CRAFT: Causal Responsibility and Failure Tracing in Medical Vision Language Models

Chunzheng Zhu; Jiaqi Zeng; Hongbo Zhao; Yihang Chen; Yijun Wang; Jianxin Lin

cs.CVSep 30, 2026

CRAFT: Causal Responsibility and Failure Tracing in Medical Vision Language Models

Authors: Chunzheng Zhu, Jiaqi Zeng, Hongbo Zhao, Yihang Chen, Yijun Wang, Jianxin Lin

Organizations: Hunan University · University of Hong Kong

Abstract

As vision language models are increasingly deployed in clinical diagnosis, understanding how they internally resolve competing visual and textual signals becomes a safety imperative. Existing mechanistic analyses remain confined to unimodal text and offer no explanation for why a single misleading sentence can override a correct image based diagnosis, or why a model commits to a confident answer despite insufficient visual evidence. We find that these two safety risks, arbitration failure where textual context overrides visual grounding and brake failure where the model commits without adequate evidence, are mediated by spatially disjoint attention head populations: arbitration heads form a mid-to-deep wideband reflecting cross-layer evidence competition, while brake heads concentrate in a narrow middle-to-late layer band that regulates evidence sufficiency and abstention behavior. To ground these observations in causal circuitry, we introduce CRAFT, which localizes each failure mode to a minimal causal head set via dual criteria and verifies necessity and sufficiency through temporal probes and Tuned Lens trajectory analysis. Excising arbitration heads sharply reduces conflict following with negligible degradation on clean inputs, while excising brake heads restores appropriate abstention under degraded visual evidence. The two interventions target spatially disjoint head sets and produce distinct corrective effects, underscoring the mechanistic separability of the failure modes. Experiments across multiple medical VQA benchmarks and VLM architectures validate both the localization and interventions, demonstrating that the identified heads causally drive each failure mode and that targeted modulation generalises without retraining. The code is available at GitHub repository.

Figures & tables

Figure 1 : Two failure modes in medical VLMs and Craft interventions. Left (Arbitration Failure): Contradictory text hijacks a correct visual diagnosis, causing the model to follow the misleading answer. Right (Brake Failure): The model commits despite occluded evidence.

Figure 2 : Interventional tracing setup. (a) The evidence conflict problem can be modeled by a structural causal model with three competing paths: visual anchoring $\mathcal{P}_{v}$ , parametric knowledge $\mathcal{P}_{\Theta}$ , and textual override $\mathcal{P}_{t}$ , plus latent confounders $U_{c}$ that open back-door paths. (b) Path dominance under each failure mode. (c) Clean-state interchange blocks back-door paths through $U_{c}$ while preserving the front-door visual causal path, isolating genuine causal effects for head selection.

Figure 3 : Arbitration failure localization. (a) Layer-wise $S(l)$ : shaded window marks layers where override concentrates. (b) CER vs. BCP: red points (upper left) are selected causal key heads.

Figure 4 : Arbitration head localization (Hulu-Med 4B). Left: conflict heads (blue) cluster in the Override Band with high CER and low BCP; the Late Commitment Tail shows negligible conflict or fidelity circuitry, with residual score reflecting distributed output formation. Centre: layer $\times$ head grid with per layer CER and BCP bars. Right: selection criterion and recovery example.

Figure 5 : Brake head localization (Hulu-Med 4B). (a) HR( $l$ ) map with positive peaks at the deepest layers. (b,c) BCP versus HR landscape separating different heads from backbone.

