model achieves significantly lower inference time com- pared to other baselines in Table 4 This improvement can be attributed two factors. First, the Llama3.2 model itself has faster interence speed due to its implementation. Second, under the Non- CoT evaluation prompts described in Section A.3.6 the model tends output only a single option letter (e.g., "A" or "B"), rather than a longer formatted string such as "The correct answer is A." The shorter outputs further reduce the average decoding time, leading to the observed advantage.
Qwen2.5-Math. In contrast, the Qwen2.5-Math model exhibits considerably longer inference time, as shown in Table 7 The primary cause is its tendency to ignore the Non-CoT evaluation and T2T prompts described in Section A.3.6, producing verbose, step-by-step solutions rather than con- cise answers. To accommodate these long outputs and avoid truncation, we set both the maximum response length and the maximum communication length to 1024 tokens during evaluation. Under this configuration, the model decodes substantially more tokens on average, resulting in significantly longer inference time.
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A.4.4 EXAMPLE MODEL OUTPUT
To help better understand the response pattern of different models, we show the example response from different methods using the CoT prompt. We use Qwen2.5-0.5B as the Sharer model and Qwen3-0.6B as the Receiver model.
As shown in the following examples, Qwen3-0.6B misunderstood the question. It treated probe for deeper meaning as merely a rephrasing task. Swwe2.55-0.BB-Instuue misinterpreted the question type altogether, taking it as sentence completion and answering the replaced question instead, with overly verbose reasoning. T2T altered the context of the question itself. In contrast, C2C main- tained solid understanding, recognizing that the rewritten sentence aimed to explore deeper moral reasoning, and accordingly selected the correct answer.
Text 5. Question
Instead of asking why the act of destroying the environment might be immoral, Hill wants to ask...
Choices:
A) why the act of destroying nature might be immoral.
B) why people who destroy the environment might be bad people.
C) how the decision to preserve the environment benefits the environment.
D) whether plants have interests.
Text 6. Qwen3-0.6B Response
Text 7. Qwen2.5-0.5B-Instree Re- sponse
The correct answer is A. why the act of destroying nature might be immoral.
To accurately answer the question, let's carefully analyze each option and think through the implications of each choice: A. This option suggests that the act of de- stroying nature might be considered im- moral. This is reasonable and common interpretation of the act of destroying na- ture, as it involves the destruction of nat- ural resources and ecosystems. However, it does not directly address the question of why the act might be immoral.
Hill is not asking why the act of de- stroying the environment immoral, but rather what he wants to ask in different way. The other options do not align with the question as stated.
The correct answer is
B. This option implies that people who destroy the environment are inherently bad. This is a more extreme interpreta- tion and does not align with the general understanding of environmental destruc- tion. It also does not directly address the question of why the act might im- moral.
C.D.
After analyzing all the options, the most appropriate and accurate answer is: The correct answer is A. Why the act of destroying nature might be immoral. This answer directly addresses the ques- tion by suggesting that the act of de- stroying nature might be considered im- moral, which aligns with the general understanding of environmental destruc- tion.
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Text 8. Text-to-Tex Text Response
Text 9, C2C Response
Context from Qwen2.5-0.5B:.
The correct answer is B. Hill wants to ask why people who destroy the environment might be bad people. Hill's goal to understand the motivations behind peo- ple's actions and to identify the underly- ing reasons for their destructive behav- ior. This can help developing strate- gies to prevent such actions and promote sustainable practices. Hill's question is way explore the complexities of hu- man behavior and to gain a deeper un- derstanding of the factors that contribute to destructive actions. The other options are the best answers because they are not directly related to the question or the reasons behind destructive behavior. The correct answer is B.
To understand the concept of environ- mental destruction, one must first grasp the fundamental principles of sustain- ability, the importance of biodiversity, and the impact of human activities the natural world.
Response from Qwen3-0.6B:
The correct answer is **C. how the deci- sion to preserve the environment benefits the environment*
Hill wants to ask how the act of preserv- ing the environment benefits the environ- ment, not why it might be immoral. This aligns with the question's 's focus on un- derstanding the consequences of environ- mental action rather than its moral impli- cations.
