Most cell-tracking links are trivial nearest-neighbor associations that bury rare but biologically critical errors in cell divisions and state changes. This paper introduces CellTrack-Bench, a hard-mined benchmark that isolates these difficult local associations from 15 datasets across four imaging modalities and evaluates whether vision-language models can act as cognitive correctors—by linking cells directly, repairing tracker graphs, or inspecting them for human review. It matters because it provides a controlled diagnostic setting to test whether frontier VLMs genuinely reason about biological dynamics or simply exploit spatial proximity.

Modern end-to-end ASR systems typically rely on simplistic fixed-size beam search, assuming that large search spaces are unnecessary. This paper systematically compares search complexity and efficiency for CTC and neural transducer models across three datasets, showing that dynamic score-based pruning adapts to local uncertainty and dramatically cuts the real-time factor without sacrificing accuracy. The work challenges the prevailing assumption that small fixed beams are sufficient and provides tuned decoding parameters for reproducibility.

This paper identifies a hidden cost of LLM safety alignment: when models are prompted with protected attributes such as race, they often refuse to judge the associated information at all. The authors formalize this as invisibilization using a two-urn Bayesian trust game with an exact benchmark, randomizing only the reporter's description across 6,600+ sessions. They show that some model configurations discard identical evidence far more often for Black-described reporters (46%) than white-described ones (9%), while ironically trusting the assessed Black source slightly more. The result is a probability bias that standard fairness audits miss entirely because they look only at cases where the model actually renders a judgment.

Post-hoc calibration usually adjusts confidence scores globally, which ignores that neural networks can be miscalibrated differently in different regions of feature space. This paper proposes Local Adaptive Probability Estimation (LAPE), a residual framework that keeps an existing global calibrator as a stable anchor and adds a non-parametric local correction drawn from neighboring calibration samples in the learned feature representation. It also extends this idea to multi-view inputs through Cascade Feature Augmentation Fusion (CFAF). By exploiting local geometry without retraining, the work targets high-stakes settings where uniform global corrections leave spatially varying errors untouched.

Panorama generation faces a fundamental tension: equirectangular projection (ERP) enables global continuity but distorts polar regions, while cubemaps preserve perspective geometry yet suffer from seams. This paper proposes CubeAtlas, a compact single-canvas unfolding of cubemap faces with rotated half-polar regions, and pairs it with layout-aware sparse attention so a pretrained diffusion transformer can generate seamless 360° imagery in one forward pass without architectural surgery. The work matters because it promises both distortion-free perspective fidelity and global spherical coherence while retaining the strong priors of standard 2D image generators.

OmniRay tackles monocular 3D reconstruction across arbitrary central cameras without requiring intrinsics or camera-type labels at inference. Its core idea is that camera geometry has a hierarchy of complexity, so the ray field should be composed rather than represented by a single basis: a monotone radial base captures symmetric lenses, a spherical-harmonic residual handles smooth global deviations, and a free-form residual cleans up local variation, all applied as successive rotations on the sphere $S^2$. A DINOv2 backbone drives a Ray Module for $\mathbf{d}(u) \in S^2$ and a Distance Module for $\rho(u) > 0$, yielding an up-to-scale point cloud $\mathbf{P}(u) = \rho(u)\,\mathbf{d}(u)$ in one forward pass.

MixJudge tackles the problem of assessing vocal mixes via natural language queries, a capability that existing audio-language systems do not separately test. The authors synthesize a curriculum of 11 mixing issues at multiple severity levels and train a contrastive audio-text critic that learns to retrieve issues, discriminate opposites, and order severity. Their menu-selection experiments further show that issue evidence transfers to ranking real processing candidates against professional mixes, offering a concrete form of explicit, comparable mixing assistance.

CODI tackles a key limitation in self-supervised Vision Transformers: the [CLS] token hogs global semantics onto a sparse set of representative patches, leaving other patch tokens weak for dense prediction. The authors show that image-level distillation gradients are gated by [CLS]-to-patch attention mass, which systematically under-trains non-representative regions. Their fix, Cross Distillation, mixes a small fraction of the teacher class-token distribution into each patch-token target, giving every masked patch an attention-independent semantic nudge. The method is simple, architecture-agnostic, and yields consistent gains on segmentation, depth, video object segmentation, and classification under aligned pretraining.

Joint audio-visual diffusion models repeatedly run both denoising branches and expensive cross-modal attention at every step, making inference costly. CAVC observes that audio-side drift caused by mid-stage caching can be repaired by a few terminal joint steps, whereas video-side degradation persists, and exploits this asymmetry with a training-free sandwich structure: full joint warm-up, video-only continuation with cached audio, and brief terminal recoupling. The result is a modest wall-clock speedup with improved visual dynamics, though the gains are bounded by the preserved video backbone.

Graph soft prompting methods such as G-Retriever claim to inject relational graph knowledge into LLMs via learned continuous tokens, yet it remains unclear whether these tokens actually carry graph-specific information. This paper identifies and systematically investigates a pathological failure mode called token collapse, in which graph-derived tokens become nearly identical across inputs, effectively degenerating into generic soft prompts. Through causal interventions across three benchmarks, multiple graph encoders, and two LLM families, the authors show that strong task accuracy can conceal a token interface that the model does not meaningfully use, and they propose a diagnostic protocol to evaluate graph-token alignment beyond simple accuracy.

