Can LLMs Introspect? A Reality Check

Can large language models detect and report their own internal states? A number of studies have argued that the answer to this question is yes. We argue, based on lessons from human metacognition research, that this conclusion may be premature: to be convinced of this conclusion we need to distinguish genuine introspection from pattern matching based on surface-level cues. Furthermore, we argue that behavioral evidence alone is inherently insufficient to establish strong introspective claims. We re-examine two recently introduced evaluation paradigms in light of this consideration. In the first paradigm, models are expected to detect whether their internal states have been tampered with. We find that models cannot reliably distinguish such interventions on their internal states from manipulations of the input, suggesting that their success in the original studies reflects their ability to detect anomalies more generally, as opposed to interventions on their internal states in particular. In the second paradigm we examine, models are tasked with predicting labels derived from their own hidden states. Here, we find that classifiers that only have access to the input achieve equivalent performance to the model's own in-context predictions, indicating that the original results do not conclusively demonstrate that the model has privileged access to its internal representations. We further introduce a relabeled control setting, where models cannot rely on the semantics of the task to solve it, and instead must rely on the internal representation; models perform closer to chance on this better-controlled version of the task. Taken together, these results indicate that current evidence is insufficient to establish that LLMs display metacognitive monitoring.

TerraSkipper: A Centimeter-Scale Robot for Multi-Terrain Skipping and Crawling

Mudskippers are unique amphibious fish capable of locomotion in diverse environments, including terrestrial surfaces, aquatic habitats, and highly viscous substrates such as mud. This versatile locomotion is largely enabled by their powerful tail, which stores and rapidly releases energy to produce impulsive jumps. Inspired by this biological mechanism, we present the design and development of a multi-terrain centimeter-scale skipping and crawling robot. The robot is predominantly 3D printed and features onboard sensing, computation, and power. It is equipped with two side fins for crawling, each integrated with a hall effect sensor for gait control, while a rotary springtail driven by a 10mm planetary gear motor enables continuous impulsive skipping across a range of substrates to achieve multi-terrain locomotion. We modeled and experimentally characterized the tail, identifying an optimal length of 25mm that maximizes the mean propulsive force (4N, peaks up to 6N) for forward motion. In addition, we evaluated skipping on substrates where fin based crawling alone fails, and varied the moisture content of uniform sand and bentonite clay powder to compare skipping with crawling. Skipping consistently produced higher mean velocities than crawling, particularly on viscous and granular media. Finally, outdoor tests on grass, loose sand, and hard ground confirmed that combining skipping on entangling and granular terrain with crawling on firm ground extends the operational range of the robot in real-world environments.

From Fold to Function: Dynamic Modeling and Simulation-Driven Design of Origami Mechanisms

Origami-inspired mechanisms can transform flat sheets into functional three-dimensional dynamic structures that are lightweight, compact, and capable of complex motion. These properties make origami increasingly valuable in robotic and deployable systems. However, accurately simulating their folding behavior and interactions with the environment remains challenging. To address this, we present a design framework for origami mechanism simulation that utilizes MuJoCo's deformable-body capabilities. In our approach, origami sheets are represented as graphs of interconnected deformable elements with user-specified constraints such as creases and actuation, defined through an intuitive graphical user interface (GUI). This framework allows users to generate physically consistent simulations that capture both the geometric structure of origami mechanisms and their interactions with external objects and surfaces. We demonstrate our method's utility through a case study on an origami catapult, where design parameters are optimized in simulation using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and validated experimentally on physical prototypes. The optimized structure achieves improved throwing performance, illustrating how our system enables rapid, simulation-driven origami design, optimization, and analysis.

PuffyBot: An Untethered Shape Morphing Robot for Multi-environment Locomotion

Amphibians adapt their morphologies and motions to accommodate movement in both terrestrial and aquatic environments. Inspired by these biological features, we present PuffyBot, an untethered shape morphing robot capable of changing its body morphology to navigate multiple environments. Our robot design leverages a scissor-lift mechanism driven by a linear actuator as its primary structure to achieve shape morphing. The transformation enables a volume change from 255.00 cm3 to 423.75 cm3, modulating the buoyant force to counteract a downward force of 3.237 N due to 330 g mass of the robot. A bell-crank linkage is integrated with the scissor-lift mechanism, which adjusts the servo-actuated limbs by 90 degrees, allowing a seamless transition between crawling and swimming modes. The robot is fully waterproof, using thermoplastic polyurethane (TPU) fabric to ensure functionality in aquatic environments. The robot can operate untethered for two hours with an onboard battery of 1000 mA h. Our experimental results demonstrate multi-environment locomotion, including crawling on the land, crawling on the underwater floor, swimming on the water surface, and bimodal buoyancy adjustment to submerge underwater or resurface. These findings show the potential of shape morphing to create versatile and energy efficient robotic platforms suitable for diverse environments.

Sequential Change Detection Under A Markov Setup With Unknown Pre-Change and Post-Change Distributions

In this work we extend the results developed in 2022 for a sequential change detection algorithm making use of Page's CUSUM statistic, the empirical distribution as an estimate of the pre-change distribution, and a universal code as a tool for estimating the post-change distribution, from the i.i.d. case to the Markov setup.

MiMiC: Minimally Modified Counterfactuals in the Representation Space

Language models often exhibit undesirable behaviors, such as gender bias or toxic language. Interventions in the representation space were shown effective in mitigating such issues by altering the LM behavior. We first show that two prominent intervention techniques, Linear Erasure and Steering Vectors, do not enable a high degree of control and are limited in expressivity. We then propose a novel intervention methodology for generating expressive counterfactuals in the representation space, aiming to make representations of a source class (e.g., "toxic") resemble those of a target class (e.g., "non-toxic"). This approach, generalizing previous linear intervention techniques, utilizes a closed-form solution for the Earth Mover's problem under Gaussian assumptions and provides theoretical guarantees on the representation space's geometric organization. We further build on this technique and derive a nonlinear intervention that enables controlled generation. We demonstrate the effectiveness of the proposed approaches in mitigating bias in multiclass classification and in reducing the generation of toxic language, outperforming strong baselines.