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ARXIV:2605.14621 · VISION-LANGUAGE MODELS · SUBMITTED 15 MAY · 20:13 UTC · FRESHNESS FRESH
ARXIV:2605.14621VISION-LANGUAGE MODELSSUBMITTED 15 MAY · 20:13 UTCFRESHNESS FRESHTian Qin · Junzhe Chen · Yuqing Shi · Tianshu Zhang · Qiang Ju · Lijie Wen · arXiv
SIRA is a training-free method to reduce hallucinations in vision-language models by reconstructing internal references without external tools.
Opportunity summary
Pain SIRA is a training-free method to reduce hallucinations in vision-language models by reconstructing internal references without external tools.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
SIRA is a training-free method to reduce hallucinations in vision-language models by reconstructing internal references without external tools. Existing contrastive decoding methods mitigate this problem by comparing predictions from the original image with those…
Large vision-language models (LVLMs) often hallucinate when language priors dominate weak or ambiguous visual evidence. Existing contrastive decoding methods mitigate this problem by comparing predictions from the original image with those from externally perturbed…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Experiments on POPE, CHAIR, and AMBER with Qwen2.5-VL and LLaVA-v1.5 show that SIRA consistently reduces hallucinations while preserving descriptive coverage and incurring lower overhead…
Vision-Language Models moved forward this cycle; last verified May 2026. Public score 5.0/10.
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SIRA is a training-free method to reduce hallucinations in vision-language models by reconstructing internal references without external tools.
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10.48550/arXiv.2605.14621SIRA is a training-free method to reduce hallucinations in vision-language models by reconstructing internal references without external tools.
Abstract
Large vision-language models (LVLMs) often hallucinate when language priors dominate weak or ambiguous visual evidence. Existing contrastive decoding methods mitigate this problem by comparing predictions from the original image with those from externally perturbed visual inputs, but such references can introduce off-manifold artifacts and require costly extra forward passes. We propose SIRA, a training-free internal contrastive decoding framework that constructs a counterfactual reference inside the same LVLM by exploiting the staged information flow of multimodal transformers. Instead of removing visual information from the input, SIRA first lets image and text tokens interact through a shared prefix, forming an aligned multimodal state that preserves prompt interpretation, decoding history, positional structure, and early visual grounding. It then forks a counterfactual branch in later transformer layers, where attention to image-token positions is masked. This branch retains the shared multimodal context but lacks continued access to fine-grained visual evidence, yielding a language-prior-dominated internal reference for token-level contrast. During decoding, SIRA suppresses tokens that remain strong without late visual access and favors predictions whose advantage depends on the full visual pathway. Experiments on POPE, CHAIR, and AMBER with Qwen2.5-VL and LLaVA-v1.5 show that SIRA consistently reduces hallucinations while preserving descriptive coverage and incurring lower overhead than two-pass contrastive decoding. SIRA requires no training, external verifier, or perturbed input, and applies to open-weight LVLMs with white-box inference access.
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PROBLEM
SIRA is a training-free method to reduce hallucinations in vision-language models by reconstructing internal references without external tools. Existing contrastive decoding methods mitigate this problem by comparing predictions from the original image with those from externally...
METHOD
Large vision-language models (LVLMs) often hallucinate when language priors dominate weak or ambiguous visual evidence. Existing contrastive decoding methods mitigate this problem by comparing predictions from the original image with those from externally perturbed visual inputs...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Experiments on POPE, CHAIR, and AMBER with Qwen2.5-VL and LLaVA-v1.5 show that SIRA consistently reduces hallucinations while preserving descriptive coverage and incurring lower overhead than two-pass con...
WHY NOW
Vision-Language Models moved forward this cycle; last verified May 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
SIRA is a training-free method to reduce hallucinations in vision-language models by reconstructing internal references without external tools. Existing contrastive decoding methods mitigate this problem by comparing predictions from the original image with those from externally perturbed visual inputs, but such references can introduce off-manifold artifacts and require costly extra forward passes.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large vision-language models (LVLMs) often hallucinate when language priors dominate weak or ambiguous visual evidence. Existing contrastive decoding methods mitigate this problem by comparing predictions from the original image with those from externally perturbed visual inputs, but such references can introduce off-manifold artifacts and require costly extra forward passes.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. Experiments on POPE, CHAIR, and AMBER with Qwen2.5-VL and LLaVA-v1.5 show that SIRA consistently reduces hallucinations while preserving descriptive coverage and incurring lower overhead than two-pass contrastive decoding.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Vision-Language Models moved forward this cycle; last verified May 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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SIRA is a training-free method to reduce hallucinations in vision-language models by reconstructing internal references without external tools.
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