Semantic Sections: An Atlas-Native Feature Ontology for Obstructed Representation Spaces

Hossein Javidnia

cs.LGMar 21, 2026

Semantic Sections: An Atlas-Native Feature Ontology for Obstructed Representation Spaces

Authors: Hossein Javidnia

Organizations: School of Computing, Dublin City University, Ireland

Abstract

Recent interpretability work often treats a feature as a single global direction, dictionary atom, or latent coordinate shared across contexts. We argue that this ontology can fail in obstructed representation spaces, where locally coherent meanings need not assemble into one globally consistent feature. We introduce an atlas-native replacement object, the semantic section: a transport-compatible family of local feature representatives defined over a context atlas. We formalize semantic sections, prove that tree-supported propagation is always pathwise realizable, and show that cycle consistency is the key criterion for genuine globalization. This yields a distinction between tree-local, globalizable, and twisted sections, with twisted sections capturing locally coherent but holonomy-obstructed meanings. We then develop a discovery-and-certification pipeline based on seeded propagation, synchronization across overlaps, defect-based pruning, cycle-aware taxonomy, and deduplication. Across layer-16 atlases for Llama 3.2 3B Instruct, Qwen 2.5 3B Instruct, and Gemma 2 2B IT, we find nontrivial populations of semantic sections, including cycle-supported globalizable and twisted regimes after deduplication. Most importantly, semantic identity is not recovered by raw global-vector similarity. Even certified globalizable sections show low cross-chart signed cosine similarity, and raw similarity baselines recover only a small fraction of true within-section pairs, often collapsing at moderate thresholds. By contrast, section-based identity recovery is perfect on certified supports. These results support semantic sections as a better feature ontology in obstructed regimes.

Figures & tables

Figure 1: Semantic sections on a cyclic chart subsystem. (A) A globalizable section, where transporting the local representative

s_{u}

around a loop

\gamma

returns a representative

T_{\gamma}s_{u}

that remains nearly aligned with

s_{u}

, yielding small loop defect

\Delta_{\gamma}\approx 0

. (B) A twisted section, where the same loop transport returns a rotated representative, so

T_{\gamma}s_{u}

no longer aligns with

s_{u}

and the loop defect is nonzero,

\Delta_{\gamma}>0

. This illustrates the difference between cycle-consistent semantic globalization and holonomy-obstructed local coherence.

Quantity	Value
Seed charts per model	$16$
Seed axes per chart	$32$
Raw seeded discoveries per model	$512$
Maximum propagation depth	$8$
Minimum overlap size	$256$
Transport conditioning threshold	$s_{\min}=0$

Table 1: Section-discovery protocol used for the cross-model section census. All runs use the same discovery and classification settings unless otherwise stated.

Model	Globalizable	Twisted	Tree-local	Fragile	Total unique
Llama 3.2 3B Instruct	1	1	2	32	36
Qwen 2.5 3B Instruct	1	2	5	16	24
Gemma 2 2B IT	14	4	8	40	66

Table 2: Deduplicated cross-model census of semantic section types at layer 16. Counts are reported after merging near-duplicate sections discovered from multiple seeds.

Model	Section type	Count	Mean support	Mean cycles	Mean edge defect	Mean alignment
Llama 3.2 3B Instruct	Globalizable	1	6.0000	1.0000	0.1367	0.9892
Llama 3.2 3B Instruct	Twisted	1	4.0000	1.0000	0.1232	0.9908
Llama 3.2 3B Instruct	Tree-local	2	4.5000	0.0000	0.1486	0.9887
Llama 3.2 3B Instruct	Fragile	32	1.9375	0.0938	0.0720	0.9946
Qwen 2.5 3B Instruct	Globalizable	1	9.0000	1.0000	0.1106	0.9915
Qwen 2.5 3B Instruct	Twisted	2	8.0000	1.5000	0.1217	0.9902

Table 3: Type-wise geometric profile of deduplicated semantic sections at layer 16. Support is the number of charts in the section support; cycles counts independent cycles in the induced support subgraph. Lower edge defect and higher alignment indicate stronger transport compatibility.

Model	Type	Seed	Support	Cycles	Mean edge defect	Mean holonomy defect	Representative chart support
Llama 3.2 3B Instruct	Globalizable	$(12,11)$	6	1	0.1367	0.3199	$\{12,26,38,78,108,111\}$
Llama 3.2 3B Instruct	Twisted	$(15,0)$	4	1	0.1232	0.3750	$\{15,27,30,101\}$
Qwen 2.5 3B Instruct	Tree-local	$(5,0)$	15	0	$\approx 0$	—	$\{0,3,5,8,10,15,28,43,72,77,79,83,93,101,115\}$
Qwen 2.5 3B Instruct	Globalizable	$(14,10)$	9	1	0.1106	0.3363	$\{1,13,14,52,63,64,96,97,126\}$
Qwen 2.5 3B Instruct	Twisted	$(14,17)$	9	2	0.1218	0.3954	$\{1,13,14,21,52,63,96,97,126\}$
Gemma 2 2B IT	Globalizable	$(14,4)$	23	9	0.0960	0.2245	$\{3,4,9,13,14,15,\dots,107,111,116\}$

Table 4: Representative deduplicated semantic sections used as exemplars in Section 5.3 , illustrating globalizable, twisted, and tree-local regimes across the three model families.

