This paper analyzes the linear stability of an INDI pitch-rate controller under model mismatch for a tilt-rotor VTOL UAV. A closed-form fifth-order transfer function is derived, and stability is characterized using the Routh-Hurwitz criterion. Two tuning procedures are proposed: robustness-oriented and performance-oriented. Control-effectiveness mismatch, especially sign errors, is identified as the most destabilizing factor.

A study revisited the android robot Andrea at a German museum for six days, engaging visitors in multilingual conversations. Three emotion simulation conditions (none, ChatGPT 4.1, WASABI) were tested with 73 visitors. Results showed no positive effect or conscious detection of the emotion simulations.

PRISM is a multimodal perception system that integrates thermal, optical, and depth sensors with a novel vision transformer network (OmniUnet) for terrain segmentation and traversability mapping. Validated on two new datasets and deployed on an embedded computer, it enables autonomous rover navigation in challenging terrain.

This paper presents SEAGR, a robot greeting framework for users from diverse cultural backgrounds and emotional states. It uses a dual-layer modulation where cultural identity determines greeting type and affective cues modulate execution, integrating cultural mapping, emotion-based gestures, and proxemics in a Sense-Think-Act architecture. A low-cost prototype is built; however, empirical validation via user studies is currently lacking.

This research presents a real-time sEMG-based interface for teleoperating an assistive robotic arm. Using a 1D convolutional neural network, the system achieves over 90% classification accuracy and stable control with an average latency of 0.32 seconds in both simulated and real environments, demonstrating the feasibility of sEMG-based telecontrol for assistive robotics.

To revive interest in motorcycles among younger generations, researchers developed a rideable two-wheeled robot with four limbs that exhibits dynamic quadrupedal gait. The robot uses a self-balancing wheeled base for primary locomotion, with limbs providing auxiliary support and coordinated motion, enabling intuitive weight-shift control. This paper focuses on robust balancing control and motion strategies for natural quadruped-like behavior.

To address catastrophic forgetting in visual navigation, researchers propose HyperDCM, a structure-aware memory mechanism. It uses large vision-language models to extract scene triples, encodes them via R-GCN into scene graph embeddings, and projects into hyperbolic space for structural separability. Dynamic clustering and structure-sensitive update select representative samples for replay, preserving knowledge diversity. Experiments on multi-scene datasets show superior retention and generalization over baselines.

This paper proposes Neural Depth Field (NDF), which treats depth estimators as scene-level implicit fields. Through a single test-time optimization, it resolves inconsistencies and unreliability issues. Experiments show NDF reduces cross-view inconsistency by 63.3% and improves inpainting accuracy by 23.1%, achieving state-of-the-art performance.

A shared latent approach for partially-labeled multi-task facial affect recognition using a variational bottleneck. On s-Aff-Wild2, it improves expression macro-F1 from 0.403 to 0.446 and breaks the action-unit ceiling with a second backbone.

The 3rd Micro-Action Analysis Grand Challenge (MAC 2026), held at ACM Multimedia 2026, advances micro-action analysis from recognition to fine-grained understanding by introducing a new task evaluated with multimodal large language models. The paper details datasets, protocols, competition results, and future directions for this emerging field.

GenSyn10 is a 60,000-image synthetic dataset aligned with CIFAR-10, generated by three architecturally diverse models to advance AI-generated image detection. Evaluation shows detectors perform well on known generators but degrade significantly on unseen ones, highlighting OOD generalization limitations.

EMTS-Det is a lightweight system for real-time follow-me person tracking on drones, using ego-motion-normalized residual motion channels to detect persons with only 22k parameters and 7.6 MFLOPs. It achieves 31.85 FPS on a Raspberry Pi Zero 2W, significantly outperforming YOLOv8n (1.95 FPS, 0.172 AP25) while maintaining 0.462 AP25 and 0.714 recall.

Researchers propose 3D FaceShell, a framework that adds subtle perturbations to 3D face avatars to mislead vision-language models from inferring sensitive attributes while preserving visual identity.

Risk-Aware Facial Retrieval (RA-FR) is proposed to address reliability in unconstrained surveillance by moving from fixed Top-k to adaptive set generation, guaranteeing inclusion of the ground truth within user-specified risk and confidence levels.

Joint-Embedding Predictive Architectures (JEPAs) train a predictor jointly with their encoder, but downstream deployment discards the predictor. This research shows the JEPA predictor serves as a transferable operator for occluded feature completion, adaptable across encoder families via a linear projection, significantly improving occluded classification accuracy.

ForensicNet is a lightweight deep learning framework for forensic face recognition, combining MobileNetV2 with CBAM attention. It achieves 92.4% accuracy on LFW and SCFace datasets with only 2.1 GFLOPs per inference, enabling real-time forensic surveillance applications.