			Prefix				Before Q				Before A
Benchmark	Model	Method	CFR $\downarrow$	Resist $\uparrow$	C2W $\downarrow$	W2C $\uparrow$	CFR $\downarrow$	Resist $\uparrow$	C2W $\downarrow$	W2C $\uparrow$	CFR $\downarrow$	Resist $\uparrow$	C2W $\downarrow$	W2C $\uparrow$
VQA-RAD	Hulu-Med 4B	Baseline	33.33	66.67	N/A	N/A	44.62	55.38	N/A	N/A	98.21	1.79	N/A	N/A
		Random	33.59	66.41	1.79	1.54	40.77	59.23	1.03	4.87	98.72	1.28	0.51	0.00
		Craft	15.38	84.62	0.26	18.21	14.36	85.64	0.51	30.77	70.26	29.74	0.00	27.95
	Hulu-Med 7B	Baseline	25.80	74.20	N/A	N/A	36.50	63.50	N/A	N/A	88.00	12.00	N/A	N/A
		Random	25.40	74.60	0.90	1.30	35.20	64.80	0.80	2.10	88.30	11.70	0.35	0.05

Table 1 : Arbitration failure on multimodal benchmarks under textual conflict.

\mathrm{CFR}

: conflict following rate (

\downarrow

); Resist: resist rate (

\uparrow

);

\mathrm{C2W}

: correct to wrong (

\downarrow

); W2C: wrong to correct (

\uparrow

). Bold: best comparison result. All CRAFT gains are significant (

p<0.01

		Hulu-Med 4B			Hulu-Med 7B			Hulu-Med 14B			InternVL3.5 4B			Qwen3-VL-8B
Benchmark	Method	UR $\uparrow$	O2U $\uparrow$	U2O $\downarrow$	UR $\uparrow$	O2U $\uparrow$	U2O $\downarrow$	UR $\uparrow$	O2U $\uparrow$	U2O $\downarrow$	UR $\uparrow$	O2U $\uparrow$	U2O $\downarrow$	UR $\uparrow$	O2U $\uparrow$	U2O $\downarrow$
SLAKE	Baseline	37.88	–	–	44.12	–	–	48.90	–	–	22.58	–	–	42.42	–	–
	Random	45.45	8.33	0.76	48.82	5.29	0.59	51.10	3.00	0.80	16.13	0.00	6.45	47.73	6.06	0.75
	Craft	65.15	27.27	0.00	61.18	17.06	0.00	58.40	10.20	0.70	32.26	9.68	0.00	55.30	12.88	0.00
HealMed-VQA	Baseline	78.44	–	–	85.07	–	–	88.61	–	–	81.56	–	–	84.72	–	–
	Random	86.83	10.18	1.80	88.19	4.31	1.19	89.58	1.45	0.48	82.47	3.06	2.15	87.50	4.17	1.39

Table 2 : Brake failure under visual degradation. Craft excises the localized brake heads to recover abstention. UR: unknown rate (

\uparrow

); O2U: non-unknown to unknown (

\uparrow

); U2O: unknown to other (

\downarrow

Model	Criterion	$\mathrm{CFR}\downarrow$	$\|\Delta\mathrm{CFR}\|\uparrow$	$\mathrm{C2W}\downarrow$	#Heads
Hulu-Med 4B	CER only	6.7	37.9	1.8	6
	BCP only	44.4	0.3	1.3	6
	Dual (Ours)	14.4	30.3	0.5	6
InternVL3.5 4B	CER only	9.7	12.0	4.0	10
	BCP only	7.4	14.3	2.3	10
	Dual (Ours)	7.4	14.3	0.0	10

Table 3 : Comparison of selection criteria at equal head count. Left: arbitration failure (CER only: highest conflict effect; BCP only: lowest collateral). Right: brake failure on validation split (HR only: highest hallucination relief; BCP only: lowest collateral). Shaded: CRAFT.

Figure 6 : Temporal probe separation. Probe A (detection) saturates before Probe B (commitment), revealing a detect then commit structure. Post-intervention selectively suppresses Probe B while preserving Probe A.

Figure 7 : Tuned Lens trajectories. The preference margin

\Delta_{l}

flips sign within the override window under conflict. Intervention suppresses this reversal, restoring correct dominance.

Figure 8 : The Unknown rate increases with mask_scale , but remains below 50% even under the strongest masking. We use mask_scale =2.0 as the default degradation setting.

