Introduces Repeated Descent framework achieving 1/046-competitive posted-price mechanism for online budget-feasible auctions with submodular valuations under secretary arrivals, plus constant-competitive for non-monotone and an Omega(log n / (log log n)^2) lower bound for XOS.
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WikiVQABench is a human-curated collection of Wikipedia-based VQA items that require both visual evidence and external knowledge from Wikidata to answer correctly.
BrepForge factorizes B-rep synthesis into face-aware autoregressive wireframe composition followed by boundary-conditioned surface instantiation using learning-free geometric priors.
QuadLink generates anisotropic quad-dominant meshes from point clouds via anchor prediction, centroid-conditioned linking, and quad-first assembly, supporting hybrid n-gon topology.
RISC reformulates self-consistency answer selection as a ranking task solved by a lightweight LambdaRank model with five hand-designed features, yielding better accuracy-efficiency trade-offs than majority voting on QA benchmarks.
DisImpact introduces a two-stage MLLM framework to classify disaster-related social media posts into ten impact categories and compute a unified physi-social impact index validated against FEMA and NASA ground-truth data.
A dataset-agnostic framework converts text tool-calling benchmarks to paired audio evaluations via TTS, speaker variation and noise, then evaluates seven omni-modal models showing model- and task-dependent performance with small text-to-voice gaps.
R-DMesh proposes a VAE-based disentanglement of base mesh, motion trajectories, and rectification offset plus Triflow Attention and rectified-flow diffusion to produce 4D meshes aligned to video despite initial pose mismatch.
DeG models 3D Gaussians via learned octree density and uses VecSeq Sobol re-indexing to turn set generation into sequence modeling, claiming SOTA quality in single-image-to-3D.
Relit-LiVE jointly predicts relit videos and viewpoint-aligned environment maps inside a single diffusion process to achieve physically consistent video relighting without camera pose input.
A single-image head reconstruction method uses coarse-to-fine optimization with normal consistency, landmarks, and geometry-aware constraints on curvature and conformality to produce meshes with industry-grade topology and preserved facial identity.
ARGUS defends LLM agents from context-aware prompt injections by tracking information provenance and verifying decisions against trustworthy evidence, reducing attack success to 3.8% while retaining 87.5% task utility.
A new MAT simplification algorithm uses explicit surface correspondence tracking and priority-controlled edge collapses to preserve structural features like fillet alignments on discrete meshes.
UniVidX unifies diverse video generation tasks into one conditional diffusion model using stochastic condition masking, decoupled gated LoRAs, and cross-modal self-attention.
A training-free technique manipulates low-frequency noise in diffusion models to control image color and structure using low-frequency priors.
TTCD uses a non-stationary feature learner and reconstruction-guided distillation inside a transformer to infer contemporaneous and lagged causal graphs from non-stationary time series without strong noise assumptions.
A relaxed Picard iteration plus heteroscedastic boundary denoising lets Monte Carlo PDE solvers solve heat equations with nonlinear radiation boundary conditions more accurately than linearization.
Lightweight networks combine bracketed smartphone exposures as convex combinations of raw pixels to produce artifact-free HDR images that generalize from synthetic training to real captures.
Establishes that no defense works against linear-proportion poisoning with unbounded noise in regularization-based continual learning and proposes verification and robust defenses for infrequent or bounded attacks.
GPC learns a motion vocabulary via Finite Scalar Quantization and end-to-end RL, then trains an autoregressive transformer for next-token control generation, achieving 99.98% motion reproduction success with emergent robustness.
Introduces BetXplain, an explanation-annotated dataset of social media betting ads collected from Instagram and Reddit for detecting manipulative and deceptive advertising.
ScaffoldAgent improves long-form report generation by modeling outline evolution as expansion, contraction, and revision guided by a utility function estimating downstream value.
DARS replaces single-shot response labels with distribution-aware supervision derived from input and output uncertainty to produce more reliable LLM routing policies.
AdMem introduces a unified bi-level memory framework with multi-agent automatic generation and reward-based long-term management that improves success on long-horizon LLM agent tasks.
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DD-GEPA: Prompt Optimization for Dialogue Disentanglement Focusing on Task Instruction and Utterance Representation
DD-GEPA decomposes and optimizes prompts with GEPA for LLM-based dialogue disentanglement, reporting accuracy gains over baseline and hand-crafted prompts on benchmarks.
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