Token Activation Map (TAM) Overview

• Token Activation Map (TAM) is a structured representation that maps individual token activations across layers to explain model behavior.
• It is applied in multimodal models, language models, and mixture-of-experts systems to analyze visual grounding, semantic saliency, and expert routing.
• TAM techniques mitigate context interference and leverage denoising, tensor pooling, and threshold-sensitive metrics for improved model interpretability.

Searching arXiv for papers on Token Activation Map and closely related activation-map/tensor formulations. Token Activation Map (TAM) denotes a family of representations and explanation methods that assign activation structure to individual tokens, typically by linking tokens to internal activations, experts, or visual regions. Across recent work, the term is used in several technically distinct but conceptually related senses: as a token-to-vision attribution map for multimodal LLMs (MLLMs), as a layer-by-token activation tensor for language-model behavior analysis, as a token-wise saliency map for transformer interpretability, and as a conceptual space–time diagram of token–expert routing in mixture-of-experts (MoE) systems. What unifies these usages is the treatment of tokens as the primary unit of analysis and the construction of structured activation objects over tokens alone or over tokens jointly with depth, experts, or image patches (Li et al., 29 Jun 2025, Bar-Shalom et al., 30 Sep 2025, Gülmez, 2 Mar 2026).

1. Definitions and scope

In MLLM explainability, a TAM is a per-token heatmap over visual patches that indicates where the model grounds a generated token in the input image. The formulation introduced for MLLMs computes, for each generated answer token $t_i^a$, a token-specific visual activation map and explicitly accounts for interference from previous context tokens, distinguishing TAM from class-level methods such as CAM that explain a single prediction rather than a sequence of autoregressive tokens (Li et al., 29 Jun 2025). In artwork analysis, the refined per-token map is written as $\bar{\mathbf{A}^a_i} \in [0,1]^{n_v}$, where $n_v$ is the number of visual patches, and span-level maps are formed by averaging per-token maps across constituent tokens (Fanelli et al., 26 Jun 2026).

A second usage appears in activation-space modeling for LLM hallucination detection. There, the central object is the Activation Tensor $A \in \mathbb{R}^{L_M \times N \times D_M}$, with axes for layers, output tokens, and hidden dimensions. This object is explicitly interpreted as a structured map over the two-dimensional grid of layers $\times$ tokens, analogous to an image with channels, and can be regarded as a TAM in tensor form (Bar-Shalom et al., 30 Sep 2025).

A third usage is token-level representational importance in LLMs. In the Activation Flow Network framework, each token receives a scalar Token Activation Strength at Layer 8 of BERT, defined by the $L_2$ norm of its hidden state. This yields a one-dimensional TAM over the token sequence rather than over a token–patch or layer–token grid (Ghosh et al., 21 May 2026).

A fourth usage is conceptual rather than primarily explanatory. In DynaMoE, a TAM is described as a space–time diagram of which experts fire for which tokens at which layers. Under this view, the hard activation tensor $A^{(\ell)} \in \{0,1\}^{T \times N_\ell}$ and its soft counterpart $W^{(\ell)}$ summarize token–expert activity layer by layer, so TAM becomes a routing object for MoE computation (Gülmez, 2 Mar 2026).

These formulations differ in modality and objective, but all treat token-indexed activation patterns as analyzable objects in their own right rather than as incidental by-products of a model forward pass.

2. Token-to-vision TAMs in multimodal LLMs

The multimodal TAM formulation arises from the observation that MLLMs generate tokens autoregressively, so later token explanations are contaminated by earlier prompt and answer tokens. In the original TAM method, visual features are denoted $\boldsymbol{F}^v \in \mathbb{R}^{n_v \times c}$, prompt features $\boldsymbol{F}^p \in \mathbb{R}^{n_p \times c}$, answer token features $\bar{\mathbf{A}^a_i} \in [0,1]^{n_v}$0, and each vocabulary token has classifier weight vector $\bar{\mathbf{A}^a_i} \in [0,1]^{n_v}$1. Raw CAM-like token activations over visual tokens are defined by

$\bar{\mathbf{A}^a_i} \in [0,1]^{n_v}$2

where $\bar{\mathbf{A}^a_i} \in [0,1]^{n_v}$3 retains only positive values (Li et al., 29 Jun 2025).