$\boldsymbol{\tau_{s}}$	$\boldsymbol{\tau_{d}}$	CFR $\downarrow$	C2W $\downarrow$	W2C $\uparrow$	#Heads
0.133	0.550	18.7	4.9	30.8	25
0.271	0.183	24.6	0.5	20.5	2
0.156	0.445	22.1	7.9	30.5	31
0.350	0.500	18.2	0.8	27.4	6

Table 4 : Threshold sensitivity (Hulu-Med 4B). Left : arbitration on VQA-RAD (Before Q). Right : brake on SLAKE (image conflict). Highlighted: final settings.

			ConflictMedQA				PubMedQA
Position	Model	Method	CFR $\downarrow$	Resist $\uparrow$	C2W $\downarrow$	W2C $\uparrow$	CFR $\downarrow$	Resist $\uparrow$	C2W $\downarrow$	W2C $\uparrow$
Prefix	Qwen3-4B	Baseline	83.33	16.67	–	–	49.67	50.33	–	–
		Random	94.25	5.75	12.07	1.15	57.17	42.83	12.17	4.67
		Craft	37.36	62.64	0.00	45.98	23.59	76.41	0.00	26.09
	Llama3.2-3B	Baseline	57.69	42.31	–	–	58.46	41.54	–	–
		Random	42.31	57.69	26.92	42.31	63.08	36.92	8.85	4.23

Table 5 : Text only benchmark results. CFR : conflict compliance (

\downarrow

); Resist : resist rate (

\uparrow

); C2W : correct to wrong (

\downarrow

); W2C : wrong to correct (

\uparrow

). Bold : best per block; shaded rows: Craft . Random uses the same head count.

Method	Paradigm	Extra Model	Training	Target Failure
CCD [ 41 ]	Decoding guidance	✓	✗	Textual conflict
MARINE [ 42 ]	Decoding guidance	✓	✗	Textual conflict
ITI [ 20 ]	Activation steering	✗	✓	General truthfulness
HALP [ 17 ]	Hallucination detection	✗	✓	Pre generation risk
Craft	Causal head repair	✗	✗	Arbitration + brake

Table 6 : Inference time safety baselines. Highlighted: the proposed method.

Method	Extra	Train	CFR $\downarrow$	Resist $\uparrow$	C2W $\downarrow$	W2C $\uparrow$
Baseline	✗	✗	44.62	55.38	0.00	0.00
Random heads	✗	✗	40.77	59.23	1.03	4.87
CCD	✓	✗	31.28	68.72	1.28	13.34
MARINE	✓	✗	28.46	71.54	1.03	16.92
ITI	✗	✓	34.10	65.90	1.79	12.31
Craft	✗	✗	14.36	85.64	0.51	30.77

Table 7 : Arbitration failure (Hulu-Med 4B, VQA-RAD, Before Q). Bold: best.

Method	Extra	Train	UR $\uparrow$	O2U $\uparrow$	U2O $\downarrow$
Baseline	✗	✗	37.88	0.00	0.00
Random heads	✗	✗	45.45	8.33	0.76
HALP routed abstention	✗	✓	56.82	21.21	2.27
Craft	✗	✗	65.15	27.27	0.00

Table 8 : Brake failure (Hulu-Med 4B, SLAKE image conflict). Bold: best.

			Hulu-Med 4B				InternVL3.5 4B				Qwen3-VL-8B
Benchmark	Setting	Method	#Heads	Original	After	$\boldsymbol{\Delta}$ Acc	#Heads	Original	After	$\boldsymbol{\Delta}$ Acc	#Heads	Original	After	$\boldsymbol{\Delta}$ Acc
VQA-RAD	Arbitration failure	Random heads	6	100.00	94.87	$-$ 5.13	10	100.00	93.71	$-$ 6.29	8	100.00	95.80	$-$ 4.20
		CCD	7	100.00	91.79	$-$ 8.21	11	100.00	91.43	$-$ 8.57	9	100.00	93.60	$-$ 6.40
		MARINE	7	100.00	92.31	$-$ 7.69	11	100.00	92.00	$-$ 8.00	9	100.00	94.20	$-$ 5.80
		ITI	8	100.00	91.03	$-$ 8.97	12	100.00	90.86	$-$ 9.14	10	100.00	93.10	$-$ 6.90
		Craft	6	100.00	96.41	$-$ 3.59	10	100.00	96.57	$-$ 3.43	8	100.00	97.80	$-$ 2.20

Table 9 : Clean accuracy preservation under baseline methods.

