Rule-VLN is the first large-scale benchmark injecting 177 regulatory categories into an urban environment, and the proposed SNRM module equips pre-trained VLN agents with zero-shot semantic reasoning and detour planning to reduce constraint violations by 19.26% and improve task completion.
Explain before you answer: A survey on compositional visual reasoning
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Proposes the Modality Translation Protocol with metrics ToS, CoS, FoS and SSC to quantify visual knowledge bottlenecks in VLMs, plus a Divergence Law hypothesis that scaling language models may increase the penalty.
Mull-Tokens are modality-agnostic latent tokens that enable free-form multimodal thinking and deliver up to 16% gains on spatial reasoning benchmarks.
COMPACT synthesizes compositional visual instruction data to reduce VIT training data by 90% while achieving 100.2% of full performance across eight multimodal benchmarks.
GlowGS improves 3D Gaussian Splatting in nighttime glow scenes via semantic feature generation from diffusion models and novel-view semantic learning with vision foundation models.
A3 adaptively selects verifiable action prefixes in VLA models using group-sampled consensus and conditional re-decoding to balance robustness and speed without manual horizon tuning.
Using lexical concreteness to guide contrastive negative mining and a new margin-based Cement loss, the Slipform framework reaches state-of-the-art on compositional benchmarks for vision-language models.
ARIS integrates a graph-based Social World Model, RAG, and agentic architecture for social robots and reports higher user ratings for intelligence, animacy, anthropomorphism, and likeability than an LLM baseline in a 23-person study with the Pepper robot.
citing papers explorer
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Rule-VLN: Bridging Perception and Compliance via Semantic Reasoning and Geometric Rectification
Rule-VLN is the first large-scale benchmark injecting 177 regulatory categories into an urban environment, and the proposed SNRM module equips pre-trained VLN agents with zero-shot semantic reasoning and detour planning to reduce constraint violations by 19.26% and improve task completion.
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The Expense of Seeing: Attaining Trustworthy Multimodal Reasoning Within the Monolithic Paradigm
Proposes the Modality Translation Protocol with metrics ToS, CoS, FoS and SSC to quantify visual knowledge bottlenecks in VLMs, plus a Divergence Law hypothesis that scaling language models may increase the penalty.
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Mull-Tokens: Modality-Agnostic Latent Thinking
Mull-Tokens are modality-agnostic latent tokens that enable free-form multimodal thinking and deliver up to 16% gains on spatial reasoning benchmarks.
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Visual Compositional Tuning
COMPACT synthesizes compositional visual instruction data to reduce VIT training data by 90% while achieving 100.2% of full performance across eight multimodal benchmarks.
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GlowGS: Generative Semantic Feature Learning for 3D Gaussian Splatting in Nighttime Glow Scenes
GlowGS improves 3D Gaussian Splatting in nighttime glow scenes via semantic feature generation from diffusion models and novel-view semantic learning with vision foundation models.
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Dynamic Execution Commitment of Vision-Language-Action Models
A3 adaptively selects verifiable action prefixes in VLA models using group-sampled consensus and conditional re-decoding to balance robustness and speed without manual horizon tuning.
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Concrete Jungle: Towards Concreteness Paved Contrastive Negative Mining for Compositional Understanding
Using lexical concreteness to guide contrastive negative mining and a new margin-based Cement loss, the Slipform framework reaches state-of-the-art on compositional benchmarks for vision-language models.
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ARIS: Agentic and Relationship Intelligence System for Social Robots
ARIS integrates a graph-based Social World Model, RAG, and agentic architecture for social robots and reports higher user ratings for intelligence, animacy, anthropomorphism, and likeability than an LLM baseline in a 23-person study with the Pepper robot.