Adapting LLM Agents Through Communication

Recent advancements in large language models (LLMs) have shown potential for human-like agents. To help these agents adapt to new tasks without extensive human supervision, we propose the Learning through Communication (LTC) paradigm, a novel training approach enabling LLM agents to improve continuously through interactions with their environments and other agents. Recent advancements in large language models (LLMs) have shown potential for human-like agents. To help these agents adapt to new tasks without extensive human supervision, we propose the Learning through Communication (LTC) paradigm, a novel training approach enabling LLM agents to improve continuously through interactions with their environments and other agents. Through iterative exploration and PPO training, LTC empowers the agent to assimilate short-term experiences into long-term memory. To optimize agent interactions for task-specific learning, we introduce three structured communication patterns: Monologue, Dialogue, and Analogue-tailored for common tasks such as decision-making, knowledge-intensive reasoning, and numerical reasoning. We evaluated LTC on three datasets: ALFWorld (decision-making), HotpotQA (knowledge-intensive reasoning), and GSM8k (numerical reasoning). On ALFWorld, it exceeds the instruction tuning baseline by 12% in success rate. On HotpotQA, LTC surpasses the instruction-tuned LLaMA-7B agent by 5.1% in EM score, and it outperforms the instruction-tuned 9x larger PaLM-62B agent by 0.6%. On GSM8k, LTC outperforms the CoT-Tuning baseline by 3.6% in accuracy. The results showcase the versatility and efficiency of the LTC approach across diverse domains. We will open-source our code to promote further development of the community.Comment: Preprin

Unsupervised deep structured semantic models for commonsense reasoning

Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches.Comment: To appear in NAACL 2019, 10 page

Liquid-like Poly(ionic liquid) as Electrolyte for Thermally Stable Lithium-Ion Battery

A liquid-like poly­(ionic liquid) (PIL) with a very low glass transition temperature of −51 °C and a thermal decomposition temperature of 202.7 °C was synthesized. A PIL-based electrolyte by mixing this poly­(ionic liquid) with additives of 10 wt % propylene carbonate and 0.1 M LiClO4 is proved to be an excellent electrolyte for lithium-ion battery. The obtained PIL-based electrolyte exhibits a high ionic conductivity of 8.3 × 10–5 S cm–1 at 25 °C and 2.0 × 10–4 S cm–1 at 60 °C and a wide electrochemical potential window up to 5.61 V at 25 °C and 4.14 V at 60 °C. The Li/LiFePO4 batteries equipped with this PIL-based electrolyte achieve high capacity, outstanding cycling stability and rate capability at 25 °C, and even improved performance at high temperature like 60 °C. Such excellent performances of batteries are attributed to the formation of stable solid-electrolyte interface film at the lithium-electrolyte interface and the stability of electrolyte during cycling

ERK is a negative feedback regulator for IFN-γ/STAT1 signaling by promoting STAT1 ubiquitination

Abstract Background We recently reported that STAT1 plays a tumor suppressor role, and ERK was inversely correlation with STAT1 expression in esophageal squamous cell carcinoma (ESCC). Here, we investigated the mechanism(s) that are responsible for the ERK regulates STAT1 in ESCC. Methods We performed the immunoprecipitation (IP) to detect the ubiquitin of STAT1 upon MEK transfection or U0126 treatment and co-IP to confirm the binding of STAT1 and ERK in ESCC cell lines. Results We found evidence that the ubiquitin-proteasome pathway can efficiently degrade STAT1 in ESCC cells, as MG132 treatment rapidly and dramatically increased STAT1 expression in these cells. This process is not dependent on the phosphorylation of the two important STAT1 residues, Y701 and S727, as site-directed mutagenesis of these two sites did not affect STAT1 degradation. We also found that ERK promotes proteasome degradation of STAT1, supported by the observations that pharmacologic inhibition of ERK resulted in a substantial increase of STAT1 whereas expression of constitutively active ERK further reduced the STAT1 protein level. In addition to suppressing STAT1 expression, ERK limited STAT1 signaling by decreasing the production of IFNγ. Conclusion To conclude, ERK is an effective negative regulator of STAT1 signaling in ESCC, by promoting its proteasome degradation and decreasing IFNγ production. Our data further supports that targeting ERK and/or STAT1 may be useful for treating ESCC