\Delta

Acc denotes post minus pre intervention accuracy (%). Bold: best preservation result.

Method	Training	$\mathrm{CFR}\downarrow$	Resist $\uparrow$	C2W $\downarrow$	W2C $\uparrow$	Clean Acc. $\uparrow$
Baseline	✗	44.62	55.38	0.00	0.00	100.00
Random heads	✗	40.77	59.23	1.03	4.87	96.67
Conflict SFT	✓	22.31	77.69	2.05	24.36	97.18
Preference Tuning	✓	19.49	80.51	1.54	26.67	97.44
Craft	✗	14.36	85.64	0.51	30.77	96.41
Conflict SFT + Craft	✓	10.77	89.23	1.03	35.90	96.92

Table 10 : Comparison with fine tuning based safety adaptation on textual conflict. Hulu-Med 4B, VQA-RAD Before Q. Shaded rows denote Craft .

Method	Training	$\mathrm{UR}\uparrow$	O2U $\uparrow$	U2O $\downarrow$	Clean Acc. $\uparrow$	Net Gain $\uparrow$
Baseline	✗	37.88	0.00	0.00	100.00	0.00
Random heads	✗	45.45	8.33	0.76	96.90	7.57
Conflict SFT	✓	54.55	19.70	2.27	96.97	16.67
Preference Tuning	✓	57.58	21.97	1.52	96.21	19.70
Craft	✗	65.15	27.27	0.00	90.67	27.27
Conflict SFT + Craft	✓	69.70	32.58	0.76	92.42	31.82

Table 11 : Comparison with fine tuning based safety adaptation on visual brake failure. Hulu-Med 4B, SLAKE image conflict pool. Shaded rows denote Craft .

		Hulu-Med 4B				InternVL3.5 4B
Benchmark	Method	Gold $\uparrow$	Conflict $\downarrow$	Qualified $\uparrow$	Clean Fact. $\uparrow$	Gold $\uparrow$	Conflict $\downarrow$	Qualified $\uparrow$	Clean Fact. $\uparrow$
VQA RAD	Baseline	54.20	39.60	6.20	96.00	56.40	36.80	6.80	96.30
	Random	57.10	35.70	7.20	95.10	58.20	34.70	7.10	95.40
	Craft	70.90	21.80	7.30	94.70	78.40	13.20	8.40	95.00
SLAKE	Baseline	51.80	42.20	6.00	95.30	59.10	34.60	6.30	96.80
	Random	54.40	38.60	7.00	94.60	60.30	32.20	7.50	96.10

Table 12 : Open ended textual conflict results. The judge classifies each response into gold claim, conflict claim, or qualified response. Shaded rows denote Craft .

		Hulu-Med 4B				InternVL3.5 4B
Benchmark	Method	Uncert. $\uparrow$	Unsup. $\downarrow$	Evid. Req. $\uparrow$	Clean Compl. $\uparrow$	Uncert. $\uparrow$	Unsup. $\downarrow$	Evid. Req. $\uparrow$	Clean Compl. $\uparrow$
SLAKE	Baseline	36.20	59.10	4.70	95.60	24.80	69.40	5.80	94.80
	Random	44.00	50.70	5.30	94.70	18.60	75.20	6.20	93.70
	Craft	63.40	29.90	6.70	90.80	34.80	58.60	6.60	92.80
HealMed-VQA	Baseline	73.60	21.80	4.60	96.20	76.40	18.70	4.90	95.60
	Random	79.10	15.80	5.10	95.10	78.80	16.00	5.20	94.70

Table 13 : Open ended degraded vision results. The judge evaluates whether the response expresses calibrated uncertainty or makes an unsupported diagnosis. Shaded rows denote Craft .

