This paper addresses multi-objective motion planning for kinodynamic systems, proposing a unified framework based on Stable Sparse-RRT (SST). By replacing the single representative node with a set of locally Pareto-optimal nodes, it yields three algorithms: lexSST, coSST, and poSST, offering theoretical guarantees and empirical validation.

This paper proposes optimizing physical workspace layouts to improve goal inference reliability in shared autonomy systems, providing probabilistic correctness guarantees. Experiments show optimized layouts reduce ambiguity and enhance inference accuracy.

A study comparing Expected Utility (EU) and Cumulative Prospect Theory (CPT) for learning reward functions from human preferences in social robot navigation. Results show CPT-based learners recover reward functions with lower regret when users are risk-sensitive, highlighting the need to model human risk sensitivity.

This paper presents a tactile-reactive gripper that integrates a Visuo-Tactile Active Palm (VTAP) and compliant, reconfigurable fingers with tactile array sensors. The design exploits structured finger-palm synergy and multi-modal perception to achieve both robust grasping and fine manipulation. A staged, gesture-conditioned retargeting framework for dexterous teleoperation is proposed. Experiments validate the system on tasks like reactive grasping, syringe reorientation, singulation of objects down to 3 mm, and peg-in-hole insertion, demonstrating high performance without high-DOF anthropomorphic designs.

This paper presents robust, repeatable, and scalable fabrication procedures for soft pneumatic actuators using two-part silicone pour casting, including methods to prevent internal cavity clogging and ensure airtight sealing, as well as a robust sensor embedding procedure for thin-film flex sensors. Finite Element Modeling, PID-controlled pneumatic experiments, and automated image processing calibration validate the actuator performance. Staircase and sinusoidal actuation tests demonstrate high repeatability and low hysteresis, validated across two operators and 24 successful fabrications.

NeuroCommitSSM is a decision-centric framework for safe commit-to-execute control in assistive robotic manipulation. It predicts a continuous commit-readiness score from synchronized EEG, EMG, and eye-tracking, and converts it into discrete commit events via dwell and hysteresis filtering. A three-state finite-state supervisor, HOLD-ASSIST-COMMIT (HAC), gates execution by requiring both sustained commit-readiness from the neural model and real-time feasibility checks. Evaluated on 32 subjects performing five ADL tasks, NeuroCommitSSM achieves 0.950 action-balanced accuracy with 0.75 false commits per 1000 REST windows, and remains robust under sensor dropout. Hardware-in-the-loop validation demonstrates reduced false starts and decision instability without sacrificing task success.

Xiaomi Robotics Team presents Xiaomi-Robotics-1, a foundational VLA model capable of following diverse language instructions in unseen environments and efficient fine-tuning for novel tasks. The two-stage training uses over 100k hours of real-world trajectories with an auto-labeling pipeline. It achieves state-of-the-art results on RoboCasa365 (57.6%) and RoboDojo (20.07). Code and models will be released.

Cross-view geo-localization matches ground-level observations to satellite imagery. Recent methods use sequential queries like video clips for richer spatiotemporal cues, but overlook route descriptions. This paper introduces SeqGeo-VL dataset (~39K video-text-satellite triplets) and TrajLoc framework that processes both video and text, leveraging dense visual and linguistic semantics. TrajMod module conditions embeddings on trajectory geometry. Experiments show significant gains over state-of-the-art on video and text geo-localization.

A study evaluating the effect of adding biopsy-confirmed cases from abnormal-enriched external datasets to screening mammography AI found that pooling datasets reduces performance due to domain shift, outweighing the benefit of additional positive cases.