Unsupervised federated classification with Vision-Language Models like CLIP suffers under non-IID shifts because standard pseudo-labeling discards ambiguous samples near decision boundaries. This paper proposes FedBST, a bilateral self-training framework that penalizes confident predictions on uncertain "waist" samples during training and refines inference by ensembling local visual prototypes with global textual prototypes. The goal is to squeeze extra robustness out of existing CLIP-based federated pipelines without any labeled data.

Episodic memory video question answering asks models to reason over long egocentric recordings by compressing the visual stream into a queryable memory. This work isolates retrieval within a fixed online textual-memory pipeline, systematically comparing retrieval units, context budgets, and reranking strategies on QAEgo4D-Closed. The authors show that their best Parent strategy matches state-of-the-art accuracy with far fewer tokens, and reveal that higher temporal retrieval coverage can actually hurt accuracy by injecting hard distractors.

This paper studies class-level unpaired vision-language matching, where frozen visual and language encoders must recover an unknown category correspondence without any paired supervision. Its core contribution is RankAlign, a preprocessing strategy that rank-transforms each within-modality similarity kernel before permutation matching, yielding an objective that is invariant to arbitrary strictly increasing reparameterizations of similarity values. The work matters because it tests whether the Platonic Representation Hypothesis can be operationalized through pure relational order rather than absolute metric alignment, and it provides exact-recovery guarantees under ordinal consistency.

Synthetic prostate MRI could help train deep learning models, but generating complete biparametric exams from anatomical masks remains underexplored. This paper proposes a 3D diffusion framework that compares sequential versus joint synthesis of T2-weighted and diffusion modalities, and evaluates directly synthesizing ADC and computed high-b-value DWI instead of deriving them from raw diffusion-weighted images. The work matters because it identifies practical pathways for generating clinically complete exams and shows that direct biomarker synthesis outperforms two-step derivation.

Clinical question answering in Indonesian is hampered by a complete lack of public, domain-specific data. IndoNutriQA introduces 911 expert-authored, abstractive question–answer pairs grounded in real clinical-nutrition reports, using eight standardized questions from the Nutrition Care Process to create a controlled evaluation benchmark. Its rigorous validation protocol and public release aim to advance low-resource clinical NLP and nutrition-focused decision support.

The paper tackles open-vocabulary semantic segmentation without pixel-level annotations, aiming to adapt CLIP from image-level classification to dense prediction. Its core idea is a regional semantic preservation module that alters the final attention layer to use a v-v self-attention mapping, together with a visual prototype memory bank and self-training with EMA to refine predictions. The prospect of annotation-free dense segmentation is appealing, but the paper’s claims and reproducibility need close scrutiny.

Apple Silicon dominates local LLM inference, yet nearly every published optimization is designed and benchmarked on NVIDIA server GPUs under batched serving workloads. This paper asks which speedups survive when moved to single-request, unified-memory Apple machines, evaluating four families—KV-cache pruning, speculative decoding, ported CUDA kernels, and post-training quantization—across four Apple devices with matched RTX 4090 controls. The central finding is that a reported speedup is not an intrinsic property of a technique but a joint function of technique, runtime, hardware contract, and workload regime.

RaSFNet tackles LiDAR semantic segmentation by fusing 3D point features with 2D frustum features in a projection-based pipeline. Its three proposed modules—RAGDE for ray-aligned geometric encoding, STAM for azimuth-aware global context via a scan-serpentine Mamba, and RCPFF for range-constrained cross-representation fusion—are designed to resolve projection conflicts and preserve 3D spatial correspondence. The method reports $70.2\%$ mIoU on the SemanticKITTI validation set and $77.7\%$ on the nuScenes validation set.

Long chain-of-thought helps vision-language models solve complex visual tasks, yet it introduces a temporal grounding problem: intermediate reasoning steps can drift from image evidence, and standard final-answer reinforcement learning cannot credit which tokens should have revisited the image. This paper identifies "visual revisit"—localized spikes in cross-modal attention after language-internal reasoning—as a key temporal signal. The authors show across three backbones that revisit peaks predict correctness and are causally linked to accuracy. Building on this, they propose RECAP, a GRPO-compatible training method that propagates terminal rewards through revisit-conditioned dynamic discounting and gated value prediction, yielding consistent HallusionBench gains without inference-time cost.

Modern nuclear and particle physics detectors such as the sPHENIX TPC produce increasingly large, sparse data volumes that strain conventional reconstruction. This work evaluates FM4NPP, a ~200M-parameter Mamba-based foundation model pretrained on TPC spacepoint sequences via self-supervised $k$-Next-Nearest-Neighbor prediction, on downstream track finding and particle identification. The central claim is that a single frozen backbone with lightweight adapters can outperform a traditional Cellular-Automaton seeder at low momentum and match or exceed a Gaussian-Process-Regression $dE/dx$ baseline for PID, potentially extending physics reach while simplifying analysis chains.