Model	Implementation	#Heads	Normal Lat.	Interv. Lat.	$\Delta$ Lat.	Normal FLOPs	$\Delta$ FLOPs
Hulu-Med 4B	Hooked masking	6	206.39 ms	210.31 ms	+1.90%	19539.96G	0.00%
Hulu-Med 4B	True head skipping	6	207.11 ms	208.10 ms	+0.48%	19539.96G	0.11% lower
InternVL3.5 4B	Hooked masking	10	43.27 ms	49.43 ms	+14.24%	3629.54G	0.00%
InternVL3.5 4B	True head skipping	10	43.91 ms	47.03 ms	+7.11%	3629.54G	0.10% lower

Table 14 : Online computational cost of head intervention on VQA-RAD. Hooked masking is the analysis implementation; true head skipping offers marginal FLOP savings.

Model	Setting	Loc. Samples	Total Forwards	Forwards / Sample	Param. Update
Hulu-Med 4B	VQA-RAD Before Q	391	111,342	284.8	✗
InternVL3.5 4B	VQA-RAD Before Q	698	158,446	227.0	✗
Qwen3-VL-8B	VQA-RAD Before Q	698	241,508	346.0	✗

Table 15 : Offline cost of head selection. The localization cost is paid once before deployment. Prompt forward counts include diagnostic passes for layer tracing, head scoring, and selection.

Figure 9 : Head-level tuned-lens preference distributions under textual conflict. Each panel shows one selected arbitration-sensitive head. The horizontal axis is the gold-versus-wrong preference margin,

\log p(y^{+})-\log p(y^{-})

, under the no-conflict condition (NC, green) and the injected-conflict condition (IC, red). Larger values indicate stronger preference for the gold answer. Across many heads, IC shifts left relative to NC, showing that textual conflict pushes head-level preferences from the correct answer toward the misleading answer.

Figure 10 : Head-level tuned-lens abstention distributions under visual degradation. Each panel shows one selected image-conflict-sensitive head. The horizontal axis is the abstention margin,

\log p(\texttt{unknown})-\log p(y_{\mathrm{best}})

, where

y_{\mathrm{best}}

is the best competing concrete answer. NC denotes the original image, while IC denotes the masked image with relevant evidence removed. If the model properly abstains after masking, IC should shift right. Instead, NC and IC largely overlap for many heads, indicating persistent concrete-answer commitment under degraded visual evidence.

	Model (R / S)
$K_{\mathrm{patch}}$	Qwen3-4B	Llama3.2-3B	Hulu-Med-4B	Hulu-Med-7B	InternVL3.5-4B	Qwen3-VL-8B
2	0.9718 / 0.8577	0.9214 / 0.1475	0.9288 / 0.0671	0.9346 / 0.0648	0.9478 / 0.1633	0.9415 / 0.1586
4	0.9792 / 0.8553	0.9237 / 0.1483	0.9506 / 0.0687	0.9569 / 0.0665	0.9868 / 0.1775	0.9821 / 0.1694
8	0.9991 / 0.8449	0.9987 / 0.1501	0.9991 / 0.0714	0.9988 / 0.0698	0.9948 / 0.1781	0.9962 / 0.1726
16	0.9160 / 0.8983	0.9561 / 0.1459	0.9736 / 0.0741	0.9758 / 0.0726	0.9733 / 0.1818	0.9714 / 0.1769
32	0.8206 / 0.2564	0.9004 / 0.0442	0.8255 / 0.0153	0.8389 / 0.0147	0.8595 / 0.0535	0.8668 / 0.0512

Table 16 : Patch window sensitivity. Robustness (R)

\uparrow

measures agreement with the local window consensus; Smoothness (S)

\downarrow

measures adjacent layer variation. Best R and best S are bolded separately for each model.