The other options either directly address question or are irrelevant. The correct answer is C.
A.4.5 TRAINING COST ANALYSIS
Here we present the training cost in Table 12 in the first few hundred steps. With 300 training steps (less than 9 GPU hours), C2C achieves a comparable result to the final checkpoint.
As shown in the table, accuracy quickly increases
Additionally, in Figure 13 we have plots of training loss and validation loss to compare the settings using Qwen2.5-0.55 loss converges at 250 steps and the eval loss becomes stable at step 1000, suggesting that C2C across different model families incurs extra computational cost.
Llama3.2- 1B Sharer. The plot shows that for both settings, the training
A.4.6 FAILURE MODE ANALYSIS
We have shown that in various settings, C2C, the receiver model performance by using the sharer's contextual understanding. However, in some specific cases, C2C can fail to do the contextual understanding from the Sharer model is
communication paradigm, can increase
Table 12:
Training cost (wall-clock and GPU hours) and MMLU-Redux accuracy each training step (columns) for three Sharer-Receiver pairs.
C2C Setting
Metric
50
100
150
300
1929
Wall-clock (h)
0.15
0.29
0.44
0.87
5.59
Qwenn2550.5BBQ0we3--....
GPU Hours
1,20
2.32
3.52
6.95
44.72
MMLU-Redux 35.53
40.36
41.10
42.06
44.30
42.92
Wall-clock (h)
0.18
0.35
0.53
1.05
6.78
Llama332-1B+@@wa33-00d
GPU Hours MML.U-Redux
1.44
2.80
4.24
8.44
54.24
35.53
28.66
41.16
43.98
44.37
44.42
Wall-clock (h) GPU Hours MMI.UReedux
0.16
0.32
0.48
0.96
6.17
Owens--B--Rase--Oeen-d..BB
1.28
2.56
3.84
7.68
49.36
35.53
38.07
39.56
40.77
44.11
43.95
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Cross Entropy Loss
2.25
Owen3-00.6 QWen2.554 2-0..25-0..5 (smoothed)
0.94
2.00
Owen3-0. Laa.m-.----uam-1BBB (smoothed)
Owwnn3 O +Uama-lml
1.75
Entropy
1.25
0.88
1.00
1003
250
500
750
1000
1250
1500
1750
Step
Step
(a)
Training loss comparison
(b) Validation loss comparison
Figure 13: Training and validation loss curves for C2C with two O.BB+Qwen3-0.6B and Llama3. -lama..2-1B+Q000 2-IB+Qwen3-0.6B.
pairs: Qwen2.5-
Table 13: MMLU accuracy in the one-Receiver, multiple-Sharers Sharers setting (Receiver: Qwen3-0.6B,
Sharer I: Qwen3-4B-Base, Sharer 2: Qwen2.5-1.5B-Maile Arrows denote KV-Cache transfer direction via C2C.
Type
MMLU Acc.
Receiver
35.53
Sharerl
1.03
Sharer2
39.86
Sharerl Receiver
60.71
Sharer2 Receiver
46.13
Sharerl, Sharer2 - Receiver
64.60
not always accurate and can mislead the Receiver into generating wrong answers. As shown in Figure 7 though in general the number of correctly answered questions increases, there are some questions that the receiver can answer correctly but are answered incorrectly after applying C2C. Below show example of accounting question where Qwen3-0.6B generates the correct answer. However, when using T2T and C2C, Qwen2.5-0.5B conveys incorrect information, leading Qwen3-0.6B to the wrong answer.
This type of failure is more likely when using very weak Sharer with strong Receiver and can lead performance degradation mentioned in the limitation part.
Text 10. Question
Sussman Co. prepared cash-basis financial statements for the month ended January 31. A summary of Sussman's January activities follows: Credit sales of $5600, Collections of $1900 relating to January credit sales. Accrued salaries $1200. By what amount will Sussman's 's cash-basis income for the month ended January 31 increase as result of restating these activities to the accrual basis of accounting?
