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- PaperFrom Outcomes to Steps: Reliable Success-Aware Critic Learning for Long-Horizon Language Agents
Reinforcement learning for long-horizon language agents requires identifying whether intermediate actions make eventual task success more or less likely when supervision
- PaperA Head Camera Is (Almost) All You Need: An Electronics-Free UMI Glove Labelled and Observed by One Moving Camera
Handheld demonstration devices decouple data collection from the robot, but conventional designs burden the hand with a wrist camera, cables and a pose tracker (SLAM, an
- PaperA Radiation-Hardened, Fault-Aware Quantized Neural Network That Self-Heals
Deep learning (DL) models are increasingly being deployed into radiation-challenging environments in spacecraft, particle accelerators, and, soon, space-based data center
- PaperLocal Allow, Global Deny: Why Per-Action Authorization Does Not Compose in LLM Agents
Autonomous LLM agents act through tools under per-action authorization: a local guard screens each tool call in isolation. We show that local authorization does not compo
- PaperProvisioning Robust and Efficient LiDAR Perception for Autonomous Driving
LiDAR-based 3D object detection is a core perception service for autonomous driving, but deployment requires robustness to point-cloud corruption while meeting latency an
- PaperLearning Inter-Graph Interactions Between Heterogeneous Substructures of Chemical Systems
Complex chemical systems containing heterogeneous substructures are common in real-world applications. Various physical phenomena of the complex chemical systems are deri
- PaperABench: Adversarily Benchmarking Large Language Models across Taxonomized Values
Ensuring the alignment of Large Language Models (LLMs) with diverse cultural values is critical for their safe global deployment. However, existing alignment benchmarks a
- PaperAuditing the Product, Not the Recursion: The Evaluation Evidence Behind Recursive Self-Improvement Claims
Recursive self-improvement (RSI) promises AI systems, such as self-evolving LLM agents, that become better at producing further improvements. Evaluating this promise requ
- PaperWaveSpec: Multi-Spectral Iris Presentation Attack Detection via Wavelength-Conditioned Spectral Coherence Adaptation
Multispectral iris presentation attack detection benefits from observing the same iris across multiple near-infrared wavelengths, but existing methods often use wavelengt
- PaperAsk to Learn: Query-Based Collaborative Reinforcement Learning
Offline reinforcement learning is fundamentally limited by the coverage of the available data, while collecting additional trajectories through online exploration may be
- PaperDART: Scaling a Shape Representation Across Anatomical Classes and Imaging Modalities
Current state-of-the-art segmentation models still produce anatomically implausible shapes, such as broken vessels and fragmented glands, on the structures that are harde
- PaperHiQGS: Hierarchical Spatiotemporal Querying for Feed-Forward Gaussian Reconstruction
Feed-forward Gaussian reconstruction from sparse posed monocular frames requires reasoning over observations that vary jointly in viewpoint and time. Existing methods eit
- PaperFrom Encoding to Generation: Towards Empathetic Large Language Models
Empathetic interaction involves two distinguishable capacities: representing another person's emotional state (cognitive empathy) and generating an appropriate response t
- PaperSelf-Decomposing Diffusion for Code Generation
Diffusion language models (DLMs) are emerging as a promising paradigm for code generation, offering the potential for fast, parallel decoding. However, generating complex
- PaperMeta Expert: Cross-Layer Expert Sharing for Storage- and Retrieval-Efficient MoE
As model parameters scale up, sparsely activated Mixture-of-Experts (MoE) approaches have become essential for model scaling. However, mainstream models currently adopt h
- PaperWORTH Benchmark: Evidence Discovery under Not-Fully-Cooperative Information -- A Two-Episode Pilot
Existing reasoning evaluations typically provide task-relevant information to models as available premises, rarely examining concurrently how models discover evidence aut
- PaperCausal Spatiotemporal State Evolution for Monocular 4D Reconstruction
While feed-forward models excel in static 3D geometry estimation, extending them to dynamic 4D scenes requires modeling visual geometry as a causally evolving state. Curr
- PaperReal In, Real Out: What If We Only Use Real Data for Scene Text Editing?