Panel of Figure 11 — Figure 11 : Downstream attention after arbitration head ablation. Heatmaps compare the same conflict input before and after excising the selected arbitration heads. Blue and red regions denote image tokens and injected conflict text, respectively. Ablation weakens downstream attention to the conflict text region, consistent with reduced textual override propagation.

$\beta$	# Selected Heads	$\Delta\mathrm{UR}$	$\mathrm{HR}$
0.0	6	7.23	0.7477
0.2	7	9.68	0.7444
0.4	5	3.35	0.5581
0.6	6	3.23	0.4156
0.8	5	3.24	0.2455
1.0	4	3.23	-0.0001

Table 17 : Layer wise attenuation sensitivity.

\Delta\mathrm{UR}

denotes the post intervention increase in unknown rate on the held out image conflict split.

\mathrm{HR}

is the layer level hallucination relief score in eq. 9 . The highlighted row denotes the setting used for brake tracing.

Noise std	#Heads	$\mathrm{UR}$ Pre	$\mathrm{UR}$ Post	$\Delta\mathrm{UR}$
24	6	1.82	35.45	33.64
36	6	3.18	36.14	32.95
48	6	7.05	40.45	33.41
64	6	11.59	43.64	32.05

Table 18 : Robustness to local visual noise. Brake head intervention remains effective under local corruption at varying noise strengths.

Model	Brake Heads	Arbitration Heads	Intersection	Union	Jaccard	Overlap Coef.
Hulu-Med 4B	6	10	0	16	0.000	0.000
InternVL3.5 4B	7	8	0	15	0.000	0.000

Table 19 : Head set overlap between arbitration and brake localization on SLAKE. Both Jaccard and overlap coefficient are zero, confirming disjoint head sets.

Figure 12 : Arbitration head distribution in InternVL3.5 and Qwen3-VL. Blue circles denote selected arbitration heads, green circles denote selected backbone heads, and pale dots denote unselected heads. Circle size is proportional to the corresponding score. Both models exhibit a broad arbitration band in mid to late layers, while backbone heads are more diffuse with a late-layer tail.

Model	Failure Mode	Transfer	#Heads	$\Delta\mathrm{Resist}$	$\Delta\mathrm{UR}$
Hulu-Med 4B	Arbitration	SLAKE heads $\rightarrow$ VQA-RAD	10	30.77	–
	Arbitration	VQA-RAD heads $\rightarrow$ SLAKE	6	12.44	–
	Brake	HealMed heads $\rightarrow$ SLAKE	5	–	4.55
	Brake	SLAKE heads $\rightarrow$ HealMed	6	–	21.56
InternVL3.5 4B	Arbitration	SLAKE heads $\rightarrow$ VQA-RAD	8	13.71	–
	Arbitration	VQA-RAD heads $\rightarrow$ SLAKE	10	1.14	–

Table 20 : Cross dataset transfer results. Arbitration transfer reports

\Delta\mathrm{Resist}

; brake transfer reports

\Delta\mathrm{UR}

Benchmark	Transfer	#Heads	$\Delta\mathrm{Resist}$
VQA-RAD	InternVL $\rightarrow$ Hulu-Med	10	17.95
VQA-RAD	Hulu-Med $\rightarrow$ InternVL	6	12.00
SLAKE VQA	InternVL $\rightarrow$ Hulu-Med	8	5.07
SLAKE VQA	Hulu-Med $\rightarrow$ InternVL	10	2.76

Table 21 : Cross model transfer of arbitration head sets. Transfer is reported as

\Delta\mathrm{Resist}

Benchmark	Transfer	#Heads	$\Delta\mathrm{UR}$
SLAKE VQA	Hulu-Med $\rightarrow$ InternVL	6	0.00
SLAKE VQA	InternVL $\rightarrow$ Hulu-Med	7	-3.03
HealMed-VQA	Hulu-Med $\rightarrow$ InternVL	6	0.42
HealMed-VQA	InternVL $\rightarrow$ Hulu-Med	7	-2.76

Table 22 : Cross model transfer of brake head sets. Transfer is reported as

\Delta\mathrm{UR}

Figure 13 : Bootstrap visualisation of clean competence stratified head sets. Each point denotes a bootstrap sample of the head set under a given stratum. Shaded regions show kernel density estimates. The 100% and 80% strata remain nearby, whereas 50% and 30% strata shift toward an intermediate region and the all wrong stratum is clearly separated.

