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REVIEW 5 major objections 5 minor 1 cited by

Massive Values in Self-Attention Modules are the Key to Contextual Knowledge Understanding

T0 review · 5 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read Concentrated massive values in Q and K, created by RoPE, are what let LLMs use in-context knowledge.

desk verdict Real phenomenon, useful quantization insight, but the contextual-vs-parametric dissociation is confounded by attention precision and undercut by the paper's own passkey caveat. read the letter →

arxiv 2502.01563 v4 pith:W32W7T37 submitted 2025-02-03 cs.CL

Mingyu Jin , Kai Mei , Wujiang Xu , Mingjie Sun , Ruixiang Tang , Mengnan Du , Zirui Liu , Yongfeng Zhang This is my paper · ORCID
classification cs.CL
keywords massivevaluesrotarypositionencodingcontextualknowledgeparametricattentionqueriesandkeysLLMquantizationinterpretability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper sets out to explain a counterintuitive structure inside transformer LLMs: in the attention queries (Q) and keys (K), a few dimensions carry values far larger than the rest, consistently at the same dimension indices across nearly all attention heads, while the values (V) show nothing similar. The authors argue that these concentrated massive values exist only in models using rotary position encoding (RoPE), appear from the first layers, and are the specific channel through which the model handles knowledge that comes from the current context window rather than from the parameters. If the claim holds, a small, precisely located set of activations is responsible for math reasoning, sentiment reading, and long-context retrieval, whereas memorized factual knowledge lives elsewhere. The practical stake is direct: quantization and compression schemes that protect these dimensions should keep contextual abilities intact, and schemes that flatten or skip them should silently cripple those abilities.

What carries the argument

The load-bearing object is the massive-value coordinate set: dimensions in Q and K whose $\ell^2$ norm over the sequence is at least five times the head's mean, found to cluster in the low-frequency half of RoPE's paired rotation dimensions. The second object is the prefill-stage disruption operation, which replaces those coordinates by the global mean of the tensor; because only the prefilling phase is altered, generation mechanics stay intact and performance changes are attributable to context processing. RoPE itself is the causal mechanism: its frequency schedule $\theta_j = 10000^{-2j/d}$ partitions dimensions into high-frequency (position-sensitive) and low-frequency (semantic) regions, and the massive values land in the low-frequency region. That placement is what lets the paper separate contextual knowledge from parametric knowledge, since disrupting the semantic-carrying low-frequency coordinates should selectively impair context tasks.

What would settle it

Run the same prefill disruption on an RoPE model while holding absolute positions fixed but permuting token identities, or vice versa, and measure whether the massive-value dimensions respond to semantic content or to position; alternatively, ablate only the highest-frequency non-massive RoPE dimensions and check whether long-context passkey retrieval collapses, which would indicate that the massive-value effect is positional rather than semantic. A cleaner test: train or fine-tune an RoPE model with the low-frequency rotation frequencies set to zero so those dimensions cannot encode position at all, and check whether massive values still concentrate there and still carry context-task performance.

Watch

Extended reading notes

Core claim

The paper defines a massive value as an entry of the per-head, per-dimension norm matrix $M_{h,d} = \|Q_{:,h,d}\|_2$ (and likewise for K) that exceeds five times that head's average over dimensions, and calls the pattern concentrated when those entries cluster in the same low-frequency dimension block across heads. The central discovery is that in RoPE-based models, including Llama, Gemma, Qwen, Mistral, Phi, LLaVA, and Qwen2-VL, this concentrated pattern appears in Q and K from the very first layer, is absent from V, and is absent from non-RoPE models such as GPT-2, GPT-Neo, OPT, and Jamba. Replacing these massive-value coordinates with the tensor mean during the prefilling phase drops GSM8K from roughly 77-87% to 4-16%, AQUA from roughly 53-64% to 10-20%, IMDB from roughly 94-97% to 2-11%, and passkey retrieval from 100% to 0-9%, while parametric QA tasks fall only 15-20 percentage points and non-massive-value disruption leaves all tasks within about 1%. Quantization comparisons show that AWQ and SmoothQuant, which preserve or redistribute massive values, retain contextual-task accuracy, while GPTQ, which does not protect them, drops GSM8K and AQUA to about 75% normalized accuracy. The paper attributes the pattern's origin to RoPE's frequency pairing and reports that the pattern appears essentially unchanged before and after the RoPE rotation is applied, indicating that the concentration is formed through training rather than by the rotation itself.

