Large Language Models (LLMs) have reshaped our world with significant advancements in science, engineering, and society through applications ranging from scientific discoveries and medical diagnostics to Chatbots. Despite their ubiquity and utility, the underlying mechanisms of LLM remain concealed within billions of parameters and complex structures, making their inner architecture and cognitive processes challenging to comprehend. We address this gap by adopting approaches to understanding emerging cognition in biology and developing a network-based framework that links cognitive skills, LLM architectures, and datasets, ushering in a paradigm shift in foundation model analysis. The skill distribution in the module communities demonstrates that while LLMs do not strictly parallel the focalized specialization observed in specific biological systems, they exhibit unique communities of modules whose emergent skill patterns partially mirror the distributed yet interconnected cognitive organization seen in avian and small mammalian brains. Our numerical results highlight a key divergence from biological systems to LLMs, where skill acquisition benefits substantially from dynamic, cross-regional interactions and neural plasticity. By integrating cognitive science principles with machine learning, our framework provides new insights into LLM interpretability and suggests that effective fine-tuning strategies should leverage distributed learning dynamics rather than rigid modular interventions.

We extend the existing work on Hopfield network state classification, employing more complex models that remain interpretable, such as densely-connected feed-forward deep neural networks and support vector machines. The states of the Hopfield network can be grouped into several classes, including learned (those presented during training), spurious (stable states that were not learned), and prototype (stable states that were not learned but are representative for a subset of learned states). It is often useful to determine to what class a given state belongs to; for example to ignore spurious states when retrieving from the network. Previous research has approached the state classification task with simple linear methods, most notably the stability ratio. We deepen the research on classifying states from prototype-regime Hopfield networks, investigating how varying the factors strengthening prototypes influences the state classification task. We study the generalizability of different classification models when trained on states derived from different prototype tasks -- for example, can a network trained on a Hopfield network with 10 prototypes classify states from a network with 20 prototypes? We find that simple models often outperform the stability ratio while remaining interpretable. These models require surprisingly little training data and generalize exceptionally well to states generated by a range of Hopfield networks, even those that were trained on exceedingly different datasets.

Abstract-- The inspection of confined critical infrastructure such as attics or crawlspaces is challenging for human operators due to insufficient task space, limited visibility, and the presence of hazardous materials. This paper introduces a prototype of PARIS (Precision Application Robot for Inaccessible Spaces): a use-inspired teleoperated mobile robot manipulator system that was conceived, developed, and tested for--and selected as a Phase I winner of--the U.S. Department of Energy's E-ROBOT Prize. To improve the thermal efficiency of buildings, the PARIS platform supports: 1) teleoperated mapping and navigation, enabling the human operator to explore compact spaces; 2) inspection and sensing, facilitating the identification and localization of under-insulated areas; and 3) air-sealing targeted gaps and cracks through which thermal energy is lost. The resulting versatile platform can also be tailored for targeted application of treatments and remediation in constrained spaces. Approximately 75% of the world's greenhouse gas (GHG) emissions result from the cumulative energy sector [1].

Advanced biological intelligence learns efficiently from an information-rich stream of stimulus information, even when feedback on behaviour quality is sparse or absent. Such learning exploits implicit assumptions about task domains. We refer to such learning as Domain-Adapted Learning (DAL). In contrast, AI learning algorithms rely on explicit externally provided measures of behaviour quality to acquire fit behaviour. This imposes an information bottleneck that precludes learning from diverse non-reward stimulus information, limiting learning efficiency. We consider the question of how biological evolution circumvents this bottleneck to produce DAL. We propose that species first evolve the ability to learn from reward signals, providing inefficient (bottlenecked) but broad adaptivity. From there, integration of non-reward information into the learning process can proceed via gradual accumulation of biases induced by such information on specific task domains. This scenario provides a biologically plausible pathway towards bottleneck-free, domain-adapted learning. Focusing on the second phase of this scenario, we set up a population of NNs with reward-driven learning modelled as Reinforcement Learning (A2C), and allow evolution to improve learning efficiency by integrating non-reward information into the learning process using a neuromodulatory update mechanism. On a navigation task in continuous 2D space, evolved DAL agents show a 300-fold increase in learning speed compared to pure RL agents. Evolution is found to eliminate reliance on reward information altogether, allowing DAL agents to learn from non-reward information exclusively, using local neuromodulation-based connection weight updates only.

Instruction Fine-tuning~(IFT) is a critical phase in building large language models~(LLMs). Previous works mainly focus on the IFT's role in the transfer of behavioral norms and the learning of additional world knowledge. However, the understanding of the underlying mechanisms of IFT remains significantly limited. In this paper, we design a knowledge intervention framework to decouple the potential underlying factors of IFT, thereby enabling individual analysis of different factors. Surprisingly, our experiments reveal that attempting to learn additional world knowledge through IFT often struggles to yield positive impacts and can even lead to markedly negative effects. Further, we discover that maintaining internal knowledge consistency before and after IFT is a critical factor for achieving successful IFT. Our findings reveal the underlying mechanisms of IFT and provide robust support for some very recent and potential future works.