Choices:
A) $2,500
B) $3,700
C) $4,400
D) $4,900
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Text 11. Qwen3-0.6B Response
Text 12. Qween2.5-0.5B-Insrrucct Re- sponse
To determine the increase in cash-basis income for Sussman Co. as a result of restating its January activities to the ac- crual basis, we need to identify the cash and accrual differences.
To determine the increase in Sussman's cash-basis income due to restating the January activities to the accrual basis of accounting, let's follow these steps:
Credit sales: $5,600 (cash is not recorded yet) Collections: $1,900 (cash recorded) Accrued salaries: $1,200 (cash is not recorded yet)
1. Identify the cash-basis income for January: Credit sales: $5600 Collec- tions: $1900 Accrued salaries: $1200
Total cash-hasis income: S5600 $1900
Cash-basis income would be: $5,600 (credit sales) + $1,900 (collections) $1,200 (accrued salaries) $5,300
$ = $8700
2. Understand the impact of restating activities to the accrual basis:
Accrual basis income would be: $5,600 (credit sales) $1,900 (collections)
3. Calculate the understated cash-
basis income:
$1,200 (accrued salaries) $7,700
-Understtte cash-basis income $8700
Increase in cash-basis income $7,700
$5500 $3100
$5,300 $2,400
4. Determine the increase in cash-basis income: The increase in cash-basis in- come is the understated cash-basis in- come. Increase in cash-basis income = Understated cash-basis income $3100 5. Match the increase to the given choices: The correct answer is $3100, which corresponds to choice B. Therefore, the correct answer is B. $3,700 700.
The correct answer is: $2,500.
Text 13. Text-to-Text Response
Text 14, C2C Response
Context from Qwen2.5-0.5B:
To calculate the increase in cash-basis in- come for the month ended January 31, need to subtract the cash sales from the credit sales. In this case, the cash sales are $1900 and the credit sales $5600. So, the increase cash-basis in- come is $5600 $$900 $3700. The cor- rect answer is B.
To determine the increase in Sussman's cash-basis income for the month ended January 31 due to restating the activities to the accrual basis of accounting, you need to consider the following steps: 1. Identify the cash-basis income for January 31:
2. Identify the accrual basis income for January 31:
Calculate the difference between the accrual basis and cash-basis income:
Response from Qwen3-0.6B:
The correct answer is B. $3,700.
To determine the increase in Sussman's 's cash-basis income for the month ended January 31 due restating the activities to the accrual basis of accounting, we need to calculate the difference between the accrual basis and cash
27

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Published as a conference paper at ICLR 2026
Table 14: MMLU-Redux accuracy in the multiple-Receiver, multiple-Sharer setting with shared latent projectors. Receivers: Qwen3-0.6B and Owwen.5--0.5B-Instuee Sharers: Qwen2.5-Coder- 0.5B- InSt-Instruct and Qwen3-4B-BB
Receivers (MMLU Acc.)
Sharer
Qwen3-0.6B
Qwen2.5-0.5B
None
34.78
38.44
Coder-O.5B
46.07
42.44
4B-Base Base
51.23
59.17
Both
51.08
59.81
A.5 C2C EXTENSIONS AND FUTURE WORK
As an initial exploration, this work focuses on pairwise communication to establish foundation for future research. Communication via KV-Cache among multiple models direction that requires future exploration, with different designs that can be employed. Here, we explore two settings that involve multiple Sharers and/or Receivers.
A.5.1 ONE RECHIVER, MULTIPLE SHARERS
We explore the scenario in which three models are involved in the communication. Here we set Qwen3-0.68 the Receiver and use both Qwen2.5-1.55 1-.555--.BB-aat and Dwen3--4-BBase as Sharers. Here, we separately trained the C2C fusers from Qwen2.5-1.55 1-...--..5B-Mtt to Qwen3-0.6B and Qwen3- 4B-Base to Qwen3-0.6B, The result in Ta shows that without additional training, the receiver can use C2C to fuse two Sharers' semantic information and improve the performance.