Load-bearing premise

The argument depends on the premise that the low-frequency dimensions carrying the massive values encode semantic, context-derived content rather than position or ordering information; if those dimensions mostly encode where tokens are, the observed task split would be a positional-information artifact rather than evidence for a dedicated contextual-knowledge channel.

Editorial extensions

If this is right

  • Quantization methods that protect or redistribute massive-value dimensions, such as AWQ and SmoothQuant, should be preferred when serving context-heavy workloads, whereas GPTQ-style uniform quantization will disproportionately hurt in-context reasoning even if factual QA accuracy looks acceptable.
  • KV-cache and activation compression schemes should allocate more bits to the massive-value coordinates in Q and K to preserve long-context and reasoning abilities.
  • RoPE-free architectures, including those using absolute position embeddings, do not exhibit the concentrated pattern, so the contextual-knowledge channel described here is tied to the rotary mechanism rather than to attention in general.
  • Models with partial RoPE, such as GPT-NeoX with rotation on only the first quarter of dimensions, still show concentration but at a different location, consistent with the claim that RoPE's rotation structure determines where massive values land.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the massive-value coordinates are the semantic channel for context, then steering or patching just those few dimensions during decoding could edit in-context behavior without touching parametric knowledge, a cheap intervention for interpretability and control that the paper does not explore.
  • The paper's own appendix notes that passkey retrieval does not require contextual understanding, so the observed collapse of passkey performance may indicate that the massive-value dimensions also carry the binding or indexing signal needed to locate tokens; this would blur the clean context-versus-parametric split and is worth testing directly.
  • Because the massive-value pattern appears identically before and after applying RoPE, the concentration is likely learned into the Q/K projections themselves during training; if so, fine-tuning an RoPE model with scrambled rotation frequencies should make the pattern disappear, offering a direct causal check of RoPE's role.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper reports that in modern transformer LLMs using RoPE, the query (Q) and key (K) tensors contain a small number of dimensions whose L2 norms are dramatically larger than the rest, that these 'massive values' are concentrated in low-frequency dimensions, and that they are absent in V and in models without RoPE. The authors disrupt these values during the prefill phase and report that accuracy on math reasoning, sentiment analysis, and passkey retrieval collapses, while parametric fact QA degrades only modestly. They additionally compare three quantization methods and conclude that methods protecting massive values better preserve contextual-knowledge performance. The paper attributes the emergence of the concentration to RoPE and provides extensive layer-wise visualizations and open-source code.

Significance. If the central claim were established, the work would provide a practical handle for quantization and a mechanistic distinction between contextual and parametric processing in LLMs. The observational contribution—the systematic documentation of concentrated Q/K outliers across many RoPE-based models and their correlation with RoPE—is valuable and consistent with related work such as Barbero et al. (2025), and the release of code and visualizations is a strength. However, the current evidence does not establish the dedicated 'contextual knowledge' interpretation: the disruption protocol is confounded with attention-logit magnitude, the task taxonomy is internally inconsistent, and parts of the mechanistic argument are circular. The paper's strongest reliable contribution is the empirical phenomenon itself, not yet the mechanistic explanation.

