What do these badges mean?
- 🚀ShippingCode exists. Multiple GitHub repos already reference this paper — people are building on it.
- 📈ClimbingCitation velocity is rising. Researchers are starting to pick it up.
- 💤QuietPublished but no notable signal yet. Most papers live here — could become anything later.
- 🎭HypeHeavy social buzz but no shipping signal. The counter-signal — defer until Twitter/X data is wired up.
- 2607.09487·Jul 10, 2026·~10 mincs.LGcs.CLstat.ML
Neural Collapse Is Forbidden: Information Floors in Language Models
Bruno Abrahao
Within-class variance in language-model representations is commonly read as incomplete neural collapse. We argue it is allocated information storage, and that the allocation obeys a law. A one-line centering identity voids a family of simplex equiangular-tight-frame claims, including our own earlier ones; in dimensionl…
- 2607.09481·Jul 10, 2026·~11 mincs.CVcs.AI
Decoupling Language Guidance from Backbones for Text-Guided Medical Segmentation
Yungeng Liu, Xuanzi Fang, Haijin Zeng, Qi Dai, +1
Text-guided medical image segmentation leverages clinical semantics to improve lesion delineation, yet many existing models bind cross-modal fusion, supervision, and decoder design into a task-specific architecture. Such tight coupling makes it difficult to reuse language guidance modules across heterogeneous vision an…
- 2607.09480·Jul 10, 2026·~8 mincs.CVcs.LGcs.NE
Foveation-Guided Dynamic Token Selection for Robust and Efficient Vision Transformers
Ibrahim Batuhan Akkaya, Kishaan Jeeveswaran, Bahram Zonooz, Elahe Arani
The human visual system (HVS) employs foveated sampling and eye movements to achieve efficient perception, conserving both metabolic energy and computational resources. Drawing inspiration from this robustness and adaptability, we introduce the Foveated Dynamic Transformer (FDT), a foveation-guided dynamic token-select…
- 2607.09474·Jul 10, 2026·~9 mincs.AI
ProofCouncil: An LLM Agent for Solving Open Mathematical Problems
Johannes Schmitt, Tim Gehrunger, Jasper Dekoninck, Gergely Bérczi, +3
Large language models (LLMs) have shown increasing promise in solving open problems in mathematics. However, their performance can be further improved through agentic workflows tailored to real-world mathematical practice. To this end, we introduce ProofCouncil, a mathematical agent that is designed to tackle open prob…
- 2607.09456·Jul 10, 2026·~11 mincs.LG
Active rejection enables reliable generalization of universal machine-learning interatomic potentials
Mingxiang Luo, Xinnan Mao, Lu Wang, Lei Bai, +2
Universal machine learning interatomic potentials (uMLIPs) bridge quantum-mechanical accuracy and large-scale molecular dynamics, but the cost of high-accuracy calculations such as r$^2$SCAN limits training to datasets that remain small relative to the open materials space. Strong average benchmark performance also doe…
- 2607.09452·Jul 10, 2026·~9 mincs.SEcs.AI
Practical Source Code Recovery from Binary Functions Using Anchor-Based Retrieval and LLM Reasoning
Charles Edward Gagnon, Steven H. H. Ding, Philippe Charland, Benjamin C. M. Fung
We present a practical pipeline for recovering source code from stripped binary functions by combining reverse engineering, anchor-based source code retrieval, and large language model reasoning. Our binary-to-source-code retrieval method attempts to identify the source function from a source code database, rather than…
- 2607.09450·Jul 10, 2026·~9 mincs.CVcs.LG
Robustifying Vision-Language Models via Test-Time Prompt Adaptation
Xingyu Zhu, Huanshen Wu, Shuo Wang, Beier Zhu, +3
Pre-trained Vision-Language Models (VLMs) such as CLIP achieve strong zero-shot generalization, but their performance degrades sharply under adversarial perturbations. Existing test-time adaptation methods typically rely on sample-level confidence heuristics, overlooking the intrinsic distributional structure of the da…
- 2607.09449·Jul 10, 2026·~8 mincs.AI
How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding
Debargha Ghosh, Silja Renooij, Anna Kononova
Bayesian causal discovery is widely used for its ability to quantify epistemic uncertainty over directed acyclic graphs (DAGs) through posterior inference. However, its behaviour under latent confounding remains poorly understood, as existing work typically notes that confounding breaks identifiability without characte…
- 2607.09443·Jul 10, 2026·~10 mincs.CVcs.AI
Parameter-Efficient Vision-Language Adaptation with Continuous Metadata Conditioning for Animal Re-Identification
Anil Osman Tur, Tonje Knutsen Sordalen, Kim Tallaksen Halvorsen, Cigdem Beyan
Long-term animal re-identification (ReID) must remain robust to gradual morphological evolution and seasonal appearance shifts. Although recent vision-language models provide strong pretrained visual representations, adapting them to longitudinal ecological settings remains challenging, particularly under identity and…
- 2607.09438·Jul 10, 2026·~11 mincs.CLcs.AIcs.LG
Test-Time Scaling for Small VLMs on Multilingual Visual MCQ
Spiros Baxevanakis, Peng-Jian Yang
Test-time scaling (TTS) reliably improves reasoning in large language models, but whether it transfers to small open vision-language models remains unclear. We examine this on EXAMS-V, a multilingual visual multiple-choice benchmark, comparing self-consistency, describe-then-reason with PRM-guided beam search, and two…