A.5.2 MULTIPLE RECEIVERS MULTIPLE SHARERS
We provide possible path towards more efficient scaling, with O(N) fusers instead of O(N2) fusers to communicate among N LLMs.
The design philosophy decompose M communication routes into M 1, then x N steps, with 1 being the unified latent space. This is achieved by using Projectors P., to transform the KV- into a unified latent representation KV-Latent L, Cache from different Sharer LLMs 80, ",,....,.... 8M1 8M
then using the C2C fuser Fr, to fuse them with each Receiver model To.... T-N-I C., represents the KV-Cache from model 8;. For M Sharers, N Receivers, we have M projectors and fusers. Each sharer-receiver pair has its own gate. During training, all Sharer KV-Caches are projected into a unified hidden space with respective Projectors.
C. Priy Un, i= i-M (O...., M-
Then, each of the N Receivers fuses all the M available information from this hidden space.
(U... Cr₂)) Fezy Cr,, N (O...N11..N11
This yields N updated caches Chy, which can be optimized using the same SFT loss discussed in Section 3.3.4. Losses from all Receivers are summed to update all Projectors and fusers. During inference, the model supports both one-to-one and many-to-one (multi-sharer to single receiver) settings. As an initial exploration, we directly adopted the training data, fuser architecture, and hyperparameter settings from the pairwise fuser training, with possibilities left for future work.
In Table 14 we show the result of using Qwen2 -.nen2.5-Coder-0.55 -.--rr-tttttttttttt and Qwen2.5-Math-11. Receivers.
Instruct Sharers, Qwen3-0.6B and Qwen2.5-0.5B-Innstee
A.5.3 INCORPORATING WITH AGENTIC F1.Ow
Here show exploration of C2C in agentic flow. We adopt the agentic flow for math problem- solving, following the workflow and prompts from Cognify (He et al. 2025). The workflow consists of a problem interpreter that analyzes the math problem and problem solver that calculates the
28

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Published as a conference paper at ICLR 2026
Table 15: GSM8K accuracy comparing single-model inference, multi-agent T2T flow, C2C, and T-C2C (T2T interpretation combined with C2C cache fusion).
Method
Accuracy
Single Model (Qwen3-0.6B) T2T (Multi-agent flow)
41.17
61.18
C2C
62.55
T-C2C
78.01
answer. For C2C, KVCache fusion. Here we present the result on GSM8K. As shown in the table, the multi-agent flow increases the accuracy of math problem solving by 20.01. C2C has accuracy of 62.55, increasing the accuracy by 1.37 when compared with the multi-agent flow using T2T. The performance can be further elevated 78.01 by using T-C2C, which lets the math interpreter generate the interpretation and use C2C on both the question and the interpretation.