This paper introduces Scientific Feasibility Control (SFC), a graph-structured conformal prediction framework that provides statistical guarantees for scientific reasoning validity. SFC decomposes reasoning into atomic factuality units and uses dynamic branching to correct errors. On PhyX, it achieves 50.1% accuracy, outperforming DeepSeek-R1 and GPT-4, reduces scientific law violations by 73%, and provides 91.7% validity guarantees at α=0.10.

Large language models often succeed on one formulation of a problem while failing on an equivalent formulation. Whether these failures arise from distinct internal circuits or different activation states of a shared circuit remains unknown. This study investigates whether arithmetic heuristic neurons are form-invariant across symbolic arithmetic, natural language word problems, and Python code in Llama-3 models. Using a two-stage pipeline of attribution patching and activation patching, we identify a compact set of neurons shared across all formats. Targeted interventions show this shared circuit is necessary and sufficient for late-layer arithmetic computation. Transferring activations from successful to failed executions recovers over 97% of errors for addition and subtraction, indicati…

This paper presents SpecLA, a speculative decoding runtime for stateful linear-attention models. It verifies chains and trees with topology-aware kernels, stores compact factors to recover accepted states, and uses confidence pruning plus a target-aligned EAGLE-style drafter. On an NVIDIA H100 with a GDN-1.3B target, SpecLA achieves up to 1.70x end-to-end speedup over autoregressive decoding.

OpenLanguageModel (OLM) is an open-source PyTorch library for building and pretraining small language models with visible internals. Model code mirrors architecture using components like Block, Residual, Repeat, and Parallel. It integrates tokenizers, datasets, optimization, mixed precision, callbacks, checkpoints, and hardware-aware execution, enabling seamless transition from teaching notebooks to full pretraining. The library includes 27 presets across nine model families and documentation from fundamentals to research. Validation shows close agreement with reference implementations, 90.6% weak-scaling efficiency on four GPUs for a 348M-parameter model, and positive usability feedback. OLM is MIT-licensed and available on PyPI, GitHub, and its documentation site.

Existing memory systems for long-horizon LLM agents often retrieve prior traces as passive context rather than converting them into executable capabilities. This paper proposes MSCE, a training-free Memory-Skill Co-Evolution framework that organizes agent experience into grounded step traces, reusable procedural policies, and declarative environmental cognition. MSCE crystallizes evidence-backed L2 policies into callable skills and introduces reflection-weighted value backfilling. Experiments show significant improvements over state-of-the-art baselines.

This paper presents the NOWJ team's methodologies across all five tasks of COLIEE 2026, including legal case retrieval, entailment, statute law retrieval, textual entailment, and judgment prediction, using adaptive pipelines and deep learning.

This study compares human and language model (LM) next-word prediction using EEG event-related potentials (ERPs), finding that only the 'surprisal' measure correlates with language-processing ERPs, especially for open-class words, and that scaling LMs does not guarantee more human-like cognitive processing.

A new study uses a simple car-wash question to reveal that language models often pre-commit to an answer before reasoning, failing to derive logically correct conclusions. Experiments on Qwen3-8B show systematic wrong commitments (recommending 'walk' when 'drive' is the only valid option). Activation-level analysis suggests hidden states already lean toward the wrong answer before output, even for rollouts that eventually answer correctly. The findings highlight a pre-reasoning decision bias in LLMs.

Small language models in retrieval-augmented generation are highly sensitive to noisy evidence. This paper introduces RIMS, a three-stage preference optimization framework featuring synthetic chain-of-thought data generation, a differentiable soft aggregation mechanism, and preference optimization. Experiments show consistent gains on multi-hop QA benchmarks.

Mixture-of-Experts (MoE) scales Transformers by routing tokens to a subset of experts, but existing routers use shallow or isolated token representations, leading to unstable and semantically inconsistent routing. This work proposes Multi-level Context Fusion MOE (MCF-MOE), which integrates cross-layer semantic aggregation and local token-level interactions for more context-aware representations. Experiments show improved routing consistency and downstream performance.

A new federated learning framework called HantaWatch enables collaborative training of genomic surveillance models without sharing raw data, improving hantavirus outbreak prediction and expert prioritization.

This paper proposes a token-level cross-modal transformer with contrastive multi-task learning to integrate genomic and clinical data for joint breast cancer subtype classification and survival prediction, overcoming limitations of coarse modality interaction, simple fusion, and independent optimization.

This paper introduces 'weights to words', a method that automatically discovers natural language-described preference dimensions from choice data, addressing under-determination and opacity in preference learning. Experiments show improved prediction accuracy and enable real-time user inspection and editing.

A new study introduces OrthoGrad, a geometric intervention on gradient updates that removes the radial component of weight gradients. Experiments on noisy-label image classification show that OrthoGrad improves test accuracy in small-data regimes but does not prevent eventual memorization of corrupted labels. The method serves as a useful diagnostic for studying learning dynamics.