· ai
[Paper] Cube Bench: A Benchmark for Spatial Visual Reasoning in MLLMs
We introduce Cube Bench, a Rubik's-cube benchmark for evaluating spatial and sequential reasoning in multimodal large language models (MLLMs). The benchmark dec...
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[Paper] Leveraging High-Fidelity Digital Models and Reinforcement Learning for Mission Engineering: A Case Study of Aerial Firefighting Under Perfect Information
As systems engineering (SE) objectives evolve from design and operation of monolithic systems to complex System of Systems (SoS), the discipline of Mission Engi...
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[Paper] Automated stereotactic radiosurgery planning using a human-in-the-loop reasoning large language model agent
Stereotactic radiosurgery (SRS) demands precise dose shaping around critical structures, yet black-box AI systems have limited clinical adoption due to opacity ...
- · ai
[Paper] Relu and softplus neural nets as zero-sum turn-based games
We show that the output of a ReLU neural network can be interpreted as the value of a zero-sum, turn-based, stopping game, which we call the ReLU net game. The ...
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[Paper] Can LLMs Predict Their Own Failures? Self-Awareness via Internal Circuits
Large language models (LLMs) generate fluent and complex outputs but often fail to recognize their own mistakes and hallucinations. Existing approaches typicall...
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[Paper] Improving ML Training Data with Gold-Standard Quality Metrics
Hand-tagged training data is essential to many machine learning tasks. However, training data quality control has received little attention in the literature, d...
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[Paper] Performative Policy Gradient: Optimality in Performative Reinforcement Learning
Post-deployment machine learning algorithms often influence the environments they act in, and thus shift the underlying dynamics that the standard reinforcement...
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[Paper] Fail Fast, Win Big: Rethinking the Drafting Strategy in Speculative Decoding via Diffusion LLMs
Diffusion Large Language Models (dLLMs) offer fast, parallel token generation, but their standalone use is plagued by an inherent efficiency-quality tradeoff. W...
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[Paper] Distilling to Hybrid Attention Models via KL-Guided Layer Selection
Distilling pretrained softmax attention Transformers into more efficient hybrid architectures that interleave softmax and linear attention layers is a promising...
- · ai
[Paper] LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Driving
Simulators can generate virtually unlimited driving data, yet imitation learning policies in simulation still struggle to achieve robust closed-loop performance...
- · ai
[Paper] Shallow Neural Networks Learn Low-Degree Spherical Polynomials with Learnable Channel Attention
We study the problem of learning a low-degree spherical polynomial of degree ell_0 = Θ(1) ge 1 defined on the unit sphere in RR^d by training an over-parameteri...
- · ai
[Paper] FlashVLM: Text-Guided Visual Token Selection for Large Multimodal Models
Large vision-language models (VLMs) typically process hundreds or thousands of visual tokens per image or video frame, incurring quadratic attention cost and su...