Aiming to modify textual content while preserving the original style, Scene Text Editing (STE) has significant practical value in many applications. STE typically require
- PaperBenefit and Cost of Random-Token Replacement under Repeated Data: A Known-Entropy Decomposition and Evidence from Ten Real Corpora
When unique pretraining data is scarce, models see the same tokens many times and are thought to memorize. Random-token replacement, an input-corruption augmentation, is
- PaperBeyond Experience: Knowledge-Guided Evolution for NPU Kernel Generation
LLM agents have become capable kernel engineers on mainstream GPU stacks, but they struggle on emerging accelerators such as Ascend NPUs, whose programming model is barel
- PaperSensitivity-Orthogonal Residual Learning for Adaptive Sideslip and Cornering Stiffness Estimation
Online adaptation of the cornering stiffness keeps a model-based sideslip estimator valid as tires change, but competes with learned model correction for the same filter
- PaperBridging Digital Deception and Physical Fidelity: A Spatio-Temporal Attack on Cognitive Radars
As cognitive radars evolve towards the end-to-end processing of one-dimensional complex baseband sequences, deep networks face severe adversarial threats. However, existi
- PaperBeyond Model Weights: Sampling-Dependent Fairness in Diffusion Language Models
Diffusion language models (DLMs) are emerging as a practical alternative to autoregressive language models (ARLMs), yet their fairness remains largely uncharacterized. DL
- PaperTeCoP: Accelerating Diffusion Large Language Model Decoding via Tentative Context Pipelining
Diffusion large language models (dLLMs) predict multiple token positions per forward pass, yet block-wise decoders serialize generation and repeatedly recompute stable pr
- PaperDuplexLedger: Benchmarking Transactional Memory in Full-Duplex Speech Agents
Full-duplex speech agents must update persistent memory while listening and speaking, yet it remains unclear which events should be stored, revised, or excluded. Existing
- PaperFactual Access States in Diffusion Language Models: From Mechanism to Knowledge Editing and Retrieval
Factual recall is a lookup: some representation inside the network serves as the key, and what that key is made of decides which queries can retrieve the fact, what an ed
- PaperWhy Static Probes Fail: Context Determines Deception Feature Composition
With increasing autonomy, Large Language Models (LLMs) may engage in deceptive behaviors, such as scheming to pursue misaligned goals or sandbagging to escape oversight.
- PaperFrom POMDP Theory to Deep RL with Particle Filters
Model-based reinforcement learning in partially observable environments requires inferring hidden states, learning latent dynamics, and planning under the resulting belie
- PaperThe Show-Up Attack: When Subagents Win Over Their Supervisor
Multi-agent systems are increasingly deployed to coordinate multiple large language model (LLM) agents. However, this also creates new attack surfaces: malicious agents m
- PaperWhen LLMs Manipulate: Measuring Behavioral, Social, and Temporal Effects
Large language models (LLMs) can influence user trust and decision-making through subtle linguistic cues, yet the behavioral effects of manipulative outputs remain poorly
- PaperRaspAssistant as an Agentic Framework for Automating Adsorption Simulation Research in Porous Materials
Porous-material adsorption studies require coordinated structure preparation, force-field selection, simulation, validation, and interpretation, making end-to-end automat
- PaperText-Anchored Modality Experts with Uncertainty-Guided Routing for Multimodal Sentiment Analysis
Multimodal sentiment analysis (MSA) aims to understand user-generated videos by integrating multiple modalities. Existing methods often treat modalities as symmetric, ove
- PaperBayesian Neural Networks via Dirichlet Process Priors on the Data-Generating Distribution for Randomized Value Functions in Deep Reinforcement Learning
Bayesian neural networks (BNNs) offer a principled framework for quantifying epistemic uncertainty. However, standard weight-space priors yield complex, high-dimensional
- PaperTwo Is Enough: Binary Classification Priors Suffice for Multi-Class In-Context Tabular Prediction
Tabular foundation models (TFMs) pretrain a transformer on large-scale synthetic priors so that a single forward pass yields zero-shot tabular prediction. TabICLv2 sample
- PaperTRCM: Topology-Aware Route-Consistency Modeling for Robust Neural Vehicle Routing
Neural combinatorial optimization has enabled fast constructive solvers for the Capacitated Vehicle Routing Problem (CVRP), yet most autoregressive models still construct
- PaperAudiPsy: Evidence-Grounded Multimodal Reasoning over Spoken Counseling Conversations for Depression Assessment
Depression assessment from counseling speech requires integrating textual and acoustic psychological evidence across turns, yet existing end-to-end classification approac
- PaperLearned Readout Tokens for Extracting Large Language Model Confidence
Large language models (LLMs) are increasingly used within everyday applications where their responses have significant downstream impacts. For responsible deployment of s
- PaperAnomalyJev: Towards Evaluating Image Anomaly Explanations via Claim-Level Verification
Recent multimodal LLM (MLLM)-based anomaly detection (AD) methods can generate textual explanations that help interpret their predictions. However, reliably and efficient
- PaperCityPaint: A Free Lunch from 2D Diffusion for City-Scale 3D Texturing
Generating high-fidelity, city-scale 3D environments plays a crucial role in applications such as virtual reality, gaming, and urban digital twins. However, the scarcity
- PaperHypoNet Nankai PS: rapid hypocenter determination for the Nankai Trough using physics-informed neural networks with three-dimensional P- and S-wave velocity models
Rapid and accurate hypocenter determination based on three-dimensional (3D) seismic velocity models is essential for seismic monitoring in subduction zones, yet it remain
Headlines and links come from the labs' own blogs, Hacker News and the GDELT news index, and from the sources our index already follows. We show a short excerpt and always link to the original. A launch reported by a news site rather than the lab is marked “Reported” until the lab itself announces it.