The central claim of TAM is that raw token maps for later tokens inherit redundant activations from correlated context tokens. To mitigate this, an estimated causal inference module constructs an interference map for token $\bar{\mathbf{A}^a_i} \in [0,1]^{n_v}$4 as a relevance-weighted combination of all earlier prompt and answer token maps,

$\bar{\mathbf{A}^a_i} \in [0,1]^{n_v}$5

with $\bar{\mathbf{A}^a_i} \in [0,1]^{n_v}$6 when the context token equals the current token (Li et al., 29 Jun 2025). A scalar $\bar{\mathbf{A}^a_i} \in [0,1]^{n_v}$7 is then fitted by least squares to align this interference estimate to the current token’s raw activation, and the interference-corrected map is denoised and rectified: $\bar{\mathbf{A}^a_i} \in [0,1]^{n_v}$8 This makes TAM a token-aware refinement of CAM-like attribution specifically designed for autoregressive multimodal generation (Li et al., 29 Jun 2025).

The same paper defines a multimodal activation vector

$\bar{\mathbf{A}^a_i} \in [0,1]^{n_v}$9

which concatenates refined visual activation with textual relevance to context tokens, enabling joint visualization of image grounding and token–token interaction (Li et al., 29 Jun 2025).

A related development, Prompt-Vision Token Activation Map (PV-TAM), shifts the semantic anchor from answer-side tokens to prompt-side tokens. The motivation is that answer-side attention is susceptible to decoding drift: for a fixed image $n_v$0 and semantic token $n_v$1, attention-based maps can vary with the generated prefix $n_v$2. This is formalized as Autoregressive Context Contamination: there exist two different prefixes with the same image and target token such that the activation maps differ (Chen et al., 22 Jun 2026). PV-TAM therefore extracts attention from prompt tokens to visual tokens,

$n_v$3

using last-layer attention and mean head aggregation (Chen et al., 22 Jun 2026).

PV-TAM also models structural bias from modality boundary markers such as special vision-delimiting tokens. If $n_v$4 is the prompt token’s raw map and $n_v$5 are the maps of adjacent structural tokens, denoising is performed by

$n_v$6

Under the paper’s additive decomposition assumptions, this cancels the shared structural component and leaves the semantic signal plus zero-mean noise (Chen et al., 22 Jun 2026).

These multimodal TAM variants are evaluated with IoU-style metrics and with attention-distribution metrics that retain activation intensity. PV-TAM introduces Target–Global Ratio, Target–Dominant Ratio, and Min Distance, arguing that binary overlap alone ignores peak distribution and diffuse background attention (Chen et al., 22 Jun 2026). This suggests a broader shift in TAM evaluation from thresholded localization alone toward distribution-sensitive alignment analysis.

3. TAMs for artwork grounding and semantic analysis

The application of TAM to artwork description uses Qwen2-VL-2B and studies whether individual generated tokens are grounded in the correct visual regions of paintings. In this setting, each painting is converted into a grid of visual tokens, with one token per $n_v$7 patch and total visual-token count between 256 and 1,280. For each generated token $n_v$8, TAM produces a refined patch-level map $n_v$9 after context interference removal and rank Gaussian denoising (Fanelli et al., 26 Jun 2026).

The paper extends per-token TAM to semantically annotated spans. If span $A \in \mathbb{R}^{L_M \times N \times D_M}$0 consists of token indices $A \in \mathbb{R}^{L_M \times N \times D_M}$1, its map is

$A \in \mathbb{R}^{L_M \times N \times D_M}$2

Averaging over constituent tokens is reported to yield the most spatially coherent maps compared with using the first token only or element-wise max (Fanelli et al., 26 Jun 2026).

Using 1,000 WikiArt paintings and 12,878 labeled spans, the study distinguishes five semantic categories: concrete visual objects, iconographic subjects, painterly style, affective or interpretive spans, and metadata (Fanelli et al., 26 Jun 2026). Localization statistics show systematic differences across categories. Concrete visual objects and iconographic tokens are more localized, with lower normalized entropy and higher concentration metrics, whereas style and affect are more diffuse. The reported values are:

Span type	Entropy $A \in \mathbb{R}^{L_M \times N \times D_M}$3	Gini $A \in \mathbb{R}^{L_M \times N \times D_M}$4	Top-10% mass $A \in \mathbb{R}^{L_M \times N \times D_M}$5
cvo	$A \in \mathbb{R}^{L_M \times N \times D_M}$6	0.421	0.303
icon	$A \in \mathbb{R}^{L_M \times N \times D_M}$7	0.443	0.312
style	$A \in \mathbb{R}^{L_M \times N \times D_M}$8	0.368	0.264
affect	$A \in \mathbb{R}^{L_M \times N \times D_M}$9	0.350	0.259
meta	$\times$0	0.392	0.282

These results indicate that TAM can distinguish sharply localized evidence for concrete entities from diffuse whole-canvas evidence for stylistic and affective terms (Fanelli et al., 26 Jun 2026).