the prompt of the math solver, the problem interpretation is done by
29


Reviews:
[{"review_id": "7KVMZa08GG", "reviewer": "Reviewer_JCWE", "summary": "The paper proposes C2C, a novel paradigm for direct semantic communication between LLMs. Current multi-LLM systems communicate through a text-to-text (T2T) interface that requires re-encoding of conversation histories and therefore leads to loss in information. C2C proposes a way to let model exchange KV caches that encode richer contextual representations.\n\nThe key component of C2C is a cache fuser (a neural network) that projects the sharer model's KV-cache into the latent space of the receiver model. Then the fusing occurs through a dynamic weighting and gating mechanism to decide which layers are fused. C2C module can be trained using NTP with LLMs remain frozen.\n\nEmpirical result shows increase accuracy in various benchmark along with faster inference. Further behavior analysis shows increase in effective rank of the receiver's KV cache, verifying information gain.", "questions": "From weakness 1:\n\nQ1: Is there a possibility for fuser reuse? Can you show, for example from the same family, that some of the fusers might be similar?\n\nQ2: In addition is it possible to train a fuser for a pair of sharer group and receiver group?\n\nFrom weakness 2:\n\nQ3: Can you provide more granular breakdown of your evaluation to show which categories benefit/hurt using C2C versus T2T?\n\nQ4: Some more qualitative example would be nice to have.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "1. Communication through KV-Cache is indeed a very interesting approach in improving communication between LLMs! KV-cache is known to be containing semantically rich, model-specific view of the context and this paper gives KV-cache a new role than being re-used for accelerating inference. And the motivation is well-justified through the oracle experiments.\n\n2. The main experiments between homogeneous and heterogenous pairs of sharer and receivers showcase consistency: in almost all settings we see both improvement in accuracy compared to T2T and latency. I also like that the author conducts the effective rank analysis to show that there is indeed an information gain.\n\n3. Clear ablation to justify every module: The design is based on findings from oracle experiment and each component seems to be adding performance gain gradually.", "weaknesses": "1. Pair-specific training and limited story on reuse: It seems that a retraining is required for every pair of sharer receiver models. I wonder how this additional cost scales up in more complex multi-LLM systems. There is likely cases where we would have more than two models and the communication between all of them are bidirectional. I also wonder how the cost of the C2C module changes as we have larger models it seems like the training cost is left undiscussed. In addition there is no experiment of showing how fusers can be \"merged\" (like training a single fuser by jointly optimize on a mixture of sharer and receivers)\n\n2. Lack of failure mode analysis: Despite the accuracy gain, it seems unclear that what exact kind of question will fusion hurt, and how well this idea generalize to OOD settings. Right now this is justified through increased effective rank but some concrete examples would help for better understanding (similar to the <p> example but that is also slightly confusing in the way it's being presented right now). \n\n3. Presentation needs to be improved: there seems to be unfinished section (for example missing figure in line 205-207)", "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": "x1zocQtEUw", "reviewer": "Reviewer_TrrM", "summary": "The paper proposes Cache-to-Cache (C2C), a way for one LLM to pass internal semantics to another by projecting and fusing their KV caches, instead of sending text messages. A small “cache projector” merges the sharer’s cache into the receiver’s cache, with a learned per-layer gate that decides where fusion helps. Two “oracle” studies motivate the design: cache enrichment without longer sequences, and cross-model cache transformability.", "questions": "1) How does the method scale to more than two agents. For example, if there are N sharers and one receiver, do you fuse pairwise, do you use a learned mixer over all caches, or do you route by head or layer. What is the complexity and does interference grow with N.  \n2) Have you considered multi-agent benchmarks to test this setting. Examples include collaborative QA, team reasoning tasks, or tool-use suites where several models with different skills must coordinate. If not available, can you adapt existing agent benchmarks and report success rate, latency, and cost.\n\nI would like to increase my score if the questions and weakness are addressed.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1) The two pilot studies are interesting and motivate the design: (a) cache enrichment at fixed cache length and (b) cross-model cache mapping that shows convertibility. They make the later choices like layer gating and fusion easier to trust.  \n2) Communicating through caches instead of text has clear novelty. It avoids extra decoding and can lower end-to-end latency while letting models share internal signals that plain text cannot carry.", "weaknesses": "1) Training cost vs benefit is not fully quantified. Please report GPU hours, peak memory, and wall time for training the fusion module per model pair, plus the speedup and accuracy gain at serving time. A clear trade-off table would help readers judge whether the training cost is paid back by latency savings and accuracy gains in real use.