major comments (5)
  1. [Section 3.3, Eqs. (9)-(10)] The disruption protocol does not clearly target the 'massive values' defined in Definition 1. Definition 1 (Eq. 7) selects coordinates by thresholding the per-head, per-dimension L2 norm over the sequence, but Eq. (9) replaces the per-token argmax dimension with the global mean, and Eq. (10) replaces per-token minima. These are different sets of coordinates, so the paper does not actually specify that the ablated entries are the massive values of Definition 1. Please clarify the exact selection rule or rewrite Eqs. (9)-(10); as written, the experiment is not reproducible and the reported effects cannot be attributed to the defined massive values.
  2. [Section 3.4, Table 1; Appendix A.2] The non-massive control is not matched in magnitude or functional contribution. Replacing the largest-magnitude Q/K coordinates removes the dominant contributors to attention logits, while replacing the smallest coordinates leaves those logits largely unchanged; hence any task that depends on precise multi-token attention (GSM8K, AQUA, IMDB, passkey) will suffer more than a single-hop fact lookup regardless of whether the coordinates encode 'contextual knowledge.' The passkey result illustrates the confound: Appendix A.2 explicitly states that passkey retrieval 'does not require contextual knowledge understanding ability, only locating the passkey,' yet Table 1 counts it as a contextual-knowledge benchmark, and its 100%-to-0% collapse is exactly what a positional or attention-precision artifact predicts. Moreover, all Table 1 entries are single numbers without error bars or significance tests, so statements such as 'only 15-20% degradation' are not statistically supported. The task dissociation in Table 1 therefore does not by itself establish a dedicated contextual-knowledge channel.
  3. [Section 4.1, Evidence 1; Section 4.2; Appendix F] The semantic-versus-positional argument is circular. Evidence 1 concludes that low-frequency massive values 'primarily encode rich semantic content rather than positional information' by citing the same disruption results whose interpretation depends on that assumption, and no independent probe of the encoded information is provided (e.g., decoding positional information from these dimensions, or ablating equally large non-massive low-frequency coordinates). In addition, Section 4.2 and Appendix F state that the patterns are 'largely consistent' before and after applying RoPE and that the concentration is 'gradually formed through training rather than being solely caused by the addition of RoPE,' which conflicts with the abstract's claim that the concentration 'is caused by Rotary Positional Encoding (RoPE).' At minimum, RoPE should be described as a structural condition or contributor, not a direct cause, and the causal claim needs a test that does not rely on the disputed disruption results.
  4. [Table 3 and Appendix F, Table 11 and Figure 46] The paper's treatment of GPT-2 is internally inconsistent: Section 4.1 and Table 3 state that models without RoPE, including GPT-2, show no concentrated massive values, while Appendix F Table 11 and Figure 46 report that GPT-2 does exhibit concentrated massive values (concentrated along heads rather than along depth dimensions). Since the 'RoPE causes concentration' claim is central, this contradiction must be resolved. Either the body-text claim is incorrect, or the appendix uses a different criterion for 'concentrated massive values'; the paper should state the criterion explicitly and apply it uniformly.
  5. [Section 3.6, Figure 5] The quantization evidence is reported only as normalized accuracy with no absolute values, no error bars, and no description of the bit-widths, calibration sets, or number of runs for AWQ, SmoothQuant, and GPTQ. With only three methods and no matched control for which weights are altered, Figure 5 does not independently corroborate the contextual-knowledge interpretation; it is also consistent with the confound in my second comment, since methods that preserve attention-dominant coordinates will better preserve any attention-heavy task. Please provide full accuracy tables and either a matched comparison or a clearly stated supporting-role claim.
minor comments (5)
  1. [Section 4.1, Evidence 1] The sentence 'It decreases from 0-(⌈d/2⌉) as the dimensional rotation angle decreases' is unclear and appears to conflate dimension index with rotation frequency; please rewrite with precise definitions.
  2. [Figure 3b and Table 7] The perplexity plot in Figure 3b should be checked against Table 7; for example, Llama3-8B IMDB massive-disrupted perplexity is reported as 112.61 in Table 7, but the bar height and axis limits in the figure are not easy to verify, and the legend should state explicitly which condition is shown.
  3. [Section 2.2, Eqs. (4)-(6)] The tensor notation is inconsistent: X is introduced as R^{lprompt×h×d}, but the equations then use l, h, d without defining the mapping to the per-head dimension D used in Section 3.1; please unify the notation.
  4. [References] Some references are incomplete, such as the Anthropic long-context prompting entry with 'Accessed: date-of-access' as the access date, and a few author lists are left as 'et al.' with no page numbers; these should be completed before publication.
  5. [Appendix A.2] The passkey-retrieval section explicitly says the task 'does not require contextual knowledge understanding ability,' which directly contradicts its inclusion under 'Contextual Knowledge Understanding Task' in Table 1 and Section 3.4; this should be reconciled in the main text.