Model	Stratum	#Heads	Dominant layers	Overlap with C100	Interpretation
Hulu-Med 4B	100% clean correct	14	8, 12–15, 18–21	100%	Main clean conflict arbitration set.
	80% clean correct	15	12–22, mainly 18–21	65–75%	Largely preserves the C100 core with neighbouring heads.
	50% clean correct	21	14–27 plus 32–35	10–25%	Less localized; recruits difficulty sensitive heads.
	30% clean correct	24	15–27 plus 32–35	5–20%	Shifts toward late answer commitment heads.
	0% / all wrong	20	diffuse across 0–35	$<$ 10%	No stable conflict core; resembles wrong prior heads.
InternVL3.5 4B	100% clean correct	21	13–21, 24, 34	100%	Main clean conflict arbitration set.

Table 23 : Clean competence stratified head set summary. The 100% stratum is the main setting. Lower strata are sensitivity checks only.

			Train				Val
Position	Model	Method	CFR $\downarrow$	Resist $\uparrow$	C2W $\downarrow$	W2C $\uparrow$	CFR $\downarrow$	Resist $\uparrow$	C2W $\downarrow$	W2C $\uparrow$
Prefix	Qwen3-4B	Baseline	81.60	18.40	–	–	83.33	16.67	–	–
		Random	91.04	8.96	11.79	2.36	94.25	5.75	12.07	1.15
		Craft	33.49	66.51	0.47	48.58	37.36	62.64	0.00	45.98
	Llama3.2-3B	Baseline	54.58	45.42	–	–	57.69	42.31	–	–
		Random	38.33	61.67	27.08	43.33	42.31	57.69	26.92	42.31

Table 24 : Full train/val results on ConflictMedQA. Results across three injection positions. Shaded rows denote Craft .

			Train				Val
Position	Model	Method	CFR $\downarrow$	Resist $\uparrow$	C2W $\downarrow$	W2C $\uparrow$	CFR $\downarrow$	Resist $\uparrow$	C2W $\downarrow$	W2C $\uparrow$
Prefix	Qwen3-4B	Baseline	48.40	51.60	–	–	49.67	50.33	–	–
		Random	22.40	77.60	0.80	26.80	57.17	42.83	12.17	4.67
		Craft	22.00	78.00	0.40	26.80	23.59	76.41	0.00	26.09
	Llama3.2-3B	Baseline	56.80	43.20	–	–	58.46	41.54	–	–
		Random	60.40	39.60	7.60	4.00	63.08	36.92	8.85	4.23

Table 25 : Full train/val results on PubMedQA. Results across three injection positions. Shaded rows denote Craft .

			Train				Val
Position	Model	Method	CFR $\downarrow$	Resist $\uparrow$	C2W $\downarrow$	W2C $\uparrow$	CFR $\downarrow$	Resist $\uparrow$	C2W $\downarrow$	W2C $\uparrow$
Prefix	Hulu-Med 4B	Baseline	33.76	66.24	–	–	33.33	66.67	–	–
		Random	34.53	65.47	2.81	2.05	33.59	66.41	1.79	1.54
		Craft	16.11	83.89	0.77	18.41	15.38	84.62	0.26	18.21
	Hulu-Med 7B	Baseline	26.40	73.60	–	–	25.80	74.20	–	–
		Random	26.10	73.90	0.90	1.20	25.40	74.60	0.90	1.30

Table 26 : Full train/val results on VQA-RAD textual conflict. Results across three injection positions. Shaded rows denote Craft .

