arXiv research

The paper teaches a reinforcement learning agent to generate diverse programs based on symbolic instructions.

Neuro-symbolic agent learns systematic generalisation from formal instructions.

The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules give rise to regularities that can be discovered through primarily unsupervised experiences and represented as abstract concepts. If such r…

NeSS combines neural and symbolic approaches for better compositional generalization.

We are increasingly surrounded by artificially intelligent technology that takes decisions and executes actions on our behalf. This creates a pressing need for general means to communicate with, instruct and guide artificial agents, with human language the most compelling means for such communication. To achieve this i…

Method constructs finance LLMs without instruction data using pretraining and model merging.

New methods predict language model out-of-distribution behaviors using causal mechanisms.

Framework enables robots to learn tasks from unlabeled human instructions.

HIGhER uses language to generate new instructions for better learning from mistakes.

Interactive learning with hindsight instruction feedback achieves better performance than traditional methods.

A method to improve LLMs by automating the construction of a mixture of expert prompts.

Improved financial sentiment analysis using simple instruction tuning of LLMs.

Agent learns complex web tasks by breaking them into simpler sub-tasks.

Improves matrix multiplication throughput for asymmetric bit-width operands.

CoT enhances transformer accuracy on serial tasks by enabling serial computation.

In this paper, a taxonomy for memory networks is proposed based on their memory organization. The taxonomy includes all the popular memory networks: vanilla recurrent neural network (RNN), long short term memory (LSTM ), neural stack and neural Turing machine and their variants. The taxonomy puts all these networks und…

Paper fine-tunes LLaMA-3-8B for financial NER using instruction and LoRA.

RAG-IT automates financial analysis using LLMs and specialized datasets.

Study shows model size inversely affects few-shot instruction accuracy.

Enhances LLMs for predicting stock movements by considering news dissemination and context.

Improved robot navigation using multi-head attention for natural language instructions.

This paper benchmarks FinGPT for financial datasets using open-source large language models.

This paper proposes a new method to connect language and physical actions in reinforcement learning.

LLM Pro Finance Suite enhances financial NLP with instruction-tuned models.

SymNoise improves language model fine-tuning by 6.7% over NEFTune, using symmetric noise.

InvestLM is a financial domain LLM tuned on LLaMA-65B for investment advice.

Predicting the number of clock cycles a processor takes to execute a block of assembly instructions in steady state (the throughput) is important for both compiler designers and performance engineers. Building an analytical model to do so is especially complicated in modern x86-64 Complex Instruction Set Computer (CISC…

Human communication takes many forms, including speech, text and instructional videos. It typically has an underlying structure, with a starting point, ending, and certain objective steps between them. In this paper, we consider instructional videos where there are tens of millions of them on the Internet. We propose a…

VaSST uses soft symbolic trees for probabilistic symbolic regression.

Framework learns useful subgoals from demonstrations and instructions.

We describe dimensionally constrained symbolic regression which has been developed for mass measurement in certain classes of events in high-energy physics (HEP). With symbolic regression, we can derive equations that are well known in HEP. However, in problems with large number of variables, we find that by constraini…

New RL framework learns task completion without prior knowledge.

WebGUM learns web navigation from multimodal data, outperforming previous methods.

Paper closes neural-symbolic learning loop with grammar model and back-search algorithm.

Geometric symbols help compute heat invariants.

Training-free looped transformers improve model performance without additional training.

Reinforcement learning and symbolic planning have both been used to build intelligent autonomous agents. Reinforcement learning relies on learning from interactions with real world, which often requires an unfeasibly large amount of experience. Symbolic planning relies on manually crafted symbolic knowledge, which may …

For an arbitrary Riemannian manifold $X$ and Hermitian vector bundles $E$ and $F$ over $X$ we define the notion of the normal symbol of a pseudodifferential operator $P$ from $E$ to $F$. The normal symbol of $P$ is a certain smooth function from the cotangent bundle $T^*X$ to the homomorphism bundle $Hom (E,F)$ and dep…

Based on the ideas of Optimal Control, we introduce the new basic characteristic of a bracket generating distribution, the Jacobi symbol. In contrast to the classical Tanaka symbol, the set of Jacobi symbols is discrete and classifiable. We give an explicit and unified algebraic procedure for the construction of the ca…

Efficient FPGA virtualization for deep learning reduces user isolation and overhead.

Symbolic knowledge in neural models can inadvertently make them more vulnerable to adversarial attacks.

We introduce mod 3 triple Milnor invariants and triple cubic residue symbols for certain primes of the Eisenstein number field $\mathbb{Q}(\sqrt{-3})$, following the analogies between knots and primes. Our triple symbol generalizes both the cubic residue symbol and Rédei's triple symbol, and describes the decomposition…

In2Core selects a coreset for efficient LLM fine-tuning with reduced data.

Qwen3-8B outperforms classical models in financial text classification.

A formula for Rademacher symbols in triangle groups is provided.

The symbolic dynamics technique is well-known for low-dimensional dynamical systems and chaotic maps, and lies at the roots of the thermodynamic formalism of dynamical systems. Here we show that this technique can also be successfully applied to time series generated by complex systems of much higher dimensionality. Ou…

Complex $b$-$6j$ symbols relate to hyperbolic tetrahedron volumes and determinants.

The paper classifies symbols of differential operators on vector bundles.