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Research Editor selected

Pseudo-Riemannian Attention Network (PRAT) for Heterophilic Graphs

This paper identifies a geometric mismatch in Graph Neural Networks on heterophilic graphs, where positive-definite metrics fail to capture dissimilarity. The authors introduce the Graph Signature Index to measure this mismatch and propose PRAT, a network using a learnable pseudo-Riemannian metric. PRAT outperforms or matches strong baselines on fifteen node-classification benchmarks, with ablations confirming the importance of the indefinite metric.

Why it matters: This work offers a novel theoretical insight and a practical method that improves GNN performance on heterophilic graphs, a challenging and active research area.

arxiv11h agoScore 82
Graph Neural NetworksGeometric Deep LearningHeterophily
Research Editor selected

NextGAN: Towards Simple and Scalable GAN Training for Image and Video Generation

NEXTGAN trains a scalar critic by regressing its input gradient onto displacements between independently paired reference and generated features at interpolated locations in frozen representation spaces. The authors state that, for a fixed generator, this regression is equivalent to an adversarial objective with a gradient-energy penalty, while the generator keeps the standard potential-based GAN update. The method adapts pre-trained image and video generators with up to 14B parameters without auxiliary losses, online teacher scores, or trajectories. On ImageNet 256x256 it reports one-step FIDs of 2.87 with pixel-space JiT-B and 1.54 with latent-space DiT-XL/1 (1.36 for its adversarial form), and a single Wan2.1-1.3B model supports one-, two- and four-step text-to-video sampling, scoring 83.13 VBench total at two steps.

Why it matters: It offers a simpler adversarial training recipe for few-step image and video generation, backed by one-step FID and VBench comparisons, which is useful for researchers and practitioners building fast generative models.

arxiv11h agoScore 80
NextGANgenerative adversarial networkstext-to-video generation
Research

OccSplat Targets Occluded Geometry in Driving-Scene Gaussian Splatting

Researchers propose OccSplat, a feed-forward 3D Gaussian Splatting framework for reconstructing driving scenes from surround-view images. Rather than inpainting missing pixels in rendered views, the method identifies regions lacking Gaussian support via Gaussian Occlusion Mapping and generates new primitives through Gaussian Occlusion Completion. These primitives are merged into the original representation, keeping feed-forward inference without per-scene optimization. Experiments on nuScenes show improvements over compared methods, with the largest gains reported in occluded regions.

arxiv11h agoScore 72
3D Gaussian Splattingautonomous drivingnovel-view synthesis
Research Editor selected

DyPSI: Joint Generation of Physics Fields and Sensor Trajectories for Dynamic Sensing

DyPSI is a new framework for dynamic physics sensing that jointly generates future spatiotemporal fields and sensor trajectories from historical observations. It uses continuous Sensor Position Fields, a shared Functional Tucker representation, and a joint diffusion model to produce temporally coherent sensing plans. Experiments on turbulent flow, sea-surface temperature, and car aerodynamics show improved error reduction over static and frame-wise placement strategies.

Why it matters: This paper introduces a novel method that addresses a key limitation in sensor placement by jointly optimizing field reconstruction and trajectory generation, with demonstrated gains on multiple real-world datasets.

arxiv11h agoScore 82
sensor placementdiffusion modelsphysics-informed AI
Research

MonoLaDN: Language-Guided DeNoising for Monocular 3D Object Detection

MonoLaDN is a training framework that transfers geometric reasoning from a frozen vision-language model into a monocular 3D detector. It encodes the VLM's structured reasoning about occlusion, depth, dimension, and angle into queries, perturbs them with noise, and trains the detector to reconstruct the clean reasoning. The deployed detector remains purely vision-based, adding no inference latency or parameters. Experiments on KITTI 3D and nuScenes report consistent improvements over state-of-the-art detectors and good generalization to new domains.

arxiv11h agoScore 78
monocular 3D object detectionvision-language modelsknowledge distillation
Research

The Split Protocol Decides the Result: Re-Evaluating Encrypted Traffic Classification