The study provides evidence that CNNs struggle to extract orientation features effectively. Using the Complex Structure Tensor, which contains compact orientation features with certainties, as input to CNNs consistently improves identification accuracy compared to grayscale inputs alone. Experiments also demonstrate that inputs provided by mini-complex convnets, combined with reduced CNN sizes, outperformed full-fledged, prevailing CNN architectures. This suggests that the upfront use of orientation features in CNNs, a strategy seen in mammalian vision, not only mitigates their limitations but also enhances their explainability and relevance to thin-clients. Experiments on the Cross-Eyed and PolyU datasets yield a 5-26% reduction in EER, providing strong empirical evidence that explicit o…

GS-RealBlur is a novel data acquisition framework that enables realistic and flexible collection of paired blurry and sharp images for training image deblurring models. It uses a handheld camera for blur shots and a gimbal for dense sharp captures, reconstructs a 3D scene representation, and refines camera poses with a Blur-aware Pose Refinement module. Models trained on the GS-RealBlur dataset outperform those trained on existing synthetic and real-world datasets across multiple benchmarks.

Falls among older adults are a major safety challenge, but continuous monitoring is difficult to sustain. This paper proposes a privacy-preserving framework using unsupervised keypoints and predictive temporal modeling to replace RGB transmission, performing segmentation and keypoint extraction locally and detecting falls via variational recurrent prediction and sequence classification. Evaluations on UR Fall Detection and Human Fall datasets show that unsupervised keypoints significantly outperform supervised methods under occlusion and partial visibility, with the gap widening under bandwidth constraints.

A Partial Information Decomposition (PID) framework is used to select the most informative MRI contrast pair prior to training, reducing computational cost for multi-contrast 3D brain tumor segmentation. Applied to T1n, T1c, T2w, and T2-FLAIR, it selected T1c+T2-FLAIR, which achieved mean Dice 0.676 vs 0.687 for all four inputs on lightweight 3D U-Nets.

Multimodal large language models struggle with part-level grounding. The proposed Object-Part Hierarchical Reflective Grounding (OP-HRG) uses a coarse-to-fine reasoning approach, first localizing the parent object then the part, with self-check and re-encoding. A part-aware GRPO framework with stage-wise rewards trains a 4B model that outperforms 7B grounding LLMs and SAM3 on several benchmarks.

This research proposes a fully training-free open-vocabulary 3D point cloud segmentation method that uses frozen vision-language models (RegionPLC) and a promptable concept segmenter (SAM3) with cross-view consistency to achieve generalized few-shot segmentation, significantly improving novel class performance on ScanNet200 and ScanNet++ benchmarks without any training or few-shot support.

AI-generated videos (AIGVs) contain subtle temporal artifacts from inter-frame inconsistencies. Standard global readouts in video backbones suppress local patch-level dynamics, hindering detection. The proposed V-PVP lightweight readout replaces the aggregation layer with parallel streams over patch velocity, adding ~0.5M parameters, achieving 95.28 AUC on AIGVDBench with frozen backbone.

This study compares HOG+SVM, LBP+Logistic Regression, and a lightweight CNN on FER-2013, CK+, and KDEF datasets. CNN achieves best overall performance, especially on complex data; HOG performs well in controlled settings; LBP performs poorly across all datasets. Dataset complexity significantly affects performance, highlighting the need for robust feature learning in real-world applications.

Large language models (LLMs) excel at answering pre-specified questions, yet their ability to navigate the open-ended, pre-conclusion stage of discovery remains largely unmeasured. This paper introduces Prospective Hypothesis Discovery (PHD), which asks models to autonomously construct grounded, discriminative, and testable hypothesis spaces from inconclusive evidence. To evaluate this, they present HypoArena, comprising HypoData (988 cases across six domains) and HypoEval (an evaluation framework). Experiments on 15 frontier LLMs reveal clear capability stratification and model-dependent effects of structured analytical skills, with gains for some lower-performing models but regressions for others, including a top performer. Arena evaluation resolves finer-grained differences and shows s…

The paper introduces BIRD, a two-stage self-reasoning distillation method that first samples concise solutions with a brevity instruction and performs prompt-switch SFT, then applies on-policy reverse-KL distillation on cleaner prefixes. On Qwen3-8B, MATH-500 accuracy improves from 86.2% to 92.0% while response length drops from 3,099 to 1,115 tokens.

This paper introduces AdaLook, an adaptive lookahead framework for masked diffusion language models. By dynamically determining rollout depth based on candidate-score variance and enabling branch expansion, AdaLook achieves a better accuracy-efficiency trade-off than existing one-step lookahead methods.