The same study also uses TAM-derived “visual reliance” to analyze hallucinations in metadata. Artist attribution is substantially more accurate than title prediction, and correct artist predictions show higher average visual reliance on the image than incorrect ones, whereas title predictions show no significant difference. This is interpreted as evidence that artist attributions are more image-grounded while title predictions are more prior-driven (Fanelli et al., 26 Jun 2026).

A further comparison with SAM 3 open-vocabulary segmentation shows that TAM maps are semantically aligned but spatially coarse. For concrete visual objects, mean IoU between Otsu-thresholded TAM maps and SAM masks is 0.233; for iconographic spans it is 0.194 (Fanelli et al., 26 Jun 2026). This suggests TAM is better suited to semantic grounding analysis than to precise boundary extraction.

4. Layer–token and scalar-token TAMs in LLMs

The activation-tensor view treats the internal state of a LLM as a TAM-like object over layers and tokens. For an LLM $\times$1, prompt $\times$2, and generated answer $\times$3 of length $\times$4, the Activation Tensor is

$\times$5

where $\times$6 is the number of layers and $\times$7 the hidden width. Each entry $\times$8 is the hidden state vector at layer $\times$9 and output token position $L_2$0 (Bar-Shalom et al., 30 Sep 2025). The ACT-ViT framework treats the $L_2$1 plane as the spatial domain of an image and hidden units as channels, then pools, linearly adapts, patches, and classifies the resulting tensor with a ViT-style backbone for hallucination detection (Bar-Shalom et al., 30 Sep 2025).

This view generalizes TAM beyond saliency. Instead of producing a scalar heatmap per token, it represents the entire processing trace of the sequence. Pooled tensors $L_2$2 are projected to a common feature size by LLM-specific linear adapters,

$L_2$3

before being patched over the layer–token plane and processed by a ViT backbone (Bar-Shalom et al., 30 Sep 2025). In-domain, ACT-ViT reportedly outperforms best token probes on 14 of 15 LLM–dataset pairs, and multi-LLM training improves performance in 12 of 15 cases, which suggests that structured layer–token TAMs contain cross-model hallucination signatures that are not captured by isolated layer-token probes (Bar-Shalom et al., 30 Sep 2025).

A simpler scalar TAM appears in the Activation Flow Network analysis of BERT. For Layer 8 hidden states $L_2$4, Token Activation Strength is

$L_2$5

This yields a token-indexed activation profile over the sequence (Ghosh et al., 21 May 2026). Tokens are partitioned into HIGH and LOW activation buckets via the upper quartile threshold $L_2$6, with

$L_2$7

The study reports that semantically meaningful content words consistently occupy the HIGH bucket and that HIGH-activation tokens dominate activation shifts under sentence perturbations (Ghosh et al., 21 May 2026).

The same framework defines Activation Shift between two aligned inputs A and B as

$L_2$8

and a HIGH-bucket contribution ratio

$L_2$9

In one illustrative example, HIGH tokens account for about 76% of total activation shift while constituting roughly 25% of the tokens, suggesting sparse concentration of representational change (Ghosh et al., 21 May 2026). This implies that even highly compressed scalar TAMs can expose semantic concentration and contextual reconfiguration in intermediate layers.

5. Token activation maps in routing and weakly supervised localization

In MoE architectures, TAM becomes a routing map rather than an attribution map. DynaMoE defines a hard TAM at layer $A^{(\ell)} \in \{0,1\}^{T \times N_\ell}$0 for $A^{(\ell)} \in \{0,1\}^{T \times N_\ell}$1 tokens and $A^{(\ell)} \in \{0,1\}^{T \times N_\ell}$2 experts as

$A^{(\ell)} \in \{0,1\}^{T \times N_\ell}$3

and a soft TAM $A^{(\ell)} \in \{0,1\}^{T \times N_\ell}$4 containing normalized expert weights over the selected experts (Gülmez, 2 Mar 2026). Dynamic routing replaces fixed Top-$A^{(\ell)} \in \{0,1\}^{T \times N_\ell}$5 with percentile-threshold selection. For gate vector $A^{(\ell)} \in \{0,1\}^{T \times N_\ell}$6, the threshold is