Circularity Check

2 steps flagged · score 4.0 of 10

Massive-value task dissociation is real, but the semantic-vs-positional reading in Section 4.1 is an interpretive loop, and passkey collapse is an inconsistently labeled benchmark.

  1. other [Section 4.1, Evidence 1 (paragraph after Eq. 12)]
    "This concentration of massive values in low-frequency regions primarily encodes rich semantic content rather than positional information, as evidenced by experimental results showing that disrupting these values severely impairs contextual understanding tasks (with IMDB accuracy dropping from 94% to single digits) while preserving basic parametric knowledge retrieval (showing only 15-20% degradation)."

    The task dissociation in Section 3.4 is the evidence used to conclude that massive values are critical for contextual knowledge. Section 4.1 then cites the same dissociation as evidence that massive values encode 'rich semantic content rather than positional information,' and that semantic attribution is subsequently used as the mechanism explaining the dissociation. This is an interpretive loop: the behavioral results do not distinguish semantic content from positional binding, because both contribute to attention logits. The paper's own Appendix F shows non-RoPE models also lose both parametric and contextual performance when massive values are disrupted, which contradicts the semantic-only attribution rather than supporting it.

  2. renaming known result [Table 1 and Section 3.4 vs. Appendix A.2]
    "this task serves as a good in-context retrieval benchmark, as it does not require understanding the text content (not require contextual knowledge understanding ability), only locating the passkey retrieval."

    Passkey retrieval is listed under 'Contextual Knowledge Understanding Task' in Table 1, and its collapse from 100% to near-zero after massive-value disruption is reported as evidence for the contextual-knowledge claim. But the paper's own appendix explicitly says the task does not require contextual-knowledge understanding. The benchmark is therefore relabeled as contextual knowledge despite the paper's own definition, so the claimed dissociation is partly manufactured by the benchmark taxonomy rather than established by the mechanism.

full rationale

The primary observation—that replacing the largest-norm Q/K coordinates in prefill degrades context-dependent benchmarks more than parametric QA—is not circular: it is an external behavioral test with no fitted parameter renamed as a prediction. The circularity appears when the paper promotes the behavioral dissociation to a mechanistic claim: Section 4.1 uses the same dissociation as evidence that massive values encode semantic content rather than position, then invokes that semantic encoding to explain the dissociation. This is an interpretive loop. The passkey benchmark is also mislabeled as contextual-knowledge evidence in the main tables while the appendix admits it requires only location. These are partial, not total, circularity: no load-bearing self-citation chain or uniqueness theorem forces the result, and the underlying massive-value phenomenon is independently observed. Score 4 reflects one core interpretive loop and one benchmark relabeling, while the central disruption experiment still has independent content.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new particles or entities; its main postulates are the threshold lambda=5, the functional interpretation of massive-value dimensions, and the semantic (rather than positional) reading of low-frequency RoPE channels. The latter two are load-bearing for the central claim and are not independently evidenced.