This paper reexamines addressee detection in multi-party dialogue, proposing that address is a continuous phenomenon rather than discrete. Using a multi-annotator corpus, they construct continuous address levels that relate to turn-taking, gaze, and backchannels. Continuous models outperform discrete ones, suggesting a graded structure of address.

Proposes PATR, a quality-aware rollout framework that uses process feedback to score partial trajectories, selectively branch from promising states, reuse shared prefixes, and stop degenerate paths, improving multi-turn RL efficiency. Achieves +5.0 points on SWE-Bench and +9.3 on FrozenLake.

SkillCorpus aggregates, curates, and evaluates over 96,000 open-source LLM agent skills from ~821,000 candidates, using a 16-class taxonomy and quality facets. Integrated with a retrieval-and-selection stack, it achieves consistent gains across benchmarks, with the largest improvement of +7.5 percentage points on SkillsBench.

arXiv:2607.15544v1 Announce Type: new Abstract: Generation of clear and accessible public health narratives is critical for communicating complex epidemiological projections to policymakers and the general public at large. Such narratives require more than simply reporting numbers: projections must be contextualized and quantitatively grounded across multiple dimensions. Further, projections are often derived from large ensemble datasets which combine intervention assumptions, geographic and demographic strata, outcomes, time horizons, and uncertainty quantiles. However, directly using large language models (LLMs) to summarize and contextualize such data often leads to inconsistencies, omissions, and fragile behavior. We introduce an agentic framework (EpiNarrate) for public health report…

Researchers using a new interpretability technique called the Jacobian lens have identified a functional structure in large language models analogous to the global workspace of human consciousness—the J-space. These representations can be reported, deliberately summoned and held, used for intermediate reasoning steps, and passed to arbitrary downstream computations, while automatic processing proceeds without them. The J-space carries coherent content only in an intermediate band of layers, holds tens of concepts at a time, and is broadcast more widely. In alignment audits, it reveals strategic deliberation, evaluation awareness, and misaligned dispositions that never appear in outputs. Post-training installs the Assistant's viewpoint. Counterfactual reflection training improves behavior…

This study proposes converting all multimodal patient data (structured measurements and free-text clinical narratives) from electronic health records into a single natural language sequence, fine-tuning a pretrained language model end-to-end without specialized fusion architectures. Evaluated on three clinical prediction tasks (in-hospital mortality, graft failure, and emergency triage), the unified serialization approach matches or exceeds task-specific multimodal baselines and outperforms a clinically deployed gradient boosting model, significantly reducing system complexity.

A new clinical foundation model, LLM4EHR, aligns clinical time series with medical event sequences using a domain-adapted large language model and a transformer time series encoder, improving performance on ICU outcome prediction tasks and enabling transferable embeddings via few-shot adaptation.

This study examines healthcare financial vulnerability before and after the COVID-19 pandemic using MEPS data from 2019 and 2021. High financial burden was defined as out-of-pocket spending exceeding 10% of family income. Poverty status, insurance coverage, and prescription drug spending were strongly associated with vulnerability. Models trained on pre-pandemic data showed only modest performance declines when applied to post-pandemic data, indicating stable predictors.

This paper introduces Stochastic Reset Pathfinding (SRP), an episodic learning problem on directed graphs with unknown edge success probabilities. The agent commits to a path each episode and resets to the source upon any edge failure. SRP models applications like quantum repeater networks and Lightning Network routing. The authors show the optimal policy is open-loop, fitting into the combinatorial cascading bandit framework. They propose PathUCB and PathTS algorithms, with a novel path-level regret bound for PathUCB. Experiments show PathTS performs best typically, though an adversarial instance causes it to diverge. PathTS is recommended as a default with caution.

This paper characterizes and compares the inherent interpretability of standard linear models and single-qubit mixed-state models for binary classification. It finds that the single-qubit model learns a hyperellipsoid instead of a hyperplane, making it the "ellipsoid version" of linear classification. The authors discuss the geometric and feature importance inductive biases of both models, offering an accessible introduction to quantum ML for readers with only linear classification knowledge.