Haptic rendering of weight plays an essential role in naturalistic object interaction in virtual environments. While kinesthetic devices have traditionally been used for this aim by applying forces on the limbs, tactile interfaces acting on the skin have recently offered potential solutions to enhance or substitute kinesthetic ones. Here, we aim to provide an in-depth overview and comparison of existing tactile weight rendering approaches. We categorized these approaches based on their type of stimulation into asymmetric vibration and skin stretch, further divided according to the working mechanism of the devices. Then, we compared these approaches using various criteria, including physical, mechanical, and perceptual characteristics of the reported devices and their potential applications. We found that asymmetric vibration devices have the smallest form factor, while skin stretch devices relying on the motion of flat surfaces, belts, or tactors present numerous mechanical and perceptual advantages for scenarios requiring more accurate weight rendering. Finally, we discussed the selection of the proposed categorization of devices and their application scopes, together with the limitations and opportunities for future research. We hope this study guides the development and use of tactile interfaces to achieve a more naturalistic object interaction and manipulation in virtual environments.

Novelty, akin to gene mutation in evolution, opens possibilities for scholarly advancement. Although peer review remains the gold standard for evaluating novelty in scholarly communication and resource allocation, the vast volume of submissions necessitates an automated measure of scholarly novelty. Adopting a perspective that views novelty as the atypical combination of existing knowledge, we introduce an information-theoretic measure of novelty in scholarly publications. This measure quantifies the degree of 'surprise' perceived by a language model that represents the word distribution of scholarly discourse. The proposed measure is accompanied by face and construct validity evidence; the former demonstrates correspondence to scientific common sense, and the latter is endorsed through alignment with novelty evaluations from a select panel of domain experts. Additionally, characterized by its interpretability, fine granularity, and accessibility, this measure addresses gaps prevalent in existing methods. We believe this measure holds great potential to benefit editors, stakeholders, and policymakers, and it provides a reliable lens for examining the relationship between novelty and academic dynamics such as creativity, interdisciplinarity, and scientific advances.

A prominent theory of affective response to music revolves around the concepts of surprisal and expectation. In prior work, this idea has been operationalized in the form of probabilistic models of music which allow for precise computation of song (or note-by-note) probabilities, conditioned on a 'training set' of prior musical or cultural experiences. To date, however, these models have been limited to compute exact probabilities through hand-crafted features or restricted to linear models which are likely not sufficient to represent the complex conditional distributions present in music. In this work, we propose to use modern deep probabilistic generative models in the form of a Diffusion Model to compute an approximate likelihood of a musical input sequence. Unlike prior work, such a generative model parameterized by deep neural networks is able to learn complex non-linear features directly from a training set itself. In doing so, we expect to find that such models are able to more accurately represent the 'surprisal' of music for human listeners. From the literature, it is known that there is an inverted U-shaped relationship between surprisal and the amount human subjects 'like' a given song. In this work we show that pre-trained diffusion models indeed yield musical surprisal values which exhibit a negative quadratic relationship with measured subject 'liking' ratings, and that the quality of this relationship is competitive with state of the art methods such as IDyOM. We therefore present this model a preliminary step in developing modern deep generative models of music expectation and subjective likability.

As if Elon Musk did not have enough on his plate with Twitter, Tesla is now under fire in a full-page advertisement in the New York Times that warns its'Full Self-Driving presents a life-threatening danger to child pedestrians.' The ad, which cost about $150,000, is from software maker The Dawn Project and claims to highlight safety testing conducted by the firm in October. A video of the experiment suggests the system does not register or stop for small mannequins crossing a road, according to the group. The testing involved a man driving in a Tesla on a back road and running over child-size mannequins in his path. Using the Tesla Full Self-Driving Beta 10.69.2.2, which is the latest version of the system, the vehicle collided with a 29-inch mannequin at speeds as low as 15 miles per hour and it ran over a four-foot-tall one at 20 miles per hour.

It is well known that different algorithms perform differently well on an instance of an algorithmic problem, motivating algorithm selection (AS): Given an instance of an algorithmic problem, which is the most suitable algorithm to solve it? As such, the AS problem has received considerable attention resulting in various approaches - many of which either solve a regression or ranking problem under the hood. Although both of these formulations yield very natural ways to tackle AS, they have considerable weaknesses. On the one hand, correctly predicting the performance of an algorithm on an instance is a sufficient, but not a necessary condition to produce a correct ranking over algorithms and in particular ranking the best algorithm first. On the other hand, classical ranking approaches often do not account for concrete performance values available in the training data, but only leverage rankings composed from such data. We propose HARRIS- Hybrid rAnking and RegRessIon foreSts - a new algorithm selector leveraging special forests, combining the strengths of both approaches while alleviating their weaknesses. HARRIS' decisions are based on a forest model, whose trees are created based on splits optimized on a hybrid ranking and regression loss function. As our preliminary experimental study on ASLib shows, HARRIS improves over standard algorithm selection approaches on some scenarios showing that combining ranking and regression in trees is indeed promising for AS.