GSHeadRelight tackles real-time, 3D-consistent portrait relighting from in-the-wild images by combining a generative 3D Gaussian Splatting backbone with a lightweight Unified Neural Radiance Transfer model. It assigns per-Gaussian spherical-harmonics coefficients that are view-conditioned to model low-frequency specular effects, trains on FFHQ under a white-light assumption without costly light-stage data, and achieves roughly 240 FPS by making relighting a simple per-Gaussian linear operation after a single forward pass.

Fetal congenital heart defect diagnosis is severely bottlenecked by the scarcity of positive cases, especially for rare subtypes that can occur in fewer than 1 in 10,000 births. This paper proposes Pos4CHD, a framework that synthesizes pseudo-positive echocardiography samples using only normal images and semantic masks, completely bypassing the need for real pathological training data. By combining clinically guided mask manipulation with a specialized generator called FE-Gen, the authors demonstrate that synthetically augmented models can approach or exceed real-data baselines on classification, detection, and segmentation tasks.

ReDence tackles the problem of detecting modern social engineering scams that go beyond credential phishing by performing adaptive, multimodal investigations of websites and their artifacts. Its core idea is a hybrid architecture that pairs a fine-tuned 4B-parameter agentic Investigator (Qwen3.5-4B) for reasoning and reporting with a specialized 4B-parameter Decider (Tev1) for fast binary and multiple-choice decisions, cutting latency while preserving depth. This matters because SE scams caused billions in losses in 2025 and existing detectors either lack interactive evidence gathering or are too slow to operate at scale.

AgentVQA unifies 23 fragmented agentic datasets into 1,000 manually verified multiple-choice questions across five domains. By doing so, it provides a scalable offline complement to costly interactive simulator evaluations for vision-language models. The paper shows that even top models achieve only 58.5% accuracy and that failures stem primarily from grounded visual perception rather than reasoning mistakes.

Training-free personalization of vision-language models usually builds a database of isolated concept descriptions, which fails when user concepts are fine-grained and confusable. ReCoG argues that recognition is inherently relational: it constructs a Concept Graph whose directed edges store pairwise discriminative cues elicited from a frozen VLM, then retrieves by having candidates compete in explicit pairwise comparisons aggregated via round-robin voting. This yields large gains on fine-grained benchmarks and consistent improvements on standard ones, with strong ablations showing the edge content—not just the voting scheme—drives performance.

GraphCAD tackles generative modeling for CAD by replacing command sequences with Edge-Adjacency Graphs (EAGs), where wireframe edges are nodes and shared B-rep vertices define adjacency. It trains a hybrid graph diffusion model that jointly denoises discrete curve types and topology along with continuous geometric parameters, grounding the denoising network via an SE(3)-equivariant point-cloud backbone that renders latent edge parameters into sampled 3D curves. The work matters because sequence-based CAD generators propagate topological errors, while prior graph methods relied on complex face-centric surfaces that are hard to constrain.

Vision-Language Models typically encode an image in a single, task-agnostic pass before reasoning, which creates a bottleneck: the model can think longer but cannot refine the visual evidence itself. LoopLook addresses this by inserting lightweight recurrent loops into a middle-to-late window of the vision encoder, conditioning each loop on the question and adaptively deciding when to stop. The result is anencoder-side recurrent perception mechanism that improves accuracy on demanding perception benchmarks with minimal inference-time overhead.

This paper challenges the prevailing FID/CLIP-based evaluation of training-free diffusion samplers, showing that step-shortening methods collapse on complex generation benchmarks despite appearing lossless. The authors propose ParaLin++, a step-parity parallel integrator that preserves the full $N$-step discretization while accelerating solver iterations via a partially linear formulation with adaptive nonlinearization and successive over-relaxation. Experiments on FLUX and Ming-Flash-Omni 2.0 demonstrate that keeping the trajectory intact preserves semantic composition, dense instruction following, and long-text rendering where few-step solvers fail.

Most cultural evaluations of LLMs reduce user identity to a single country label, ignoring that real users hold multi-layered identities. This paper introduces a composite-identity setup where a system-prompt persona names one country while a user turn signals a second identity, either explicitly by naming a country or implicitly through query language. By probing three open-weight models with WVS multiple-choice questions and 1.6 million open-ended responses, the authors show that language and country cues shift model values in different, non-interchangeable ways—a finding with direct implications for how multilingual and multicultural personalization should be engineered.

Intra-operative CT imaging of the liver often shows poor contrast for hepatocellular carcinoma, making tumour localisation difficult and curating labelled datasets expensive. This paper proposes guiding an RL-based search on intra-operative CT using weak supervision from a tumour-presence classifier trained on pre-operative MR images, eliminating the need for paired cross-modal data. The core idea is that an MR-trained classifier still assigns higher tumour probabilities to cancerous windows on CT, and these probabilities drive an agent to explore and aggregate a tumour score map. The work matters because it offers a way to transfer pre-operative appearance knowledge into the operating room without deformable registration or manual CT segmentations.