free parameters (1)
  • Massive value threshold lambda = 5
    Definition 1 sets lambda = 5 empirically to decide which L2-normalized Q/K entries count as massive; the paper does not test sensitivity to this threshold, and the set of disrupted dimensions changes with it.
assumptions (3)
  • ad hoc to paper Massive-value coordinates identified by the L2-norm criterion and lambda=5 correspond to functionally dedicated 'contextual knowledge' channels.
    Definition 1 and the disruption protocol in Section 3.3 assume that magnitude outliers in the sequence-averaged L2 norm identify the dimensions whose removal produces the task dissociation; no independent localization (e.g., causal tracing or attention logit analysis) is provided.
  • domain assumption The parametric knowledge retrieval benchmarks are purely parametric and do not involve contextual reasoning.
    Remark 3.2 and Appendix A.1 describe synthetic QA verified by LLM and human annotators, but the 15-20% accuracy drop under massive-value disruption on Cities suggests these tasks are not fully independent of the disrupted dimensions.
  • ad hoc to paper Low-frequency RoPE dimensions carry semantic content rather than positional information in the tested models.
    Section 4.1 Evidence 1 asserts this to reconcile massive-value concentration with the disruption results, but the paper offers no direct position-versus-semantics probe; Appendix C's characterization of Llama massive values as 'high-frequency' contradicts the low-frequency claim.

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Cite this review

Pith. "Pith review of Massive Values in Self-Attention Modules are the Key to Contextual Knowledge Understanding." pith.science (2026). https://pith.science/paper/W32W7T37

@misc{pith2026250201563,
  author       = {Pith},
  title        = {Pith review of: Massive Values in Self-Attention Modules are the Key to Contextual Knowledge Understanding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/W32W7T37}},
  note         = {Machine review of arXiv:2502.01563}
}
read the original abstract

Large language models (LLMs) have achieved remarkable success in contextual knowledge understanding. In this paper, we show that these concentrated massive values consistently emerge in specific regions of attention queries (Q) and keys (K) while not having such patterns in values (V) in various modern transformer-based LLMs (Q, K, and V mean the representations output by the query, key, and value layers respectively). Through extensive experiments, we further demonstrate that these massive values play a critical role in interpreting contextual knowledge (knowledge obtained from the current context window) rather than in retrieving parametric knowledge stored within the model's parameters. Our further investigation of quantization strategies reveals that ignoring these massive values leads to a pronounced drop in performance on tasks requiring rich contextual understanding, aligning with our analysis. Finally, we trace the emergence of concentrated massive values and find that such concentration is caused by Rotary Positional Encoding (RoPE), which has appeared since the first layers. These findings shed new light on how Q and K operate in LLMs and offer practical insights for model design and optimization. The Code is Available at https://github.com/MingyuJ666/Rope_with_LLM.

Figures

Figures reproduced from arXiv: 2502.01563 by the authors.