$A^{(\ell)} \in \{0,1\}^{T \times N_\ell}$7

and selected experts are

$A^{(\ell)} \in \{0,1\}^{T \times N_\ell}$8

with fallback to $A^{(\ell)} \in \{0,1\}^{T \times N_\ell}$9 if none are selected (Gülmez, 2 Mar 2026). The number of active experts becomes variable: $W^{(\ell)}$0

Because DynaMoE also varies the number of experts by layer according to schedules such as descending, ascending, pyramid, and wave, TAM width changes with depth. For a descending 4-layer schedule with $W^{(\ell)}$1 and $W^{(\ell)}$2, the expert counts are $W^{(\ell)}$3, and reported average active experts per token decrease from about 3.2 in layer 1 to about 1.2 by layer 4 (Gülmez, 2 Mar 2026). This makes the TAM wide and moderately sparse in early layers and narrow and dense in late layers.

Theoretical analysis in DynaMoE relates TAM diversity to expressivity. Fixed Top-$W^{(\ell)}$4 routing yields $W^{(\ell)}$5 possible activation subsets, whereas percentile routing yields

$W^{(\ell)}$6

with strict inequality over fixed Top-$W^{(\ell)}$7 when $W^{(\ell)}$8 (Gülmez, 2 Mar 2026). This is interpreted as an increase in routing-pattern diversity and therefore in the set of possible token–expert maps.

In vision localization, a related but not identical token-based map is TS-CAM. Patch tokens from a vision transformer are endowed with class semantics through a semantic re-allocation branch producing class-specific token maps $W^{(\ell)}$9, while class-token attention averaged across layers yields a semantic-agnostic attention map $\boldsymbol{F}^v \in \mathbb{R}^{n_v \times c}$0. The final token semantic coupled attention map is

$\boldsymbol{F}^v \in \mathbb{R}^{n_v \times c}$1

where $\boldsymbol{F}^v \in \mathbb{R}^{n_v \times c}$2 reshapes the attention vector to the token grid and $\boldsymbol{F}^v \in \mathbb{R}^{n_v \times c}$3 is element-wise multiplication (Gao et al., 2021). This can be interpreted as a class-conditioned TAM over image patches. On CUB-200-2011, TS-CAM with DeiT-S reports Top-1 localization 71.3%, Top-5 localization 83.8%, and GT-Known localization 87.7%, outperforming several CNN-CAM baselines (Gao et al., 2021).

A CNN analogue is the low-level feature based activation map framework for weakly supervised object localization. There, a generator predicts a dense activation map $\boldsymbol{F}^v \in \mathbb{R}^{n_v \times c}$4 from shallow feature maps $\boldsymbol{F}^v \in \mathbb{R}^{n_v \times c}$5, and the map directly masks the features used by the classifier,

$\boldsymbol{F}^v \in \mathbb{R}^{n_v \times c}$6

Stage 2 refines $\boldsymbol{F}^v \in \mathbb{R}^{n_v \times c}$7 using evaluation cross-entropy, weighted entropy loss, area loss, and attentive erasing to yield a well-separated and thresholdable object map (Xie et al., 2021). Although formulated for pixels rather than tokens, the paper explicitly notes the close analogy to a TAM in token-based models (Xie et al., 2021).

6. Methodological themes, metrics, and limitations

Across the literature, TAM methods differ most clearly along four axes: activation domain, scalarization, context handling, and evaluation target.

Setting	TAM domain	Core signal	Main objective
MLLM TAM	token $\boldsymbol{F}^v \in \mathbb{R}^{n_v \times c}$8 visual patches	classifier-row projection + causal subtraction	token-level visual explanation (Li et al., 29 Jun 2025)
PV-TAM	prompt token $\boldsymbol{F}^v \in \mathbb{R}^{n_v \times c}$9 visual patches	prompt-to-vision attention	vision–language consistency (Chen et al., 22 Jun 2026)
ACT-ViT	layers $\boldsymbol{F}^p \in \mathbb{R}^{n_p \times c}$0 tokens $\boldsymbol{F}^p \in \mathbb{R}^{n_p \times c}$1 hidden units	hidden-state tensor	hallucination detection (Bar-Shalom et al., 30 Sep 2025)
AFN	token sequence	$\boldsymbol{F}^p \in \mathbb{R}^{n_p \times c}$2 norm of Layer-8 hidden states	semantic saliency analysis (Ghosh et al., 21 May 2026)
DynaMoE	tokens $\boldsymbol{F}^p \in \mathbb{R}^{n_p \times c}$3 experts $\boldsymbol{F}^p \in \mathbb{R}^{n_p \times c}$4 layers	routing decisions and weights	adaptive computation analysis (Gülmez, 2 Mar 2026)

A recurring methodological issue is context contamination. In MLLMs, prior tokens can induce visually similar maps for later tokens even when they are semantically distinct, motivating the estimated causal interference subtraction of TAM (Li et al., 29 Jun 2025). PV-TAM makes a related but different intervention by relocating the semantic anchor to prompt-side tokens and subtracting structural-token bias (Chen et al., 22 Jun 2026). In the artwork setting, context interference removal is essential because generated art descriptions mix local object labels, global style terms, and metadata-like claims that can be strongly prior-driven (Fanelli et al., 26 Jun 2026).

