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ARXIV:2605.14054 · VISION-LANGUAGE MODELS · SUBMITTED 15 MAY · 20:14 UTC · FRESHNESS FRESH
ARXIV:2605.14054VISION-LANGUAGE MODELSSUBMITTED 15 MAY · 20:14 UTCFRESHNESS FRESHHaozhe Wang · Qixin Xu · Changpeng Wang · Taofeng Xue · Chong Peng · Wenhu Chen · +1 at arXiv
A reinforcement learning framework that improves vision-language model synergy by rewarding perception fidelity, addressing the 'bad seeing or bad thinking' ambiguity.
Opportunity summary
Pain A reinforcement learning framework that improves vision-language model synergy by rewarding perception fidelity, addressing the 'bad seeing or bad thinking' ambiguity.
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A reinforcement learning framework that improves vision-language model synergy by rewarding perception fidelity, addressing the 'bad seeing or bad thinking' ambiguity. Recent advancements have pursued this goal via architectural designs or agentic workflows.
Achieving robust perception-reasoning synergy is a central goal for advanced Vision-Language Models (VLMs). Recent advancements have pursued this goal via architectural designs or agentic workflows.
ScienceToStartup currently rates this 4.0/10 on the public viability pass. To resolve this, we introduce a reinforcement learning framework that improves perception-reasoning synergy by reliably rewarding the perception fidelity.
Vision-Language Models moved forward this cycle; last verified May 2026. Public score 4.0/10.
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A reinforcement learning framework that improves vision-language model synergy by rewarding perception fidelity, addressing the 'bad seeing or bad thinking' ambiguity.
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10.48550/arXiv.2605.14054A reinforcement learning framework that improves vision-language model synergy by rewarding perception fidelity, addressing the 'bad seeing or bad thinking' ambiguity.
Abstract
Achieving robust perception-reasoning synergy is a central goal for advanced Vision-Language Models (VLMs). Recent advancements have pursued this goal via architectural designs or agentic workflows. However, these approaches are often limited by static textual reasoning or complicated by the significant compute and engineering burden of external agentic complexity. Worse, this heavy investment does not yield proportional gains, often witnessing a "seesaw effect" on perception and reasoning. This motivates a fundamental rethinking of the true bottleneck. In this paper, we argue that the root cause of this trade-off is an ambiguity in modality credit assignment: when a VLM fails, is it due to flawed perception ("bad seeing") or flawed logic ("bad thinking")? To resolve this, we introduce a reinforcement learning framework that improves perception-reasoning synergy by reliably rewarding the perception fidelity. We explicitly decompose the generation process into interleaved perception and reasoning steps. This decoupling enables targeted supervision on perception. Crucially, we introduce Perception Verification (PV), leveraging a "blindfolded reasoning" proxy to reward perceptual fidelity independently of reasoning outcomes. Furthermore, to scale training across free-form VL tasks, we propose Structured Verbal Verification, which replaces high-variance LLM judging with structured algorithmic execution. These techniques are integrated into a Modality-Aware Credit Assignment (MoCA) mechanism, which routes rewards to the specific source of error -- either bad seeing or bad thinking -- enabling a single VLM to achieve simultaneous performance gains across a wide task spectrum.
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PROBLEM
A reinforcement learning framework that improves vision-language model synergy by rewarding perception fidelity, addressing the 'bad seeing or bad thinking' ambiguity. Recent advancements have pursued this goal via architectural designs or agentic workflows.
METHOD
Achieving robust perception-reasoning synergy is a central goal for advanced Vision-Language Models (VLMs). Recent advancements have pursued this goal via architectural designs or agentic workflows.
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. To resolve this, we introduce a reinforcement learning framework that improves perception-reasoning synergy by reliably rewarding the perception fidelity.
WHY NOW
Vision-Language Models moved forward this cycle; last verified May 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A reinforcement learning framework that improves vision-language model synergy by rewarding perception fidelity, addressing the 'bad seeing or bad thinking' ambiguity. Recent advancements have pursued this goal via architectural designs or agentic workflows.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Achieving robust perception-reasoning synergy is a central goal for advanced Vision-Language Models (VLMs). Recent advancements have pursued this goal via architectural designs or agentic workflows.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. To resolve this, we introduce a reinforcement learning framework that improves perception-reasoning synergy by reliably rewarding the perception fidelity.
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 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A reinforcement learning framework that improves vision-language model synergy by rewarding perception fidelity, addressing the 'bad seeing or bad thinking' ambiguity.
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