Model	Type	Seed	Support	Cycles	Mean edge defect	Mean path defect	Mean holonomy defect	Pairwise mean cosine	Pairwise min cosine	Fraction $\geq 0.5$	Fraction $\geq 0.7$
Llama 3.2 3B Instruct , layer 16	Globalizable	$(12,11)$	6	1	0.1367	0.1003	0.3199	0.2139	0.0796	0.0000	0.0000
Llama 3.2 3B Instruct , layer 16	Twisted	$(15,0)$	4	1	0.1232	0.0832	0.3750	0.2475	0.0615	0.1667	0.0000
Llama 3.2 3B Instruct , layer 16	Tree-local	$(11,9)$	5	0	0.1419	0.1366	—	0.3028	0.0701	0.3000	0.0000
Llama 3.2 3B Instruct , layer 16	Tree-local	$(0,5)$	4	0	0.1553	0.1294	—	0.1246	0.0284	0.1667	0.0000
Qwen 2.5 3B Instruct , layer 16	Tree-local	$(5,0)$	15	0	0.0000	0.0000	—	0.3483	0.0055	0.3143	0.0571
Qwen 2.5 3B Instruct , layer 16	Globalizable	$(14,10)$	9	1	0.1106	0.0942	0.3363	0.1814	0.0069	0.0000	0.0000

Table 5: Semantic-section coherence versus raw cross-chart vector similarity for the representative exemplars in Section 5.3 , together with a small number of additional tree-local baseline sections. Even when a candidate object is section-coherent, its raw local representatives are not close as a single shared vector across charts.

Model	Type	Seed	Support	Cycles	Pairs	Section recovery	Raw @ $0.3$	Raw @ $0.5$	Raw @ $0.7$	Mean cosine	Min cosine
Llama 3.2 3B Instruct , layer 16	Globalizable	$(12,11)$	6	1	15	1.0000	0.2667	0.0000	0.0000	0.2139	0.0796
Llama 3.2 3B Instruct , layer 16	Twisted	$(15,0)$	4	1	6	1.0000	0.3333	0.1667	0.0000	0.2475	0.0615
Llama 3.2 3B Instruct , layer 16	Tree-local	$(11,9)$	5	0	10	1.0000	0.5000	0.3000	0.0000	0.3028	0.0701
Llama 3.2 3B Instruct , layer 16	Tree-local	$(0,5)$	4	0	6	1.0000	0.1667	0.1667	0.0000	0.1246	0.0284
Qwen 2.5 3B Instruct , layer 16	Tree-local	$(5,0)$	15	0	105	1.0000	0.5619	0.3143	0.0571	0.3483	0.0055
Qwen 2.5 3B Instruct , layer 16	Globalizable	$(14,10)$	9	1	36	1.0000	0.2500	0.0000	0.0000	0.1814	0.0069

Table 6: Semantic identity recovery: sections versus raw global similarity. The section criterion recovers all within-section pairs by construction, while raw signed-cosine similarity recovers only a small fraction of them.

Model	Type	$n$ sections	Mean support	Mean cycles	Total pairs	Section	Raw @ $0.3$	Raw @ $0.5$
Llama 3.2 3B Instruct , layer 12	Globalizable	1	8.0000	8.0000	28	1.0000	0.1071	0.0714
Llama 3.2 3B Instruct , layer 12	Tree-local	5	6.4000	0.0000	101	1.0000	0.0792	0.0000
Llama 3.2 3B Instruct , layer 16	Globalizable	1	6.0000	1.0000	15	1.0000	0.2667	0.0000
Llama 3.2 3B Instruct , layer 16	Twisted	1	4.0000	1.0000	6	1.0000	0.3333	0.1667
Llama 3.2 3B Instruct , layer 16	Tree-local	2	4.5000	0.0000	16	1.0000	0.3750	0.2500
Llama 3.2 3B Instruct , layer 20	Tree-local	2	4.5000	0.0000	16	1.0000	0.2500	0.0000

Table 7: Aggregate semantic identity recovery by section type. Across models and layers, section-based recovery is perfect on certified supports, while raw global-similarity recovery remains poor and collapses to zero at stricter thresholds.

Figure 2: Aggregate semantic identity recovery across section types. Section-based recovery remains perfect on certified supports, while raw global-similarity recovery is consistently lower and typically collapses at stricter thresholds.

Model / layer	Globalizable	Twisted	Tree-local	Fragile	Total unique
Llama 3.2 3B Instruct , layer 12	1	0	5	17	23
Llama 3.2 3B Instruct , layer 16	1	1	2	32	36
Llama 3.2 3B Instruct , layer 20	0	0	2	21	23

Table 8: Deduplicated semantic-section census across Llama layers.

Model / layer	Globalizable	Twisted	Tree-local	Fragile	Total unique
Llama 3.2 3B Instruct , layer 12	$0$ – $1$	$0$ – $0$	$5$ – $5$	$17$ – $18$	$23$ – $23$
Llama 3.2 3B Instruct , layer 16	$0$ – $1$	$1$ – $1$	$2$ – $2$	$32$ – $33$	$36$ – $36$
Qwen 2.5 3B Instruct , layer 16	$0$ – $1$	$1$ – $2$	$4$ – $5$	$16$ – $17$	$22$ – $24$
Gemma 2 2B IT , layer 16	$13$ – $18$	$2$ – $5$	$8$ – $8$	$40$ – $47$	$64$ – $74$

Table 9: Range of deduplicated section counts under threshold variation. Each entry reports the minimum and maximum count across the

27

threshold settings.

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