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This article describes a system design for AI therapy that uses a deterministic pipeline to decide clinical actions, preventing the LLM from making autonomous decisions. It involves scoring, state buckets, an admission table, action selection, micro-practices, and crisis pre-screening, with the LLM only used for scoring and generation. The article also discusses the costs and limitations of this approach.

Researchers propose a physics-informed residual dynamics learning framework that preserves differential flatness for tight quadrotor formation flight. The approach enables a computationally efficient feedback linearization controller, reducing tracking errors by 31% compared to baselines. It matches NMPC performance with an order of magnitude less computation, requiring under 30 seconds of training data and a 5ms loop rate.

DiffRadar is a real-time radar SLAM system that models radar observations as a differentiable, physics-aware Gaussian field rather than discrete scans. It achieves substantial reductions in trajectory error on benchmarks, especially in feature-poor corridor motion, more than doubles map consistency, and maintains real-time performance at 70 FPS.

This paper introduces a contract-grounded architecture for behavior tree synthesis, where a coding agent queries a robot-side MCP server to retrieve a skill library and operators, enabling non-expert users to issue natural language commands without knowing robot internals. Evaluations show near-perfect validation and high task success across 110 simulated and 14 physical tasks.

A study investigates how robot gaze affects human visual attention in a collaborative word association game using a NAO robot. Findings show that robot gaze orientation does not influence fixation time on proposed words, but participants gaze more at the robot when seeking confirmation. The verbal aspect overshadows referential gaze in cognitively demanding tasks.

This work investigates using robotic manipulator motions for propellant-free spacecraft attitude control. By formulating a trajectory optimization problem with joint and collision avoidance constraints, complex maneuvers are demonstrated, showing manipulators can serve as redundant or primary attitude control systems, especially for in-space assembly.

This paper presents a corpus-level analysis of behavior diagrams from Sony's R-CODE sample set for the ERS-111 AIBO, identifying a compact embodied grammar centered on initialization, sensing, iterative action, synchronization, and recovery, and argues for its utility as an intermediate representation for building new behaviors on constrained robotic systems.

GaitSpan is a novel framework for growing humanoid locomotion from walking to running. It uses a pretrained walking policy as a seed skill, expanding it through rhythm generation, stride shaping, and residual adaptation, achieving continuous speed range, morphology transfer, and zero-shot deployment.

This paper introduces two complementary physics priors to improve robustness in robotic in-hand rolling: a global grasp-quality prior from classical grasp analysis and a local contact-geometry prior based on fingertip curvature. Experiments show significant gains in rotation efficiency, grasp stability, and disturbance rejection, enhancing sim-to-real transfer.

Proposes an unsupervised image translation framework to convert daytime plant-row RGB images to near-infrared (NIR) nighttime counterparts without pixel supervision, enabling reuse of daytime semantic labels for training nighttime perception models. Leverages pre-trained CLIP model for semantic consistency and introduces a visibility mask for limited NIR illumination. Evaluated on AgriNight dataset (428 day, 549 night images) as the first benchmark for nighttime agricultural visual navigation. Real robot experiments confirm effectiveness.

Multi-robot teams in confined environments must adapt formation geometry and topology. Existing methods model deformation and reconfiguration independently or with handcrafted rules, leading to deadlock. EFLUX is a geometry-grounded LLM agentic framework that jointly reasons over deformation and reconfiguration actions via a closed-loop pipeline. Experiments show reduced deadlock and navigation failures.

A new study investigates the interplay between representation space and reference selection in training-free reference-based synthetic image attribution. Using representations from different layers of CLIP and DINOv2 along with three reference selection methods, the authors show that attribution accuracy peaks at intermediate layers and that semantically constrained references reduce query-reference mismatch, improving performance especially under limited reference budgets.

This paper presents a systematic evaluation of continual learning methods for heterogeneous medical visual question answering tasks, including classification, multi-label classification, detection, cell counting, and report generation. Findings show existing methods struggle to maintain stability-plasticity balance when tasks with different objectives are interleaved.

SymbOmni is a novel AI model addressing the 'perpetual novice' problem—the inability of current models to learn cumulatively and evolve autonomously. It employs Symbolic Concept Learning with an optimizable memory module that abstracts low-level operations into reusable symbolic workflow instructions, operating via an induction-transduction cycle. Experiments show it outperforms existing agent systems and closed-source models in image quality and task success, reduces token consumption by over 40%, and achieves state-of-the-art continual learning results.

MetaView is a diffusion-based monocular novel view synthesis framework that enables rendering under large view changes from a single image. It combines implicit geometry priors with metric depth to achieve geometry consistency and precise controllability, outperforming existing methods.