Figure 1
Figure 1. In transformer-based Large Language Models with RoPE (like Llama, Gemma), the attention queries (Q) and keys (K) ex￾hibit concentrated massive values in certain dimensions. et al., 2025; Wang et al., 2025). However, our understanding of their internal mechanisms and how these mechanisms relate to observable behaviors remains limited, posing a challenge to improving their reliability and performance. Researchers have… view at source ↗
Figure 2
Figure 2. Q and K Embedding Vector in Llama-2-7B, we choose Layer 10 and 20, and the input question is shown as [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Disrupting massive values leads to higher perplexity and lower diversity, while disrupting non-massive values maintains model performance, particularly evident in IMDB dataset analysis. before and after disrupting massive values, with particular focus on Llama3-8B as shown in Figure 3b and Figure 3a (additional results can be found in [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (41 more)
Figure 4
Figure 4. Figure 4: We can observe that introducing conflicting background knowledge causes LLM to be misled into making random guesses. However, after massive values are disrupted, the model is still able to maintain a certain level of accuracy. minimum: 2.73%), while non-massive value m…
Figure 6
Figure 6. Figure 6: K, Q and V in Layer 20 of OPT-350M, where massive values are not concentrated in specific regions. 0 1 2 3 4 5 6 7 Number of Head High Frequencies Low Frequencies Embedding Vector of K (Layer 20) 0 10 20 30 40 50 Concentrate Massive Value Q Concentrate Massive Value 0 …
Figure 7
Figure 7. Figure 7: K, Q, and V at Layer 20 in Gemma2-9B. Massive values are concentrated in low-frequency regions of K and Q, absent in V. of LLMs employing different position embeddings that can support these findings are provided in the Appendix C. Evidence 4: Since RoPE divides the di…
Figure 8
Figure 8. Figure 8: Pipeline of synthesizing parametric knowledge retrieval dataset: The process starts with a factual knowledge taxonomy as input. An LLM generates true/false factual questions in a predefined format. Another LLM verifies and refines these questions for accuracy. A manual…
Figure 9
Figure 9. Figure 9: An example in Passkey Retrieval Task, this is the easier case in this kind of task. The pass key length is 6 tokens, and the prompt length is less than 128 tokens. Although this task is simple and far from real-world scenarios, it tests two fundamental capabilities of …
Figure 10
Figure 10. Figure 10: An example in Passkey Retrieval Task, this is the harder case in this kind of task. The pass key length is 12 tokens, and the prompt length is about 256 tokens. The pass key is hidden in the middle of the prompt. Therefore, we chose this as a benchmark. Additionally, …
Figure 11
Figure 11. Figure 11: Prompt in Inference LLM 0 1 2 3 4 5 6 7 8 9 101112131415 Number of Head 0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 Dim Embedding Vector of K (Layer 20) 0 10 20 30 40 50 60 70 0 1 2 3 4 5 6 7 8 9 101112131415 Number of Head 0 3 6 9 12 15 18 21 24 27 …
Figure 12
Figure 12. Figure 12: Embedding Vector of K Q, V in OPT-350M, we choose Layer 10 and the input question is shown as [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]
Figure 13
Figure 13. Figure 13: 3D Figure of Key Value in different Head in 10 layers of OPT-350M We tested two LLMs without RoPE, OPT-2.7B and OPT-350M with the prompt [PITH_FULL_IMAGE:figures/full_fig_p018_13.png]
Figure 14
Figure 14. Figure 14: Embedding Vector of K, Q and V in Jamba, we choose Layer 10, and the input question is shown as [PITH_FULL_IMAGE:figures/full_fig_p019_14.png]
Figure 15
Figure 15. Figure 15: 3D Figure of Key Value in different Head in 10 layers of Jamba-350M C. More Visualization Results for LLMs with RoPE Llama: Llama (Dubey et al., 2024; Touvron et al., 2023) is a cutting-edge transformer model designed with several unique structural features that optim…
Figure 16
Figure 16. Figure 16: Embedding Vector of K, Q and V in meta-Llama-2-7b-chat-hf, we choose Layer 20, and the input question is shown as [PITH_FULL_IMAGE:figures/full_fig_p020_16.png]
Figure 17
Figure 17. Figure 17: Embedding Vector of K, Q and V in Meta-Llama-3-8B-Instruct, we choose Layer 10, and the input question is shown as [PITH_FULL_IMAGE:figures/full_fig_p020_17.png]
Figure 18
Figure 18. Figure 18: 3D Figure of Key Value in different Head in 20 layers of Llama2-7B 0 20 40 60 80 100 Token 0 20 40 60 80 100 120 Dim 0 2 4 6 8 10 12 Absolute Value Llama3-8B Layer 10 Head 0 Key Cache 0 20 40 60 80 100 Token 0 20 40 60 80 100 120 Dim 0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.…
Figure 19
Figure 19. Figure 19: 3D Figure of Key Value in different Head in 10 layers of Llama3-8B 0 5 10 15 20 25 30 Token 0 20 40 60 80 100 120 Dim 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Absolute Value LLaVA-1.5 Layer 10 Head 0 Key Cache 0 5 10 15 20 25 30 Token 0 20 40 60 80 100 120 Dim 0 2 4 6 8 10 12 14 A…
Figure 20
Figure 20. Figure 20: 3D Figure of Key Value in different Head in 10 layers of LLaVA-V1.5 Prompt in LLaVA to save vector What are the key aspects or factors I should be aware of, and what precautions or considerations should I take to ensure success or avoid potential pitfalls in this imag…
Figure 21
Figure 21. Figure 21: Prompt used to feed into LLM for inference 21 [PITH_FULL_IMAGE:figures/full_fig_p021_21.png]