This arXiv paper shows that encrypted traffic classifiers can score macro-F1 0.992 under a standard evaluation protocol but only 0.567 when flows from one packet capture are kept on one side of the split. Across five split protocols on backbone QUIC traffic, server-disjoint splitting costs 0.192 macro-F1, while random, temporal and client-disjoint splits are statistically indistinguishable from session-disjoint splitting. The authors propose single-identifier probes as a pre-training diagnostic for identifying the relevant split axis, and report that several pre-registered architectural claims fail under the corrected evaluation.

arxiv11h agoScore 76
encrypted traffic classificationevaluation methodologydata leakage
Research Editor selected

A.S.E 2.0: How Repository Context Impacts the Security of LLM-Generated Code

This paper introduces CASE-Bench, a benchmark of 145 repository-level tasks derived from real CVEs across 51 projects, to study how repository context affects the security of LLM-generated code. Experiments with 13 context construction methodshol and six LLMs show that functionality-oriented context improves functional pass rates more than secure pass rates, and that withholding safeguards or inserting insecure examples significantly increases exploitable code. Coding agents observe relevant safeguards in only 27-36% of trajectories, highlighting the need for security-aware context construction.

Why it matters: Provides concrete, recent evidence and a new benchmark on a critical but understudied aspect of LLM code security, offering actionable insights for developers and researchers.

arxiv11h agoScore 85
LLM code securityrepository contextsecure code generation
Research

Multi-Resident Data Association in Smart Homes via MDP Modeling and Behavior Cloning

This paper introduces a novel framework for multi-resident data association in smart homes, reformulating it as a Markov Decision Process solved via behavior cloning. The approach uses a lightweight Transformer and a fine-tuned LLM, outperforming state-of-the-art baselines on three datasets, especially for concurrent events. The LLM shows preliminary cross-environment transfer potential, though real-world robustness needs further validation.

arxiv11h agoScore 78
multi-resident activity recognitionambient sensorssequential decision making
Research

STEK: Structural-Terrain-Enhanced Koopman-Inspired Cross-Channel Regularization for Retrospective Battery Multi-Target Estimation

The paper introduces STEK, a framework for retrospective battery multi-target estimation that combines cell-level lifecycle dQ/dV terrain with endpoint-derived structural proxies via quality-proxy-conditioned gating. It uses bounded FiLM and a validity-gated residual head to refine endpoint-curve representations, plus a Koopman-inspired low-rank linear map for cross-channel consistency. Experiments under the MIX-battery protocol show improvements over endpoint-only baselines and direct concatenation, with graceful fallback when descriptors are removed.

arxiv11h agoScore 72
battery health estimationmachine learningKoopman theory
Research

Engineering Bounded Halting and Observable Universality Tests

This paper reframes the halting problem as a bounded-halting decision with a step budget Taine, and shows that no deterministic checker can decide all instances within a fixed fraction of T below one. It also proposes adversarial tests for finitary universality that check resource growth and absence of fixed deliberation bounds, with implications for machine-learning systems.

arxiv11h agoScore 72
halting problemcomputational complexityuniversal machines
Research

Reproduction Study Finds Risk-Controlled Throughput Forecasting Fails Under Temporal Splits and Low Budgets

A reproduction of the BG-CFQS method for risk-budgeted LEO satellite throughput forecasting shows that its risk guarantee holds only under random calibration splits, not the contiguous temporal splits seen in deployment. The method cannot serve risk budgets below 0.15, overshooting by up to 3.7x at the tightest budget. Online calibration tracks budgets well, but group-conditional recalibration often underperforms simple conditioning.

arxiv11h agoScore 78
risk-controlled forecastingLEO satellite networksconformal prediction
Research

Conformal Prediction Framework for Distribution-to-Distribution Regression

Researchers propose a new conformal prediction framework for distribution-to-distribution regression, a setting where both inputs and outputs are probability distributions. They show that naive Split Conformal Prediction produces prediction sets that are marginally valid but lack interpretable volume or tractable sampling. Their approach operates in an orthogonal basis space to yield interpretable prediction sets while preserving coverage guarantees, with an adaptive variant giving asymptotic conditional coverage. Empirical results on synthetic and real-world datasets validate the method, and code is publicly released.

arxiv11h agoScore 72
conformal predictiondistribution-to-distribution regressionuncertainty quantification
Research Editor selected

EnzymeRSI: A dual-loop recursive self-improvement system for enzyme design

EnzymeRSI is a dual-loop recursive self-improvement system that evolves harness assets and algorithms without changing model weights, closing the loop with a cloud laboratory. It improved ProteinGym Spearman correlations by 0.044 on average, reached a PCC of 0.849 on ThermoMPNN, and demonstrated wet-lab success including a 31.1-fold catalytic efficiency increase in KOD-E polymerase. The system offers a new paradigm for autonomous enzyme engineering by treating workflow and algorithm development as a single falsifiable dry-to-wet loop.

