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ARXIV:2603.15008 · VIDEO REASONING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.15008VIDEO REASONINGSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
ClueNet enhances video question answering by improving visual clue extraction and reasoning alignment.
Opportunity summary
Pain ClueNet enhances video question answering by improving visual clue extraction and reasoning alignment.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
ClueNet enhances video question answering by improving visual clue extraction and reasoning alignment. Prevailing end-to-end MLLM frameworks lack explicit structured reasoning between visual perception and answer derivation, causing severe hallucinations and poor interpretability.
Multi-modal Large Language Models (MLLMs) have significantly advanced video reasoning, yet Video Question Answering (VideoQA) remains challenging due to its demand for temporal causal reasoning and evidence-grounded answer generation. Prevailing end-to-end MLLM frameworks lack…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Experiments on NExT-QA, STAR, and MVBench show that ClueNet outperforms state-of-the-art methods by $\ge$ 1.1%, with superior generalization, hallucination mitigation, inference efficiency, and cross-backbone…
Video Reasoning moved forward this cycle; last verified April 2026. Public score 3.0/10.
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ClueNet enhances video question answering by improving visual clue extraction and reasoning alignment.
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10.48550/arXiv.2603.15008ClueNet enhances video question answering by improving visual clue extraction and reasoning alignment.
Abstract
Multi-modal Large Language Models (MLLMs) have significantly advanced video reasoning, yet Video Question Answering (VideoQA) remains challenging due to its demand for temporal causal reasoning and evidence-grounded answer generation. Prevailing end-to-end MLLM frameworks lack explicit structured reasoning between visual perception and answer derivation, causing severe hallucinations and poor interpretability. Existing methods also fail to address three core gaps: faithful visual clue extraction, utility-aware clue filtering, and end-to-end clue-answer alignment. Inspired by hierarchical human visual cognition, we propose ClueNet, a clue-aware video reasoning framework with a two-stage supervised fine-tuning paradigm without extensive base model modifications. Decoupled supervision aligns clue extraction and chain-based reasoning, while inference supervision with an adaptive clue filter refines high-order reasoning, alongside lightweight modules for efficient inference. Experiments on NExT-QA, STAR, and MVBench show that ClueNet outperforms state-of-the-art methods by $\ge$ 1.1%, with superior generalization, hallucination mitigation, inference efficiency, and cross-backbone compatibility. This work bridges the perception-to-generation gap in MLLM video understanding, providing an interpretable, faithful reasoning paradigm for high-stakes VideoQA applications.
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PROBLEM
ClueNet enhances video question answering by improving visual clue extraction and reasoning alignment. Prevailing end-to-end MLLM frameworks lack explicit structured reasoning between visual perception and answer derivation, causing severe hallucinations and poor interpretabilit...
METHOD
Multi-modal Large Language Models (MLLMs) have significantly advanced video reasoning, yet Video Question Answering (VideoQA) remains challenging due to its demand for temporal causal reasoning and evidence-grounded answer generation. Prevailing end-to-end MLLM frameworks lack e...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Experiments on NExT-QA, STAR, and MVBench show that ClueNet outperforms state-of-the-art methods by $\ge$ 1.1%, with superior generalization, hallucination mitigation, inference efficiency, and cross-back...
WHY NOW
Video Reasoning moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
ClueNet enhances video question answering by improving visual clue extraction and reasoning alignment. Prevailing end-to-end MLLM frameworks lack explicit structured reasoning between visual perception and answer derivation, causing severe hallucinations and poor interpretability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Multi-modal Large Language Models (MLLMs) have significantly advanced video reasoning, yet Video Question Answering (VideoQA) remains challenging due to its demand for temporal causal reasoning and evidence-grounded answer generation. Prevailing end-to-end MLLM frameworks lack explicit structured reasoning between visual perception and answer derivation, causing severe hallucinations and poor interpretability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Experiments on NExT-QA, STAR, and MVBench show that ClueNet outperforms state-of-the-art methods by $\ge$ 1.1%, with superior generalization, hallucination mitigation, inference efficiency, and cross-backbone compatibility.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Video Reasoning moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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ClueNet enhances video question answering by improving visual clue extraction and reasoning alignment.
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Video Reasoning
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