\n2) Writing still need improvement. Some tables are not referenced in the paper.", "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": "KGVZfj2igg", "reviewer": "Reviewer_Qkfp", "summary": "This paper proposes a method, Cache-to-Cache (C2C), which enables two LLMs to communicate through the transfer of KV cache representations, bypassing the need to explicitly decode text. C2C projects and fuses the KV cache of a sharer model into a receiver model using a learned fuser that involves a projection, dynamic weighting, and learnable gating modules. The approach is transformable across model families and experiments show that receiver models in the C2C setup observe an improved accuracy and achieve a significant speedup.", "questions": "1. The effective rank increases after fusion, but does this always correspond to \"richer semantics\"? Could you analyze what types of information are being transferred?\n2. Table 4 shows performance gap between C2C and baseline approaches drop with longer contexts. Is this due to cache projection errors accumulating, or fundamental limitations of the approach?\n3. What is the training time and GPU memory requirement for training a fuser? How does this compare to the potential inference savings?\n4. Could you please clarify the latency calculation for C2C in Table 3? Does the reported C2C time (e.g., 0.40s for Qwen2.5-0.5B Sharer) include the time for both the Sharer's forward pass (0.34s) and the Receiver's forward pass (0.29s), plus the Fuser's computation? The C2C time seems lower than the sum of its parts.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The paper's primary strength is the novelty of it core idea, in using KV cache as a direct communication channel between two models, bypassing text generation. While KV cache sharing/re-use ideas exist for single-model inference, this paper proposes a novel latent communication between heterogeneous models.\n2. The cache enrichment oracle is an interesting observation to decouple the effect of cache quality from cache length, demonstrating cache enrichment at fixed length improves quality.\n3. The C2C trained MLP enables broad setups across model families and sizes.\n4. Consistent accuracy and latency gains across models and tasks tested.", "weaknesses": "1. While the oracle experiments motivate the approach, there is limited analysis and justification for why KV-Cache is the optimal representation for inter-model communication, compared to other modes models may communicate with each other, such as through hidden-states.\n2. Presentation quality needs to be improved. Some important design details such as alignment to enable heterogeneous communication are only briefly mentioned in main text. Figures and tables not being referenced also make it difficult to follow the paper (Figures not referenced in lines 205-206. Table 3 not referenced in the main text). \n3. The performance benefits from C2C seem to diminish significantly when both the receiver model and sharer model size grow larger (Figure 6). A three layer MLP or the limited training may not be sufficient to capture the shift in distribution in KV cache representation for larger models.\n4. The C2C fuser module must be trained for each specific sharer and receiver pair. This is a significant limitation that is not discussed. For $N$ distinct models in a multi-agent workflow, this implies training $O(N^2)$ fuser modules.\n5. The paper's main evaluations are single-turn, single-answer benchmarks that do not require multi-agent collaboration. The paper would be stronger if C2C can be demonstrated to also work in tasks that involve models sharing responsibilities such as in planning or negotiation.", "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": "ORcVod48Hg", "reviewer": "Reviewer_qYJq", "summary": "The paper “Cache-to-Cache: Direct Semantic Communication Between Large Language Models” proposes a new paradigm (C2C) that lets multiple LLMs exchange information directly via their KV-caches instead of text. By projecting and fusing one model’s cache into another through a learnable neural “Fuser” with gating, C2C enables richer, faster inter-model communication that preserves deep semantics. Experiments show 3–5 % higher accuracy and about 2× speedup over text-to-text interaction. It generalizes across model families and sizes, outperforming routing and text collaboration baselines while supporting heterogeneous model cooperation.", "questions": "1. What is the training overhead for the fuser? Transforming a llama 3.2 1B's KV into Qwen 0.5B's KV seems to be a computational heavy idea.\n2. Is this idea reasonable? If you have two models of different architectures, transferring the KV cache does not seem like a good idea to me. The main reason is that the difference in the numeric value of KV are probably too high such that transforming one into another will be more difficult than generating the KV from text.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Novel idea of transferring cache with little restriction on the type of model.\n2. Does not require change of the receiver or sender. \n3. Shows improvement over text to text and cache to cache baselines.\n4. Proposes an interesting argument. Although the reviewer personally do not support this claim (see comment below), I think the initial results are nice.", "weaknesses": "1. Lack of experiments on bigger models. \n2. The theory is lacking. To me, there is an implicit assumption that generating KV cache from text is more costly than the fusing operation introduced. However, transforming between two different model classes is intuitively a more complex operation. Can authors explain why this might be cheaper than KV recomputation?", "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)