🧬 Computational Biology

🎞️ ECCV2026 · 6 paper notes

📌 Same area in other venues: 📷 CVPR2026 (21) · 🔬 ICLR2026 (156) · 💬 ACL2026 (5) · 🧪 ICML2026 (52) · 🤖 AAAI2026 (20) · 🧠 NeurIPS2025 (76)

🔥 Top topics: Alignment/RLHF ×2

CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning

Without touching the generative architecture, CellFluxRL treats seven automatically computable biological evaluators (MoA consistency, nucleus-in-cytoplasm containment, nuclear roundness, and size/count statistics of nuclei and cytoplasm) as rewards, and post-trains the flow-matching virtual cell model CellFlux with RL to push "visually realistic" cell images toward "biologically meaningful" ones; all seven rewards improve over the baselines, and best-of-N test-time scaling amplifies the gains further.

FPicker: Topology-Guided Evolution for Filament Tracing in Low-SNR Microscopy

Tsinghua University researchers present FPicker, which constructs a coarse topological skeletal prior via a box-free center-endpoint representation and iteratively deforms it through an open-boundary graph convolutional snake module with bridge curriculum training, eliminating ghost center drift and tip shrinking in low-SNR Cryo-EM filament tracing (SNR = -20 dB).

Revisiting Scene Graph Generation from the Perspective of Detector-Conditioned Reachability

This paper quantitatively analyzes prediction discrepancies between detector-based and query-based SGG methods through the lens of detector-conditioned reachability, and proposes Dual-SGG—a unified framework combining top-down and bottom-up triplet queries within a single decoder to achieve state-of-the-art relationship detection without complex post-hoc calibration.

Score-Based Matching with Target Guidance for Cryo-EM Denoising

Addressing the extreme low-SNR and lack of clean ground-truth supervision in cryo-electron microscopy (cryo-EM), this paper introduces a target-guided score matching (TSM) framework leveraging low-pass reference density projections, which stabilizes near-manifold score learning and suppresses structured background to consistently improve downstream particle picking and 3D reconstruction resolution.

Together, Then Apart: Balancing Alignment and Distinctiveness for Multimodal Survival Analysis

Addressing the issue of over-alignment collapse where aggressive cross-modal fusion suppresses modality-specific evidence in multimodal survival analysis, this paper proposes the Together-Then-Apart (TTA) framework: it first discovers shared prognostic patterns via shared prototypes and semi-relaxed unbalanced optimal transport with a curriculum mass schedule, and then preserves modality-specific features using learnable anchors and contrastive regularization, consistently outperforming recent SOTA models across five TCGA cohorts.

Trust-Region Noise Search for Black-Box Alignment of Diffusion and Flow Models

Addressing GPU memory overhead, inapplicability to non-differentiable/expensive rewards, and off-manifold drift in existing inference-time alignment methods, this paper proposes Trust-Region Noise Search (TRS), which adaptively searches and optimizes the source noise using multi-center hypercubes and coordinate masks, significantly outperforming prior black-box, gradient-based, and trajectory-search baselines across text-to-image, molecule, and protein generation tasks.