TACO combines Differential Answer-Probe Reward (DAPR) and Outcome-Gated Advantage Routing (OGAR) to assign credit to tool calls in agentic visual reasoning, producing accuracy gains on multimodal benchmarks.
PatchCue: Enhancing Vision-Language Model Reasoning with Patch- Based Visual Cues
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 6verdicts
UNVERDICTED 6representative citing papers
Instruction understanding is reframed as an evolving Instruction-as-State variable conditioned on perceptual state and realized via the S-EGIU coarse-to-fine framework, reporting a +2.68% SPL gain on REVERIE Test Unseen.
ELVA uses rule-based RL rewards to rank negatives by similarity, reducing grain blindness in universal multimodal retrieval and reporting a 13.1% gain on a new multi-grain benchmark.
ATT-CR introduces triangular attention and feature-selected gating to reduce computational cost and cloudy-pixel interference in remote sensing cloud removal.
SDB balances behavioral diversity and learning stability in VLN self-improvement by expanding decisions into latent hypotheses, performing reliability-aware aggregation, and applying a regularizer, yielding gains such as SPL 33.73 to 35.93 on REVERIE val-unseen.
HRNav decomposes image-goal navigation into VLM-based short-horizon planning and RL-based execution with a wandering suppression penalty to improve performance in complex unseen settings.
citing papers explorer
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TACO: Tool-Augmented Credit Optimization for Agentic Tool Use
TACO combines Differential Answer-Probe Reward (DAPR) and Outcome-Gated Advantage Routing (OGAR) to assign credit to tool calls in agentic visual reasoning, producing accuracy gains on multimodal benchmarks.
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Instruction-as-State: Environment-Guided and State-Conditioned Semantic Understanding for Embodied Navigation
Instruction understanding is reframed as an evolving Instruction-as-State variable conditioned on perceptual state and realized via the S-EGIU coarse-to-fine framework, reporting a +2.68% SPL gain on REVERIE Test Unseen.
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ELVA: Exploring Ranking-Driven Universal Multimodal Retrieval
ELVA uses rule-based RL rewards to rank negatives by similarity, reducing grain blindness in universal multimodal retrieval and reporting a 13.1% gain on a new multi-grain benchmark.
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ATT-CR: Adaptive Triangular Transformer for Cloud Removal
ATT-CR introduces triangular attention and feature-selected gating to reduce computational cost and cloudy-pixel interference in remote sensing cloud removal.
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The Essence of Balance for Self-Improving Agents in Vision-and-Language Navigation
SDB balances behavioral diversity and learning stability in VLN self-improvement by expanding decisions into latent hypotheses, performing reliability-aware aggregation, and applying a regularizer, yielding gains such as SPL 33.73 to 35.93 on REVERIE val-unseen.
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Think before Go: Hierarchical Reasoning for Image-goal Navigation
HRNav decomposes image-goal navigation into VLM-based short-horizon planning and RL-based execution with a wandering suppression penalty to improve performance in complex unseen settings.