Episodes

  • Seeing is Free, Speaking is Not: Uncovering the True Energy Bottleneck in Edge VLM Inference

    Seeing is Free, Speaking is Not: Uncovering the True Energy Bottleneck in Edge VLM Inference
    Jul 15 2026
    Efforts to make vision-language models efficient on edge devices have focused on reducing visual tokens, assuming visual processing dominates energy costs. This paper's systematic energy profiling across multiple models and hardware platforms overturns that assumption: inference power is nearly constant regardless of input, while output token count - driven by slower per-token decode time - is the true energy driver, with image complexity affecting energy mainly through longer generated responses. Applications include redesigning edge AI efficiency strategies to prioritize controlling output length over visual token pruning, informing hardware and software optimization for battery-powered robots, drones, and mobile AI assistants. Authors: Junfei Zhan, Haoxun Shen, Mingang Guo, Zixuan Huang, Tengjiao He Paper: https://arxiv.org/abs/2607.09520v1
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    3 mins
  • SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction

    SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction
    Jul 15 2026
    Cancer survival prediction models typically assume all diagnostic data (from demographics to costly genomic tests) is available, ignoring that acquiring each modality carries real clinical burden and cost. This paper frames modality acquisition as a sequential decision problem, introducing a self-evolving LLM agent that decides, patient by patient, whether further testing is justified, using episodic memory of similar cases and accumulated decision patterns. Tested on glioma patient cohorts, it maintains competitive prediction accuracy while cutting diagnostic burden by 55%. Applications include cost- and burden-aware clinical decision support, reducing unnecessary invasive testing in oncology workflows. Authors: Chongyu Qu, Can Cui, Zhengyi Lu, Junchao Zhu, Tianyuan Yao, Junlin Guo, Juming Xiong, Yanfan Zhu, Yuechen Yang, Bennett A. Landman, Yuankai Huo Paper: https://arxiv.org/abs/2607.09521v1
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    4 mins
  • ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts

    ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts
    Jul 15 2026
    Pathology foundation models are typically trained with narrow objectives on limited data scales, fragmenting complementary strengths across separate models. This paper presents a unified pathology foundation model built through staged distillation, combining knowledge from eight vision-only, vision-language, and slide-level teacher models into one backbone, trained on nearly 25 million pathology images. Evaluated across 96 downstream tasks and 48 data sources, it achieves top average performance across tissue-level, multimodal, and whole-slide clinical tasks. Applications include a single, versatile AI backbone for computational pathology supporting cancer diagnosis, tissue analysis, and clinical decision support across diverse tasks and institutions. Authors: Jiawen Li, Tian Guan, Huijuan Shi, Xitong Ling, Mingxi Fu, Anjia Han, Chao He, Yonghong He Paper: https://arxiv.org/abs/2607.09526v1
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    3 mins
  • Beyond Fixed Representations: The Vocabulary and Verifier Gaps in Open-Ended AI

    Beyond Fixed Representations: The Vocabulary and Verifier Gaps in Open-Ended AI
    Jul 15 2026
    Current AI systems, even strong reasoners and coders, typically operate within a fixed vocabulary of concepts and fixed success criteria set in advance. This paper argues genuine open-ended intelligence requires systems that can invent, stabilize, and reuse new representational primitives rather than just recombining existing ones. The authors identify two core obstacles - the "vocabulary gap" (inventing new concepts) and "verifier gap" (judging a new concept's value before it's proven useful) - and propose a framework and roadmap involving persistent memory and evolving verification. Applications include guiding future AI research toward genuine scientific discovery, creative innovation, and long-horizon autonomous research capabilities. Authors: Yuan Cao, Haiqian Yang Paper: https://arxiv.org/abs/2607.09560v1
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    3 mins
  • TCLA: Training-Free Class-wise Logit Adaptation for Medical Vision-Language Models

    TCLA: Training-Free Class-wise Logit Adaptation for Medical Vision-Language Models
    Jul 15 2026
    Medical vision-language models perform well in zero-shot settings but degrade when applied to new imaging domains due to distribution shifts and class imbalance from pretraining. This paper introduces a training-free method that adjusts inference logits using only a handful of support examples, improving class separation without adding trainable components - important since low-data regimes (like one-shot) are often unstable for existing adaptation techniques. Tested across nine datasets spanning X-ray, ultrasound, MRI, CT, and histopathology, it outperforms most training-based alternatives. Applications include rapid, lightweight adaptation of medical AI diagnostic tools across imaging modalities and hospital settings with minimal labeled data. Authors: Tianyou Jiang, Ziyu Zhou Paper: https://arxiv.org/abs/2607.09562v1
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    3 mins
  • Large-Scale Portfolio Optimization Problem Under Cardinality Constraint With Enhanced Multi-Objective Evolutionary Algorithms

    Large-Scale Portfolio Optimization Problem Under Cardinality Constraint With Enhanced Multi-Objective Evolutionary Algorithms
    Jul 15 2026
    Selecting optimal investment portfolios becomes an NP-hard problem once realistic constraints, like limiting the number of assets held, are introduced, making exact solutions impractical at scale. This paper enhances multi-objective evolutionary algorithms with new solution representations, operators, and repair mechanisms tailored to asset-count-constrained portfolio problems, combined with improved mating strategies. Tested against traditional algorithms using established market indices, the method converges faster and finds better solutions without performance loss as market size grows. Applications include practical portfolio construction tools for asset managers and individual investors needing to balance diversification against transaction costs and monitoring overhead. Authors: Danial Ramezani, Mostafa Abouei Ardakan Paper: https://arxiv.org/abs/2607.09566v1
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    3 mins
  • Conceptual Networks for Cross-Linguistic Idiomatic Expressions: A Feature-Based Graph Approach

    Conceptual Networks for Cross-Linguistic Idiomatic Expressions: A Feature-Based Graph Approach
    Jul 15 2026
    Idiomatic expressions carry figurative meaning that's difficult to capture with standard word embeddings, especially across languages. This paper builds an interpretable graph-based framework annotating 160 idioms from eight languages with cognitive-linguistic features (like containment or concealment), connecting them via similarity graphs. The resulting network clusters idioms by conceptual schema rather than language, aids automatic idiom detection, and enables cross-lingual identification of equivalent expressions better than embedding-based methods. Applications include machine translation systems handling figurative language, cross-lingual NLP tools, and computational tools for linguistics and cognitive science research into shared human conceptual structures. Authors: Kiran Pala, Punam Silu, Lixun Yu Paper: https://arxiv.org/abs/2607.09576v1
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    4 mins
  • Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining

    Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining
    Jul 15 2026
    Pre-demolition audits, central to sustainable urban material recovery, require decisions that are defensible - legible, well-sourced, and contestable - not just accurate. This paper argues that explainable AI and knowledge graphs each solve part of this problem and proposes four integration modes (Lifting, Constraining, Typing, Revising) explaining how combining them produces defensibility properties neither achieves alone, illustrated through a building-materials example using open building-data standards. Applications include supporting auditors and regulators in urban mining and demolition assessments, improving transparency and accountability in AI-assisted environmental compliance decisions, and informing broader design of explainable decision-support systems in regulated domains. Authors: Jan Gronewald, Andreas Emrich, Nijat Mehdiyev Paper: https://arxiv.org/abs/2607.09578v1
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    4 mins