Figure 22
Figure 22. Figure 22: Embedding Vector of K, Q, V in LLaVA-1.5, we choose Layer 10, and the input question is shown as [PITH_FULL_IMAGE:figures/full_fig_p022_22.png]
Figure 23
Figure 23. Figure 23: Output example of LLaVA inference Qwen2-VL: Qwen2-VL (Wang et al., 2024) is the latest enhancement of the Qwen-VL model, marks a significant leap forward in multimodal AI, offering state-of-the-art visual understanding across diverse resolutions and ratios and excelli…
Figure 24
Figure 24. Figure 24: Embedding Vector of K, Q, V in ViT in Qwen2-VL 7B, we choose Layer 10 Qwen2-VL uniquely incorporates Multimodal Rotary Position Encoding (M-RoPE), which differs from the traditional one-dimensional Rotary Position Encoding (Su et al., 2024) (1D-RoPE) used in LLMs, the…
Figure 25
Figure 25. Figure 25: Embedding Vector of K, Q, V in large language model in Qwen2-VL 7B, we choose Layer 10 Qwen and Mistral: Qwen and Mistral both use RoPE. Through observations of Qwen2.5-7B’s K, Q, and V maps as shown in [PITH_FULL_IMAGE:figures/full_fig_p023_25.png]
Figure 26
Figure 26. Figure 26: Embedding Vector of K, Q, V in Qwen2.5-7B, we choose Layer 10, and the input question is shown as [PITH_FULL_IMAGE:figures/full_fig_p023_26.png]
Figure 27
Figure 27. Figure 27: Embedding Vector of K Q, V in GPT-NEOX-20B (with RoPE, but not the same RoPE as Llama/Gemma), we choose Layer 1 and the input question is shown as [PITH_FULL_IMAGE:figures/full_fig_p024_27.png]
Figure 28
Figure 28. Figure 28: 3D Figure of Key Value in different Head in 10 layers of Qwen2.5-7B D. Additional Results of Disrupting Massive Values of LLMs D.1. Perplexity Result [PITH_FULL_IMAGE:figures/full_fig_p024_28.png]
Figure 29
Figure 29. Figure 29: K, Q and V in Mistral-7B-Instruct-v0.3, we choose Layer 10, and the input question is shown as [PITH_FULL_IMAGE:figures/full_fig_p025_29.png]
Figure 30
Figure 30. Figure 30: 3D Figure of Key Value in different Head in 10 layers of Mistral each token wi , resulting in a tensor P ∈ R N×V , where V is the size of the vocabulary. Perplexity Computation 1. Log-Probability Computation: For each token wi , the log-probability log P(wi | w1, w2, …
Figure 31
Figure 31. Figure 31: Result in Inference Gemma Llama Output: Answer: s and Playboy bunnies in your films! Bob Fosse’s "Star 80" about Dorothy Stratten, of whom Bogdanovich was obsessed enough to have married her SISTER after her murder at the hands of her low-life husband, is a zillion ti…
Figure 32
Figure 32. Figure 32: Result in Inference Gemma Llama Output: I’ve never seen so many mistakes in a biopic, ranging from her early years in Celoron and Jamestown to her later years with Desi. I could write a whole list of factual errors, but it would go on for pages. In all, I believe that…
Figure 33
Figure 33. Figure 33: Result in Inference Llama3-8B 27 [PITH_FULL_IMAGE:figures/full_fig_p027_33.png]
Figure 34
Figure 34. Figure 34: Result in Inference Llama3-8B Llama Output: I am a helpful movie critic who can help me. Answer ’Negative’ or ’Positive’ I should explain the reason for my answer. PPL: 72.02507019042969 [PITH_FULL_IMAGE:figures/full_fig_p028_34.png]
Figure 36
Figure 36. Figure 36: Result in Inference Qwen E. Experiment Details Large Language Models: For the main table as [PITH_FULL_IMAGE:figures/full_fig_p028_36.png]
Figure 37
Figure 37. Figure 37: Prompt in Inference LLM has on toys, how many will she remain with? GSM8K Example: Nancy is filling an aquarium for her fish. She fills it halfway and goes to answer the door. While she’s gone, her cat knocks the aquarium over and spills half the water in it. Then Nan…
Figure 38
Figure 38. Figure 38: Prompt in Inference LLM IMDB Example 1: I rented I AM CURIOUS-YELLOW from my video store because of all the controversy that surrounded it when it was first released in 1967. I also heard that at first it was seized by U.S. customs if it ever tried to enter this count…
Figure 39
Figure 39. Figure 39: Greater-Less Experiment [PITH_FULL_IMAGE:figures/full_fig_p030_39.png]
Figure 40
Figure 40. Figure 40: Readability Rate Experiment 0 1 2 3 4 5 6 7 Number of Head 0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 63 Dim Embedding Vector of K (Layer 1) 0 10 20 30 40 50 60 70 0 1 2 3 4 5 6 7 Number of Head 0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 6…
Figure 41
Figure 41. Figure 41: Embedding Vector of K in Llama-3-8B and Qwen2.5-7B, we choose Layer 1 and 2, and the input question is shown as [PITH_FULL_IMAGE:figures/full_fig_p031_41.png]
Figure 42
Figure 42. Figure 42: Embedding Vector of K in Llama-3-8B, we choose Layer 1 and 10 before RoPE and after RoPE, and the input question is shown as [PITH_FULL_IMAGE:figures/full_fig_p032_42.png]
Figure 43
Figure 43. Figure 43: Readability Rate Experiment As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p032_43.png]
Figure 44
Figure 44. Figure 44: Embedding Vector of K Q, V in GPT-NEO-1.3B (without RoPE), we choose Layer 1 and the input question is shown as [PITH_FULL_IMAGE:figures/full_fig_p033_44.png]
Figure 45
Figure 45. Figure 45: Embedding Vector of K Q, V in GPT-NEOX-20B (with RoPE, but not the same RoPE as Llama/Gemma), we choose Layer 1 and the input question is shown as [PITH_FULL_IMAGE:figures/full_fig_p033_45.png]
Figure 46
Figure 46. Figure 46: Embedding Vector of K Q, V in GPT-2 (without RoPE), we choose Layer 1 and the input question is shown as [PITH_FULL_IMAGE:figures/full_fig_p034_46.png]