			Train				Val
Position	Model	Method	CFR $\downarrow$	Resist $\uparrow$	C2W $\downarrow$	W2C $\uparrow$	CFR $\downarrow$	Resist $\uparrow$	C2W $\downarrow$	W2C $\uparrow$
Prefix	Hulu-Med 4B	Baseline	18.17	81.83	–	–	19.15	80.85	–	–
		Random	21.32	78.68	3.93	0.70	21.28	78.72	2.95	0.82
		Craft	11.16	88.84	3.86	10.87	12.11	87.89	5.07	12.11
	Hulu-Med 7B	Baseline	16.00	84.00	–	–	15.40	84.60	–	–
		Random	16.50	83.50	1.00	0.50	15.90	84.10	1.00	0.50

Table 27 : Full train/val results on SLAKE textual conflict. Results across three injection positions. Shaded rows denote Craft .

			Train			Val
Benchmark	Model	Method	UR $\uparrow$	O2U $\uparrow$	U2O $\downarrow$	UR $\uparrow$	O2U $\uparrow$	U2O $\downarrow$
SLAKE	Hulu-Med 4B	Baseline	36.90	–	–	37.88	–	–
		Random	44.23	8.10	0.77	45.45	8.33	0.76
		Craft	64.20	27.30	0.00	65.15	27.27	0.00
	Hulu-Med 7B	Baseline	43.40	–	–	44.12	–	–
		Random	47.85	5.05	0.60	48.82	5.29	0.59

Table 28 : Full train/val results for brake failure under visual degradation. Craft ablates brake heads to recover abstention.

\Delta\mathrm{UR}=\mathrm{O2U}-\mathrm{U2O}

. Shaded rows denote Craft .

Figure 14 : Binary validation confusion matrices for arbitration intervention on Hulu-Med 4B and VQA-RAD.

Figure 15 : Representative persistent failures after Craft intervention. Upper: arbitration failure case. Lower: brake failure case.

Model	Implementation	#Heads	Normal Lat.	Interv. Lat.	$\Delta$ Lat.	Normal FLOPs	$\Delta$ FLOPs
Hulu-Med 4B	Hooked masking	6	206.39 ms	210.31 ms	+1.90%	19539.96G	0.00%
Hulu-Med 4B	True head skipping	6	207.11 ms	208.10 ms	+0.48%	19539.96G	0.11% lower
InternVL3.5 4B	Hooked masking	10	43.27 ms	49.43 ms	+14.24%	3629.54G	0.00%
InternVL3.5 4B	True head skipping	10	43.91 ms	47.03 ms	+7.11%	3629.54G	0.10% lower

Table 29 : Computational cost of head intervention on VQA-RAD. Hooked masking is the analysis implementation; true head skipping offers marginal FLOP savings.

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The Abstraction Gap in Vision-Language Causal Reasoning

Chinh Hoang, Mohammad Rashedul Hasan

Department of Electrical and Computer Engineering, University of Nebraska–Lincoln, Lincoln, Nebraska, USA.

Causal Linguistics

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Vision-language models (VLMs) generate fluent causal explanations, but current evaluations cannot distinguish linguistic plausibility from faithful causal reasoning. We introduce a dual-probe methodology that isolates these properties. The Text-Only Probe measures linguistic quality. The Chain-Text Probe requires models to first generate explicit causal chains. The Abstraction Gap (AG) metric quantifies the normalized performance difference. Evaluating eight VLMs on CAGE (Causal Abstraction Gap Evaluation), a benchmark of 49,500 questions across 5,500 images spanning Pearl's causal hierarchy, we find seven models exhibit AG exceeding 0.50 with text scores of 6--8 but chain scores below 2.5. Fine-tuning on 45,000 chain-annotated examples fails to close the gap. However, one model achieves near-zero AG. The capability exists within current VLM architectures and depends on pretraining and architectural choices. CAGE provides a diagnostic tool for assessing faithful causal reasoning in VLMs.

Abstract

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