Paper Tracking

An automated weekly digest. Every Monday an agent scans arxiv and HuggingFace Daily Papers, downloads the PDFs, and summarizes the top 3 picks per topic.

Week of Β· View archive β†’

Graph Γ— LLM

Graph-structured reasoning, graph foundation models, and LLM-augmented GNNs.

R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video

↑ 0 πŸ“š 45123 β˜… 0 Aug 11
  • Problem: Object-centric question answering in long egocentric video requires preserving persistent object identity and structured spatial change, which caption-based methods fail to capture.
  • Model: R4DSG: Relative 4D scene graph memory that indexes video by time, place, persistent objects, and anchor-relative transitions without requiring global world coordinates.
  • Code: https://dualtransparency.github.io/R4DSG/

Vision-Language Models

Multimodal VLMs, vision encoders, and image-text foundation models.

Intern-S2-Preview: Scientific Agentic Foundation Model

↑ 56 β˜… 0 Aug 12
  • Problem: AI systems need to reason over heterogeneous scientific modalities, interact with tools, and sustain progress over long task horizons for scientific discovery.
  • Model: "Intern-S2-Preview-397B": multimodal scientific foundation model with time series forecasting, agentic RL training, and modular Memory Decoder for domain specialization.
  • Code: InternLM/xtuner

AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss

↑ 26 πŸ“š 9561 β˜… 0 Aug 10
  • Problem: Static pretrained feature spaces used in FrΓ©chet distance losses enable FrΓ©chet hacking, where optimized metrics improve while visual quality degrades.
  • Model: "AdvFD: Adversarial FrΓ©chet Distance" β€” combines static pretrained encoders with an adversarially learned adaptive representation and real-feature whitening to expose residual distribution mismatches during generator post-training.
  • Code: not released

World Models

Predictive world models, JEPA-style learning, video generation, and embodied simulators.

Alaya-EVOKE: From Linear-Scaling Supervision to Endless World

↑ 119 πŸ“š 14838 β˜… 0 Aug 13
  • Problem: Interactive world models struggle to balance persistent memory, low-latency response, and long-horizon generation due to conflicting computational demands.
  • Model: "Evoke": world model with externalized scene geometry bank and linear-attention teacher for long-horizon supervision enabling few-step student generation.
  • Code: not released
  • ⚠ Interested, but agent could not fetch the PDF β€” summary based on abstract only.

DreamX-Phi 1.0: Action-Conditioned Video World Model for Robotic Manipulation

↑ 93 πŸ“š 16421 β˜… 0 Aug 13
  • Problem: Action-conditioned video world models struggle to faithfully predict robot manipulation while preserving arm identity, object state, and scene geometry.
  • Model: DreamX-Phi 1.0: action-conditioned video world model using PRoPE-style SE(3) geometric encoding, depth supervision, SAM3 masks, and V-JEPA teacher for faithful robotic manipulation prediction.
  • Code: AMAP-ML/DreamX-Phi

PlayWorld: Benchmarking World Models with Agent Players over Long-Horizon Objectives

↑ 43 πŸ“š 37560 β˜… 0 Aug 13
  • Problem: Existing world model benchmarks use fixed action sequences that fail to enable fair comparison across models with different action granularities and response dynamics.
  • Model: PlayWorld: benchmark with multi-modal Agent Player that adaptively executes long-horizon objectives to evaluate world models on geometry consistency, interaction fidelity, and state evolution.
  • Code: kxding/PlayWorld

Spatial Single-Cell Study

Single-cell + spatial omics foundation models, virtual cells, biological world models, and biomedical digital twins.

Disentangled Shared Representations Improve Morpho-Transcriptomic Integration

↑ 0 β˜… 0 Aug 14
  • Problem: Standard multimodal models for spatial transcriptomics compress H&E and gene expression into shared latent space without explicitly separating shared versus modality-specific variation.
  • Model: approach: Disentangled multimodal representation learning comparing VAE-based (MMVAE+, MMVAE+sg) and contrastive (disSSL) models that factorize shared and private latent components for H&E and spatial transcriptomics integration.
  • Code: not released

Program-space Diffusion for Morphology-to-Transcriptomics Prediction

↑ 0 β˜… 0 Aug 14
  • Problem: Predicting spatial gene expression from histology is computationally expensive and ignores coordinated transcriptional variation.
  • Model: approach: Conditional diffusion model operating in transcriptional program space learned via consensus non-negative matrix factorization (cNMF), with Pearson residual normalization for count data.
  • Code: not released