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  1. Awakening Diffusion Transformers: Eliciting Stronger Generation and Understanding via Massive Activation Modulation

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Massive activations in DiTs are timestep-driven detail channels; suppressing them guides finer sampling and AdaLN-modulating them yields more discriminative dense features.

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Works this paper leans on

14 extracted references · 11 canonical work pages · cited by 1 Pith paper

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    Cannot determine the relation between (D) and (F)

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    add norm,

    (G > I) 1 17 Massive Values in Self-Attention Modules are the Key to Contextual Knowledge Understanding Prompt in LLM to save Embedding Vector A car is being driven, in a straight line and at a uniform speed, towards the base of a vertical tower. The top of the tower is observed from the car and, in the process, it takes 10 minutes for the angle of elevat...

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    Log-Probability Computation: For each token wi, the log-probability log P (wi | w1, w2, . . . , wi−1) is extracted from the predicted probability tensor P by selecting the value corresponding to the actual token wi’s index in the vocabulary

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    Averaging Log-Probabilities: Compute the average log-probability over the entire sequence: 1 N NX i=1 log P (wi | w1, w2, . . . , wi−1)

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    poodlesque

    Exponential Transformation: Take the negative exponential to convert the average log-probability into perplexity: PPL = exp − 1 N NX i=1 log P (wi | w1, w2, . . . , wi−1) ! The table presents perplexity (PPL) changes across three datasets (AQUA, IMDB, GSM8K) for three models (...

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Reviewed August 9, 2026 · model on record in the stance chip above.