Why it matters: This paper presents a concrete, recent AI system with measurable improvements in both computational benchmarks and prospective wet-lab validations, offering significant value for readers interested in autonomous scientific discovery and enzyme engineering.

arxiv11h agoScore 85
enzyme engineeringrecursive self-improvementautonomous laboratories
Research Editor selected

Automated Discovery of Datasets with Surprising Side Effects on Language Models

Researchers propose a method to automatically discover training datasets that cause unexpected side effects in language models, such as inducing misalignment or outdated personas. They define three objectives for these datasets and use LLM judges with evolutionary and reinforcement learning to find them, producing 6,575 datasets across 8 behaviors. The findings show that automated metrics align with human judgments and that frontier models can predict side effects better than humans.

Why it matters: This paper introduces a novel automated pipeline for discovering training data side effects, offering a testbed for studying generalization and improving model safety.

arxiv11h agoScore 85
language model generalizationtraining data discoveryAI safety
Research Editor selected

Prism: Bidirectional Self-Teaching for Unified Multimodal Models

Prism is a post-training method for unified multimodal models that derives bidirectional supervision from self-generated interleaved rollouts)Skip. It decouples trajectories into text and image chains, assesses their reliability, and constructs training targets accordingly. Experiments on SenseNova-U1 and BAGEL show improvements in both generation and understanding, e.g., GenEval +3.4 and MME +29 points, without external verifiers or annotated corrections.

Why it matters: This presents a novel training method with concrete benchmark gains, offering practical value for multimodal model development.

arxiv11h agoScore 82
Unified Multimodal ModelsSelf-supervised LearningModel Post-training
Research Editor selected

Incentivizing Learning in AI-Assisted Work: A Model of Skill Dynamics

This paper extends an accept-or-solve model to sequential tasks where worker skills evolve through independent practice or reliance on AI. It shows that myopic workers can get trapped at low skill levels or lose skills, while strategic workers may choose independent work to preserve future capabilities. The authors characterize incentive payments that encourage skill acquisition and retention, finding that independent work can become self-sustaining and that higher skill targets may reduce long-run costs.

Why it matters: Provides a concrete economic model and actionable insights for organizations designing AI-assisted workflows to sustain human expertise.

arxiv11h agoScore 85
AI-assisted workskill dynamicsincentive design
Research Editor selected

Traffic-VL: Reinforced Vision-Language Model for End-to-End Traffic Signal Control

Traffic-VL is an 8-billion-parameter vision-language model that directly maps multi-view surveillance images and natural-language phase descriptions to traffic signal decisions, bypassing intermediate structured states. It is trained with offline reinforcement learning on photorealistic synthetic data (SynTra-V) and online stepwise trajectory optimization, matching structured-state baselines while outperforming image-based controllers and general-purpose VLMs. Evaluations include unseen topologies, degraded sensing, real footage, and a three-week deployment across ten intersections.

Why it matters: This paper presents a concrete, recent AI development with strong experimental validation, including real-world deployment, offering practical value for autonomous traffic management.

arxiv11h agoScore 85
Traffic signal controlVision-language modelsReinforcement learning
Research

Corralling AI Risks with Grassroots Contracts

The paper proposes grassroots contracts, a digital adaptation of Proudhon's mutualism, to address three major AI risks on digital platforms: AI control of global platforms, unaccountable AI agents, and unattributed deepfakes. It presents implementations for a social graph with accountable AI agents and a social network with digital provenance, along with working smartphone apps. The approach aims to restore accountability and provenance through decentralized, user-driven agreements.

arxiv11h agoScore 72
AI governancedigital platformsAI agents