A teacher-student framework for predicting perineural invasion (PNI) in intrahepatic cholangiocarcinoma from T2-weighted MRI. During training, the teacher uses tumor and liver masks to learn dense token routing; the student distills this to retain informative tokens under a fixed budget. No masks required at inference. Achieves AUROC 0.750 in 155 patients with 1.43 GFLOPs and 8.02 ms per case.

SpikeDS is a novel spiking neural network architecture that efficiently predicts perineural invasion in cholangiocarcinoma from 3D MRI by leveraging both activation sparsity and spatial sparsity, achieving an AUC of 0.753 with only 14.4 mJ energy consumption on a cohort of 139 patients.

Self-supervised learning is promising for medical imaging where labeled data is scarce. COJEPA combines joint-embedding predictive architecture and contrastive loss for volumetric brain MRI, trained on 2,286 T1-weighted scans. It achieves state-of-the-art results in twin retrieval, age regression, and tumor segmentation.

This paper proposes a method that detects anomalous frames between two task videos using VLM-generated frame descriptions and intra-video self-similarity to extract candidate scenes containing expert-specific actions and contextual decision-making. In simulated distribution board maintenance experiments (27 tasks), it achieved 65% and 61% extraction rates for actions and decisions respectively, outperforming conventional methods (59% and 33%).

GenDiff is a novel diffusion-based framework for low-dose CT reconstruction that jointly models continuous radiation dose and anatomical information. It integrates a Dose-Anatomy Encoder, cold diffusion backbone, physics-consistency update, and Structural Prior Refinement Module, outperforming existing methods on multi-anatomy clinical datasets with strong robustness and generalization.

TSCA-Net proposes three complementary modules (Temporal-Spatial Clique Attention, Cross-Pedestrian Clique Potential, Adaptive KAN Grid Refinement) to improve pedestrian trajectory prediction in crowded environments, achieving state-of-the-art results on ETH/UCY and SDD benchmarks.

Two hybrid continual learning methods, RA-EWC and CGKD, are proposed to identify endangered Australian Aboriginal languages using limited data, outperforming fine-tuning and existing CL baselines on Warlpiri, Dalabon, and Dharawal.

A new paper reveals how large language models separate a character's belief from reality through a value slot and routing mechanism. The research shows two distinct mechanisms: a generic value slot for binding attributed values, and a router that selects which frame (belief or reality) to read from. The slot is filled either directly or via visibility-gated lookback, and the separation resides in routing subspaces rather than the slot itself. These findings hold across architectures and emerge between 3B and 7B parameters.

The paper introduces MAGE, a framework for studying component interactions in prompt optimization, discovering the Prompt Optimization Coupling Effect (POCE) where multiple stochastic signals interact to improve performance while amplifying variance. Key findings: failure-grounded reflection is crucial; MAGE outperforms GEPA on GSM8K-Hard; increasing candidate diversity amplifies POCE; POCE is headroom-dependent; in low-data settings, fixed prompts outperform reflective optimizers.

This work fine-tunes LLaMA 3 (8B) as an efficient drop-in reranker via supervised fine-tuning and 4-bit quantization, replacing cross-encoders in RAG pipelines. It achieves 14-21% improvement in answer relevancy, context precision, answer similarity, and answer correctness on a domain-specific QA benchmark while reducing inference overhead.

This paper proposes a text dataset distillation framework using Trajectory-Aware Knowledge Estimation (TAKE), which leverages influence functions and training trajectories to compute knowledge scores for each sample, and selects prototypes via optimal transport, compressing corpora to 0.1% of original size while preserving downstream task fidelity. Evaluated on text classification and natural language inference tasks.

This paper proposes a graph-based framework combining weak supervision with propagation graph analysis for detecting disinformation narratives in Telegram ecosystems. It aggregates semantically related claims into narrative-level clusters and models their diffusion across interconnected channels, enabling scalable detection of coordinated amplification.

A study evaluating state-of-the-art LLMs on Korean-Braille translation finds poor, unstable outputs and weak human agreement. Fine-tuning a small T5-small model yields large gains, revealing systematic limitations in current LLMs for accessibility-critical modalities.

This paper proposes G-SHARE, a framework that operationalizes the CNNP nine-step human-factor event diagnosis guideline into a multi-stage pipeline including evidence extraction, stepwise reasoning, and post-hoc consistency repair. Evaluated on real nuclear industry data, G-SHARE significantly outperforms one-shot prompting and machine learning baselines, demonstrating the value of structured reasoning and consistency enforcement for robust diagnosis.