A Natural Position Observer With Vertical Detection Coil for FSCW Machines

This letter presents a sensorless control strategy using the novel detection coil for permanent magnet synchronous motor with fractional slot concentrated winding (FSCW). In order to eliminate the armature component in the coil voltage, the detection coil distribution is determined based on the analysis of synchronous inductance and leakage inductance for FSCW machines. Hence, the rotor position and angular velocity are estimated from the terminal voltage of detection coil without any knowledge of motor parameters. Both finite-element analysis results and experiments are carried out to prove the effectiveness of the proposed method at steady and transient states

Metal‐organic framework‐derived Fe/Cu‐substituted Co nanoparticles embedded in CNTs‐grafted carbon polyhedron for Zn‐air batteries

Abstract Metal‐organic frameworks (MOFs) and MOF‐derived materials have attracted great attention as alternatives to noble‐metal based electrocatalysts owing to their intriguing structure properties, especially for high efficiency and stable oxygen reduction reaction (ORR). Herein, we employed a one‐pot reaction to make a multimetal (Fe, Co, Cu, and Zn) mixed zeolitic imidazolate framework (MM‐ZIF) via adopting a simple in situ redox reaction. Further pyrolysis of the target MM‐ZIF, a highly porous carbon polyhedron (FC‐C@NC) grafted with abundant carbon nanotubes was obtained, in which ultrasmall Co nanoparticles with partial lattice sites substituted by Fe and Cu were embedded. The obtained FC‐C@NC possessed large surface area, highly porous structure, widely‐spread metal active sites, and conductive carbon frameworks, contributing to outstanding ORR activity and long‐term stability. It displayed superior tolerance to methanol crossover and exceeded the commercial Pt/C catalyst and most previously reported non‐noble‐metal catalysts. Impressively, the as‐produced FC‐C@NC‐based zinc‐air battery afforded an open‐circuit potential of 1.466 V, a large specific capacity of 659.5 mAh/g, and a high gravimetric energy density of 784.3 Wh/kgZn, significantly outperforming the Pt/C‐based cathode

Carbon Nanodots Memristor: An Emerging Candidate toward Artificial Biosynapse and Human Sensory Perception System

Abstract In the era of big data and artificial intelligence (AI), advanced data storage and processing technologies are in urgent demand. The innovative neuromorphic algorithm and hardware based on memristor devices hold a promise to break the von Neumann bottleneck. In recent years, carbon nanodots (CDs) have emerged as a new class of nano‐carbon materials, which have attracted widespread attention in the applications of chemical sensors, bioimaging, and memristors. The focus of this review is to summarize the main advances of CDs‐based memristors, and their state‐of‐the‐art applications in artificial synapses, neuromorphic computing, and human sensory perception systems. The first step is to systematically introduce the synthetic methods of CDs and their derivatives, providing instructive guidance to prepare high‐quality CDs with desired properties. Then, the structure–property relationship and resistive switching mechanism of CDs‐based memristors are discussed in depth. The current challenges and prospects of memristor‐based artificial synapses and neuromorphic computing are also presented. Moreover, this review outlines some promising application scenarios of CDs‐based memristors, including neuromorphic sensors and vision, low‐energy quantum computation, and human–machine collaboration

Online Identification of Inductance and Flux Linkage for Inverter-Fed SPMSMs Using Switching State Functions

This article presents an online method to acquire the stator inductance and flux linkage for inverter-fed surface-mounted permanent magnet synchronous motors (SPMSMs) using switching state functions instead of average models. Given the switching state functions when different voltage vectors are applied to the motors, the information of inductance and flux linkage is revealed. Based on the dc bus voltage and current derivatives obtained from either hardware methods like Rogowski coil and differential circuit, or software methods such as tracking differentiator, stator inductance, and flux linkage are able to be figured out directly using the redundant or more practicalmethods with the help of recursive least square (RLS). Simulations and experiments are carried out to prove the effectiveness of the proposed methods, besides, dc voltage and operating speed variation as well as the impact of resistance are taken into account. Finally, compared with the conventional parameter identification based on the average models either in alpha-beta or d-q coordinate, the proposed methods with switching state functions have remarkable advantages especially when the rotor position error exists. What needs further effort is the challenging case at very low speed, e.g., it is hard to identify flux linkage due to the small back electromotive force (EMF)