Multibeam imaging sonar is widely used for underwater perception, but its intensity images are degraded by coupled coherent speckle and system-induced blur, and clean reflectivity ground truth is almost never available. This paper derives the conditional-mean forward operator $A x = x * |h|^2$ from a stochastic coherent image-formation model and uses it to train a self-supervised reconstruction network from registered multi-frame observations. By projecting one frame's latent reconstruction through $A$ and matching it to a second registered frame, then regularizing with an adapted robust equivariant imaging loss, the framework targets joint despeckling and deblurring without any clean targets. The work matters because it replaces ad hoc intensity-domain speckle models with a physics-consistent inverse problem for real sonar platforms.

RheOPT addresses the data bottleneck in non-Newtonian flow simulation by pretraining a transformer-based neural operator on inexpensive Newtonian simulations before fine-tuning on a handful of complex-fluid examples. The core hypothesis is that geometry-conditioned momentum transport—not the Newtonian constitutive law itself—transfers across the rheological boundary. In geometry-matched settings, the authors show that a model fine-tuned on as few as four non-Newtonian samples can match or exceed one trained from scratch on sixteen.

Whole-body vision-language-action (VLA) policies for humanoid loco-manipulation usually condition only on the current view, so they fail when brief object cues vanish before the robot must act. This paper proposes WholeBodyMem, which uses a frozen SAM3 to track task objects, detects object events (appear, disappear, move, change shape) to causally select up to $B_{\max}=7$ key frames, and feeds those native tokens together with mask-pooled object tokens into a flow-matching action expert via cross-attention and FiLM. On the new HuMeGo benchmark and standard SIMPLE tasks, the method raises the $\Psi_0$ base policy's simulated success from $29.1\%$ to $72.6\%$ and real-world Unitree G1 success from $36.7\%$ to $88.3\%$, while also improving reactive tasks to $86.9\%$.

Context augmentation methods are widely used to improve large language model performance at inference time, yet it remains unclear whether reported accuracy gains reflect genuine reasoning improvements or mere statistical noise. This paper introduces a systematic sentence-level evaluation framework spanning five intervention types—from blank filler tokens to teacher-generated explanations—across seven models and three question-answering datasets. By decomposing accuracy into correct flips ($\mathrm{Corr}$) and broken predictions ($\mathrm{Brk}$) and applying a sign-randomisation test, the authors show that many gains are undirected perturbations rather than directed progress, especially for methods that add computation without new information.

The paper asks whether language models can transfer hierarchical syntactic generalizations across languages. It introduces a cross-lingual poverty-of-stimulus (CPOS) design where positive evidence for hierarchical subject-verb agreement is present only in the source language (L1), and tests transfer to an ambiguous target language (L2). The core finding is that transfer is weak and only reliably detectable when the two languages share vocabulary tokens, challenging the view that models abstract language-universal hierarchical rules independently of lexical overlap.

Existing UAV vision-language navigation benchmarks generate trajectories without kinodynamic constraints, leading to jerky, collision-prone flight. KinoUAV introduces a dataset built from acceleration-driven, kinodynamically feasible trajectories with directional free-space distance labels and a two-stage Cruise–Seek task. The proposed KinoUAV-Agent hierarchically plans waypoints and predicts continuous accelerations, reporting state-of-the-art success and stability on the benchmark.

Diagnosing combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is difficult because tumors contain mixed hepatocellular and cholangiocellular morphology. This work attacks the problem with weakly supervised multiple-instance learning: it classifies HES-stained whole-slide images into HCC, CCA, and cHCC-CCA, and introduces an attention-supervision loss and a dual-branch model to force heatmaps onto tumor regions and separate HCC-like from CCA-like components. The clinical aim is to improve both slide-level diagnosis and characterization of intratumoral heterogeneity without costly patch-level annotations.

IQuest-Q1 is a 320-billion-parameter sparse Mixture-of-Experts foundation model (15 billion activated) optimized for agentic command-line interaction. It merges Stable SplitNorm, the Muon optimizer, and $\mu$P parameterization within a three-stage pipeline—progressive pre-training, context-extending mid-training, and multi-scaffold post-training using reinforcement learning and multi-teacher distillation—to advance open-weight CLI agent capabilities. While the paper integrates several validated training techniques and releases model weights openly, benchmark results reveal it still trails frontier proprietary systems on the hardest coding and reasoning tasks.

Current chest X-ray report generators that concatenate all available patient records into the decoder prompt produce long, variable-length inputs that can dilute attention and introduce image-irrelevant details. ICGF-CXRMate tackles this by conditioning learnable queries on the current radiograph to compress heterogeneous patient data into a fixed number of clinically relevant tokens, then selectively fusing them into the visual stream via gated cross-attention. On linked MIMIC-CXR and MIMIC-IV-ED examinations, this yields shorter prompts and stronger clinical metrics, offering a practical way to embed emergency-department context without overwhelming the language model.

High-quality presentation generation remains labor-intensive, and existing agentic frameworks rely mostly on foundation-model priors without task-specific training. This paper presents a unified training pipeline integrating cross-validated data synthesis, guided teacher enhancement for supervised fine-tuning, and verifiable aesthetic rewards via GRPO-based reinforcement learning. Experiments on PresentBench and GDPVal show that the pipeline improves both content quality and visual design, with compact models surpassing larger zero-shot baselines.