Another recurring issue is threshold sensitivity and intensity preservation. Classical IoU-style localization discards activation magnitude through binarization. PV-TAM therefore introduces TGR, TDR, and Min-Dist to capture activation concentration and peak geometry (Chen et al., 22 Jun 2026). The low-level WSOL framework similarly emphasizes the importance of producing activation maps with bimodal histograms and stable threshold sensitivity across images (Xie et al., 2021). In artwork grounding, normalized entropy, Gini coefficient, and top-10% mass are used to quantify concentration without collapsing maps to binary masks (Fanelli et al., 26 Jun 2026).

Noise and coarseness are also common limitations. The original TAM addresses salt-and-pepper artifacts in transformer maps through a rank Gaussian filter, which combines the robustness of rank-based filtering with Gaussian weighting over ranks (Li et al., 29 Jun 2025). Yet even after refinement, artwork TAM maps remain coarser than SAM 3 masks (Fanelli et al., 26 Jun 2026). ACT-ViT faces a different resolution problem: the full activation tensor is large, so it requires max-pooling over layers and tokens, which the authors note is lossy (Bar-Shalom et al., 30 Sep 2025).

Finally, several works note that activation maps are not identical to causal explanations. AFN assumes that large $\boldsymbol{F}^p \in \mathbb{R}^{n_p \times c}$5 norm implies representational importance, a heuristic not validated against human labels in that work (Ghosh et al., 21 May 2026). PV-TAM explicitly notes that attention is not causality, even though prompt-side attention is more stable than answer-side attention (Chen et al., 22 Jun 2026). DynaMoE’s TAMs can also collapse in principle without explicit load balancing, though catastrophic collapse is not observed at the studied scales (Gülmez, 2 Mar 2026).

7. Significance and emerging directions

The research trajectory around TAM indicates a general move from static class-level explanations toward sequence-aware, token-indexed activation analysis. In multimodal models, TAM provides an interpretable unit for asking whether a specific generated word is visually grounded or merely inherited from prior linguistic context (Li et al., 29 Jun 2025, Fanelli et al., 26 Jun 2026). In VLM consistency evaluation, prompt-side TAMs suggest that semantic grounding can be analyzed more cleanly before decoding drift accumulates (Chen et al., 22 Jun 2026). In language-model analysis, layer–token tensors and scalar token activation profiles show that TAM-like structures can support hallucination detection, transfer across LLMs, and the study of semantic concentration in intermediate layers (Bar-Shalom et al., 30 Sep 2025, Ghosh et al., 21 May 2026).

A plausible implication is that TAM has become less a single algorithm than a representational paradigm. In this broader sense, a TAM is any structured activation object whose primary coordinates include tokens and whose analysis reveals token-specific evidence, routing, or representational salience. Under that interpretation, visual token grounding in MLLMs, layer–token activation tensors for LLM diagnostics, token-wise semantic saliency in BERT, and token–expert routing diagrams in MoE models are all instances of the same methodological turn toward token-centered activation analysis (Li et al., 29 Jun 2025, Bar-Shalom et al., 30 Sep 2025, Gülmez, 2 Mar 2026).

Current work also suggests several unresolved directions. One is improved compression or multi-scale modeling of layer–token TAMs beyond fixed max-pooling (Bar-Shalom et al., 30 Sep 2025). Another is combining semantically aligned but coarse TAM maps with boundary-precise segmentation systems (Fanelli et al., 26 Jun 2026). A third is integrating TAM-style objectives into model training rather than using them only post hoc, as suggested by the online generation and refinement of activation maps in weakly supervised localization (Xie et al., 2021). In MoE settings, learned capacity schedules driven directly by TAM statistics rather than predefined schedules remain an open direction (Gülmez, 2 Mar 2026).

Taken together, these developments establish Token Activation Map as a unifying concept for analyzing how tokenized neural systems allocate computation, represent semantics, and ground outputs in input structure across language, vision, and multimodal generation.